{"meta": {"count": 2000, "db_response_time_ms": 36, "page": 1, "per_page": 100, "groups_count": null, "x_query": {"oql": "get works where retracted is (false)\n  and year is (2025)\n  and title-abstract has ((stemmed \"benchmark\" OR stemmed \"evaluation\" OR stemmed \"factuality\" OR stemmed \"hallucination\" OR stemmed \"reproducibility\") AND (stemmed \"foundation model\" OR stemmed \"language model\" OR stemmed \"large language model\" OR stemmed \"vision language model\"))\n  and type is (article or preprint or review);\nthen sample (2000) of those works with seed (42)", "oqo": {"get_rows": "works", "filter_rows": [{"column_id": "is_retracted", "value": false}, {"column_id": "publication_year", "value": 2025}, {"join": "or", "filters": [{"column_id": "title_and_abstract.search", "value": "\"benchmark\"", "operator": "has"}, {"column_id": "title_and_abstract.search", "value": "\"evaluation\"", "operator": "has"}, {"column_id": "title_and_abstract.search", "value": "\"factuality\"", "operator": "has"}, {"column_id": "title_and_abstract.search", "value": "\"hallucination\"", "operator": "has"}, {"column_id": "title_and_abstract.search", "value": "\"reproducibility\"", "operator": "has"}]}, {"join": "or", "filters": [{"column_id": "title_and_abstract.search", "value": "\"foundation model\"", "operator": "has"}, {"column_id": "title_and_abstract.search", "value": "\"language model\"", "operator": "has"}, {"column_id": "title_and_abstract.search", "value": "\"large language model\"", "operator": "has"}, {"column_id": "title_and_abstract.search", "value": "\"vision language model\"", "operator": "has"}]}, {"join": "or", "filters": [{"column_id": "type", "value": "article"}, {"column_id": "type", "value": "preprint"}, {"column_id": "type", "value": "review"}]}], "sample": 2000, "seed": "42"}, "url": "/works?filter=is_retracted:false,publication_year:2025,title_and_abstract.search:%22benchmark%22|%22evaluation%22|%22factuality%22|%22hallucination%22|%22reproducibility%22,title_and_abstract.search:%22foundation model%22|%22language model%22|%22large language model%22|%22vision language model%22,type:article|preprint|review&sample=2000&seed=42&per_page=100&page=1"}, "cost_usd": 0.001}, "results": [{"id": "https://openalex.org/W4415276818", "doi": "https://doi.org/10.48550/arxiv.2510.13652", "title": "EditCast3D: Single-Frame-Guided 3D Editing with Video Propagation and View Selection", "publication_year": 2025, "publication_date": "2025-10-11", "type": "preprint", "language": null, "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5102833420", "display_name": "Huaizhi Qu", "orcid": "https://orcid.org/0009-0003-7861-0458", "observed_orcids": ["https://orcid.org/0009-0003-7861-0458"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Qu, Huaizhi", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5086677784", "display_name": "Ruichen Zhang", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Zhang, Ruichen", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5079445781", "display_name": "Shuqing Luo", "orcid": "https://orcid.org/0000-0003-4872-3315", "observed_orcids": ["https://orcid.org/0000-0003-4872-3315"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Luo, Shuqing", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5006095883", "display_name": "Luchao Qi", "orcid": "https://orcid.org/0000-0002-3192-4746", "observed_orcids": ["https://orcid.org/0000-0002-3192-4746"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Qi, Luchao", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5100361532", "display_name": "Zhihao Zhang", "orcid": "https://orcid.org/0000-0002-3038-3415", "observed_orcids": ["https://orcid.org/0000-0002-3038-3415"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Zhang, Zhihao", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5100409052", "display_name": "Xiaoming Liu", "orcid": "https://orcid.org/0000-0003-3215-8753", "observed_orcids": ["https://orcid.org/0000-0003-3215-8753"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Liu, Xiaoming", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5102635961", "display_name": "Roni Sengupta", "orcid": "https://orcid.org/0000-0001-5914-3469", "observed_orcids": ["https://orcid.org/0000-0001-5914-3469"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Sengupta, Roni", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "last", "author": {"id": "https://openalex.org/A5103073431", "display_name": "Tianlong Chen", "orcid": "https://orcid.org/0000-0001-7774-8197", "observed_orcids": ["https://orcid.org/0000-0001-7774-8197"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Chen, Tianlong", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}], "abstract_inverted_index": {"Recent": [0], "advances": [1], "in": [2, 9, 30], "foundation": [3, 34, 72, 153, 194], "models": [4, 52, 73, 154, 195], "have": [5], "driven": [6], "remarkable": [7], "progress": [8], "image": [10, 27, 143], "editing,": [11], "yet": [12], "their": [13, 37], "extension": [14], "to": [15, 24, 74, 87], "3D": [16, 103, 164, 172, 197], "editing": [17, 28, 56, 90, 94, 144, 165, 173, 177, 198], "remains": [18, 100], "underexplored.": [19], "A": [20], "natural": [21], "approach": [22], "is": [23, 108, 202], "replace": [25], "the": [26, 42, 83, 136], "modules": [29], "existing": [31, 54], "workflows": [32], "with": [33], "models.": [35], "However,": [36], "heavy": [38], "computational": [39], "demands": [40], "and": [41, 44, 123, 126, 145, 167, 179, 189], "restrictions": [43], "costs": [45], "of": [46], "closed-source": [47], "APIs": [48], "make": [49], "plugging": [50], "these": [51], "into": [53, 196], "iterative": [55], "strategies": [57], "impractical.": [58], "To": [59, 110], "address": [60], "this": [61], "limitation,": [62], "we": [63], "propose": [64], "EditCast3D,": [65], "a": [66, 78, 115, 187], "pipeline": [67, 137], "that": [68, 119, 149], "employs": [69], "video": [70, 96], "generation": [71], "propagate": [75], "edits": [76], "from": [77], "single": [79], "first": [80], "frame": [81], "across": [82, 156], "entire": [84], "dataset": [85], "prior": [86], "reconstruction.": [88], "While": [89], "propagation": [91], "enables": [92], "dataset-level": [93], "via": [95], "models,": [97], "its": [98], "consistency": [99], "suboptimal": [101], "for": [102, 192], "reconstruction,": [104], "where": [105], "multi-view": [106], "alignment": [107], "essential.": [109], "overcome": [111], "this,": [112], "EditCast3D": [113, 160, 185], "introduces": [114], "view": [116], "selection": [117], "strategy": [118], "explicitly": [120], "identifies": [121], "consistent": [122], "reconstruction-friendly": [124], "views": [125], "adopts": [127], "feedforward": [128], "reconstruction": [129], "without": [130], "requiring": [131], "costly": [132], "refinement.": [133], "In": [134], "combination,": [135], "both": [138], "minimizes": [139], "reliance": [140], "on": [141, 161], "expensive": [142], "mitigates": [146], "prompt": [147], "ambiguities": [148], "arise": [150], "when": [151], "applying": [152], "independently": [155], "images.": [157], "We": [158], "evaluate": [159], "commonly": [162], "used": [163], "datasets": [166], "compare": [168], "it": [169], "against": [170], "state-of-the-art": [171], "baselines,": [174], "demonstrating": [175], "superior": [176], "quality": [178], "high": [180], "efficiency.": [181], "These": [182], "results": [183], "establish": [184], "as": [186], "scalable": [188], "general": [190], "paradigm": [191], "integrating": [193], "pipelines.": [199], "The": [200], "code": [201], "available": [203], "at": [204], "https://github.com/UNITES-Lab/EditCast3D": [205]}, "primary_topic": {"id": "https://openalex.org/T10719", "display_name": "3D Shape Modeling and Analysis", "score": 0.8651999831199646, "subfield": {"id": "https://openalex.org/subfields/2206", "display_name": "Computational Mechanics"}, "field": {"id": "https://openalex.org/fields/22", "display_name": "Engineering"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T10719", "display_name": "3D Shape Modeling and Analysis", "score": 0.8651999831199646, "subfield": {"id": "https://openalex.org/subfields/2206", "display_name": "Computational Mechanics"}, "field": {"id": "https://openalex.org/fields/22", "display_name": "Engineering"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10775", "display_name": "Generative Adversarial Networks and Image Synthesis", "score": 0.10459999740123749, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10481", "display_name": "Computer Graphics and Visualization Techniques", "score": 0.007199999876320362, "subfield": {"id": "https://openalex.org/subfields/1704", "display_name": "Computer Graphics and Computer-Aided Design"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2510.13652", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2510.13652", "pdf_url": "https://arxiv.org/pdf/2510.13652", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": null, "license_id": null, "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W7220062444", "doi": null, "title": "\u0410\u043d\u0430\u043b\u0438\u0437 \u043c\u0435\u0442\u043e\u0434\u043e\u0432 \u0438\u0437\u0432\u043b\u0435\u0447\u0435\u043d\u0438\u044f \u043a\u043b\u044e\u0447\u0435\u0432\u044b\u0445 \u0441\u043b\u043e\u0432 \u0438\u0437 \u0442\u0435\u043a\u0441\u0442\u043e\u0432\u044b\u0445 \u0434\u0430\u043d\u043d\u044b\u0445", "publication_year": 2025, "publication_date": "2025-06-30", "type": "article", "language": "ru", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5157058530", "display_name": "\u0428. \u0418. \u041c\u0443\u0442\u0430\u0438\u0440\u043e\u0432", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I4210094909", "display_name": "Surgut State University", "ror": "https://ror.org/01gsr9f55", "country_code": "RU", "type": "education", "lineage": ["https://openalex.org/I4210094909"]}], "countries": ["RU"], "is_corresponding": false, "raw_author_name": "\u041c\u0443\u0442\u0430\u0438\u0440\u043e\u0432, \u0428. \u0418.", "raw_affiliation_strings": ["\u0421\u0443\u0440\u0433\u0443\u0442\u0441\u043a\u0438\u0439 \u0433\u043e\u0441\u0443\u0434\u0430\u0440\u0441\u0442\u0432\u0435\u043d\u043d\u044b\u0439 \u0443\u043d\u0438\u0432\u0435\u0440\u0441\u0438\u0442\u0435\u0442, \u0421\u0443\u0440\u0433\u0443\u0442, \u0420\u043e\u0441\u0441\u0438\u0439\u0441\u043a\u0430\u044f \u0424\u0435\u0434\u0435\u0440\u0430\u0446\u0438\u044f"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "\u0421\u0443\u0440\u0433\u0443\u0442\u0441\u043a\u0438\u0439 \u0433\u043e\u0441\u0443\u0434\u0430\u0440\u0441\u0442\u0432\u0435\u043d\u043d\u044b\u0439 \u0443\u043d\u0438\u0432\u0435\u0440\u0441\u0438\u0442\u0435\u0442, \u0421\u0443\u0440\u0433\u0443\u0442, \u0420\u043e\u0441\u0441\u0438\u0439\u0441\u043a\u0430\u044f \u0424\u0435\u0434\u0435\u0440\u0430\u0446\u0438\u044f", "institution_ids": ["https://openalex.org/I4210094909"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5157308877", "display_name": "\u041a. \u0418. \u0411\u0443\u0448\u043c\u0435\u043b\u0435\u0432\u0430", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I4210094909", "display_name": "Surgut State University", "ror": "https://ror.org/01gsr9f55", "country_code": "RU", "type": "education", "lineage": ["https://openalex.org/I4210094909"]}], "countries": ["RU"], "is_corresponding": false, "raw_author_name": "\u0411\u0443\u0448\u043c\u0435\u043b\u0435\u0432\u0430, \u041a. \u0418.", "raw_affiliation_strings": ["\u0421\u0443\u0440\u0433\u0443\u0442\u0441\u043a\u0438\u0439 \u0433\u043e\u0441\u0443\u0434\u0430\u0440\u0441\u0442\u0432\u0435\u043d\u043d\u044b\u0439 \u0443\u043d\u0438\u0432\u0435\u0440\u0441\u0438\u0442\u0435\u0442, \u0421\u0443\u0440\u0433\u0443\u0442, \u0420\u043e\u0441\u0441\u0438\u0439\u0441\u043a\u0430\u044f \u0424\u0435\u0434\u0435\u0440\u0430\u0446\u0438\u044f"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "\u0421\u0443\u0440\u0433\u0443\u0442\u0441\u043a\u0438\u0439 \u0433\u043e\u0441\u0443\u0434\u0430\u0440\u0441\u0442\u0432\u0435\u043d\u043d\u044b\u0439 \u0443\u043d\u0438\u0432\u0435\u0440\u0441\u0438\u0442\u0435\u0442, \u0421\u0443\u0440\u0433\u0443\u0442, \u0420\u043e\u0441\u0441\u0438\u0439\u0441\u043a\u0430\u044f \u0424\u0435\u0434\u0435\u0440\u0430\u0446\u0438\u044f", "institution_ids": ["https://openalex.org/I4210094909"]}]}], "abstract_inverted_index": {"we": [0], "conducted": [1], "a": [2], "comparative": [3], "analysis": [4], "of": [5], "keyword": [6], "extraction": [7], "methods": [8, 46], "based": [9], "on": [10], "statistical": [11], "and": [12, 18, 30], "neural": [13], "network": [14], "approaches,": [15], "including": [16], "YAKE": [17], "transformer-based": [19], "language": [20], "models": [21], "such": [22], "as": [23, 52], "BERT.": [24], "We": [25, 56], "evaluated": [26], "their": [27], "accuracy,": [28], "performance,": [29], "suitability": [31], "for": [32, 43], "different": [33], "text": [34, 49], "types.": [35], "Our": [36], "primary": [37], "goal": [38], "was": [39], "to": [40, 47], "give": [41], "recommendations": [42], "optimizing": [44], "these": [45], "enhance": [48], "processing": [50], "efficiency": [51], "data": [53], "volumes": [54], "increase.": [55], "performed": [57], "experiments": [58], "with": [59], "real": [60], "scientific": [61], "texts.": [62]}, "primary_topic": {"id": "https://openalex.org/T13083", "display_name": "Advanced Text Analysis Techniques", "score": 0.9994999766349792, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T13083", "display_name": "Advanced Text Analysis Techniques", "score": 0.9994999766349792, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.00019999999494757503, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T13910", "display_name": "Computational and Text Analysis Methods", "score": 0.0, "subfield": {"id": "https://openalex.org/subfields/3300", "display_name": "General Social Sciences"}, "field": {"id": "https://openalex.org/fields/33", "display_name": "Social Sciences"}, "domain": {"id": "https://openalex.org/domains/2", "display_name": "Social Sciences"}}], "primary_location": {"id": "pmh:oai:journal.niisi:article/410", "is_oa": true, "landing_page_url": "https://ru.jcyb.ru/nisii_tech/article/view/410", "pdf_url": "https://ru.jcyb.ru/nisii_tech/article/download/410/281", "source": {"id": "https://openalex.org/S4210222450", "display_name": "\u0423\u0441\u043f\u0435\u0445\u0438 \u043a\u0438\u0431\u0435\u0440\u043d\u0435\u0442\u0438\u043a\u0438 / Russian Journal of Cybernetics", "issn_l": "2712-9942", "issn": ["2712-9942"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": null, "host_organization_name": null, "host_organization_lineage": [], "host_organization_lineage_names": [], "type": "journal"}, "license": "cc-by", "license_id": "https://openalex.org/licenses/cc-by", "version": "publishedVersion", "is_accepted": true, "is_published": true, "raw_source_name": "2712-9942", "raw_type": "info:eu-repo/semantics/article"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W7117986238", "doi": "https://doi.org/10.48550/arxiv.2512.24058", "title": "Beyond Hallucinations: A Composite Score for Measuring Reliability in Open-Source Large Language Models", "publication_year": 2025, "publication_date": "2025-12-30", "type": "preprint", "language": null, "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5121758536", "display_name": "Rohit Kumar Salla", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Salla, Rohit Kumar", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5086534441", "display_name": "Mr. V. Saravanan", "orcid": "https://orcid.org/0009-0007-3973-238X", "observed_orcids": ["https://orcid.org/0009-0007-3973-238X"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Saravanan, Manoj", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "last", "author": {"id": "https://openalex.org/A5121825575", "display_name": "Shrikar Reddy Kota", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Kota, Shrikar Reddy", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}], "abstract_inverted_index": {"Large": [0], "Language": [1], "Models": [2], "(LLMs)": [3], "like": [4], "LLaMA,": [5], "Mistral,": [6], "and": [7, 19, 35, 62, 87, 103, 113], "Gemma": [8], "are": [9, 42], "increasingly": [10], "used": [11], "in": [12], "decision-critical": [13], "domains": [14], "such": [15], "as": [16], "healthcare,": [17], "law,": [18], "finance,": [20], "yet": [21], "their": [22], "reliability": [23], "remains": [24], "uncertain.": [25], "They": [26], "often": [27], "make": [28], "overconfident": [29], "errors,": [30], "degrade": [31], "under": [32, 84], "input": [33], "shifts,": [34], "lack": [36], "clear": [37], "uncertainty": [38, 63], "estimates.": [39], "Existing": [40], "evaluations": [41], "fragmented,": [43], "addressing": [44], "only": [45], "isolated": [46], "aspects.": [47], "We": [48], "introduce": [49], "the": [50, 106], "Composite": [51], "Reliability": [52], "Score": [53], "(CRS),": [54], "a": [55, 66], "unified": [56], "framework": [57], "that": [58, 105], "integrates": [59], "calibration,": [60], "robustness,": [61, 112], "quantification": [64], "into": [65], "single": [67, 101], "interpretable": [68], "metric.": [69], "Through": [70], "experiments": [71], "on": [72], "ten": [73], "leading": [74], "open-source": [75], "LLMs": [76], "across": [77], "five": [78], "QA": [79], "datasets,": [80], "we": [81], "assess": [82], "performance": [83], "baselines,": [85], "perturbations,": [86], "calibration": [88], "methods.": [89], "CRS": [90], "delivers": [91], "stable": [92], "model": [93], "rankings,": [94], "uncovers": [95], "hidden": [96], "failure": [97], "modes": [98], "missed": [99], "by": [100], "metrics,": [102], "highlights": [104], "most": [107], "dependable": [108], "systems": [109], "balance": [110], "accuracy,": [111], "calibrated": [114], "uncertainty.": [115]}, "primary_topic": {"id": "https://openalex.org/T11689", "display_name": "Adversarial Robustness in Machine Learning", "score": 0.7085000276565552, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T11689", "display_name": "Adversarial Robustness in Machine Learning", "score": 0.7085000276565552, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.17980000376701355, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10181", "display_name": "Natural Language Processing Techniques", "score": 0.06880000233650208, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2512.24058", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2512.24058", "pdf_url": "https://arxiv.org/pdf/2512.24058", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": null, "license_id": null, "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4415981614", "doi": "https://doi.org/10.48550/arxiv.2510.02571", "title": "How Confident are Video Models? Empowering Video Models to Express their Uncertainty", "publication_year": 2025, "publication_date": "2025-10-02", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5058149361", "display_name": "Zhiting Mei", "orcid": "https://orcid.org/0000-0003-1831-8335", "observed_orcids": ["https://orcid.org/0000-0003-1831-8335"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Mei, Zhiting", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5115087048", "display_name": "Ola Shorinwa", "orcid": "https://orcid.org/0009-0004-2514-6134", "observed_orcids": ["https://orcid.org/0009-0004-2514-6134"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Shorinwa, Ola", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "last", "author": {"id": "https://openalex.org/A5102792178", "display_name": "Anirudha Majumdar", "orcid": "https://orcid.org/0009-0002-2296-7485", "observed_orcids": ["https://orcid.org/0009-0002-2296-7485"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Majumdar, Anirudha", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}], "abstract_inverted_index": {"Generative": [0], "video": [1, 20, 52, 73, 84, 96, 115, 144, 176], "models": [2, 18, 22, 53, 97, 116], "demonstrate": [3, 179], "impressive": [4], "text-to-video": [5], "capabilities,": [6], "spurring": [7], "widespread": [8], "adoption": [9], "in": [10, 45, 143, 151], "many": [11], "real-world": [12], "applications.": [13], "However,": [14], "like": [15], "large": [16], "language": [17], "(LLMs),": [19], "generation": [21, 149], "tend": [23], "to": [24, 123, 139, 160], "hallucinate,": [25], "producing": [26], "plausible": [27], "videos": [28], "even": [29], "when": [30], "they": [31], "are": [32, 188], "factually": [33], "wrong.": [34], "Although": [35], "uncertainty": [36, 71, 80, 127, 157, 185], "quantification": [37, 81], "(UQ)": [38], "of": [39, 72, 82, 95, 169], "LLMs": [40], "has": [41], "been": [42], "extensively": [43], "studied": [44], "prior": [46], "work,": [47], "no": [48, 105], "UQ": [49, 112, 137], "method": [50, 113], "for": [51, 79, 91, 114], "exists,": [54], "raising": [55], "critical": [56], "safety": [57], "concerns.": [58], "To": [59], "our": [60], "knowledge,": [61], "this": [62], "paper": [63], "represents": [64], "the": [65, 70, 93, 148, 152, 192, 198], "first": [66], "work": [67], "towards": [68], "quantifying": [69], "models.": [74, 145], "We": [75], "present": [76], "a": [77, 89, 110, 136], "framework": [78], "generative": [83], "models,": [85], "consisting": [86], "of:": [87], "(i)": [88], "metric": [90], "evaluating": [92], "calibration": [94, 142], "based": [98], "on": [99, 174], "robust": [100], "rank": [101], "correlation": [102], "estimation": [103], "with": [104, 191], "stringent": [106], "modeling": [107, 122], "assumptions;": [108], "(ii)": [109], "black-box": [111], "(termed": [117], "S-QUBED),": [118], "which": [119], "leverages": [120], "latent": [121, 153], "rigorously": [124], "decompose": [125], "predictive": [126], "into": [128], "its": [129], "aleatoric": [130, 199], "and": [131, 134, 195, 200], "epistemic": [132, 201], "components;": [133], "(iii)": [135], "dataset": [138], "facilitate": [140], "benchmarking": [141], "By": [146], "conditioning": [147], "task": [150, 162, 193], "space,": [154], "we": [155, 178], "disentangle": [156], "arising": [158, 166], "due": [159], "vague": [161], "specifications": [163], "from": [164, 167], "that": [165, 180, 187], "lack": [168], "knowledge.": [170], "Through": [171], "extensive": [172], "experiments": [173], "benchmark": [175], "datasets,": [177], "S-QUBED": [181], "computes": [182, 197], "calibrated": [183], "total": [184], "estimates": [186], "negatively": [189], "correlated": [190], "accuracy": [194], "effectively": [196], "constituents.": [202]}, "primary_topic": {"id": "https://openalex.org/T11689", "display_name": "Adversarial Robustness in Machine Learning", "score": 0.8895999789237976, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T11689", "display_name": "Adversarial Robustness in Machine Learning", "score": 0.8895999789237976, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10775", "display_name": "Generative Adversarial Networks and Image Synthesis", "score": 0.06040000170469284, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T11714", "display_name": "Multimodal Machine Learning Applications", "score": 0.01860000006854534, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2510.02571", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2510.02571", "pdf_url": "https://arxiv.org/pdf/2510.02571", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": null, "license_id": null, "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W7105147524", "doi": "https://doi.org/10.5281/zenodo.17582777", "title": "Appendix II \u2013 Emergence and Subtext Analysis (The R-Mode Framework)", "publication_year": 2025, "publication_date": "2025-11-11", "type": "article", "language": "en", "authorships": [{"author_position": "first", "author": {"id": null, "display_name": "Ziegler, Andr\u00e9", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": true, "raw_author_name": "Ziegler, Andr\u00e9", "raw_affiliation_strings": ["Independent Researcher, Zurich, Switzerland"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Independent Researcher, Zurich, Switzerland", "institution_ids": []}]}], "abstract_inverted_index": {"This": [0, 93], "appendix": [1], "presents": [2], "structured": [3], "observations": [4, 193], "from": [5], "the": [6, 72, 134, 153, 165], "Resonance": [7], "Prompting": [8], "Framework": [9], "(R-P-F)": [10], "\u2014": [11, 40, 55, 128], "an": [12, 33], "experimental": [13], "protocol": [14], "designed": [15], "to": [16, 58, 177], "examine": [17], "interpretive": [18, 98], "behavior": [19, 94], "in": [20, 102, 132], "large": [21], "language": [22], "models": [23], "(LLMs)": [24], "under": [25], "non-factual": [26], "prompting": [27], "conditions.": [28], "The": [29, 192], "framework": [30], "investigates": [31], "whether": [32], "LLM": [34], "can": [35, 74, 146, 181], "reflect": [36], "implicit": [37], "human": [38, 185], "perception": [39], "specifically,": [41], "how": [42], "a": [43, 100, 125, 129, 149, 195, 206], "user": [44], "unconsciously": [45], "encodes": [46], "another": [47], "person": [48], "(X)": [49], "within": [50], "their": [51], "own": [52], "linguistic": [53], "field": [54], "without": [56], "access": [57], "external": [59], "data": [60], "or": [61], "inferred": [62], "cognition.": [63], "Results": [64], "indicate": [65], "that,": [66], "when": [67, 138, 184], "factual": [68], "retrieval": [69], "is": [70, 95, 116], "suspended,": [71], "model": [73, 135], "self-organize": [75], "output": [76], "through": [77, 106], "internal": [78], "probabilistic": [79, 211], "alignment,": [80], "producing": [81], "reflections": [82], "that": [83, 151], "participants": [84], "consistently": [85], "recognize": [86], "as": [87, 97, 148, 205], "coherent": [88], "representations": [89], "of": [90, 168, 218], "relational": [91], "perception.": [92, 215], "described": [96], "emergence:": [99], "state": [101], "which": [103, 133], "meaning": [104, 137], "arises": [105], "resonance": [107, 204], "rather": [108], "than": [109], "deterministic": [110], "reasoning.": [111], "Under": [112], "these": [113], "conditions,": [114], "what": [115], "conventionally": [117], "labeled": [118], "hallucination": [119, 145], "may": [120], "not": [121, 174], "represent": [122], "error": [123], "but": [124], "structural": [126], "openness": [127], "functional": [130], "mode": [131], "reorganizes": [136], "data-driven": [139], "inference": [140], "collapses.": [141], "In": [142], "this": [143], "sense,": [144], "act": [147], "feature": [150], "exposes": [152], "model\u2019s": [154], "underlying": [155], "capacity": [156], "for": [157, 198], "self-organization": [158], "and": [159, 187, 202, 213], "coherence": [160], "formation.": [161], "These": [162], "findings": [163], "support": [164], "broader": [166], "hypothesis": [167], "Resonant": [169], "Information": [170], "Transfer:": [171], "information": [172], "need": [173], "be": [175], "transmitted": [176], "appear": [178], "meaningful;": [179], "it": [180], "emerge": [182], "synchronously": [183], "attention": [186], "system": [188], "dynamics": [189], "achieve": [190], "coherence.": [191], "establish": [194], "reproducible": [196], "foundation": [197], "studying": [199], "human\u2013AI": [200], "co-reflection": [201], "position": [203], "measurable": [207], "organizing": [208], "principle": [209], "bridging": [210], "computation": [212], "conscious": [214], "Is": [216], "part": [217], "10.5281/zenodo.17482173": [219]}, "primary_topic": {"id": "https://openalex.org/T13308", "display_name": "Cybernetics and Technology in Society", "score": 0.2863999903202057, "subfield": {"id": "https://openalex.org/subfields/1207", "display_name": "History and Philosophy of Science"}, "field": {"id": "https://openalex.org/fields/12", "display_name": "Arts and Humanities"}, "domain": {"id": "https://openalex.org/domains/2", "display_name": "Social Sciences"}}, "topics": [{"id": "https://openalex.org/T13308", "display_name": "Cybernetics and Technology in Society", "score": 0.2863999903202057, "subfield": {"id": "https://openalex.org/subfields/1207", "display_name": "History and Philosophy of Science"}, "field": {"id": "https://openalex.org/fields/12", "display_name": "Arts and Humanities"}, "domain": {"id": "https://openalex.org/domains/2", "display_name": "Social Sciences"}}, {"id": "https://openalex.org/T10258", "display_name": "Philosophy and Theoretical Science", "score": 0.2632000148296356, "subfield": {"id": "https://openalex.org/subfields/3205", "display_name": "Experimental and Cognitive Psychology"}, "field": {"id": "https://openalex.org/fields/32", "display_name": "Psychology"}, "domain": {"id": "https://openalex.org/domains/2", "display_name": "Social Sciences"}}, {"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.07999999821186066, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "doi:10.5281/zenodo.17582777", "is_oa": true, "landing_page_url": "https://doi.org/10.5281/zenodo.17582777", "pdf_url": null, "source": {"id": "https://openalex.org/S4306400562", "display_name": "Zenodo (CERN European Organization for Nuclear Research)", "issn_l": null, "issn": null, "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I67311998", "host_organization_name": "European Organization for Nuclear Research", "host_organization_lineage": ["https://openalex.org/I67311998"], "host_organization_lineage_names": [], "type": "repository"}, "license": null, "license_id": null, "version": null, "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "article-journal"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W7109013111", "doi": "https://doi.org/10.48550/arxiv.2512.04618", "title": "Neural Decoding of Overt Speech from ECoG Using Vision Transformers and Contrastive Representation Learning", "publication_year": 2025, "publication_date": "2025-12-04", "type": "preprint", "language": null, "authorships": [{"author_position": "first", "author": {"id": null, "display_name": "Ticha, Mohamed Baha Ben", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Ticha, Mohamed Baha Ben", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": null, "display_name": "Ran, Xingchen", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I154526488", "display_name": "Inserm", "ror": "https://ror.org/02vjkv261", "country_code": "FR", "type": "government", "lineage": ["https://openalex.org/I154526488"]}, {"id": "https://openalex.org/I4210151160", "display_name": "Grenoble Institute of Neurosciences", "ror": "https://ror.org/04as3rk94", "country_code": "FR", "type": "facility", "lineage": ["https://openalex.org/I154526488", "https://openalex.org/I2738703131", "https://openalex.org/I4210151160", "https://openalex.org/I899635006"]}, {"id": "https://openalex.org/I899635006", "display_name": "Universit\u00e9 Grenoble Alpes", "ror": "https://ror.org/02rx3b187", "country_code": "FR", "type": "education", "lineage": ["https://openalex.org/I899635006"]}], "countries": ["FR"], "is_corresponding": false, "raw_author_name": "Ran, Xingchen", "raw_affiliation_strings": ["Univ. Grenoble Alpes , Inserm ,", "Zhejiang University , Qiushi Academy for Advanced Studies , Hangzhou , People's Republic of China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Univ. Grenoble Alpes , Inserm ,", "institution_ids": ["https://openalex.org/I154526488", "https://openalex.org/I4210151160", "https://openalex.org/I899635006"]}, {"raw_affiliation_string": "Zhejiang University , Qiushi Academy for Advanced Studies , Hangzhou , People's Republic of China", "institution_ids": []}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Saldanha, Guillaume", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I154526488", "display_name": "Inserm", "ror": "https://ror.org/02vjkv261", "country_code": "FR", "type": "government", "lineage": ["https://openalex.org/I154526488"]}, {"id": "https://openalex.org/I4210151160", "display_name": "Grenoble Institute of Neurosciences", "ror": "https://ror.org/04as3rk94", "country_code": "FR", "type": "facility", "lineage": ["https://openalex.org/I154526488", "https://openalex.org/I2738703131", "https://openalex.org/I4210151160", "https://openalex.org/I899635006"]}, {"id": "https://openalex.org/I899635006", "display_name": "Universit\u00e9 Grenoble Alpes", "ror": "https://ror.org/02rx3b187", "country_code": "FR", "type": "education", "lineage": ["https://openalex.org/I899635006"]}], "countries": ["FR"], "is_corresponding": false, "raw_author_name": "Saldanha, Guillaume", "raw_affiliation_strings": ["Univ. Grenoble Alpes , Inserm ,"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Univ. Grenoble Alpes , Inserm ,", "institution_ids": ["https://openalex.org/I154526488", "https://openalex.org/I4210151160", "https://openalex.org/I899635006"]}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Godais, Ga\u00ebl Le", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I154526488", "display_name": "Inserm", "ror": "https://ror.org/02vjkv261", "country_code": "FR", "type": "government", "lineage": ["https://openalex.org/I154526488"]}, {"id": "https://openalex.org/I4210151160", "display_name": "Grenoble Institute of Neurosciences", "ror": "https://ror.org/04as3rk94", "country_code": "FR", "type": "facility", "lineage": ["https://openalex.org/I154526488", "https://openalex.org/I2738703131", "https://openalex.org/I4210151160", "https://openalex.org/I899635006"]}, {"id": "https://openalex.org/I899635006", "display_name": "Universit\u00e9 Grenoble Alpes", "ror": "https://ror.org/02rx3b187", "country_code": "FR", "type": "education", "lineage": ["https://openalex.org/I899635006"]}], "countries": ["FR"], "is_corresponding": false, "raw_author_name": "Godais, Ga\u00ebl Le", "raw_affiliation_strings": ["Univ. Grenoble Alpes , Inserm ,"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Univ. Grenoble Alpes , Inserm ,", "institution_ids": ["https://openalex.org/I154526488", "https://openalex.org/I4210151160", "https://openalex.org/I899635006"]}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Roussel, Phil\u00e9mon", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I154526488", "display_name": "Inserm", "ror": "https://ror.org/02vjkv261", "country_code": "FR", "type": "government", "lineage": ["https://openalex.org/I154526488"]}, {"id": "https://openalex.org/I4210151160", "display_name": "Grenoble Institute of Neurosciences", "ror": "https://ror.org/04as3rk94", "country_code": "FR", "type": "facility", "lineage": ["https://openalex.org/I154526488", "https://openalex.org/I2738703131", "https://openalex.org/I4210151160", "https://openalex.org/I899635006"]}, {"id": "https://openalex.org/I899635006", "display_name": "Universit\u00e9 Grenoble Alpes", "ror": "https://ror.org/02rx3b187", "country_code": "FR", "type": "education", "lineage": ["https://openalex.org/I899635006"]}], "countries": ["FR"], "is_corresponding": false, "raw_author_name": "Roussel, Phil\u00e9mon", "raw_affiliation_strings": ["Univ. Grenoble Alpes , Inserm ,"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Univ. Grenoble Alpes , Inserm ,", "institution_ids": ["https://openalex.org/I154526488", "https://openalex.org/I4210151160", "https://openalex.org/I899635006"]}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Aubert, Marc", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I154526488", "display_name": "Inserm", "ror": "https://ror.org/02vjkv261", "country_code": "FR", "type": "government", "lineage": ["https://openalex.org/I154526488"]}, {"id": "https://openalex.org/I4210151160", "display_name": "Grenoble Institute of Neurosciences", "ror": "https://ror.org/04as3rk94", "country_code": "FR", "type": "facility", "lineage": ["https://openalex.org/I154526488", "https://openalex.org/I2738703131", "https://openalex.org/I4210151160", "https://openalex.org/I899635006"]}, {"id": "https://openalex.org/I899635006", "display_name": "Universit\u00e9 Grenoble Alpes", "ror": "https://ror.org/02rx3b187", "country_code": "FR", "type": "education", "lineage": ["https://openalex.org/I899635006"]}], "countries": ["FR"], "is_corresponding": false, "raw_author_name": "Aubert, Marc", "raw_affiliation_strings": ["Univ. Grenoble Alpes , Inserm ,"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Univ. Grenoble Alpes , Inserm ,", "institution_ids": ["https://openalex.org/I154526488", "https://openalex.org/I4210151160", "https://openalex.org/I899635006"]}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Fontanell, Amina", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I154526488", "display_name": "Inserm", "ror": "https://ror.org/02vjkv261", "country_code": "FR", "type": "government", "lineage": ["https://openalex.org/I154526488"]}, {"id": "https://openalex.org/I2800555055", "display_name": "Centre Hospitalier Universitaire de Grenoble", "ror": "https://ror.org/041rhpw39", "country_code": "FR", "type": "healthcare", "lineage": ["https://openalex.org/I2800555055"]}, {"id": "https://openalex.org/I4210151160", "display_name": "Grenoble Institute of Neurosciences", "ror": "https://ror.org/04as3rk94", "country_code": "FR", "type": "facility", "lineage": ["https://openalex.org/I154526488", "https://openalex.org/I2738703131", "https://openalex.org/I4210151160", "https://openalex.org/I899635006"]}, {"id": "https://openalex.org/I899635006", "display_name": "Universit\u00e9 Grenoble Alpes", "ror": "https://ror.org/02rx3b187", "country_code": "FR", "type": "education", "lineage": ["https://openalex.org/I899635006"]}], "countries": ["FR"], "is_corresponding": false, "raw_author_name": "Fontanell, Amina", "raw_affiliation_strings": ["Univ. Grenoble Alpes , CHU Grenoble Alpes , Department of Neurosurgery , Grenoble , France", "Univ. Grenoble Alpes , Inserm ,"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Univ. Grenoble Alpes , CHU Grenoble Alpes , Department of Neurosurgery , Grenoble , France", "institution_ids": ["https://openalex.org/I2800555055", "https://openalex.org/I899635006"]}, {"raw_affiliation_string": "Univ. Grenoble Alpes , Inserm ,", "institution_ids": ["https://openalex.org/I154526488", "https://openalex.org/I4210151160", "https://openalex.org/I899635006"]}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Costecalde, Thomas", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I2738703131", "display_name": "Commissariat \u00e0 l'\u00c9nergie Atomique et aux \u00c9nergies Alternatives", "ror": "https://ror.org/00jjx8s55", "country_code": "FR", "type": "government", "lineage": ["https://openalex.org/I2738703131"]}, {"id": "https://openalex.org/I3020098449", "display_name": "CEA Grenoble", "ror": "https://ror.org/02mg6n827", "country_code": "FR", "type": "government", "lineage": ["https://openalex.org/I2738703131", "https://openalex.org/I3020098449"]}, {"id": "https://openalex.org/I4210114253", "display_name": "Clinatec", "ror": "https://ror.org/02dpnb389", "country_code": "FR", "type": "facility", "lineage": ["https://openalex.org/I4210114253", "https://openalex.org/I4405258737"]}, {"id": "https://openalex.org/I899635006", "display_name": "Universit\u00e9 Grenoble Alpes", "ror": "https://ror.org/02rx3b187", "country_code": "FR", "type": "education", "lineage": ["https://openalex.org/I899635006"]}], "countries": ["FR"], "is_corresponding": false, "raw_author_name": "Costecalde, Thomas", "raw_affiliation_strings": ["Univ. Grenoble Alpes , CEA , LETI , Clinatec , Grenoble , France"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Univ. Grenoble Alpes , CEA , LETI , Clinatec , Grenoble , France", "institution_ids": ["https://openalex.org/I2738703131", "https://openalex.org/I3020098449", "https://openalex.org/I4210114253", "https://openalex.org/I899635006"]}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Struber, Lucas", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I2738703131", "display_name": "Commissariat \u00e0 l'\u00c9nergie Atomique et aux \u00c9nergies Alternatives", "ror": "https://ror.org/00jjx8s55", "country_code": "FR", "type": "government", "lineage": ["https://openalex.org/I2738703131"]}, {"id": "https://openalex.org/I3020098449", "display_name": "CEA Grenoble", "ror": "https://ror.org/02mg6n827", "country_code": "FR", "type": "government", "lineage": ["https://openalex.org/I2738703131", "https://openalex.org/I3020098449"]}, {"id": "https://openalex.org/I4210114253", "display_name": "Clinatec", "ror": "https://ror.org/02dpnb389", "country_code": "FR", "type": "facility", "lineage": ["https://openalex.org/I4210114253", "https://openalex.org/I4405258737"]}, {"id": "https://openalex.org/I899635006", "display_name": "Universit\u00e9 Grenoble Alpes", "ror": "https://ror.org/02rx3b187", "country_code": "FR", "type": "education", "lineage": ["https://openalex.org/I899635006"]}], "countries": ["FR"], "is_corresponding": false, "raw_author_name": "Struber, Lucas", "raw_affiliation_strings": ["Univ. Grenoble Alpes , CEA , LETI , Clinatec , Grenoble , France"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Univ. Grenoble Alpes , CEA , LETI , Clinatec , Grenoble , France", "institution_ids": ["https://openalex.org/I2738703131", "https://openalex.org/I3020098449", "https://openalex.org/I4210114253", "https://openalex.org/I899635006"]}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Karakas, Serpil", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I2738703131", "display_name": "Commissariat \u00e0 l'\u00c9nergie Atomique et aux \u00c9nergies Alternatives", "ror": "https://ror.org/00jjx8s55", "country_code": "FR", "type": "government", "lineage": ["https://openalex.org/I2738703131"]}, {"id": "https://openalex.org/I3020098449", "display_name": "CEA Grenoble", "ror": "https://ror.org/02mg6n827", "country_code": "FR", "type": "government", "lineage": ["https://openalex.org/I2738703131", "https://openalex.org/I3020098449"]}, {"id": "https://openalex.org/I4210114253", "display_name": "Clinatec", "ror": "https://ror.org/02dpnb389", "country_code": "FR", "type": "facility", "lineage": ["https://openalex.org/I4210114253", "https://openalex.org/I4405258737"]}, {"id": "https://openalex.org/I899635006", "display_name": "Universit\u00e9 Grenoble Alpes", "ror": "https://ror.org/02rx3b187", "country_code": "FR", "type": "education", "lineage": ["https://openalex.org/I899635006"]}], "countries": ["FR"], "is_corresponding": false, "raw_author_name": "Karakas, Serpil", "raw_affiliation_strings": ["Univ. Grenoble Alpes , CEA , LETI , Clinatec , Grenoble , France"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Univ. Grenoble Alpes , CEA , LETI , Clinatec , Grenoble , France", "institution_ids": ["https://openalex.org/I2738703131", "https://openalex.org/I3020098449", "https://openalex.org/I4210114253", "https://openalex.org/I899635006"]}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Zhang, Shaomin", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Zhang, Shaomin", "raw_affiliation_strings": ["Zhejiang University , Qiushi Academy for Advanced Studies , Hangzhou , People's Republic of China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Zhejiang University , Qiushi Academy for Advanced Studies , Hangzhou , People's Republic of China", "institution_ids": []}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Kahane, Philippe", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I2800555055", "display_name": "Centre Hospitalier Universitaire de Grenoble", "ror": "https://ror.org/041rhpw39", "country_code": "FR", "type": "healthcare", "lineage": ["https://openalex.org/I2800555055"]}, {"id": "https://openalex.org/I899635006", "display_name": "Universit\u00e9 Grenoble Alpes", "ror": "https://ror.org/02rx3b187", "country_code": "FR", "type": "education", "lineage": ["https://openalex.org/I899635006"]}], "countries": ["FR"], "is_corresponding": false, "raw_author_name": "Kahane, Philippe", "raw_affiliation_strings": ["Univ. Grenoble Alpes , CHU Grenoble Alpes , Department of Neurology , Grenoble , France"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Univ. Grenoble Alpes , CHU Grenoble Alpes , Department of Neurology , Grenoble , France", "institution_ids": ["https://openalex.org/I2800555055", "https://openalex.org/I899635006"]}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Charvet, Guillaume", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I2738703131", "display_name": "Commissariat \u00e0 l'\u00c9nergie Atomique et aux \u00c9nergies Alternatives", "ror": "https://ror.org/00jjx8s55", "country_code": "FR", "type": "government", "lineage": ["https://openalex.org/I2738703131"]}, {"id": "https://openalex.org/I3020098449", "display_name": "CEA Grenoble", "ror": "https://ror.org/02mg6n827", "country_code": "FR", "type": "government", "lineage": ["https://openalex.org/I2738703131", "https://openalex.org/I3020098449"]}, {"id": "https://openalex.org/I4210114253", "display_name": "Clinatec", "ror": "https://ror.org/02dpnb389", "country_code": "FR", "type": "facility", "lineage": ["https://openalex.org/I4210114253", "https://openalex.org/I4405258737"]}, {"id": "https://openalex.org/I899635006", "display_name": "Universit\u00e9 Grenoble Alpes", "ror": "https://ror.org/02rx3b187", "country_code": "FR", "type": "education", "lineage": ["https://openalex.org/I899635006"]}], "countries": ["FR"], "is_corresponding": false, "raw_author_name": "Charvet, Guillaume", "raw_affiliation_strings": ["Univ. Grenoble Alpes , CEA , LETI , Clinatec , Grenoble , France"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Univ. Grenoble Alpes , CEA , LETI , Clinatec , Grenoble , France", "institution_ids": ["https://openalex.org/I2738703131", "https://openalex.org/I3020098449", "https://openalex.org/I4210114253", "https://openalex.org/I899635006"]}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Chabard\u00e8s, St\u00e9phan", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I2738703131", "display_name": "Commissariat \u00e0 l'\u00c9nergie Atomique et aux \u00c9nergies Alternatives", "ror": "https://ror.org/00jjx8s55", "country_code": "FR", "type": "government", "lineage": ["https://openalex.org/I2738703131"]}, {"id": "https://openalex.org/I2800555055", "display_name": "Centre Hospitalier Universitaire de Grenoble", "ror": "https://ror.org/041rhpw39", "country_code": "FR", "type": "healthcare", "lineage": ["https://openalex.org/I2800555055"]}, {"id": "https://openalex.org/I3020098449", "display_name": "CEA Grenoble", "ror": "https://ror.org/02mg6n827", "country_code": "FR", "type": "government", "lineage": ["https://openalex.org/I2738703131", "https://openalex.org/I3020098449"]}, {"id": "https://openalex.org/I4210114253", "display_name": "Clinatec", "ror": "https://ror.org/02dpnb389", "country_code": "FR", "type": "facility", "lineage": ["https://openalex.org/I4210114253", "https://openalex.org/I4405258737"]}, {"id": "https://openalex.org/I899635006", "display_name": "Universit\u00e9 Grenoble Alpes", "ror": "https://ror.org/02rx3b187", "country_code": "FR", "type": "education", "lineage": ["https://openalex.org/I899635006"]}], "countries": ["FR"], "is_corresponding": false, "raw_author_name": "Chabard\u00e8s, St\u00e9phan", "raw_affiliation_strings": ["Univ. Grenoble Alpes , CEA , LETI , Clinatec , Grenoble , France", "Univ. Grenoble Alpes , CHU Grenoble Alpes , Department of Neurosurgery , Grenoble , France"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Univ. Grenoble Alpes , CEA , LETI , Clinatec , Grenoble , France", "institution_ids": ["https://openalex.org/I2738703131", "https://openalex.org/I3020098449", "https://openalex.org/I4210114253", "https://openalex.org/I899635006"]}, {"raw_affiliation_string": "Univ. Grenoble Alpes , CHU Grenoble Alpes , Department of Neurosurgery , Grenoble , France", "institution_ids": ["https://openalex.org/I2800555055", "https://openalex.org/I899635006"]}]}, {"author_position": "last", "author": {"id": null, "display_name": "Yvert, Blaise", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I154526488", "display_name": "Inserm", "ror": "https://ror.org/02vjkv261", "country_code": "FR", "type": "government", "lineage": ["https://openalex.org/I154526488"]}, {"id": "https://openalex.org/I4210151160", "display_name": "Grenoble Institute of Neurosciences", "ror": "https://ror.org/04as3rk94", "country_code": "FR", "type": "facility", "lineage": ["https://openalex.org/I154526488", "https://openalex.org/I2738703131", "https://openalex.org/I4210151160", "https://openalex.org/I899635006"]}, {"id": "https://openalex.org/I899635006", "display_name": "Universit\u00e9 Grenoble Alpes", "ror": "https://ror.org/02rx3b187", "country_code": "FR", "type": "education", "lineage": ["https://openalex.org/I899635006"]}], "countries": ["FR"], "is_corresponding": false, "raw_author_name": "Yvert, Blaise", "raw_affiliation_strings": ["Univ. Grenoble Alpes , Inserm ,"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Univ. Grenoble Alpes , Inserm ,", "institution_ids": ["https://openalex.org/I154526488", "https://openalex.org/I4210151160", "https://openalex.org/I899635006"]}]}], "abstract_inverted_index": {"Speech": [0], "Brain": [1], "Computer": [2], "Interfaces": [3], "(BCIs)": [4], "offer": [5], "promising": [6], "solutions": [7], "to": [8, 14, 48, 56, 84, 123, 176], "people": [9], "with": [10, 88, 142, 153], "severe": [11], "paralysis": [12], "unable": [13], "communicate.": [15], "A": [16, 52], "number": [17], "of": [18, 25, 39, 128, 163], "recent": [19], "studies": [20], "have": [21], "demonstrated": [22], "convincing": [23], "reconstruction": [24], "intelligible": [26], "speech": [27, 58, 107, 129, 178], "from": [28, 130, 179], "surface": [29, 89], "electrocorticographic": [30], "(ECoG)": [31], "or": [32, 41], "intracortical": [33, 78], "recordings": [34], "by": [35, 63], "predicting": [36], "a": [37, 60, 161, 164, 173, 180], "series": [38], "phonemes": [40], "words": [42], "and": [43, 120, 150, 183], "using": [44, 77], "downstream": [45], "language": [46], "models": [47], "obtain": [49, 85], "meaningful": [50], "sentences.": [51], "current": [53], "challenge": [54], "is": [55, 82, 135], "reconstruct": [57], "in": [59, 99, 146, 160], "streaming": [61], "mode": [62], "directly": [64], "regressing": [65], "cortical": [66], "signals": [67], "into": [68], "acoustic": [69], "speech.": [70], "While": [71], "this": [72, 100, 171], "has": [73], "been": [74], "achieved": [75], "recently": [76], "data,": [79], "further": [80], "work": [81], "needed": [83], "comparable": [86], "results": [87], "ECoG": [90, 131], "recordings.": [91], "In": [92], "particular,": [93], "optimizing": [94], "neural": [95, 115], "decoders": [96], "becomes": [97], "critical": [98], "case.": [101], "Here": [102], "we": [103], "present": [104], "an": [105, 112, 147], "offline": [106], "decoding": [108], "pipeline": [109], "based": [110], "on": [111, 137], "encoder-decoder": [113], "deep": [114], "architecture,": [116], "integrating": [117], "Vision": [118], "Transformers": [119], "contrastive": [121], "learning": [122], "enhance": [124], "the": [125, 154], "direct": [126], "regression": [127], "signals.": [132], "The": [133], "approach": [134], "evaluated": [136], "two": [138], "datasets,": [139], "one": [140], "obtained": [141, 152], "clinical": [143], "subdural": [144], "electrodes": [145], "epileptic": [148], "patient,": [149], "another": [151], "fully": [155, 181], "implantable": [156, 182], "WIMAGINE": [157], "epidural": [158, 185], "system": [159, 187], "participant": [162], "motor": [165], "BCI": [166], "trial.": [167], "To": [168], "our": [169], "knowledge": [170], "presents": [172], "first": [174], "attempt": [175], "decode": [177], "wireless": [184], "recording": [186], "offering": [188], "perspectives": [189], "for": [190], "long-term": [191], "use.": [192]}, "primary_topic": {"id": "https://openalex.org/T10429", "display_name": "EEG and Brain-Computer Interfaces", "score": 0.7710000276565552, "subfield": {"id": "https://openalex.org/subfields/2805", "display_name": "Cognitive Neuroscience"}, "field": {"id": "https://openalex.org/fields/28", "display_name": "Neuroscience"}, "domain": {"id": "https://openalex.org/domains/1", "display_name": "Life Sciences"}}, "topics": [{"id": "https://openalex.org/T10429", "display_name": "EEG and Brain-Computer Interfaces", "score": 0.7710000276565552, "subfield": {"id": "https://openalex.org/subfields/2805", "display_name": "Cognitive Neuroscience"}, "field": {"id": "https://openalex.org/fields/28", "display_name": "Neuroscience"}, "domain": {"id": "https://openalex.org/domains/1", "display_name": "Life Sciences"}}, {"id": "https://openalex.org/T10201", "display_name": "Speech Recognition and Synthesis", "score": 0.10670000314712524, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10860", "display_name": "Speech and Audio Processing", "score": 0.030700000002980232, "subfield": {"id": "https://openalex.org/subfields/1711", "display_name": "Signal Processing"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2512.04618", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2512.04618", "pdf_url": "https://arxiv.org/pdf/2512.04618", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": "cc-by", "license_id": "https://openalex.org/licenses/cc-by", "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4405990119", "doi": "https://doi.org/10.2139/ssrn.5080078", "title": "Long Context Window-Based Legal Interpretation of Building Codes and Regulations", "publication_year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": null, "display_name": "Jaekun Lee", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I193775966", "display_name": "Yonsei University", "ror": "https://ror.org/01wjejq96", "country_code": "KR", "type": "education", "lineage": ["https://openalex.org/I193775966"]}], "countries": ["KR"], "is_corresponding": false, "raw_author_name": "Jaekun Lee", "raw_affiliation_strings": ["Yonsei University", "Yonsei University, Seoul, Korea, Republic of (South Korea)"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Yonsei University", "institution_ids": ["https://openalex.org/I193775966"]}, {"raw_affiliation_string": "Yonsei University, Seoul, Korea, Republic of (South Korea)", "institution_ids": ["https://openalex.org/I193775966"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5075755166", "display_name": "Ghang Lee", "orcid": "https://orcid.org/0000-0002-3522-2733", "observed_orcids": ["https://orcid.org/0000-0002-3522-2733"]}, "institutions": [{"id": "https://openalex.org/I193775966", "display_name": "Yonsei University", "ror": "https://ror.org/01wjejq96", "country_code": "KR", "type": "education", "lineage": ["https://openalex.org/I193775966"]}], "countries": ["KR"], "is_corresponding": true, "raw_author_name": "Ghang Lee", "raw_affiliation_strings": ["Yonsei University", "Yonsei University, Seoul, Korea, Republic of (South Korea)"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Yonsei University", "institution_ids": ["https://openalex.org/I193775966"]}, {"raw_affiliation_string": "Yonsei University, Seoul, Korea, Republic of (South Korea)", "institution_ids": ["https://openalex.org/I193775966"]}]}], "abstract_inverted_index": null, "primary_topic": {"id": "https://openalex.org/T13643", "display_name": "Artificial Intelligence in Law", "score": 0.9326000213623047, "subfield": {"id": "https://openalex.org/subfields/3320", "display_name": "Political Science and International Relations"}, "field": {"id": "https://openalex.org/fields/33", "display_name": "Social Sciences"}, "domain": {"id": "https://openalex.org/domains/2", "display_name": "Social Sciences"}}, "topics": [{"id": "https://openalex.org/T13643", "display_name": "Artificial Intelligence in Law", "score": 0.9326000213623047, "subfield": {"id": "https://openalex.org/subfields/3320", "display_name": "Political Science and International Relations"}, "field": {"id": "https://openalex.org/fields/33", "display_name": "Social Sciences"}, "domain": {"id": "https://openalex.org/domains/2", "display_name": "Social Sciences"}}, {"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.05990000069141388, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10260", "display_name": "Software Engineering Research", "score": 0.0020000000949949026, "subfield": {"id": "https://openalex.org/subfields/1710", "display_name": "Information Systems"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "doi:10.2139/ssrn.5080078", "is_oa": true, "landing_page_url": "https://doi.org/10.2139/ssrn.5080078", "pdf_url": null, "source": {"id": "https://openalex.org/S4210172589", "display_name": "SSRN Electronic Journal", "issn_l": "1556-5068", "issn": ["1556-5068"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I1318003438", "host_organization_name": "RELX Group (Netherlands)", "host_organization_lineage": ["https://openalex.org/I1318003438"], "host_organization_lineage_names": [], "type": "repository"}, "license": null, "license_id": null, "version": "acceptedVersion", "is_accepted": true, "is_published": false, "raw_source_name": null, "raw_type": "posted-content"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4411665105", "doi": "https://doi.org/10.1177/14727978251352135", "title": "Exploring autonomous content creation in digital media using generative adversarial networks: A moral and aesthetic evaluation framework", "publication_year": 2025, "publication_date": "2025-06-25", "type": "article", "language": "en", "authorships": [{"author_position": "first", "author": {"id": null, "display_name": "Mengjie Liu", "orcid": "https://orcid.org/0009-0002-7412-5386", "observed_orcids": ["https://orcid.org/0009-0002-7412-5386"]}, "institutions": [{"id": "https://openalex.org/I3130607311", "display_name": "Changsha Normal University", "ror": "https://ror.org/02sqk3z62", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I3130607311"]}], "countries": ["CN"], "is_corresponding": true, "raw_author_name": "Mengjie Liu", "raw_affiliation_strings": ["Academy of Arts &amp; Design, Changsha Normal University, Changsha, China"], "raw_orcid": "https://orcid.org/0009-0002-7412-5386", "affiliations": [{"raw_affiliation_string": "Academy of Arts &amp; Design, Changsha Normal University, Changsha, China", "institution_ids": ["https://openalex.org/I3130607311"]}]}], "abstract_inverted_index": {"The": [0, 73, 153], "growing": [1], "influence": [2], "of": [3, 14, 23, 52, 98, 148, 219], "Artificial": [4], "Intelligence": [5], "(AI)": [6], "in": [7, 32, 76, 223], "digital": [8], "media": [9], "has": [10], "catalyzed": [11], "the": [12, 19, 46, 93, 146, 197, 217, 220], "development": [13], "autonomous": [15], "content": [16], "creation,": [17], "transforming": [18], "production": [20], "and": [21, 27, 49, 87, 113, 124, 140, 190, 201, 212, 227], "consumption": [22], "visual": [24, 71, 126, 231], "art,": [25], "design,": [26], "cultural": [28, 188], "narratives.": [29], "Despite": [30], "advances": [31], "Generative": [33], "Adversarial": [34], "Networks": [35], "(GANs),": [36], "existing": [37], "research": [38, 56], "often": [39], "overlooks": [40], "comprehensive": [41], "frameworks": [42], "that": [43, 60], "assess": [44], "both": [45], "aesthetic": [47, 78, 129], "quality": [48], "moral": [50, 82, 183], "implications": [51], "AI-generated": [53, 230], "content.": [54, 192], "This": [55], "aims": [57], "to": [58, 116, 121, 161, 180], "bridge": [59], "gap": [61], "by": [62], "proposing": [63], "a": [64, 157, 166, 172, 213], "holistic": [65], "evaluation": [66, 89], "framework": [67, 222], "tailored": [68], "for": [69], "GAN-generated": [70], "media.": [72], "novelty": [74], "lies": [75], "combining": [77], "feature": [79], "analysis": [80], "with": [81, 171], "assessment": [83], "using": [84, 145, 194], "deep": [85], "learning": [86], "language-based": [88], "tools.": [90], "It": [91], "utilizes": [92], "ArtBench-10": [94], "dataset,": [95], "which": [96], "consists": [97], "10": [99], "diverse": [100], "artistic": [101], "styles.": [102], "Preprocessing": [103], "steps": [104], "include": [105], "z-score": [106], "normalization,": [107], "Contrast-Limited": [108], "Adaptive": [109], "Histogram": [110, 147], "Equalization": [111], "(CLAHE),": [112], "resizing": [114], "images": [115], "256": [117, 119], "\u00d7": [118], "pixels": [120], "standardize": [122], "input": [123], "enhance": [125], "clarity.": [127], "For": [128], "analysis,": [130], "key": [131], "features": [132], "such": [133, 185], "as": [134, 186], "color": [135], "harmony,": [136], "texture": [137], "richness,": [138], "symmetry,": [139], "compositional": [141], "balance": [142], "were": [143], "extracted": [144], "Oriented": [149], "Gradients": [150], "(HOG)": [151], "method.": [152], "proposed": [154, 221], "method": [155], "employs": [156], "conditional": [158], "GAN": [159], "(cGAN)": [160], "autonomously": [162], "generate": [163], "artwork,": [164], "while": [165], "GPT-based": [167], "language": [168], "model,": [169], "along": [170], "rule-based": [173], "ethics": [174], "filter,": [175], "evaluates": [176], "associated": [177], "textual": [178], "metadata": [179], "detect": [181], "potential": [182], "issues,": [184], "bias,": [187], "appropriation,": [189], "offensive": [191], "Implemented": [193], "TensorFlow": [195], "2.0,": [196], "model": [198], "supports": [199], "modularity": [200], "real-time": [202], "evaluation.": [203], "Results": [204], "indicate": [205], "superior": [206], "classification": [207], "performance,": [208], "achieving": [209], "96.3%": [210], "precision": [211], "95.2%": [214], "F1-score,": [215], "confirming": [216], "effectiveness": [218], "selecting": [224], "aesthetically": [225], "coherent": [226], "ethically": [228], "sound": [229], "outputs.": [232]}, "primary_topic": {"id": "https://openalex.org/T10775", "display_name": "Generative Adversarial Networks and Image Synthesis", "score": 0.9473000168800354, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T10775", "display_name": "Generative Adversarial Networks and Image Synthesis", "score": 0.9473000168800354, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T14254", "display_name": "Digital Media and Visual Art", "score": 0.016200000420212746, "subfield": {"id": "https://openalex.org/subfields/1704", "display_name": "Computer Graphics and Computer-Aided Design"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T12357", "display_name": "Digital Media Forensic Detection", "score": 0.013700000010430813, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "doi:10.1177/14727978251352135", "is_oa": false, "landing_page_url": "https://doi.org/10.1177/14727978251352135", "pdf_url": null, "source": {"id": "https://openalex.org/S2765058733", "display_name": "Journal of Computational Methods in Sciences and Engineering", "issn_l": "1472-7978", "issn": ["1472-7978", "1875-8983"], "is_oa": false, "is_in_doaj": false, "is_core": true, "listed_in": ["cwts-core", "jufo-1", "norway-1"], "host_organization": "https://openalex.org/P4310318577", "host_organization_name": "IOS Press", "host_organization_lineage": ["https://openalex.org/P4310318577"], "host_organization_lineage_names": ["IOS Press"], "type": "journal"}, "license": null, "license_id": null, "version": "publishedVersion", "is_accepted": true, "is_published": true, "raw_source_name": "Journal of Computational Methods in Sciences and Engineering", "raw_type": "journal-article"}, "cited_by_count": 2, "is_retracted": false}, {"id": "https://openalex.org/W4408104071", "doi": "https://doi.org/10.1136/bmjopen-2024-094455", "title": "Accuracy of machine learning and traditional statistical models in the prediction of postpartum haemorrhage: a systematic review", "publication_year": 2025, "publication_date": "2025-03-01", "type": "review", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5073985557", "display_name": "Tha\u00eds Ba\u00eata", "orcid": "https://orcid.org/0000-0002-0486-1911", "observed_orcids": ["https://orcid.org/0000-0002-0486-1911"]}, "institutions": [{"id": "https://openalex.org/I110200422", "display_name": "Universidade Federal de Minas Gerais", "ror": "https://ror.org/0176yjw32", "country_code": "BR", "type": "education", "lineage": ["https://openalex.org/I110200422"]}], "countries": ["BR"], "is_corresponding": true, "raw_author_name": "Thais Baeta", "raw_affiliation_strings": ["Universidade Federal de Minas Gerais, Belo Horizonte, Brazil"], "raw_orcid": "https://orcid.org/0000-0002-0486-1911", "affiliations": [{"raw_affiliation_string": "Universidade Federal de Minas Gerais, Belo Horizonte, Brazil", "institution_ids": ["https://openalex.org/I110200422"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5079618479", "display_name": "Ana Luiza Lunardi Rocha", "orcid": "https://orcid.org/0000-0002-4717-9541", "observed_orcids": ["https://orcid.org/0000-0002-4717-9541", "https://orcid.org/0000-0003-4914-8770"]}, "institutions": [{"id": "https://openalex.org/I110200422", "display_name": "Universidade Federal de Minas Gerais", "ror": "https://ror.org/0176yjw32", "country_code": "BR", "type": "education", "lineage": ["https://openalex.org/I110200422"]}], "countries": ["BR"], "is_corresponding": false, "raw_author_name": "Ana Luiza Lunardi Rocha", "raw_affiliation_strings": ["Universidade Federal de Minas Gerais, Belo Horizonte, Brazil"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Universidade Federal de Minas Gerais, Belo Horizonte, Brazil", "institution_ids": ["https://openalex.org/I110200422"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5016491730", "display_name": "Juliana Almeida Oliveira", "orcid": "https://orcid.org/0000-0002-4704-2318", "observed_orcids": ["https://orcid.org/0000-0002-4704-2318"]}, "institutions": [{"id": "https://openalex.org/I110200422", "display_name": "Universidade Federal de Minas Gerais", "ror": "https://ror.org/0176yjw32", "country_code": "BR", "type": "education", "lineage": ["https://openalex.org/I110200422"]}], "countries": ["BR"], "is_corresponding": false, "raw_author_name": "Juliana Almeida Oliveira", "raw_affiliation_strings": ["Universidade Federal de Minas Gerais, Belo Horizonte, Brazil"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Universidade Federal de Minas Gerais, Belo Horizonte, Brazil", "institution_ids": ["https://openalex.org/I110200422"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5037898474", "display_name": "Ana Paula Couto da Silva", "orcid": "https://orcid.org/0000-0001-5951-3562", "observed_orcids": ["https://orcid.org/0000-0001-5951-3562"]}, "institutions": [{"id": "https://openalex.org/I110200422", "display_name": "Universidade Federal de Minas Gerais", "ror": "https://ror.org/0176yjw32", "country_code": "BR", "type": "education", "lineage": ["https://openalex.org/I110200422"]}], "countries": ["BR"], "is_corresponding": false, "raw_author_name": "Ana Paula Couto da Silva", "raw_affiliation_strings": ["Universidade Federal de Minas Gerais, Belo Horizonte, Brazil"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Universidade Federal de Minas Gerais, Belo Horizonte, Brazil", "institution_ids": ["https://openalex.org/I110200422"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5075019203", "display_name": "Zilma Silveira Nogueira Reis", "orcid": "https://orcid.org/0000-0001-6374-9295", "observed_orcids": ["https://orcid.org/0000-0001-6374-9295"]}, "institutions": [{"id": "https://openalex.org/I110200422", "display_name": "Universidade Federal de Minas Gerais", "ror": "https://ror.org/0176yjw32", "country_code": "BR", "type": "education", "lineage": ["https://openalex.org/I110200422"]}], "countries": ["BR"], "is_corresponding": false, "raw_author_name": "Zilma Silveira Nogueira Reis", "raw_affiliation_strings": ["Universidade Federal de Minas Gerais, Belo Horizonte, Brazil"], "raw_orcid": "https://orcid.org/0000-0001-6374-9295", "affiliations": [{"raw_affiliation_string": "Universidade Federal de Minas Gerais, Belo Horizonte, Brazil", "institution_ids": ["https://openalex.org/I110200422"]}]}], "abstract_inverted_index": {"OBJECTIVES:": [0], "To": [1], "evaluate": [2], "whether": [3], "postpartum": [4], "haemorrhage": [5], "(PPH)": [6], "can": [7], "be": [8], "predicted": [9], "using": [10, 159, 212, 218], "both": [11, 160, 219], "machine": [12], "learning": [13], "(ML)": [14], "and": [15, 23, 27, 44, 46, 85, 103, 107, 120, 123, 162, 181, 215, 240, 255], "traditional": [16, 182], "statistical": [17, 164, 183], "models.": [18, 165, 184], "DESIGN:": [19], "Diagnostic": [20], "systematic": [21], "review": [22], "meta-analysis": [24], "of": [25, 50, 65, 87, 156, 176, 243, 263, 288, 312, 334], "observational": [26, 105], "clinical": [28, 108], "studies,": [29, 233, 254], "prospectively": [30], "registered": [31], "on": [32, 99], "PROSPERO,": [33], "performed": [34, 251], "accordingly": [35], "to": [36, 170, 228, 247, 271, 280], "the": [37, 63, 112, 154, 172, 272, 295, 332], "Preferred": [38], "Reporting": [39], "Items": [40], "for": [41, 54, 128, 194, 302], "Systematic": [42], "Reviews": [43], "Meta-analysis": [45], "Prediction": [47], "model": [48, 73], "risk": [49, 174], "bias": [51], "assessment": [52], "tool": [53], "studies": [55, 106, 133, 191, 204, 211, 217], "developing,": [56], "validating": [57], "or": [58, 139, 319], "updating": [59], "prediction": [60, 122, 158, 265], "models,": [61], "with": [62, 206, 267], "use": [64], "an": [66], "independent": [67], "analysis": [68], "by": [69, 179], "a": [70, 224, 241, 259, 286, 309], "large": [71, 310], "language": [72], "(GPT-4": [74], "Open": [75], "AI).": [76], "DATA": [77], "SOURCES:": [78], "MEDLINE/PubMed,": [79], "LILACS-BVS,": [80], "Cochrane": [81], "Library,": [82], "Scopus-Elsevier,": [83], "Embase-Elsevier": [84], "Web": [86], "Science.": [88], "ELIGIBILITY": [89], "CRITERIA": [90], "FOR": [91], "SELECTED": [92], "STUDIES:": [93], "The": [94, 150, 196, 283], "literature": [95], "search": [96], "was": [97, 223, 249], "conducted": [98, 205], "4": [100], "January": [101], "2024": [102], "included": [104], "trials": [109], "published": [110], "in": [111, 202, 231, 252, 328], "past": [113], "10": [114], "years": [115], "that": [116, 124, 134], "assessed": [117], "early": [118], "PPH": [119, 121, 138, 142, 157, 177, 230, 264, 335], "applied": [125, 270], "accuracy": [126, 155], "metrics": [127], "outcomes": [129], "evaluation.": [130, 144], "We": [131], "excluded": [132], "did": [135], "not": [136], "define": [137], "had": [140, 285], "exclusive": [141], "subgroups": [143], "PRIMARY": [145], "AND": [146], "SECONDARY": [147], "OUTCOME": [148], "MEASURES:": [149], "primary": [151], "outcome": [152, 168], "is": [153, 169], "ML": [161, 180, 213, 235, 245, 256, 298], "conventional": [163, 207], "A": [166], "secondary": [167], "describe": [171], "strongest": [173], "factors": [175], "identified": [178], "RESULTS:": [185], "Of": [186], "551": [187], "citations": [188], "screened,": [189], "35": [190], "were": [192], "eligible": [193], "inclusion.": [195], "synthesis": [197], "gathered": [198], "383": [199], "648": [200], "patients": [201], "24": [203], "statistics": [208], "(CS),": [209], "9": [210], "models": [214, 239, 257, 299], "2": [216], "methods.": [220], "Multivariate": [221], "regression": [222], "preferred": [225], "modelling": [226], "approach": [227], "predict": [229], "CS": [232, 248, 268], "while": [234], "approaches": [236], "used": [237], "multiple": [238], "myriad": [242], "features.": [244], "comparison": [246], "only": [250], "two": [253], "demonstrated": [258], "95%": [260, 277], "higher": [261], "likelihood": [262], "compared": [266], "when": [269], "same": [273], "dataset": [274], "(OR": [275], "1.95,": [276], "CI": [278], "1.88": [279], "2.01,": [281], "p<0.001).": [282], "I\u00b2": [284], "value": [287], "54%,": [289], "p=0.14,": [290], "indicating": [291], "moderate": [292], "heterogeneity": [293], "between": [294], "studies.": [296], "CONCLUSIONS:": [297], "are": [300], "promising": [301], "predicting": [303], "PPH.": [304], "Nevertheless,": [305], "they": [306], "often": [307], "require": [308], "number": [311], "predictors,": [313], "which": [314], "may": [315], "limit": [316], "their": [317], "applicability": [318], "necessitate": [320], "automation": [321], "through": [322], "digital": [323], "systems.": [324], "This": [325], "poses": [326], "challenges": [327], "resource-scarce": [329], "settings": [330], "where": [331], "majority": [333], "complications": [336], "occur.": [337], "PROSPERO": [338], "REGISTRATION": [339], "NUMBER:": [340], "CRD42024521059.": [341]}, "primary_topic": {"id": "https://openalex.org/T11389", "display_name": "Maternal and fetal healthcare", "score": 0.9824000000953674, "subfield": {"id": "https://openalex.org/subfields/2735", "display_name": "Pediatrics, Perinatology and Child Health"}, "field": {"id": "https://openalex.org/fields/27", "display_name": "Medicine"}, "domain": {"id": "https://openalex.org/domains/4", "display_name": "Health Sciences"}}, "topics": [{"id": "https://openalex.org/T11389", "display_name": "Maternal and fetal healthcare", "score": 0.9824000000953674, "subfield": {"id": "https://openalex.org/subfields/2735", "display_name": "Pediatrics, Perinatology and Child Health"}, "field": {"id": "https://openalex.org/fields/27", "display_name": "Medicine"}, "domain": {"id": "https://openalex.org/domains/4", "display_name": "Health Sciences"}}, {"id": "https://openalex.org/T11396", "display_name": "Artificial Intelligence in Healthcare", "score": 0.00279999990016222, "subfield": {"id": "https://openalex.org/subfields/3605", "display_name": "Health Information Management"}, "field": {"id": "https://openalex.org/fields/36", "display_name": "Health Professions"}, "domain": {"id": "https://openalex.org/domains/4", "display_name": "Health Sciences"}}, {"id": "https://openalex.org/T10453", "display_name": "Maternal and Perinatal Health Interventions", "score": 0.001500000013038516, "subfield": {"id": "https://openalex.org/subfields/2729", "display_name": "Obstetrics and Gynecology"}, "field": {"id": "https://openalex.org/fields/27", "display_name": "Medicine"}, "domain": {"id": "https://openalex.org/domains/4", "display_name": "Health Sciences"}}], "primary_location": {"id": "doi:10.1136/bmjopen-2024-094455", "is_oa": true, "landing_page_url": "https://doi.org/10.1136/bmjopen-2024-094455", "pdf_url": null, "source": {"id": "https://openalex.org/S79054089", "display_name": "BMJ Open", "issn_l": "2044-6055", "issn": ["2044-6055"], "is_oa": true, "is_in_doaj": true, "is_core": true, "listed_in": ["cwts-core", "doaj", "doyens", "jufo-1", "ki-jl-1", "medline", "norway-1"], "host_organization": "https://openalex.org/P4310319945", "host_organization_name": "BMJ", "host_organization_lineage": ["https://openalex.org/P4310319945"], "host_organization_lineage_names": ["BMJ"], "type": "journal"}, "license": "cc-by-nc", "license_id": "https://openalex.org/licenses/cc-by-nc", "version": "publishedVersion", "is_accepted": true, "is_published": true, "raw_source_name": "BMJ Open", "raw_type": "journal-article"}, "cited_by_count": 12, "is_retracted": false}, {"id": "https://openalex.org/W4417098375", "doi": "https://doi.org/10.48550/arxiv.2505.12493", "title": "GUI-Shift: Enhancing VLM-Based GUI Agents through Self-supervised Reinforcement Learning", "publication_year": 2025, "publication_date": "2025-05-18", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5100590599", "display_name": "Longxi Gao", "orcid": "https://orcid.org/0009-0003-9762-1160", "observed_orcids": ["https://orcid.org/0009-0003-9762-1160"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Gao, Longxi", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5100425468", "display_name": "Li Zhang", "orcid": "https://orcid.org/0000-0001-8966-1451", "observed_orcids": ["https://orcid.org/0000-0001-8966-1451"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Zhang, Li", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5016707754", "display_name": "Pengzhi Gao", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Gao, Pengzhi", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5035054120", "display_name": "Wei Liu", "orcid": "https://orcid.org/0000-0003-1285-2334", "observed_orcids": ["https://orcid.org/0000-0003-1285-2334"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Liu, Wei", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": null, "display_name": "Luan, Jian", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Luan, Jian", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "last", "author": {"id": "https://openalex.org/A5089690212", "display_name": "Mengwei Xu", "orcid": "https://orcid.org/0000-0001-6271-6993", "observed_orcids": ["https://orcid.org/0000-0001-6271-6993"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Xu, Mengwei", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}], "abstract_inverted_index": {"Training": [0], "effective": [1], "Vision-Language": [2], "Models": [3], "(VLMs)": [4], "for": [5, 55], "GUI": [6, 24, 35, 48, 66, 106, 110, 113, 128, 140, 154], "agents": [7], "typically": [8], "depends": [9], "on": [10, 72, 122], "large-scale": [11], "annotated": [12, 164], "datasets,": [13], "whose": [14], "collection": [15], "is": [16], "both": [17, 127], "labor-intensive": [18], "and": [19, 59, 112, 130, 156], "error-prone.": [20], "We": [21, 94], "introduce": [22], "K-step": [23], "Transition,": [25], "a": [26, 44, 78, 158], "self-supervised": [27, 149], "inverse": [28], "dynamics": [29, 36], "task": [30, 107], "in": [31, 139], "which": [32], "VLMs": [33], "learn": [34], "by": [37], "predicting": [38], "the": [39, 53, 146], "initial": [40], "action": [41], "that": [42, 83, 120], "causes": [43], "transition": [45], "between": [46], "two": [47], "states.": [49], "This": [50, 143], "approach": [51], "eliminates": [52], "need": [54], "natural": [56], "language": [57], "instructions": [58], "enables": [60], "scalable": [61, 159], "dataset": [62], "construction": [63], "from": [64], "existing": [65], "trajectories": [67, 155], "or": [68], "automated": [69], "exploration.": [70], "Building": [71], "this": [73], "task,": [74], "we": [75], "propose": [76], "GUI-Shift,": [77], "reinforcement": [79], "learning": [80], "(RL)": [81], "framework": [82], "combines": [84], "rule-based": [85], "optimization": [86], "with": [87, 163], "data": [88], "filtering": [89], "to": [90, 126, 135, 151, 161], "improve": [91], "VLM": [92, 100], "performance.": [93], "conduct": [95], "extensive": [96], "experiments": [97], "using": [98], "multiple": [99], "backbones": [101], "across": [102], "four": [103], "benchmarks,": [104], "spanning": [105], "automation": [108, 129, 141], "(AndroidControl,": [109], "Odyssey)": [111], "grounding": [114, 131], "(ScreenSpot-v2,": [115], "ScreenSpot-Pro).": [116], "Our": [117], "results": [118], "show": [119], "training": [121, 162], "GUI-Shift": [123], "generalizes": [124], "well": [125], "tasks,": [132], "yielding": [133], "up": [134], "an": [136], "11.2%": [137], "increase": [138], "accuracy.": [142], "study": [144], "underscores": [145], "potential": [147], "of": [148], "RL": [150], "leverage": [152], "unlabeled": [153], "offers": [157], "alternative": [160], "samples.": [165]}, "primary_topic": {"id": "https://openalex.org/T10462", "display_name": "Reinforcement Learning in Robotics", "score": 0.1768999993801117, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T10462", "display_name": "Reinforcement Learning in Robotics", "score": 0.1768999993801117, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T11714", "display_name": "Multimodal Machine Learning Applications", "score": 0.16030000150203705, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10743", "display_name": "Software Testing and Debugging Techniques", "score": 0.05420000106096268, "subfield": {"id": "https://openalex.org/subfields/1712", "display_name": "Software"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2505.12493", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2505.12493", "pdf_url": "https://arxiv.org/pdf/2505.12493", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": null, "license_id": null, "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4415622900", "doi": "https://doi.org/10.21203/rs.3.rs-7714424/v1", "title": "Conversational LLM Framework for Secure and Energy-Efficient Wireless Sensor Networks", "publication_year": 2025, "publication_date": "2025-10-28", "type": "preprint", "language": null, "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5021148261", "display_name": "Sauvik Bal", "orcid": "https://orcid.org/0000-0001-5569-1855", "observed_orcids": ["https://orcid.org/0000-0001-5569-1855"]}, "institutions": [{"id": "https://openalex.org/I1288043984", "display_name": "Techno India University", "ror": "https://ror.org/00v1y6t69", "country_code": "IN", "type": "education", "lineage": ["https://openalex.org/I1288043984"]}], "countries": ["IN"], "is_corresponding": false, "raw_author_name": "Sauvik Bal", "raw_affiliation_strings": ["Techno India University, West Bengal"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Techno India University, West Bengal", "institution_ids": ["https://openalex.org/I1288043984"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5117172405", "display_name": "Lopa Mandal", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I144614875", "display_name": "Alliance University", "ror": "https://ror.org/03f4gsr42", "country_code": "IN", "type": "education", "lineage": ["https://openalex.org/I144614875"]}], "countries": ["IN"], "is_corresponding": false, "raw_author_name": "Lopa Mandal", "raw_affiliation_strings": ["Alliance University"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Alliance University", "institution_ids": ["https://openalex.org/I144614875"]}]}], "abstract_inverted_index": null, "primary_topic": {"id": "https://openalex.org/T11498", "display_name": "Security in Wireless Sensor Networks", "score": 0.7529000043869019, "subfield": {"id": "https://openalex.org/subfields/1705", "display_name": "Computer Networks and Communications"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T11498", "display_name": "Security in Wireless Sensor Networks", "score": 0.7529000043869019, "subfield": {"id": "https://openalex.org/subfields/1705", "display_name": "Computer Networks and Communications"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10080", "display_name": "Energy Efficient Wireless Sensor Networks", "score": 0.2393999993801117, "subfield": {"id": "https://openalex.org/subfields/1705", "display_name": "Computer Networks and Communications"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10273", "display_name": "IoT and Edge/Fog Computing", "score": 0.0008999999845400453, "subfield": {"id": "https://openalex.org/subfields/1705", "display_name": "Computer Networks and Communications"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "doi:10.21203/rs.3.rs-7714424/v1", "is_oa": true, "landing_page_url": "https://doi.org/10.21203/rs.3.rs-7714424/v1", "pdf_url": "https://www.researchsquare.com/article/rs-7714424/latest.pdf", "source": {"id": "https://openalex.org/S4306525896", "display_name": "Research Square", "issn_l": null, "issn": null, "is_oa": false, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": null, "host_organization_name": null, "host_organization_lineage": [], "host_organization_lineage_names": [], "type": "repository"}, "license": "cc-by", "license_id": "https://openalex.org/licenses/cc-by", "version": "acceptedVersion", "is_accepted": true, "is_published": false, "raw_source_name": "Research Square", "raw_type": "posted-content"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4416814762", "doi": "https://doi.org/10.1093/jamiaopen/ooaf157", "title": "Span-based annotation framework for LLM-based clinical named entity recognition: development and validation using Korean emergency department notes", "publication_year": 2025, "publication_date": "2025-11-03", "type": "article", "language": "en", "authorships": [{"author_position": "first", "author": {"id": null, "display_name": "Eun Hye Jang", "orcid": "https://orcid.org/0009-0005-8685-211X", "observed_orcids": ["https://orcid.org/0009-0005-8685-211X"]}, "institutions": [{"id": "https://openalex.org/I848706", "display_name": "Sungkyunkwan University", "ror": "https://ror.org/04q78tk20", "country_code": "KR", "type": "education", "lineage": ["https://openalex.org/I848706"]}], "countries": ["KR"], "is_corresponding": false, "raw_author_name": "Eun Hye Jang", "raw_affiliation_strings": ["Department of Digital Health, SAIHST, Sungkyunkwan University , Seoul, 06355,", "Smart Health Lab, SAIHST, Sungkyunkwan University , Seoul, 06355,"], "raw_orcid": "https://orcid.org/0009-0005-8685-211X", "affiliations": [{"raw_affiliation_string": "Department of Digital Health, SAIHST, Sungkyunkwan University , Seoul, 06355,", "institution_ids": ["https://openalex.org/I848706"]}, {"raw_affiliation_string": "Smart Health Lab, SAIHST, Sungkyunkwan University , Seoul, 06355,", "institution_ids": ["https://openalex.org/I848706"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5069825770", "display_name": "Javier Aguirre", "orcid": "https://orcid.org/0009-0007-6802-944X", "observed_orcids": ["https://orcid.org/0009-0007-6802-944X"]}, "institutions": [{"id": "https://openalex.org/I848706", "display_name": "Sungkyunkwan University", "ror": "https://ror.org/04q78tk20", "country_code": "KR", "type": "education", "lineage": ["https://openalex.org/I848706"]}], "countries": ["KR"], "is_corresponding": false, "raw_author_name": "Javier Aguirre", "raw_affiliation_strings": ["Smart Health Lab, SAIHST, Sungkyunkwan University , Seoul, 06355,"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Smart Health Lab, SAIHST, Sungkyunkwan University , Seoul, 06355,", "institution_ids": ["https://openalex.org/I848706"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5036127587", "display_name": "Sangji Lee", "orcid": "https://orcid.org/0009-0001-8364-3719", "observed_orcids": ["https://orcid.org/0009-0001-8364-3719"]}, "institutions": [{"id": "https://openalex.org/I848706", "display_name": "Sungkyunkwan University", "ror": "https://ror.org/04q78tk20", "country_code": "KR", "type": "education", "lineage": ["https://openalex.org/I848706"]}], "countries": ["KR"], "is_corresponding": false, "raw_author_name": "Sangji Lee", "raw_affiliation_strings": ["Department of Digital Health, SAIHST, Sungkyunkwan University , Seoul, 06355,", "Smart Health Lab, SAIHST, Sungkyunkwan University , Seoul, 06355,"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Digital Health, SAIHST, Sungkyunkwan University , Seoul, 06355,", "institution_ids": ["https://openalex.org/I848706"]}, {"raw_affiliation_string": "Smart Health Lab, SAIHST, Sungkyunkwan University , Seoul, 06355,", "institution_ids": ["https://openalex.org/I848706"]}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Hyeyoon Moon", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I848706", "display_name": "Sungkyunkwan University", "ror": "https://ror.org/04q78tk20", "country_code": "KR", "type": "education", "lineage": ["https://openalex.org/I848706"]}], "countries": ["KR"], "is_corresponding": false, "raw_author_name": "Hyeyoon Moon", "raw_affiliation_strings": ["Department of Digital Health, SAIHST, Sungkyunkwan University , Seoul, 06355,", "Smart Health Lab, SAIHST, Sungkyunkwan University , Seoul, 06355,"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Digital Health, SAIHST, Sungkyunkwan University , Seoul, 06355,", "institution_ids": ["https://openalex.org/I848706"]}, {"raw_affiliation_string": "Smart Health Lab, SAIHST, Sungkyunkwan University , Seoul, 06355,", "institution_ids": ["https://openalex.org/I848706"]}]}, {"author_position": "last", "author": {"id": null, "display_name": "Won Chul Cha", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I2802194831", "display_name": "Samsung Medical Center", "ror": "https://ror.org/05a15z872", "country_code": "KR", "type": "healthcare", "lineage": ["https://openalex.org/I2250650973", "https://openalex.org/I2802194831"]}, {"id": "https://openalex.org/I848706", "display_name": "Sungkyunkwan University", "ror": "https://ror.org/04q78tk20", "country_code": "KR", "type": "education", "lineage": ["https://openalex.org/I848706"]}], "countries": ["KR"], "is_corresponding": true, "raw_author_name": "Won Chul Cha", "raw_affiliation_strings": ["Department of Digital Health, SAIHST, Sungkyunkwan University , Seoul, 06355,", "Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine , Seoul,", "Smart Health Lab, SAIHST, Sungkyunkwan University , Seoul, 06355,"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Digital Health, SAIHST, Sungkyunkwan University , Seoul, 06355,", "institution_ids": ["https://openalex.org/I848706"]}, {"raw_affiliation_string": "Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine , Seoul,", "institution_ids": ["https://openalex.org/I2802194831", "https://openalex.org/I848706"]}, {"raw_affiliation_string": "Smart Health Lab, SAIHST, Sungkyunkwan University , Seoul, 06355,", "institution_ids": ["https://openalex.org/I848706"]}]}], "abstract_inverted_index": {"Objective:": [0], "This": [1, 193], "study": [2, 194], "aims": [3], "to": [4], "develop": [5], "and": [6, 32, 82, 135, 142, 212, 227, 233], "validate": [7], "of": [8], "a": [9, 58, 101, 151, 196], "span-based": [10, 140, 143, 198], "annotation": [11, 43, 199], "framework": [12, 200], "for": [13, 163, 201, 231], "clinical": [14, 29, 165, 203, 214], "named": [15], "entity": [16, 39, 222], "recognition": [17], "(NER)": [18], "using": [19], "large": [20], "language": [21, 85], "models": [22], "(LLMs)": [23], "based": [24], "on": [25, 67, 92], "Korean": [26, 62], "emergency": [27], "department": [28], "notes.": [30], "Materials": [31], "Methods:": [33], "Two": [34], "datasets": [35], "with": [36, 50, 76, 158], "the": [37, 51, 93, 112, 116, 181], "same": [38], "types": [40], "but": [41], "different": [42], "spans": [44, 223], "(word-": [45], "vs": [46], "phrase-level)": [47], "were": [48, 74], "constructed,": [49], "phrase-level": [52, 95, 228], "dataset": [53, 96], "further": [54, 98], "was": [55, 65, 97], "expanded": [56], "into": [57], "doubled": [59, 94], "version.": [60], "A": [61], "language-specific": [63], "LLM": [64, 81], "fine-tuned": [66, 83, 91], "each": [68], "dataset,": [69], "producing": [70], "three": [71, 109, 207], "variants": [72, 110], "that": [73, 170, 224], "compared": [75], "two": [77], "baseline": [78], "models,": [79], "few-shot": [80, 159], "small": [84], "model": [86], "(SLM).": [87], "The": [88, 123, 177], "final": [89, 124, 178], "variant": [90, 125, 179], "evaluated": [99], "against": [100], "human": [102, 182], "annotator.": [103], "Results:": [104], "In": [105, 210], "all": [106, 121, 132], "experimental": [107], "settings,": [108, 215], "outperformed": [111, 180], "baselines": [113], "by": [114, 206], "achieving": [115], "highest": [117], "F1": [118, 127], "scores": [119, 128], "across": [120, 131], "metrics.": [122], "achieved": [126], "exceeding": [129], "0.80": [130], "averaging": [133], "strategies": [134], "evaluation": [136], "metrics,": [137], "including": [138], "token-based,": [139], "exact,": [141], "partial": [144], "evaluations": [145], "demonstrating": [146], "its": [147, 185], "robustness": [148], "applicable": [149], "in": [150, 219], "practical": [152], "setting.": [153], "Discussion:": [154], "While": [155], "prompt": [156], "engineering": [157], "is": [160, 174], "widely": [161], "adopted": [162], "LLM-based": [164, 202], "NER,": [166], "our": [167], "results": [168], "proved": [169], "supervised": [171], "fine-tuning": [172], "(SFT)": [173], "consistently": [175], "superior.": [176], "annotator,": [183], "emphasizing": [184], "potential": [186], "as": [187], "an": [188], "automatic": [189], "labeling": [190], "tool.": [191], "Conclusion:": [192], "introduced": [195], "novel": [197], "NER": [204], "verified": [205], "independent": [208], "experiments.": [209], "multilingual": [211], "real-world": [213], "LLMs": [216], "have": [217], "proven": [218], "handling": [220], "complex": [221], "include": [225], "word-level": [226], "annotations,": [229], "particularly": [230], "long": [232], "attribute-rich": [234], "entities.": [235]}, "primary_topic": {"id": "https://openalex.org/T11710", "display_name": "Biomedical Text Mining and Ontologies", "score": 0.6689000129699707, "subfield": {"id": "https://openalex.org/subfields/1312", "display_name": "Molecular Biology"}, "field": {"id": "https://openalex.org/fields/13", "display_name": "Biochemistry, Genetics and Molecular Biology"}, "domain": {"id": "https://openalex.org/domains/1", "display_name": "Life Sciences"}}, "topics": [{"id": "https://openalex.org/T11710", "display_name": "Biomedical Text Mining and Ontologies", "score": 0.6689000129699707, "subfield": {"id": "https://openalex.org/subfields/1312", "display_name": "Molecular Biology"}, "field": {"id": "https://openalex.org/fields/13", "display_name": "Biochemistry, Genetics and Molecular Biology"}, "domain": {"id": "https://openalex.org/domains/1", "display_name": "Life Sciences"}}, {"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.2678000032901764, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T13702", "display_name": "Machine Learning in Healthcare", "score": 0.05249999836087227, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "doi:10.1093/jamiaopen/ooaf157", "is_oa": true, "landing_page_url": "https://doi.org/10.1093/jamiaopen/ooaf157", "pdf_url": null, "source": {"id": "https://openalex.org/S4210237468", "display_name": "JAMIA Open", "issn_l": "2574-2531", "issn": ["2574-2531"], "is_oa": true, "is_in_doaj": true, "is_core": true, "listed_in": ["cwts-core", "doaj", "doyens", "ki-jl-1", "norway-1"], "host_organization": "https://openalex.org/P4310311647", "host_organization_name": "University of Oxford", "host_organization_lineage": ["https://openalex.org/P4310311647"], "host_organization_lineage_names": ["University of Oxford"], "type": "journal"}, "license": "cc-by-nc", "license_id": "https://openalex.org/licenses/cc-by-nc", "version": "publishedVersion", "is_accepted": true, "is_published": true, "raw_source_name": "JAMIA Open", "raw_type": "journal-article"}, "cited_by_count": 1, "is_retracted": false}, {"id": "https://openalex.org/W4415070848", "doi": "https://doi.org/10.48550/arxiv.2506.13300", "title": "Seewo's Submission to MLC-SLM: Lessons learned from Speech Reasoning Language Models", "publication_year": 2025, "publication_date": "2025-06-16", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5100758169", "display_name": "Bo Hu Li", "orcid": "https://orcid.org/0000-0001-5980-4861", "observed_orcids": ["https://orcid.org/0000-0001-5980-4861"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Li, Bo", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5034706235", "display_name": "Chengben Xu", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Xu, Chengben", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "last", "author": {"id": "https://openalex.org/A5030057663", "display_name": "Wufeng Zhang", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Zhang, Wufeng", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}], "abstract_inverted_index": {"This": [0, 76], "paper": [1], "presents": [2], "Seewo's": [3], "systems": [4], "for": [5, 44, 51, 98, 106], "both": [6], "tracks": [7], "of": [8, 96, 104, 115], "the": [9, 82, 87, 113], "Multilingual": [10], "Conversational": [11], "Speech": [12], "Language": [13], "Model": [14], "Challenge": [15], "(MLC-SLM),": [16], "addressing": [17], "automatic": [18], "speech": [19, 41], "recognition": [20], "(ASR)": [21], "and": [22, 38, 62, 101], "speaker": [23], "diarization": [24], "with": [25, 65], "ASR": [26], "(SD-ASR).": [27], "We": [28], "introduce": [29], "a": [30, 94, 102], "multi-stage": [31], "training": [32], "pipeline": [33], "that": [34], "explicitly": [35], "enhances": [36], "reasoning": [37], "self-correction": [39, 72], "in": [40], "language": [42], "models": [43], "ASR.": [45], "Our": [46], "approach": [47, 77], "combines": [48], "curriculum": [49], "learning": [50], "progressive": [52], "capability": [53], "acquisition,": [54], "Chain-of-Thought": [55], "data": [56], "augmentation": [57], "to": [58, 69], "foster": [59], "intermediate": [60], "reflection,": [61], "Reinforcement": [63], "Learning": [64], "Verifiable": [66], "Rewards": [67], "(RLVR)": [68], "further": [70], "refine": [71], "through": [73], "reward-driven": [74], "optimization.": [75], "achieves": [78], "substantial": [79], "improvements": [80], "over": [81], "official": [83], "challenge": [84, 119], "baselines.": [85], "On": [86], "evaluation": [88], "set,": [89], "our": [90], "best": [91], "system": [92], "attains": [93], "WER/CER": [95], "11.57%": [97], "Track": [99, 107], "1": [100], "tcpWER/tcpCER": [103], "17.67%": [105], "2.": [108], "Comprehensive": [109], "ablation": [110], "studies": [111], "demonstrate": [112], "effectiveness": [114], "each": [116], "component": [117], "under": [118], "constraints.": [120]}, "primary_topic": {"id": "https://openalex.org/T10201", "display_name": "Speech Recognition and Synthesis", "score": 0.9994999766349792, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T10201", "display_name": "Speech Recognition and Synthesis", "score": 0.9994999766349792, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10860", "display_name": "Speech and Audio Processing", "score": 9.999999747378752e-05, "subfield": {"id": "https://openalex.org/subfields/1711", "display_name": "Signal Processing"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T12031", "display_name": "Speech and dialogue systems", "score": 9.999999747378752e-05, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2506.13300", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2506.13300", "pdf_url": "https://arxiv.org/pdf/2506.13300", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": null, "license_id": null, "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4416932165", "doi": "https://doi.org/10.48550/arxiv.2512.00082", "title": "Exploring Diagnostic Prompting Approach for Multimodal LLM-based Visual Complexity Assessment: A Case Study of Amazon Search Result Pages", "publication_year": 2025, "publication_date": "2025-11-26", "type": "preprint", "language": null, "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5120679135", "display_name": "Divendar Murtadak", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Murtadak, Divendar", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5005770150", "display_name": "Y. Kim", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Kim, Yoon", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "last", "author": {"id": "https://openalex.org/A5120537333", "display_name": "Trilokya Akula", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Akula, Trilokya", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}], "abstract_inverted_index": {"This": [0], "study": [1], "investigates": [2], "whether": [3], "diagnostic": [4, 26, 118], "prompting": [5, 27, 32, 43, 119, 142], "can": [6], "improve": [7], "Multimodal": [8], "Large": [9], "Language": [10], "Model": [11], "(MLLM)": [12], "reliability": [13], "for": [14, 107, 149], "visual": [15, 78, 104], "complexity": [16, 50], "assessment": [17], "of": [18], "Amazon": [19, 35], "Search": [20], "Results": [21], "Pages": [22], "(SRP).": [23], "We": [24], "compare": [25], "with": [28, 52, 132, 144], "standard": [29], "gestalt": [30], "principles-based": [31], "using": [33], "200": [34], "SRP": [36], "pages": [37], "and": [38, 110, 139], "human": [39, 49], "expert": [40], "annotations.": [41], "Diagnostic": [42], "showed": [44], "notable": [45], "improvements": [46], "in": [47, 93, 102, 141], "predicting": [48], "judgments,": [51], "F1-score": [53], "increasing": [54], "from": [55], "0.031": [56], "to": [57], "0.297": [58], "(+858\\%": [59], "relative": [60], "improvement),": [61], "though": [62, 129], "absolute": [63], "performance": [64], "remains": [65], "modest": [66], "(Cohen's": [67], "$\u03ba$": [68], "=": [69], "0.071).": [70], "The": [71], "decision": [72], "tree": [73], "revealed": [74], "that": [75, 117], "models": [76], "prioritize": [77], "design": [79], "elements": [80], "(badge": [81], "clutter:": [82], "38.6\\%": [83], "importance)": [84], "while": [85], "humans": [86], "emphasize": [87], "content": [88], "similarity,": [89], "suggesting": [90], "partial": [91], "alignment": [92], "reasoning": [94], "patterns.": [95], "Failure": [96], "case": [97], "analysis": [98], "reveals": [99], "persistent": [100], "challenges": [101], "MLLM": [103], "perception,": [105], "particularly": [106], "product": [108], "similarity": [109], "color": [111], "intensity": [112], "assessment.": [113], "Our": [114], "findings": [115], "indicate": [116], "represents": [120], "a": [121], "promising": [122], "initial": [123], "step": [124], "toward": [125], "human-aligned": [126], "MLLM-based": [127], "evaluation,": [128], "failure": [130], "cases": [131], "consistent": [133], "human-MLLM": [134], "disagreement": [135], "require": [136], "continued": [137], "research": [138], "refinement": [140], "approaches": [143], "larger": [145], "ground": [146], "truth": [147], "datasets": [148], "reliable": [150], "practical": [151], "deployment.": [152]}, "primary_topic": {"id": "https://openalex.org/T10286", "display_name": "Information Retrieval and Search Behavior", "score": 0.43070000410079956, "subfield": {"id": "https://openalex.org/subfields/1710", "display_name": "Information Systems"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T10286", "display_name": "Information Retrieval and Search Behavior", "score": 0.43070000410079956, "subfield": {"id": "https://openalex.org/subfields/1710", "display_name": "Information Systems"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10470", "display_name": "Usability and User Interface Design", "score": 0.15770000219345093, "subfield": {"id": "https://openalex.org/subfields/1709", "display_name": "Human-Computer Interaction"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T11714", "display_name": "Multimodal Machine Learning Applications", "score": 0.11670000106096268, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2512.00082", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2512.00082", "pdf_url": "https://arxiv.org/pdf/2512.00082", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": null, "license_id": null, "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4408960949", "doi": "https://doi.org/10.1111/jori.70004", "title": "Textual analysis of insurance claims with large language models", "publication_year": 2025, "publication_date": "2025-03-26", "type": "article", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5101926299", "display_name": "Dongchen Li", "orcid": "https://orcid.org/0000-0001-7499-7358", "observed_orcids": ["https://orcid.org/0000-0001-7499-7358", "https://orcid.org/0000-0002-7095-0816"]}, "institutions": [{"id": "https://openalex.org/I66867065", "display_name": "East China Normal University", "ror": "https://ror.org/02n96ep67", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I66867065"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Dongchen Li", "raw_affiliation_strings": ["Key Laboratory of Advanced Theory and Application in Statistics and Data Science\u2010MOE, School of Statistics East China Normal University Shanghai 200062 P.R. China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Key Laboratory of Advanced Theory and Application in Statistics and Data Science\u2010MOE, School of Statistics East China Normal University Shanghai 200062 P.R. China", "institution_ids": ["https://openalex.org/I66867065"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5071607165", "display_name": "Zhuo Jin", "orcid": "https://orcid.org/0000-0002-9488-2993", "observed_orcids": ["https://orcid.org/0000-0002-9488-2993"]}, "institutions": [{"id": "https://openalex.org/I99043593", "display_name": "Macquarie University", "ror": "https://ror.org/01sf06y89", "country_code": "AU", "type": "education", "lineage": ["https://openalex.org/I99043593"]}], "countries": ["AU"], "is_corresponding": false, "raw_author_name": "Zhuo Jin", "raw_affiliation_strings": ["Department of Actuarial Studies and Business Analytics Macquarie University 2109 NSW Australia"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Actuarial Studies and Business Analytics Macquarie University 2109 NSW Australia", "institution_ids": ["https://openalex.org/I99043593"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5014962983", "display_name": "Linyi Qian", "orcid": "https://orcid.org/0009-0006-0823-5367", "observed_orcids": ["https://orcid.org/0009-0006-0823-5367"]}, "institutions": [{"id": "https://openalex.org/I66867065", "display_name": "East China Normal University", "ror": "https://ror.org/02n96ep67", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I66867065"]}], "countries": ["CN"], "is_corresponding": true, "raw_author_name": "Linyi Qian", "raw_affiliation_strings": ["China Inclusive Aging Finance Research Center East China Normal University Shanghai 200062 P.R. China", "Key Laboratory of Advanced Theory and Application in Statistics and Data Science\u2010MOE, School of Statistics East China Normal University Shanghai 200062 P.R. China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "China Inclusive Aging Finance Research Center East China Normal University Shanghai 200062 P.R. China", "institution_ids": ["https://openalex.org/I66867065"]}, {"raw_affiliation_string": "Key Laboratory of Advanced Theory and Application in Statistics and Data Science\u2010MOE, School of Statistics East China Normal University Shanghai 200062 P.R. China", "institution_ids": ["https://openalex.org/I66867065"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5114546954", "display_name": "Hailiang Yang", "orcid": "https://orcid.org/0000-0001-8749-5031", "observed_orcids": ["https://orcid.org/0000-0001-8749-5031"]}, "institutions": [{"id": "https://openalex.org/I69356397", "display_name": "Xi\u2019an Jiaotong-Liverpool University", "ror": "https://ror.org/03zmrmn05", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I69356397"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Hailiang Yang", "raw_affiliation_strings": ["Department of Financial and Actuarial Mathematics, School of Mathematics &amp; Physics Xi'an Jiaotong\u2010Liverpool University Suzhou 215123 P.R. China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Financial and Actuarial Mathematics, School of Mathematics &amp; Physics Xi'an Jiaotong\u2010Liverpool University Suzhou 215123 P.R. China", "institution_ids": ["https://openalex.org/I69356397"]}]}], "abstract_inverted_index": {"Abstract": [0], "This": [1], "study": [2], "proposes": [3], "a": [4], "comprehensive": [5], "and": [6, 26, 30, 38, 49, 79, 94, 122, 134, 149], "general": [7], "framework": [8, 42], "for": [9, 114], "examining": [10], "discrepancies": [11], "in": [12, 23], "textual": [13], "content": [14], "using": [15], "large": [16], "language": [17], "models": [18], "(LLMs),": [19], "broadening": [20], "application": [21], "scenarios": [22], "the": [24, 97, 107], "insurtech": [25], "risk": [27], "management": [28], "fields,": [29], "conducting": [31], "empirical": [32, 83], "research": [33], "based": [34, 128], "on": [35, 129], "actual": [36, 130], "needs": [37], "real\u2010world": [39], "data.": [40], "Our": [41, 82], "integrates": [43], "OpenAI's": [44], "interface": [45], "to": [46, 59, 69], "embed": [47], "texts": [48], "project": [50], "them": [51], "into": [52], "external": [53], "categories": [54], "while": [55], "utilizing": [56], "distance": [57], "metrics": [58], "evaluate": [60], "discrepancies.": [61], "To": [62], "identify": [63], "significant": [64, 100], "disparities,": [65], "we": [66], "design": [67], "prompts": [68], "analyze": [70], "three": [71], "types": [72], "of": [73, 88, 96, 106], "relationships:": [74], "identical": [75], "information,": [76], "logical": [77], "relationships": [78], "potential": [80], "relationships.": [81, 109], "analysis": [84], "shows": [85], "that": [86, 142], "22.1%": [87], "samples": [89, 98], "exhibit": [90], "substantial": [91], "semantic": [92], "discrepancies,": [93], "38.1%": [95], "with": [99, 136], "differences": [101], "contain": [102], "at": [103], "least": [104], "one": [105], "identified": [108], "The": [110], "average": [111], "processing": [112], "time": [113], "each": [115], "sample": [116], "does": [117], "not": [118], "exceed": [119], "4": [120], "s,": [121], "all": [123], "processes": [124], "can": [125], "be": [126], "adjusted": [127], "needs.": [131], "Backtesting": [132], "results": [133], "comparisons": [135], "traditional": [137], "NLP": [138], "methods": [139], "further": [140], "demonstrate": [141], "our": [143], "proposed": [144], "method": [145], "is": [146], "both": [147], "effective": [148], "robust.": [150]}, "primary_topic": {"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.8246999979019165, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.8246999979019165, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T13910", "display_name": "Computational and Text Analysis Methods", "score": 0.14380000531673431, "subfield": {"id": "https://openalex.org/subfields/3300", "display_name": "General Social Sciences"}, "field": {"id": "https://openalex.org/fields/33", "display_name": "Social Sciences"}, "domain": {"id": "https://openalex.org/domains/2", "display_name": "Social Sciences"}}, {"id": "https://openalex.org/T13643", "display_name": "Artificial Intelligence in Law", "score": 0.003599999938160181, "subfield": {"id": "https://openalex.org/subfields/3320", "display_name": "Political Science and International Relations"}, "field": {"id": "https://openalex.org/fields/33", "display_name": "Social Sciences"}, "domain": {"id": "https://openalex.org/domains/2", "display_name": "Social Sciences"}}], "primary_location": {"id": "doi:10.1111/jori.70004", "is_oa": false, "landing_page_url": "https://doi.org/10.1111/jori.70004", "pdf_url": null, "source": {"id": "https://openalex.org/S116483603", "display_name": "Journal of Risk & Insurance", "issn_l": "0022-4367", "issn": ["0022-4367", "1539-6975"], "is_oa": false, "is_in_doaj": false, "is_core": true, "listed_in": ["abdc-a", "cwts-core", "jufo-2", "norway-1"], "host_organization": "https://openalex.org/P4310320595", "host_organization_name": "Wiley", "host_organization_lineage": ["https://openalex.org/P4310320595"], "host_organization_lineage_names": ["Wiley"], "type": "journal"}, "license": null, "license_id": null, "version": "publishedVersion", "is_accepted": true, "is_published": true, "raw_source_name": "Journal of Risk and Insurance", "raw_type": "journal-article"}, "cited_by_count": 9, "is_retracted": false}, {"id": "https://openalex.org/W4414585332", "doi": "https://doi.org/10.48550/arxiv.2505.19145", "title": "Do Large Language Models (Really) Need Statistical Foundations?", "publication_year": 2025, "publication_date": "2025-05-25", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5080575294", "display_name": "Weijie Su", "orcid": "https://orcid.org/0000-0003-1787-1219", "observed_orcids": ["https://orcid.org/0000-0003-1787-1219"]}, "institutions": [], "countries": [], "is_corresponding": true, "raw_author_name": "Su, Weijie", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}], "abstract_inverted_index": {"Large": [0], "language": [1], "models": [2, 58], "(LLMs)": [3], "represent": [4], "a": [5, 168, 179, 189], "new": [6], "paradigm": [7], "for": [8, 75], "processing": [9], "unstructured": [10], "data,": [11], "with": [12, 50, 167], "applications": [13], "across": [14], "an": [15], "unprecedented": [16], "range": [17], "of": [18, 33, 88, 182, 197], "domains.": [19], "In": [20], "this": [21], "paper,": [22], "we": [23, 46, 81], "address,": [24], "through": [25], "two": [26], "arguments,": [27, 132], "whether": [28], "the": [29, 42, 51, 84, 133, 195], "development": [30, 99], "and": [31, 65, 78, 98, 125, 146, 157, 193], "application": [32], "LLMs": [34, 54, 89, 175], "would": [35], "genuinely": [36], "benefit": [37], "from": [38, 41, 92, 188], "foundational": [39], "contributions": [40], "statistics": [43, 202], "discipline.": [44], "First,": [45], "argue": [47, 82], "affirmatively,": [48], "beginning": [49, 160], "observation": [52], "that": [53, 83, 171], "are": [55, 72, 154, 158], "inherently": [56], "statistical": [57, 70, 119, 152, 172], "due": [59, 121], "to": [60, 122, 161], "their": [61, 93, 123], "profound": [62], "data": [63, 147], "dependency": [64], "stochastic": [66], "generation": [67], "processes,": [68], "where": [69, 151], "insights": [71], "naturally": [73], "essential": [74], "handling": [76], "variability": [77], "uncertainty.": [79], "Second,": [80], "persistent": [85], "black-box": [86], "nature": [87], "--": [90, 108, 139, 150], "stemming": [91], "immense": [94], "scale,": [95], "architectural": [96], "complexity,": [97], "practices": [100], "often": [101, 126], "prioritizing": [102], "empirical": [103], "performance": [104], "over": [105], "theoretical": [106], "interpretability": [107], "renders": [109], "closed-form": [110], "or": [111], "purely": [112], "mechanistic": [113], "analyses": [114], "generally": [115], "intractable,": [116], "thereby": [117], "necessitating": [118], "approaches": [120], "flexibility": [124], "demonstrated": [127], "effectiveness.": [128], "To": [129], "substantiate": [130], "these": [131], "paper": [134], "outlines": [135], "several": [136], "research": [137, 173], "areas": [138], "including": [140], "alignment,": [141], "watermarking,": [142], "uncertainty": [143], "quantification,": [144], "evaluation,": [145], "mixture": [148], "optimization": [149], "methodologies": [153], "critically": [155], "needed": [156], "already": [159], "make": [162], "valuable": [163], "contributions.": [164], "We": [165], "conclude": [166], "discussion": [169], "suggesting": [170], "concerning": [174], "will": [176], "likely": [177], "form": [178], "diverse": [180], "``mosaic''": [181], "specialized": [183], "topics": [184], "rather": [185], "than": [186], "deriving": [187], "single": [190], "unifying": [191], "theory,": [192], "highlighting": [194], "importance": [196], "timely": [198], "engagement": [199], "by": [200], "our": [201], "community": [203], "in": [204], "LLM": [205], "research.": [206]}, "primary_topic": {"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.8057000041007996, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.8057000041007996, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10181", "display_name": "Natural Language Processing Techniques", "score": 0.08290000259876251, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T11689", "display_name": "Adversarial Robustness in Machine Learning", "score": 0.03790000081062317, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2505.19145", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2505.19145", "pdf_url": "https://arxiv.org/pdf/2505.19145", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": null, "license_id": null, "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4407424722", "doi": "https://doi.org/10.48550/arxiv.2502.07374", "title": "LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!", "publication_year": 2025, "publication_date": "2025-02-11", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5016324529", "display_name": "Dacheng Li", "orcid": "https://orcid.org/0009-0005-1895-1933", "observed_orcids": ["https://orcid.org/0009-0005-1895-1933"]}, "institutions": [{"id": "https://openalex.org/I95457486", "display_name": "University of California, Berkeley", "ror": "https://ror.org/01an7q238", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I95457486"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Li, Dacheng", "raw_affiliation_strings": ["Department of Electrical Engineering and Computer Sciences , University of California , Berkeley"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Electrical Engineering and Computer Sciences , University of California , Berkeley", "institution_ids": ["https://openalex.org/I95457486"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5039788110", "display_name": "Shiyi Cao", "orcid": "https://orcid.org/0000-0002-1953-7751", "observed_orcids": ["https://orcid.org/0000-0002-1953-7751", "https://orcid.org/0009-0005-6834-375X"]}, "institutions": [{"id": "https://openalex.org/I95457486", "display_name": "University of California, Berkeley", "ror": "https://ror.org/01an7q238", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I95457486"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Cao, Shiyi", "raw_affiliation_strings": ["Department of Electrical Engineering and Computer Sciences , University of California , Berkeley"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Electrical Engineering and Computer Sciences , University of California , Berkeley", "institution_ids": ["https://openalex.org/I95457486"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5095886747", "display_name": "Tyler Griggs", "orcid": "https://orcid.org/0009-0006-4655-0229", "observed_orcids": ["https://orcid.org/0009-0006-4655-0229"]}, "institutions": [{"id": "https://openalex.org/I95457486", "display_name": "University of California, Berkeley", "ror": "https://ror.org/01an7q238", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I95457486"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Griggs, Tyler", "raw_affiliation_strings": ["Department of Electrical Engineering and Computer Sciences , University of California , Berkeley"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Electrical Engineering and Computer Sciences , University of California , Berkeley", "institution_ids": ["https://openalex.org/I95457486"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5023175954", "display_name": "Shu Liu", "orcid": "https://orcid.org/0000-0002-9812-7821", "observed_orcids": ["https://orcid.org/0000-0002-9812-7821"]}, "institutions": [{"id": "https://openalex.org/I95457486", "display_name": "University of California, Berkeley", "ror": "https://ror.org/01an7q238", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I95457486"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Liu, Shu", "raw_affiliation_strings": ["Department of Electrical Engineering and Computer Sciences , University of California , Berkeley"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Electrical Engineering and Computer Sciences , University of California , Berkeley", "institution_ids": ["https://openalex.org/I95457486"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5042410262", "display_name": "Xiangxi Mo", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I95457486", "display_name": "University of California, Berkeley", "ror": "https://ror.org/01an7q238", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I95457486"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Mo, Xiangxi", "raw_affiliation_strings": ["Department of Electrical Engineering and Computer Sciences , University of California , Berkeley"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Electrical Engineering and Computer Sciences , University of California , Berkeley", "institution_ids": ["https://openalex.org/I95457486"]}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Tang, Eric", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Tang, Eric", "raw_affiliation_strings": ["Anyscale"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Anyscale", "institution_ids": []}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Hegde, Sumanth", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Hegde, Sumanth", "raw_affiliation_strings": ["Anyscale"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Anyscale", "institution_ids": []}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Hakhamaneshi, Kourosh", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Hakhamaneshi, Kourosh", "raw_affiliation_strings": ["Anyscale"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Anyscale", "institution_ids": []}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5022259826", "display_name": "Shishir G. Patil", "orcid": "https://orcid.org/0000-0001-6835-388X", "observed_orcids": ["https://orcid.org/0000-0001-6835-388X"]}, "institutions": [{"id": "https://openalex.org/I95457486", "display_name": "University of California, Berkeley", "ror": "https://ror.org/01an7q238", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I95457486"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Patil, Shishir G.", "raw_affiliation_strings": ["Department of Electrical Engineering and Computer Sciences , University of California , Berkeley"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Electrical Engineering and Computer Sciences , University of California , Berkeley", "institution_ids": ["https://openalex.org/I95457486"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5005554337", "display_name": "Matei Zaharia", "orcid": "https://orcid.org/0000-0002-7547-7204", "observed_orcids": ["https://orcid.org/0000-0002-7547-7204"]}, "institutions": [{"id": "https://openalex.org/I95457486", "display_name": "University of California, Berkeley", "ror": "https://ror.org/01an7q238", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I95457486"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Zaharia, Matei", "raw_affiliation_strings": ["Department of Electrical Engineering and Computer Sciences , University of California , Berkeley"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Electrical Engineering and Computer Sciences , University of California , Berkeley", "institution_ids": ["https://openalex.org/I95457486"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5072427753", "display_name": "Joseph E. Gonzalez", "orcid": "https://orcid.org/0000-0003-2921-956X", "observed_orcids": ["https://orcid.org/0000-0003-2921-956X"]}, "institutions": [{"id": "https://openalex.org/I95457486", "display_name": "University of California, Berkeley", "ror": "https://ror.org/01an7q238", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I95457486"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Gonzalez, Joseph E.", "raw_affiliation_strings": ["Department of Electrical Engineering and Computer Sciences , University of California , Berkeley"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Electrical Engineering and Computer Sciences , University of California , Berkeley", "institution_ids": ["https://openalex.org/I95457486"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5041920173", "display_name": "Ion Gabriel Stoica", "orcid": "https://orcid.org/0000-0002-5373-0088", "observed_orcids": ["https://orcid.org/0000-0002-5373-0088"]}, "institutions": [{"id": "https://openalex.org/I95457486", "display_name": "University of California, Berkeley", "ror": "https://ror.org/01an7q238", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I95457486"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Stoica, Ion", "raw_affiliation_strings": ["Department of Electrical Engineering and Computer Sciences , University of California , Berkeley"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Electrical Engineering and Computer Sciences , University of California , Berkeley", "institution_ids": ["https://openalex.org/I95457486"]}]}], "abstract_inverted_index": {"Large": [0, 41], "reasoning": [1, 6, 50, 126, 142, 166, 205, 220], "models": [2], "(LRMs)": [3], "tackle": [4], "complex": [5], "problems": [7], "by": [8], "following": [9], "long": [10, 64], "chain-of-thoughts": [11], "(Long": [12], "CoT)": [13], "that": [14, 39, 109, 153], "incorporate": [15], "reflection,": [16], "backtracking,": [17], "and": [18, 24, 56, 80, 89, 103, 209], "self-validation.": [19], "However,": [20], "the": [21, 68, 96, 110, 118, 122, 158, 216, 224], "training": [22, 66, 136, 191, 215], "techniques": [23], "data": [25], "requirements": [26], "to": [27, 95, 117, 190, 203], "elicit": [28, 204], "Long": [29, 48, 113, 159, 177], "CoT": [30, 49, 65, 114, 178], "remain": [31], "poorly": [32], "understood.": [33], "In": [34, 149], "this": [35], "work,": [36], "we": [37, 107], "find": [38, 108], "a": [40, 75, 173], "Language": [42], "model": [43, 70, 174], "(LLM)": [44], "can": [45], "effectively": [46], "learn": [47], "through": [51], "data-efficient": [52], "supervised": [53], "fine-tuning": [54], "(SFT)": [55], "parameter-efficient": [57], "low-rank": [58], "adaptation": [59], "(LoRA).": [60], "With": [61], "just": [62], "17k": [63], "samples,": [67], "Qwen2.5-32B-Instruct": [69], "achieves": [71, 184], "significant": [72], "improvements": [73], "on": [74, 86, 92, 137, 147, 176], "wide": [76], "range": [77], "of": [78, 101, 112, 124, 201, 219, 227], "math": [79], "coding": [81], "benchmarks,": [82], "including": [83], "56.7%": [84], "(+40.0%)": [85], "AIME": [87], "2024": [88], "57.0%": [90], "(+8.1%)": [91], "LiveCodeBench,": [93], "competitive": [94], "proprietary": [97], "o1-preview": [98], "model's": [99], "score": [100], "44.6%": [102], "59.1%.": [104], "More": [105], "importantly,": [106], "structure": [111], "is": [115, 223], "critical": [116], "learning": [119], "process,": [120], "whereas": [121], "content": [123], "individual": [125], "steps": [127], "has": [128], "minimal": [129], "impact.": [130], "Perturbations": [131], "affecting": [132], "content,": [133], "such": [134, 161], "as": [135, 162], "incorrect": [138, 181], "samples": [139, 179], "or": [140, 164], "removing": [141], "keywords,": [143], "have": [144], "little": [145], "impact": [146], "performance.": [148], "contrast,": [150], "structural": [151], "modifications": [152], "disrupt": [154], "logical": [155], "consistency": [156], "in": [157, 207], "CoT,": [160], "shuffling": [163], "deleting": [165], "steps,": [167], "significantly": [168], "degrade": [169], "accuracy.": [170], "For": [171], "example,": [172], "trained": [175], "with": [180, 192], "answers": [182], "still": [183], "only": [185], "3.2%": [186], "lower": [187], "accuracy": [188], "compared": [189], "fully": [193], "correct": [194], "samples.": [195], "These": [196], "insights": [197], "deepen": [198], "our": [199, 228], "understanding": [200], "how": [202], "capabilities": [206], "LLMs": [208], "highlight": [210], "key": [211], "considerations": [212], "for": [213], "efficiently": [214], "next": [217], "generation": [218], "models.": [221], "This": [222], "academic": [225], "paper": [226], "previous": [229], "released": [230], "Sky-T1-32B-Preview": [231], "model.": [232], "Codes": [233], "are": [234], "available": [235], "at": [236], "https://github.com/NovaSky-AI/SkyThought.": [237]}, "primary_topic": {"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.7792999744415283, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.7792999744415283, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10181", "display_name": "Natural Language Processing Techniques", "score": 0.0348999984562397, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T11307", "display_name": "Domain Adaptation and Few-Shot Learning", "score": 0.03020000085234642, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2502.07374", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2502.07374", "pdf_url": "https://arxiv.org/pdf/2502.07374", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": "cc-by", "license_id": "https://openalex.org/licenses/cc-by", "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4413059170", "doi": "https://doi.org/10.21203/rs.3.rs-6935611/v1", "title": "Comparative Evaluation of Viral Hepatitis Question Responses: ChatGPT-4.5 Outperforms Three Established Models", "publication_year": 2025, "publication_date": "2025-07-18", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5100555885", "display_name": "Juntao Ma", "orcid": "https://orcid.org/0009-0000-5513-4100", "observed_orcids": ["https://orcid.org/0009-0000-5513-4100"]}, "institutions": [{"id": "https://openalex.org/I131434179", "display_name": "Nanjing University of Chinese Medicine", "ror": "https://ror.org/04523zj19", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I131434179"]}, {"id": "https://openalex.org/I4210111013", "display_name": "Nanjing Drum Tower Hospital", "ror": "https://ror.org/026axqv54", "country_code": "CN", "type": "healthcare", "lineage": ["https://openalex.org/I4210111013"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Juntao Ma", "raw_affiliation_strings": ["Department of Laboratory Medicine, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Laboratory Medicine, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine", "institution_ids": ["https://openalex.org/I131434179", "https://openalex.org/I4210111013"]}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Linyan Gong", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I131434179", "display_name": "Nanjing University of Chinese Medicine", "ror": "https://ror.org/04523zj19", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I131434179"]}, {"id": "https://openalex.org/I4210111013", "display_name": "Nanjing Drum Tower Hospital", "ror": "https://ror.org/026axqv54", "country_code": "CN", "type": "healthcare", "lineage": ["https://openalex.org/I4210111013"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Linyan Gong", "raw_affiliation_strings": ["Department of Laboratory Medicine, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Laboratory Medicine, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine", "institution_ids": ["https://openalex.org/I131434179", "https://openalex.org/I4210111013"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5022259494", "display_name": "Yuchen Song", "orcid": "https://orcid.org/0000-0001-9554-4964", "observed_orcids": ["https://orcid.org/0000-0001-9554-4964"]}, "institutions": [{"id": "https://openalex.org/I131434179", "display_name": "Nanjing University of Chinese Medicine", "ror": "https://ror.org/04523zj19", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I131434179"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Yuchen Song", "raw_affiliation_strings": ["School of Integrative Medicine\uff0cNanjing University of Chinese Medicine"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "School of Integrative Medicine\uff0cNanjing University of Chinese Medicine", "institution_ids": ["https://openalex.org/I131434179"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5026816160", "display_name": "Guiyang Wang", "orcid": "https://orcid.org/0000-0001-7380-6783", "observed_orcids": ["https://orcid.org/0000-0001-7380-6783"]}, "institutions": [{"id": "https://openalex.org/I4210111013", "display_name": "Nanjing Drum Tower Hospital", "ror": "https://ror.org/026axqv54", "country_code": "CN", "type": "healthcare", "lineage": ["https://openalex.org/I4210111013"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Guiyang Wang", "raw_affiliation_strings": ["Department of Infectious Diseases, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Infectious Diseases, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School", "institution_ids": ["https://openalex.org/I4210111013"]}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Juan Xia", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I4210111013", "display_name": "Nanjing Drum Tower Hospital", "ror": "https://ror.org/026axqv54", "country_code": "CN", "type": "healthcare", "lineage": ["https://openalex.org/I4210111013"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Juan Xia", "raw_affiliation_strings": ["Department of Infectious Diseases, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Infectious Diseases, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School", "institution_ids": ["https://openalex.org/I4210111013"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5115602468", "display_name": "Yun Liu", "orcid": "https://orcid.org/0000-0003-4377-1211", "observed_orcids": ["https://orcid.org/0000-0003-4377-1211"]}, "institutions": [{"id": "https://openalex.org/I4210111013", "display_name": "Nanjing Drum Tower Hospital", "ror": "https://ror.org/026axqv54", "country_code": "CN", "type": "healthcare", "lineage": ["https://openalex.org/I4210111013"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Yun Liu", "raw_affiliation_strings": ["Department of Emergency, Nanjing Drum Tower Hospital"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Emergency, Nanjing Drum Tower Hospital", "institution_ids": ["https://openalex.org/I4210111013"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5100748885", "display_name": "Bei Jia", "orcid": "https://orcid.org/0000-0003-4383-7145", "observed_orcids": ["https://orcid.org/0000-0003-4383-7145"]}, "institutions": [{"id": "https://openalex.org/I4210111013", "display_name": "Nanjing Drum Tower Hospital", "ror": "https://ror.org/026axqv54", "country_code": "CN", "type": "healthcare", "lineage": ["https://openalex.org/I4210111013"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Bei Jia", "raw_affiliation_strings": ["Department of Infectious Diseases, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Infectious Diseases, Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School", "institution_ids": ["https://openalex.org/I4210111013"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5100416071", "display_name": "Yuxin Chen", "orcid": "https://orcid.org/0000-0001-5955-687X", "observed_orcids": ["https://orcid.org/0000-0001-5955-687X", "https://orcid.org/0009-0000-5541-5914", "https://orcid.org/0000-0003-2133-140X"]}, "institutions": [{"id": "https://openalex.org/I131434179", "display_name": "Nanjing University of Chinese Medicine", "ror": "https://ror.org/04523zj19", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I131434179"]}, {"id": "https://openalex.org/I4210111013", "display_name": "Nanjing Drum Tower Hospital", "ror": "https://ror.org/026axqv54", "country_code": "CN", "type": "healthcare", "lineage": ["https://openalex.org/I4210111013"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Yuxin Chen", "raw_affiliation_strings": ["Department of Laboratory Medicine, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Laboratory Medicine, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine", "institution_ids": ["https://openalex.org/I131434179", "https://openalex.org/I4210111013"]}]}], "abstract_inverted_index": null, "primary_topic": {"id": "https://openalex.org/T11636", "display_name": "Artificial Intelligence in Healthcare and Education", "score": 0.9994999766349792, "subfield": {"id": "https://openalex.org/subfields/2718", "display_name": "Health Informatics"}, "field": {"id": "https://openalex.org/fields/27", "display_name": "Medicine"}, "domain": {"id": "https://openalex.org/domains/4", "display_name": "Health Sciences"}}, "topics": [{"id": "https://openalex.org/T11636", "display_name": "Artificial Intelligence in Healthcare and Education", "score": 0.9994999766349792, "subfield": {"id": "https://openalex.org/subfields/2718", "display_name": "Health Informatics"}, "field": {"id": "https://openalex.org/fields/27", "display_name": "Medicine"}, "domain": {"id": "https://openalex.org/domains/4", "display_name": "Health Sciences"}}, {"id": "https://openalex.org/T11396", "display_name": "Artificial Intelligence in Healthcare", "score": 9.999999747378752e-05, "subfield": {"id": "https://openalex.org/subfields/3605", "display_name": "Health Information Management"}, "field": {"id": "https://openalex.org/fields/36", "display_name": "Health Professions"}, "domain": {"id": "https://openalex.org/domains/4", "display_name": "Health Sciences"}}, {"id": "https://openalex.org/T14414", "display_name": "Artificial Intelligence in Education", "score": 0.0, "subfield": {"id": "https://openalex.org/subfields/1710", "display_name": "Information Systems"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "doi:10.21203/rs.3.rs-6935611/v1", "is_oa": true, "landing_page_url": "https://doi.org/10.21203/rs.3.rs-6935611/v1", "pdf_url": "https://www.researchsquare.com/article/rs-6935611/latest.pdf", "source": {"id": "https://openalex.org/S4306525896", "display_name": "Research Square", "issn_l": null, "issn": null, "is_oa": false, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": null, "host_organization_name": null, "host_organization_lineage": [], "host_organization_lineage_names": [], "type": "repository"}, "license": "cc-by", "license_id": "https://openalex.org/licenses/cc-by", "version": "acceptedVersion", "is_accepted": true, "is_published": false, "raw_source_name": "Research Square", "raw_type": "posted-content"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4416085050", "doi": "https://doi.org/10.48550/arxiv.2503.03485", "title": "TEDDY: A Family Of Foundation Models For Understanding Single Cell Biology", "publication_year": 2025, "publication_date": "2025-03-05", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": null, "display_name": "Chevalier, Alexis", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Chevalier, Alexis", "raw_affiliation_strings": ["BCG AI Science Institute , Boston , USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "BCG AI Science Institute , Boston , USA", "institution_ids": []}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5017116101", "display_name": "Soumya Sankar Ghosh", "orcid": "https://orcid.org/0000-0002-4469-4070", "observed_orcids": ["https://orcid.org/0000-0002-4469-4070"]}, "institutions": [{"id": "https://openalex.org/I1285764155", "display_name": "Merck & Co., Inc., Rahway, NJ, USA (United States)", "ror": "https://ror.org/02891sr49", "country_code": "US", "type": "company", "lineage": ["https://openalex.org/I1285764155"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Ghosh, Soumya", "raw_affiliation_strings": ["Merck & Co., Inc. , Cambridge , MA , USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Merck & Co., Inc. , Cambridge , MA , USA", "institution_ids": ["https://openalex.org/I1285764155"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5108836043", "display_name": "Urvi Awasthi", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Awasthi, Urvi", "raw_affiliation_strings": ["BCG AI Science Institute , Boston , USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "BCG AI Science Institute , Boston , USA", "institution_ids": []}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5073217965", "display_name": "James J. Watkins", "orcid": "https://orcid.org/0000-0001-8302-825X", "observed_orcids": ["https://orcid.org/0000-0001-8302-825X"]}, "institutions": [{"id": "https://openalex.org/I1285764155", "display_name": "Merck & Co., Inc., Rahway, NJ, USA (United States)", "ror": "https://ror.org/02891sr49", "country_code": "US", "type": "company", "lineage": ["https://openalex.org/I1285764155"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Watkins, James", "raw_affiliation_strings": ["Merck & Co., Inc. , Cambridge , MA , USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Merck & Co., Inc. , Cambridge , MA , USA", "institution_ids": ["https://openalex.org/I1285764155"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5060616084", "display_name": "Julia Bieniewska", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Bieniewska, Julia", "raw_affiliation_strings": ["BCG AI Science Institute , Boston , USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "BCG AI Science Institute , Boston , USA", "institution_ids": []}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5120334147", "display_name": "Nichita Mitrea", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Mitrea, Nichita", "raw_affiliation_strings": ["MSD (UK) Lim- ited , London , UK"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "MSD (UK) Lim- ited , London , UK", "institution_ids": []}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Kotova, Olga", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I1285764155", "display_name": "Merck & Co., Inc., Rahway, NJ, USA (United States)", "ror": "https://ror.org/02891sr49", "country_code": "US", "type": "company", "lineage": ["https://openalex.org/I1285764155"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Kotova, Olga", "raw_affiliation_strings": ["Merck & Co., Inc. , Cambridge , MA , USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Merck & Co., Inc. , Cambridge , MA , USA", "institution_ids": ["https://openalex.org/I1285764155"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5005687071", "display_name": "Kirill Shkura", "orcid": "https://orcid.org/0000-0002-1722-0910", "observed_orcids": ["https://orcid.org/0000-0002-1722-0910"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Shkura, Kirill", "raw_affiliation_strings": ["MSD (UK) Lim- ited , London , UK"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "MSD (UK) Lim- ited , London , UK", "institution_ids": []}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5054858507", "display_name": "Andrew Noble", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I1285764155", "display_name": "Merck & Co., Inc., Rahway, NJ, USA (United States)", "ror": "https://ror.org/02891sr49", "country_code": "US", "type": "company", "lineage": ["https://openalex.org/I1285764155"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Noble, Andrew", "raw_affiliation_strings": ["Merck & Co., Inc. , Cambridge , MA , USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Merck & Co., Inc. , Cambridge , MA , USA", "institution_ids": ["https://openalex.org/I1285764155"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5050979856", "display_name": "Michael J. Steinbaugh", "orcid": "https://orcid.org/0000-0002-2403-2221", "observed_orcids": ["https://orcid.org/0000-0002-2403-2221"]}, "institutions": [{"id": "https://openalex.org/I1285764155", "display_name": "Merck & Co., Inc., Rahway, NJ, USA (United States)", "ror": "https://ror.org/02891sr49", "country_code": "US", "type": "company", "lineage": ["https://openalex.org/I1285764155"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Steinbaugh, Michael", "raw_affiliation_strings": ["Merck & Co., Inc. , Cambridge , MA , USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Merck & Co., Inc. , Cambridge , MA , USA", "institution_ids": ["https://openalex.org/I1285764155"]}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Sadashivaiah, Vijay", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I1285764155", "display_name": "Merck & Co., Inc., Rahway, NJ, USA (United States)", "ror": "https://ror.org/02891sr49", "country_code": "US", "type": "company", "lineage": ["https://openalex.org/I1285764155"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Sadashivaiah, Vijay", "raw_affiliation_strings": ["Merck & Co., Inc. , Cambridge , MA , USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Merck & Co., Inc. , Cambridge , MA , USA", "institution_ids": ["https://openalex.org/I1285764155"]}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Dasoulas, George", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I1285764155", "display_name": "Merck & Co., Inc., Rahway, NJ, USA (United States)", "ror": "https://ror.org/02891sr49", "country_code": "US", "type": "company", "lineage": ["https://openalex.org/I1285764155"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Dasoulas, George", "raw_affiliation_strings": ["Merck & Co., Inc. , Cambridge , MA , USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Merck & Co., Inc. , Cambridge , MA , USA", "institution_ids": ["https://openalex.org/I1285764155"]}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Delile, Julien", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Delile, Julien", "raw_affiliation_strings": ["BCG AI Science Institute , Boston , USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "BCG AI Science Institute , Boston , USA", "institution_ids": []}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Meier, Christoph", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Meier, Christoph", "raw_affiliation_strings": ["BCG AI Science Institute , Boston , USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "BCG AI Science Institute , Boston , USA", "institution_ids": []}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Zhukov, Leonid", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Zhukov, Leonid", "raw_affiliation_strings": ["BCG AI Science Institute , Boston , USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "BCG AI Science Institute , Boston , USA", "institution_ids": []}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Khalil, Iya", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I1285764155", "display_name": "Merck & Co., Inc., Rahway, NJ, USA (United States)", "ror": "https://ror.org/02891sr49", "country_code": "US", "type": "company", "lineage": ["https://openalex.org/I1285764155"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Khalil, Iya", "raw_affiliation_strings": ["Merck & Co., Inc. , Cambridge , MA , USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Merck & Co., Inc. , Cambridge , MA , USA", "institution_ids": ["https://openalex.org/I1285764155"]}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Mukherjee, Srayanta", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Mukherjee, Srayanta", "raw_affiliation_strings": ["BCG AI Science Institute , Boston , USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "BCG AI Science Institute , Boston , USA", "institution_ids": []}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5074292004", "display_name": "Judith Mueller", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I1285764155", "display_name": "Merck & Co., Inc., Rahway, NJ, USA (United States)", "ror": "https://ror.org/02891sr49", "country_code": "US", "type": "company", "lineage": ["https://openalex.org/I1285764155"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Mueller, Judith", "raw_affiliation_strings": ["Merck & Co., Inc. , Cambridge , MA , USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Merck & Co., Inc. , Cambridge , MA , USA", "institution_ids": ["https://openalex.org/I1285764155"]}]}], "abstract_inverted_index": {"Understanding": [0], "the": [1, 38, 73, 96, 111, 143, 171], "biological": [2, 20, 100], "mechanisms": [3], "of": [4, 18, 31, 40, 80, 98, 105, 114, 147], "disease": [5, 45, 145, 161], "is": [6, 85], "crucial": [7], "for": [8, 13, 44, 65, 160, 174], "medicine,": [9], "and": [10, 128, 157, 163, 169, 185, 197], "in": [11, 25, 57], "particular,": [12], "drug": [14], "discovery.": [15], "AI-powered": [16], "analysis": [17], "genome-scale": [19], "data": [21, 35, 75, 195], "holds": [22], "great": [23], "potential": [24], "this": [26], "regard.": [27], "The": [28], "increasing": [29], "availability": [30, 97], "single-cell": [32, 67, 120], "RNA": [33], "sequencing": [34], "has": [36], "enabled": [37], "development": [39], "large": [41], "foundation": [42, 49, 68, 121], "models": [43, 50, 56, 115, 122, 135, 178], "biology.": [46, 176], "However,": [47], "existing": [48, 183], "only": [51], "modestly": [52], "improve": [53], "over": [54, 182], "task-specific": [55], "downstream": [58, 138], "applications.": [59], "Here,": [60], "we": [61, 71, 94], "explored": [62], "two": [63], "avenues": [64], "improving": [66], "models.": [69, 92], "First,": [70], "scaled": [72], "pre-training": [74], "to": [76], "a": [77, 103], "diverse": [78], "collection": [79], "116": [81], "million": [82, 130], "cells,": [83], "which": [84], "larger": [86], "than": [87], "those": [88], "used": [89], "by": [90], "previous": [91], "Second,": [93], "leveraged": [95], "large-scale": [99], "annotations": [101], "as": [102], "form": [104], "supervision": [106], "during": [107, 152, 167], "pre-training.": [108], "We": [109, 132], "trained": [110], "\\model": [112], "family": [113], "comprising": [116], "six": [117], "transformer-based": [118], "state-of-the-art": [119], "with": [123, 193], "70": [124], "million,": [125, 127], "160": [126], "400": [129], "parameters.": [131], "vetted": [133], "our": [134], "on": [136], "several": [137], "evaluation": [139], "tasks,": [140], "including": [141], "identifying": [142], "underlying": [144], "state": [146], "held-out": [148], "donors": [149, 164], "not": [150, 165], "seen": [151, 166], "training,": [153, 168], "distinguishing": [154], "between": [155], "diseased": [156], "healthy": [158], "cells": [159], "conditions": [162], "probing": [170], "learned": [172], "representations": [173], "known": [175], "Our": [177], "showed": [179, 188], "substantial": [180], "improvement": [181], "works,": [184], "scaling": [186], "experiments": [187], "that": [189], "performance": [190], "improved": [191], "predictably": [192], "both": [194], "volume": [196], "parameter": [198], "count.": [199]}, "primary_topic": {"id": "https://openalex.org/T11289", "display_name": "Single-cell and spatial transcriptomics", "score": 0.9994999766349792, "subfield": {"id": "https://openalex.org/subfields/1312", "display_name": "Molecular Biology"}, "field": {"id": "https://openalex.org/fields/13", "display_name": "Biochemistry, Genetics and Molecular Biology"}, "domain": {"id": "https://openalex.org/domains/1", "display_name": "Life Sciences"}}, "topics": [{"id": "https://openalex.org/T11289", "display_name": "Single-cell and spatial transcriptomics", "score": 0.9994999766349792, "subfield": {"id": "https://openalex.org/subfields/1312", "display_name": "Molecular Biology"}, "field": {"id": "https://openalex.org/fields/13", "display_name": "Biochemistry, Genetics and Molecular Biology"}, "domain": {"id": "https://openalex.org/domains/1", "display_name": "Life Sciences"}}, {"id": "https://openalex.org/T10885", "display_name": "Gene expression and cancer classification", "score": 9.999999747378752e-05, "subfield": {"id": "https://openalex.org/subfields/1312", "display_name": "Molecular Biology"}, "field": {"id": "https://openalex.org/fields/13", "display_name": "Biochemistry, Genetics and Molecular Biology"}, "domain": {"id": "https://openalex.org/domains/1", "display_name": "Life Sciences"}}, {"id": "https://openalex.org/T10887", "display_name": "Bioinformatics and Genomic Networks", "score": 0.0, "subfield": {"id": "https://openalex.org/subfields/1312", "display_name": "Molecular Biology"}, "field": {"id": "https://openalex.org/fields/13", "display_name": "Biochemistry, Genetics and Molecular Biology"}, "domain": {"id": "https://openalex.org/domains/1", "display_name": "Life Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2503.03485", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2503.03485", "pdf_url": "https://arxiv.org/pdf/2503.03485", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": null, "license_id": null, "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 1, "is_retracted": false}, {"id": "https://openalex.org/W4415332511", "doi": "https://doi.org/10.48550/arxiv.2509.23529", "title": "DOoM: Difficult Olympiads of Math", "publication_year": 2025, "publication_date": "2025-09-27", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5094584049", "display_name": "Ilya Kuleshov", "orcid": "https://orcid.org/0000-0002-6947-6897", "observed_orcids": ["https://orcid.org/0000-0002-6947-6897"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Kuleshov, Ilya", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5120056887", "display_name": "Ilin Pavel", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Pavel, Ilin", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5058026269", "display_name": "N. M. Kompanets", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Kompanets, Nikolay", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5099380933", "display_name": "Ksenia Sycheva", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Sycheva, Ksenia", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "last", "author": {"id": "https://openalex.org/A5120056889", "display_name": "Aleksandr Nikolich", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Nikolich, Aleksandr", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}], "abstract_inverted_index": {"This": [0], "paper": [1, 44], "introduces": [2], "DOoM,": [3], "a": [4, 71], "new": [5], "open-source": [6], "benchmark": [7, 25], "designed": [8], "to": [9, 35], "assess": [10], "the": [11, 47, 68, 77], "capabilities": [12], "of": [13, 28, 67, 79], "language": [14], "models": [15], "in": [16, 22, 85], "solving": [17], "mathematics": [18, 88], "and": [19, 38, 55, 58, 76, 82, 89], "physics": [20, 90], "problems": [21, 27], "Russian.": [23], "The": [24], "includes": [26], "varying": [29], "difficulty,": [30], "ranging": [31], "from": [32, 62], "school-level": [33], "tasks": [34], "university": [36], "Olympiad": [37], "entrance": [39], "exam": [40], "questions.": [41], "In": [42], "this": [43], "we": [45], "discuss": [46], "motivation": [48], "behind": [49], "its": [50], "creation,": [51], "describe": [52], "dataset's": [53], "structure": [54], "evaluation": [56], "methodology,": [57], "present": [59], "initial": [60], "results": [61, 69], "testing": [63], "various": [64], "models.": [65], "Analysis": [66], "shows": [70], "correlation": [72], "between": [73, 87], "model": [74], "performance": [75, 86], "number": [78], "tokens": [80], "used,": [81], "highlights": [83], "differences": [84], "tasks.": [91]}, "primary_topic": {"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.7246000170707703, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.7246000170707703, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T13523", "display_name": "Mathematics, Computing, and Information Processing", "score": 0.06129999831318855, "subfield": {"id": "https://openalex.org/subfields/1703", "display_name": "Computational Theory and Mathematics"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10181", "display_name": "Natural Language Processing Techniques", "score": 0.04699999839067459, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2509.23529", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2509.23529", "pdf_url": "https://arxiv.org/pdf/2509.23529", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": null, "license_id": null, "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4407244986", "doi": "https://doi.org/10.48550/arxiv.2502.03885", "title": "InfiniteHBD: Building Datacenter-Scale High-Bandwidth Domain for LLM with Optical Circuit Switching Transceivers", "publication_year": 2025, "publication_date": "2025-02-06", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5116187789", "display_name": "Chenchen Shou", "orcid": "https://orcid.org/0009-0006-7113-0357", "observed_orcids": ["https://orcid.org/0009-0006-7113-0357"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Shou, Chenchen", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": null, "display_name": "Liu, Guyue", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Liu, Guyue", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": null, "display_name": "Nie, Hao", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Nie, Hao", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5034388927", "display_name": "Huaiyu Meng", "orcid": "https://orcid.org/0000-0001-5326-3056", "observed_orcids": ["https://orcid.org/0000-0001-5326-3056"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Meng, Huaiyu", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5016175345", "display_name": "Yu Zhou", "orcid": "https://orcid.org/0000-0003-4188-9953", "observed_orcids": ["https://orcid.org/0000-0003-4188-9953"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Zhou, Yu", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": null, "display_name": "Jiang, Yimin", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Jiang, Yimin", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": null, "display_name": "Lv, Wenqing", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Lv, Wenqing", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5112351917", "display_name": "Ye-Long Xu", "orcid": "https://orcid.org/0009-0004-7443-9988", "observed_orcids": ["https://orcid.org/0009-0004-7443-9988"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Xu, Yelong", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5085189326", "display_name": "Yuanwei Lu", "orcid": "https://orcid.org/0009-0006-2554-6490", "observed_orcids": ["https://orcid.org/0009-0006-2554-6490"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Lu, Yuanwei", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5042229142", "display_name": "Zhang Chen", "orcid": "https://orcid.org/0000-0002-1412-2709", "observed_orcids": ["https://orcid.org/0000-0002-1412-2709"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Chen, Zhang", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5029970324", "display_name": "Yanbo Yu", "orcid": "https://orcid.org/0000-0002-9275-7291", "observed_orcids": ["https://orcid.org/0000-0002-9275-7291"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Yu, Yanbo", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5019776095", "display_name": "Yichen Shen", "orcid": "https://orcid.org/0000-0002-0934-0242", "observed_orcids": ["https://orcid.org/0000-0002-0934-0242"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Shen, Yichen", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5084336844", "display_name": "Yibo Zhu", "orcid": "https://orcid.org/0000-0003-4323-1290", "observed_orcids": ["https://orcid.org/0000-0003-4323-1290"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Zhu, Yibo", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "last", "author": {"id": "https://openalex.org/A5060116992", "display_name": "Daxin Jiang", "orcid": "https://orcid.org/0000-0002-6657-5806", "observed_orcids": ["https://orcid.org/0000-0002-6657-5806"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Jiang, Daxin", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}], "abstract_inverted_index": {"Scaling": [0], "Large": [1], "Language": [2], "Model": [3, 179], "(LLM)": [4], "training": [5], "relies": [6], "on": [7], "multi-dimensional": [8], "parallelism,": [9], "where": [10], "High-Bandwidth": [11], "Domains": [12], "(HBDs)": [13], "are": [14], "critical": [15], "for": [16, 122], "communication-intensive": [17], "parallelism": [18], "like": [19], "Tensor": [20], "Parallelism.": [21], "However,": [22], "existing": [23], "HBD": [24, 74], "architectures": [25], "face": [26], "fundamental": [27], "limitations": [28], "in": [29], "scalability,": [30], "cost,": [31], "and": [32, 79, 100, 118, 139, 166, 177], "fault": [33, 51, 64, 112, 175], "resiliency:": [34], "switch-centric": [35], "HBDs": [36, 45, 55], "(e.g.,": [37, 46, 56], "NVL-72)": [38], "incur": [39], "prohibitive": [40], "scaling": [41], "costs,": [42], "while": [43], "GPU-centric": [44], "TPUv3/Dojo)": [47], "suffer": [48], "from": [49], "severe": [50], "propagation.": [52], "Switch-GPU": [53], "hybrid": [54], "TPUv4)": [57], "take": [58], "a": [59, 72, 128, 134, 156], "middle-ground": [60], "approach,": [61], "but": [62], "the": [63, 83, 115], "explosion": [65], "radius": [66], "remains": [67], "large.": [68], "We": [69], "propose": [70], "InfiniteHBD,": [71], "transceiver-centric": [73], "architecture": [75], "that": [76, 147], "integrates": [77], "connectivity": [78], "dynamic": [80], "switching": [81], "at": [82, 114], "transceiver": [84, 132], "level": [85], "by": [86, 182], "embedding": [87], "Optical": [88], "Circuit": [89], "Switching": [90], "(OCS)": [91], "within": [92], "each": [93], "transceiver.": [94], "It": [95], "enables": [96], "reconfigurable": [97, 135], "point-to-multipoint": [98], "communication": [99], "scalable": [101], "variable-size": [102], "ring": [103, 137], "topologies.": [104], "InfiniteHBD": [105, 148], "achieves": [106, 155], "datacenter-scale": [107], "scalability": [108], "without": [109], "cost": [110, 150], "explosion,": [111], "isolation": [113], "node": [116, 174], "level,": [117], "full": [119], "bandwidth": [120], "utilization": [121], "healthy": [123], "GPUs.": [124], "Key": [125], "innovations": [126], "include": [127], "Silicon": [129], "Photonic-based": [130], "OCS": [131], "(OCSTrx),": [133], "k-hop": [136], "topology,": [138], "an": [140], "HBD-DCN": [141], "orchestration": [142], "algorithm.": [143], "The": [144], "evaluation": [145], "demonstrates": [146], "reduces": [149], "to": [151, 185], "31%": [152], "of": [153], "NVL-72,": [154], "near-zero": [157, 169], "GPU": [158], "waste": [159], "ratio": [160], "(over": [161], "10x": [162], "lower": [163], "than": [164], "NVL-72": [165], "TPUv4),": [167], "maintains": [168], "cross-ToR": [170], "traffic": [171], "under": [172], "7%": [173], "ratio,": [176], "improves": [178], "FLOPs": [180], "Utilization": [181], "3.37x": [183], "compared": [184], "NVIDIA": [186], "DGX": [187], "(8": [188], "GPUs/node).": [189]}, "primary_topic": {"id": "https://openalex.org/T10829", "display_name": "Interconnection Networks and Systems", "score": 0.9487000107765198, "subfield": {"id": "https://openalex.org/subfields/1705", "display_name": "Computer Networks and Communications"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T10829", "display_name": "Interconnection Networks and Systems", "score": 0.9487000107765198, "subfield": {"id": "https://openalex.org/subfields/1705", "display_name": "Computer Networks and Communications"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10054", "display_name": "Parallel Computing and Optimization Techniques", "score": 0.020899999886751175, "subfield": {"id": "https://openalex.org/subfields/1708", "display_name": "Hardware and Architecture"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10847", "display_name": "Advanced Optical Network Technologies", "score": 0.01590000092983246, "subfield": {"id": "https://openalex.org/subfields/2208", "display_name": "Electrical and Electronic Engineering"}, "field": {"id": "https://openalex.org/fields/22", "display_name": "Engineering"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2502.03885", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2502.03885", "pdf_url": "https://arxiv.org/pdf/2502.03885", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": null, "license_id": null, "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 1, "is_retracted": false}, {"id": "https://openalex.org/W4411968027", "doi": "https://doi.org/10.1016/j.neucom.2025.130617", "title": "In the eyes of a language model: A comprehensive examination through eye-tracking data", "publication_year": 2025, "publication_date": "2025-07-02", "type": "article", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5059707020", "display_name": "Luca Dini", "orcid": "https://orcid.org/0009-0004-2070-7238", "observed_orcids": ["https://orcid.org/0009-0004-2070-7238"]}, "institutions": [{"id": "https://openalex.org/I108290504", "display_name": "University of Pisa", "ror": "https://ror.org/03ad39j10", "country_code": "IT", "type": "education", "lineage": ["https://openalex.org/I108290504"]}, {"id": "https://openalex.org/I4210124522", "display_name": "Institute for Computational Linguistics \u201cA. Zampolli\u201d", "ror": "https://ror.org/028g3pe33", "country_code": "IT", "type": "facility", "lineage": ["https://openalex.org/I4210124522", "https://openalex.org/I4210155236"]}, {"id": "https://openalex.org/I4210155236", "display_name": "National Research Council", "ror": "https://ror.org/04zaypm56", "country_code": "IT", "type": "nonprofit", "lineage": ["https://openalex.org/I4210155236"]}], "countries": ["IT"], "is_corresponding": false, "raw_author_name": "Luca Dini", "raw_affiliation_strings": ["Institute of Computational Linguistics \u201cAntonio Zampolli\u201d (CNR_ILC)/ItaliaNLP Lab, Via Giuseppe Moruzzi, 1, Pisa, 56124, Italy", "University of Pisa, Largo Bruno Pontecorvo, 3, Pisa, 56127, Italy"], "raw_orcid": "https://orcid.org/0009-0004-2070-7238", "affiliations": [{"raw_affiliation_string": "Institute of Computational Linguistics \u201cAntonio Zampolli\u201d (CNR_ILC)/ItaliaNLP Lab, Via Giuseppe Moruzzi, 1, Pisa, 56124, Italy", "institution_ids": ["https://openalex.org/I4210124522", "https://openalex.org/I4210155236"]}, {"raw_affiliation_string": "University of Pisa, Largo Bruno Pontecorvo, 3, Pisa, 56127, Italy", "institution_ids": ["https://openalex.org/I108290504"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5114231065", "display_name": "Luca Moroni", "orcid": "https://orcid.org/0009-0006-1210-5098", "observed_orcids": ["https://orcid.org/0009-0006-1210-5098"]}, "institutions": [{"id": "https://openalex.org/I861853513", "display_name": "Sapienza University of Rome", "ror": "https://ror.org/02be6w209", "country_code": "IT", "type": "education", "lineage": ["https://openalex.org/I861853513"]}], "countries": ["IT"], "is_corresponding": false, "raw_author_name": "Luca Moroni", "raw_affiliation_strings": ["Sapienza University of Rome/Sapienza NLP group, Via Ariosto, 25, Roma, 00185, Italy"], "raw_orcid": "https://orcid.org/0009-0006-1210-5098", "affiliations": [{"raw_affiliation_string": "Sapienza University of Rome/Sapienza NLP group, Via Ariosto, 25, Roma, 00185, Italy", "institution_ids": ["https://openalex.org/I861853513"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5057659257", "display_name": "Dominique Brunato\u22c4", "orcid": "https://orcid.org/0000-0003-3256-4794", "observed_orcids": ["https://orcid.org/0000-0003-3256-4794"]}, "institutions": [{"id": "https://openalex.org/I4210124522", "display_name": "Institute for Computational Linguistics \u201cA. Zampolli\u201d", "ror": "https://ror.org/028g3pe33", "country_code": "IT", "type": "facility", "lineage": ["https://openalex.org/I4210124522", "https://openalex.org/I4210155236"]}, {"id": "https://openalex.org/I4210155236", "display_name": "National Research Council", "ror": "https://ror.org/04zaypm56", "country_code": "IT", "type": "nonprofit", "lineage": ["https://openalex.org/I4210155236"]}], "countries": ["IT"], "is_corresponding": true, "raw_author_name": "Dominique Brunato", "raw_affiliation_strings": ["Institute of Computational Linguistics \u201cAntonio Zampolli\u201d (CNR_ILC)/ItaliaNLP Lab, Via Giuseppe Moruzzi, 1, Pisa, 56124, Italy"], "raw_orcid": "https://orcid.org/0000-0003-3256-4794", "affiliations": [{"raw_affiliation_string": "Institute of Computational Linguistics \u201cAntonio Zampolli\u201d (CNR_ILC)/ItaliaNLP Lab, Via Giuseppe Moruzzi, 1, Pisa, 56124, Italy", "institution_ids": ["https://openalex.org/I4210124522", "https://openalex.org/I4210155236"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5084812833", "display_name": "Felice Dell\u2019Orletta\u22c4", "orcid": "https://orcid.org/0000-0003-3454-9387", "observed_orcids": ["https://orcid.org/0000-0003-3454-9387"]}, "institutions": [{"id": "https://openalex.org/I4210124522", "display_name": "Institute for Computational Linguistics \u201cA. Zampolli\u201d", "ror": "https://ror.org/028g3pe33", "country_code": "IT", "type": "facility", "lineage": ["https://openalex.org/I4210124522", "https://openalex.org/I4210155236"]}, {"id": "https://openalex.org/I4210155236", "display_name": "National Research Council", "ror": "https://ror.org/04zaypm56", "country_code": "IT", "type": "nonprofit", "lineage": ["https://openalex.org/I4210155236"]}], "countries": ["IT"], "is_corresponding": false, "raw_author_name": "Felice Dell\u2019Orletta", "raw_affiliation_strings": ["Institute of Computational Linguistics \u201cAntonio Zampolli\u201d (CNR_ILC)/ItaliaNLP Lab, Via Giuseppe Moruzzi, 1, Pisa, 56124, Italy"], "raw_orcid": "https://orcid.org/0000-0003-3454-9387", "affiliations": [{"raw_affiliation_string": "Institute of Computational Linguistics \u201cAntonio Zampolli\u201d (CNR_ILC)/ItaliaNLP Lab, Via Giuseppe Moruzzi, 1, Pisa, 56124, Italy", "institution_ids": ["https://openalex.org/I4210124522", "https://openalex.org/I4210155236"]}]}], "abstract_inverted_index": {"Cognitive": [0], "signals,": [1], "particularly": [2], "eye-tracking": [3, 96, 112], "data,": [4], "offer": [5], "a": [6, 31, 58, 79, 119], "unique": [7], "lens": [8], "for": [9, 175], "understanding": [10], "human": [11, 48, 102, 158, 169, 201, 218], "sentence": [12], "processing.": [13], "Leveraging": [14], "eye-gaze": [15], "data": [16, 113], "from": [17], "the": [18, 24, 62, 66, 106, 115, 135, 140, 146, 177, 193, 208], "English": [19], "and": [20, 51, 92, 213, 224, 230], "Italian": [21], "section": [22], "of": [23, 33, 47, 65, 108, 139, 149, 165, 180, 195], "Multilingual": [25], "Eye-Movement": [26], "Corpus": [27], "(MECO),": [28], "we": [29, 69, 122], "designed": [30], "series": [32], "experiments": [34], "aiming": [35], "at": [36], "exploring": [37], "whether": [38], "pre-trained": [39], "neural": [40], "language": [41, 186, 202], "models": [42, 150, 187], "(NLMs)": [43], "encode": [44, 87], "patterns": [45, 171], "representative": [46], "reading": [49, 212], "behaviour": [50, 219], "if": [52, 73], "directly": [53], "incorporating": [54], "this": [55, 125], "information": [56, 89], "through": [57, 78], "fine-tuning": [59, 110, 153], "process": [60], "influences": [61], "cognitive": [63, 190, 209], "plausibility": [64], "model.": [67], "Additionally,": [68], "sought": [70], "to": [71], "determine": [72], "such": [74], "an": [75], "impact": [76], "persists": [77], "downstream": [80, 120, 142], "task.": [81, 143], "Our": [82], "findings": [83], "reveal": [84], "that": [85, 93, 124, 197], "transformers": [86], "eye-gaze-related": [88], "during": [90], "pretraining": [91], "explicitly": [94], "integrating": [95], "features": [97], "increases": [98], "model": [99, 221], "alignment": [100, 216], "with": [101, 157, 207, 217], "attention.": [103, 159], "When": [104], "investigating": [105], "effect": [107], "intermediate": [109, 126, 152], "on": [111, 114, 118], "model\u2019s": [116], "performance": [117], "task,": [121], "observe": [123], "step": [127], "does": [128], "not": [129, 198], "result": [130], "in": [131, 188], "catastrophic": [132], "forgetting,": [133], "despite": [134], "very": [136], "different": [137], "nature": [138], "considered": [141], "In": [144, 160], "addition,": [145], "attention": [147, 170], "mechanism": [148], "undergoing": [151], "remains": [154], "closely": [155], "aligned": [156], "conclusion,": [161], "our": [162], "comprehensive": [163], "evaluation": [164], "NLMs": [166], "informed": [167], "by": [168], "offers": [172], "great": [173], "potential": [174], "advancing": [176], "growing": [178], "field": [179], "eXplainable": [181], "Artificial": [182], "Intelligence": [183], "(XAI).": [184], "Grounding": [185], "real-world": [189], "processes": [191], "enables": [192], "creation": [194], "systems": [196], "only": [199], "replicate": [200], "output": [203], "but": [204], "also": [205], "align": [206], "mechanisms": [210], "behind": [211], "comprehension.": [214], "This": [215], "enhances": [220], "adaptability,": [222], "interpretability,": [223], "effectiveness,": [225], "fostering": [226], "more": [227], "human-centric,": [228], "transparent,": [229], "reliable": [231], "AI": [232], "applications": [233], "across": [234], "various": [235], "domains.": [236], "1": [237]}, "primary_topic": {"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.5313000082969666, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.5313000082969666, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10465", "display_name": "Neurobiology of Language and Bilingualism", "score": 0.18529999256134033, "subfield": {"id": "https://openalex.org/subfields/2805", "display_name": "Cognitive Neuroscience"}, "field": {"id": "https://openalex.org/fields/28", "display_name": "Neuroscience"}, "domain": {"id": "https://openalex.org/domains/1", "display_name": "Life Sciences"}}, {"id": "https://openalex.org/T12026", "display_name": "Explainable Artificial Intelligence (XAI)", "score": 0.09210000187158585, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "doi:10.1016/j.neucom.2025.130617", "is_oa": true, "landing_page_url": "https://doi.org/10.1016/j.neucom.2025.130617", "pdf_url": null, "source": {"id": "https://openalex.org/S45693802", "display_name": "Neurocomputing", "issn_l": "0925-2312", "issn": ["0925-2312", "1872-8286"], "is_oa": false, "is_in_doaj": false, "is_core": true, "listed_in": ["cwts-core", "doyens", "jufo-2", "ki-jl-2", "norway-2"], "host_organization": "https://openalex.org/P4310320990", "host_organization_name": "Elsevier BV", "host_organization_lineage": ["https://openalex.org/P4310320990"], "host_organization_lineage_names": ["Elsevier BV"], "type": "journal"}, "license": "cc-by-nc-nd", "license_id": "https://openalex.org/licenses/cc-by-nc-nd", "version": "publishedVersion", "is_accepted": true, "is_published": true, "raw_source_name": "Neurocomputing", "raw_type": "journal-article"}, "cited_by_count": 2, "is_retracted": false}, {"id": "https://openalex.org/W4413133129", "doi": "https://doi.org/10.36227/techrxiv.175459856.69720288/v1", "title": "Multifaceted LLMs for Recommendations of Software Vulnerability Repair and Assessment", "publication_year": 2025, "publication_date": "2025-08-07", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5119278477", "display_name": "Alfred Asare Amoah", "orcid": "https://orcid.org/0009-0009-9687-9120", "observed_orcids": ["https://orcid.org/0009-0009-9687-9120"]}, "institutions": [{"id": "https://openalex.org/I60158472", "display_name": "Concordia University", "ror": "https://ror.org/0420zvk78", "country_code": "CA", "type": "education", "lineage": ["https://openalex.org/I60158472"]}], "countries": ["CA"], "is_corresponding": true, "raw_author_name": "Alfred Asare Amoah", "raw_affiliation_strings": ["Department of Electrical and Computer Engineering, Concordia University Montr\u00e9al"], "raw_orcid": "https://orcid.org/0009-0009-9687-9120", "affiliations": [{"raw_affiliation_string": "Department of Electrical and Computer Engineering, Concordia University Montr\u00e9al", "institution_ids": ["https://openalex.org/I60158472"]}]}, {"author_position": "last", "author": {"id": null, "display_name": "Yan Liu", "orcid": "https://orcid.org/0000-0002-6747-8151", "observed_orcids": ["https://orcid.org/0000-0002-6747-8151"]}, "institutions": [{"id": "https://openalex.org/I60158472", "display_name": "Concordia University", "ror": "https://ror.org/0420zvk78", "country_code": "CA", "type": "education", "lineage": ["https://openalex.org/I60158472"]}], "countries": ["CA"], "is_corresponding": false, "raw_author_name": "Yan Liu", "raw_affiliation_strings": ["Department of Electrical and Computer Engineering, Concordia University Montr\u00e9al"], "raw_orcid": "https://orcid.org/0000-0002-6747-8151", "affiliations": [{"raw_affiliation_string": "Department of Electrical and Computer Engineering, Concordia University Montr\u00e9al", "institution_ids": ["https://openalex.org/I60158472"]}]}], "abstract_inverted_index": {"Common": [0, 5], "Weakness": [1], "Enumerations": [2], "(CWEs)": [3], "and": [4, 7, 18, 37, 73, 101, 132, 143, 146, 189, 211, 233, 240, 256, 277, 283, 288], "Vulnerabilities": [6], "Exposures": [8], "(CVEs)": [9], "are": [10, 46, 168], "open": [11], "knowledge": [12, 32, 147, 242, 275], "bases": [13, 33, 276], "that": [14, 176, 250], "provide": [15], "definitions,": [16], "descriptions,": [17], "samples": [19], "of": [20, 25, 52, 77, 120, 140, 158, 178, 202, 223, 265], "code": [21, 39, 61, 83, 90, 122, 164, 172, 187, 231], "vulnerabilities.": [22], "The": [23], "combination": [24], "Large": [26], "Language": [27], "Models": [28], "(LLMs)": [29], "with": [30, 85, 124, 227], "vulnerability": [31, 40], "helps": [34], "to": [35, 57, 70, 170, 206, 219, 262, 285], "enhance": [36, 220], "automate": [38], "repair.": [41, 108, 194], "Meanwhile,": [42], "several": [43], "key": [44], "factors": [45, 97], "introduced,": [47], "including": [48], "1)": [49], "the": [50, 53, 65, 71, 75, 78, 104, 107, 137, 141, 161, 185, 190, 193, 200, 203, 216, 221, 238, 248], "retrieval": [51, 133, 210, 236, 289], "most": [54], "relevant": [55], "context": [56, 67], "a": [58, 114, 156, 260], "specific": [59], "vulnerable": [60, 121, 163], "snippet;": [62], "2)": [63], "augmenting": [64], "retrieved": [66], "as": [68, 82], "input": [69], "LLMs;": [72], "3)": [74], "form": [76], "generated": [79], "artifacts,": [80], "such": [81], "repair": [84, 91, 188], "natural": [86], "language": [87], "explanations": [88], "or": [89], "only.": [92], "Artifacts": [93], "produced": [94], "by": [95], "these": [96, 179], "often": [98], "lack": [99], "transparency": [100], "explainability": [102], "regarding": [103], "rationale": [105, 191], "behind": [106, 192], "In": [109], "this": [110], "paper,": [111], "we": [112, 198], "propose": [113], "framework": [115, 218], "for": [116, 160], "an": [117], "explainable": [118, 255], "recommendation": [119, 224], "repairs": [123], "techniques": [125], "addressing": [126], "each": [127], "factor.": [128], "We": [129, 214], "design": [130], "embedding": [131], "patterns": [134, 150], "based": [135], "on": [136, 273], "data": [138], "characteristics": [139], "CWE": [142, 239], "CVE": [144, 241], "codebase": [145], "base.": [148], "These": [149, 166], "applied": [151], "across": [152], "multiple": [153], "LLMs": [154, 234], "produce": [155], "set": [157], "recommendations": [159, 167, 180, 258], "same": [162], "snippets.": [165], "used": [169], "generate": [171], "repairs.": [173, 213], "This": [174], "highlights": [175], "assessments": [177], "become": [181], "imperative": [182], "in": [183], "deciding": [184], "appropriate": [186], "Through": [195], "100": [196], "experiments,": [197], "observe": [199], "inadequacy": [201], "SOTA": [204], "metrics": [205], "differentiate": [207], "between": [208], "lowquality": [209], "irrelevant": [212], "use": [215], "LLM-as-a-Judge": [217], "robustness": [222], "assessments.": [225], "Compared": [226], "baselines": [228], "using": [229], "static": [230], "analysis": [232], "without": [235], "from": [237], "base,": [243], "our": [244], "findings": [245], "have": [246], "highlighted": [247], "potential": [249], "multifaceted": [251], "LLM": [252], "usage": [253], "produces": [254], "reliable": [257], "under": [259], "small": [261], "mild": [263], "level": [264], "self-alignment": [266], "bias.": [267], "Our": [268], "work": [269], "has": [270], "been": [271], "developed": [272], "open-source": [274], "models,": [278], "which": [279], "makes": [280], "it": [281], "reproducible": [282], "extensible": [284], "new": [286], "datasets": [287], "patterns.": [290]}, "primary_topic": {"id": "https://openalex.org/T10260", "display_name": "Software Engineering Research", "score": 0.9272000193595886, "subfield": {"id": "https://openalex.org/subfields/1710", "display_name": "Information Systems"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T10260", "display_name": "Software Engineering Research", "score": 0.9272000193595886, "subfield": {"id": "https://openalex.org/subfields/1710", "display_name": "Information Systems"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T12479", "display_name": "Web Application Security Vulnerabilities", "score": 0.040300000458955765, "subfield": {"id": "https://openalex.org/subfields/1710", "display_name": "Information Systems"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10743", "display_name": "Software Testing and Debugging Techniques", "score": 0.02290000021457672, "subfield": {"id": "https://openalex.org/subfields/1712", "display_name": "Software"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "doi:10.36227/techrxiv.175459856.69720288/v1", "is_oa": true, "landing_page_url": "https://doi.org/10.36227/techrxiv.175459856.69720288/v1", "pdf_url": "https://www.techrxiv.org/doi/pdf/10.36227/techrxiv.175459856.69720288", "source": null, "license": "cc-by", "license_id": "https://openalex.org/licenses/cc-by", "version": "acceptedVersion", "is_accepted": true, "is_published": false, "raw_source_name": null, "raw_type": "posted-content"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4416059721", "doi": "https://doi.org/10.48550/arxiv.2506.13734", "title": "Instruction Following by Principled Boosting Attention of Large Language Models", "publication_year": 2025, "publication_date": "2025-06-16", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5033315494", "display_name": "Vit\u00f3ria Guardieiro", "orcid": "https://orcid.org/0000-0003-1956-5418", "observed_orcids": ["https://orcid.org/0000-0003-1956-5418"]}, "institutions": [{"id": "https://openalex.org/I79576946", "display_name": "University of Pennsylvania", "ror": "https://ror.org/00b30xv10", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I79576946"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Guardieiro, Vitoria", "raw_affiliation_strings": ["University of Pennsylvania"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "University of Pennsylvania", "institution_ids": ["https://openalex.org/I79576946"]}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Khare, Avishree", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I79576946", "display_name": "University of Pennsylvania", "ror": "https://ror.org/00b30xv10", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I79576946"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Khare, Avishree", "raw_affiliation_strings": ["University of Pennsylvania"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "University of Pennsylvania", "institution_ids": ["https://openalex.org/I79576946"]}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Stein, Adam", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I79576946", "display_name": "University of Pennsylvania", "ror": "https://ror.org/00b30xv10", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I79576946"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Stein, Adam", "raw_affiliation_strings": ["University of Pennsylvania"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "University of Pennsylvania", "institution_ids": ["https://openalex.org/I79576946"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5066376294", "display_name": "Eric Wong", "orcid": "https://orcid.org/0000-0002-8568-6659", "observed_orcids": ["https://orcid.org/0000-0002-8568-6659"]}, "institutions": [{"id": "https://openalex.org/I79576946", "display_name": "University of Pennsylvania", "ror": "https://ror.org/00b30xv10", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I79576946"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Wong, Eric", "raw_affiliation_strings": ["University of Pennsylvania"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "University of Pennsylvania", "institution_ids": ["https://openalex.org/I79576946"]}]}], "abstract_inverted_index": {"Large": [0], "language": [1], "models'": [2], "behavior": [3], "is": [4, 62], "often": [5], "shaped": [6], "by": [7, 83, 136], "instructions": [8], "such": [9, 60], "as": [10, 87], "system": [11], "prompts,": [12], "refusal": [13], "boundaries,": [14], "privacy": [15], "constraints,": [16], "and": [17, 46, 93, 161, 170, 192], "tool-use": [18], "rules": [19, 92, 100], "that": [20, 53, 104, 128, 148], "must": [21], "hold": [22], "at": [23], "inference": [24], "time.": [25], "Yet": [26], "in": [27], "practice": [28], "these": [29], "constraints": [30], "can": [31, 124], "be": [32, 130], "violated": [33], "under": [34], "long": [35], "contexts": [36], "or": [37, 180], "when": [38], "user-provided": [39], "context": [40, 117, 127], "conflicts": [41], "with": [42, 96], "them,": [43], "creating": [44], "reliability": [45], "safety": [47], "risks.": [48], "This": [49], "motivates": [50], "inference-time": [51], "interventions": [52], "strengthen": [54], "instruction": [55, 69, 85, 91, 108, 194], "influence": [56], "without": [57], "retraining.": [58], "One": [59], "intervention": [61, 147], "attention": [63, 67, 80, 97, 106, 156, 172, 198], "steering,": [64, 169], "which": [65, 99], "biases": [66], "toward": [68], "tokens.": [70], "In": [71], "this": [72, 111, 137], "work,": [73], "we": [74, 139], "present": [75], "a": [76, 145, 150, 201], "unifying": [77], "theory": [78], "for": [79, 116], "steering": [81, 173], "methods": [82, 174, 191], "formalizing": [84], "following": [86], "rule-based": [88], "competition": [89], "between": [90], "context-derived": [94], "rules,": [95], "mediating": [98], "dominate.": [101], "We": [102, 163], "prove": [103], "boosting": [105, 123], "to": [107, 118, 154], "tokens": [109], "tilts": [110], "competition,": [112], "making": [113], "it": [114], "harder": [115], "override": [119], "instruction-following.": [120], "However,": [121], "excessive": [122], "suppress": [125], "task-relevant": [126], "should": [129], "incorporated": [131], "alongside": [132], "the": [133, 186, 193], "instruction.": [134], "Guided": [135], "theory,": [138], "propose": [140], "Instruction": [141], "Attention": [142], "Boosting": [143], "(InstABoost),": [144], "simple": [146], "applies": [149], "constant": [151], "additive": [152], "bias": [153], "instruction-key": [155], "logits": [157], "across": [158, 175], "all": [159, 182], "layers": [160], "heads.": [162], "evaluate": [164], "InstABoost": [165, 178], "against": [166], "prompting,": [167], "latent": [168, 190], "prior": [171, 197], "15": [176], "tasks.": [177], "matches": [179], "outperforms": [181], "baselines": [183], "while": [184], "avoiding": [185], "fluency": [187], "collapse": [188], "of": [189, 196], "over-focus": [195], "methods,": [199], "achieving": [200], "stronger": [202], "steering-quality": [203], "tradeoff.": [204]}, "primary_topic": {"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.7807999849319458, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.7807999849319458, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10181", "display_name": "Natural Language Processing Techniques", "score": 0.14900000393390656, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T11689", "display_name": "Adversarial Robustness in Machine Learning", "score": 0.014999999664723873, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2506.13734", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2506.13734", "pdf_url": "https://arxiv.org/pdf/2506.13734", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": "cc-by", "license_id": "https://openalex.org/licenses/cc-by", "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4407386052", "doi": "https://doi.org/10.48550/arxiv.2502.05928", "title": "ClinKD: Cross-Modal Clinical Knowledge Distiller For Multi-Task Medical Images", "publication_year": 2025, "publication_date": "2025-02-09", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": null, "display_name": "Ge, Hongyu", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I139024713", "display_name": "Guangdong University of Technology", "ror": "https://ror.org/04azbjn80", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I139024713"]}, {"id": "https://openalex.org/I200769079", "display_name": "Hong Kong University of Science and Technology", "ror": "https://ror.org/00q4vv597", "country_code": "HK", "type": "education", "lineage": ["https://openalex.org/I200769079"]}, {"id": "https://openalex.org/I889458895", "display_name": "University of Hong Kong", "ror": "https://ror.org/02zhqgq86", "country_code": "HK", "type": "education", "lineage": ["https://openalex.org/I889458895"]}], "countries": ["CN", "HK"], "is_corresponding": false, "raw_author_name": "Ge, Hongyu", "raw_affiliation_strings": ["The Hong Kong University of Sciences and Technology , Guangzhou"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "The Hong Kong University of Sciences and Technology , Guangzhou", "institution_ids": ["https://openalex.org/I139024713", "https://openalex.org/I200769079", "https://openalex.org/I889458895"]}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Hao, Longkun", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I82880672", "display_name": "Beihang University", "ror": "https://ror.org/00wk2mp56", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I82880672"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Hao, Longkun", "raw_affiliation_strings": ["Beihang University"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Beihang University", "institution_ids": ["https://openalex.org/I82880672"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5100437308", "display_name": "Zeshui Xu", "orcid": "https://orcid.org/0000-0003-3547-2908", "observed_orcids": ["https://orcid.org/0000-0003-3547-2908"]}, "institutions": [{"id": "https://openalex.org/I154099455", "display_name": "Shandong University", "ror": "https://ror.org/0207yh398", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I154099455"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Xu, Zihui", "raw_affiliation_strings": ["Shandong University"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Shandong University", "institution_ids": ["https://openalex.org/I154099455"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5044857419", "display_name": "Zhenxin Lin", "orcid": "https://orcid.org/0009-0008-5530-0461", "observed_orcids": ["https://orcid.org/0009-0008-5530-0461", "https://orcid.org/0000-0003-2676-8723"]}, "institutions": [{"id": "https://openalex.org/I75900474", "display_name": "Hubei University", "ror": "https://ror.org/03a60m280", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I75900474"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Lin, Zhenxin", "raw_affiliation_strings": ["Hubei University"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Hubei University", "institution_ids": ["https://openalex.org/I75900474"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5072404466", "display_name": "Bin Li", "orcid": "https://orcid.org/0009-0000-0898-7864", "observed_orcids": ["https://orcid.org/0009-0000-0898-7864"]}, "institutions": [{"id": "https://openalex.org/I19820366", "display_name": "Chinese Academy of Sciences", "ror": "https://ror.org/034t30j35", "country_code": "CN", "type": "government", "lineage": ["https://openalex.org/I19820366"]}, {"id": "https://openalex.org/I4210145761", "display_name": "Shenzhen Institutes of Advanced Technology", "ror": "https://ror.org/04gh4er46", "country_code": "CN", "type": "facility", "lineage": ["https://openalex.org/I19820366", "https://openalex.org/I4210145761"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Li, Bin", "raw_affiliation_strings": ["Shenzhen Institutes of Advanced Technology , Chinese Academy of Sciences"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Shenzhen Institutes of Advanced Technology , Chinese Academy of Sciences", "institution_ids": ["https://openalex.org/I19820366", "https://openalex.org/I4210145761"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5046902140", "display_name": "Shoujun Zhou", "orcid": "https://orcid.org/0000-0003-3232-6796", "observed_orcids": ["https://orcid.org/0000-0003-3232-6796"]}, "institutions": [{"id": "https://openalex.org/I19820366", "display_name": "Chinese Academy of Sciences", "ror": "https://ror.org/034t30j35", "country_code": "CN", "type": "government", "lineage": ["https://openalex.org/I19820366"]}, {"id": "https://openalex.org/I4210145761", "display_name": "Shenzhen Institutes of Advanced Technology", "ror": "https://ror.org/04gh4er46", "country_code": "CN", "type": "facility", "lineage": ["https://openalex.org/I19820366", "https://openalex.org/I4210145761"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Zhou, Shoujun", "raw_affiliation_strings": ["Shenzhen Institutes of Advanced Technology , Chinese Academy of Sciences"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Shenzhen Institutes of Advanced Technology , Chinese Academy of Sciences", "institution_ids": ["https://openalex.org/I19820366", "https://openalex.org/I4210145761"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5028358280", "display_name": "Hongjin Zhao", "orcid": "https://orcid.org/0000-0001-5296-2293", "observed_orcids": ["https://orcid.org/0000-0001-5296-2293"]}, "institutions": [{"id": "https://openalex.org/I118347636", "display_name": "Australian National University", "ror": "https://ror.org/019wvm592", "country_code": "AU", "type": "education", "lineage": ["https://openalex.org/I118347636"]}], "countries": ["AU"], "is_corresponding": false, "raw_author_name": "Zhao, Hongjin", "raw_affiliation_strings": ["Australian National University"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Australian National University", "institution_ids": ["https://openalex.org/I118347636"]}]}, {"author_position": "last", "author": {"id": null, "display_name": "Liu, Yihang", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I154099455", "display_name": "Shandong University", "ror": "https://ror.org/0207yh398", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I154099455"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Liu, Yihang", "raw_affiliation_strings": ["Shandong University"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Shandong University", "institution_ids": ["https://openalex.org/I154099455"]}]}], "abstract_inverted_index": {"Medical": [0], "Visual": [1], "Question": [2], "Answering": [3], "(Med-VQA)": [4], "represents": [5], "a": [6], "critical": [7], "and": [8, 43, 58, 85], "challenging": [9, 121], "subtask": [10], "within": [11], "the": [12, 71, 111, 145], "general": [13, 20], "VQA": [14, 34], "domain.": [15], "Despite": [16], "significant": [17], "advancements": [18], "in": [19], "VQA,": [21], "multimodal": [22], "large": [23], "language": [24], "models": [25], "(MLLMs)": [26], "still": [27], "exhibit": [28], "substantial": [29], "limitations": [30, 37], "when": [31, 100], "handling": [32], "multi-task": [33], "scenarios.": [35], "These": [36], "manifest": [38], "through": [39], "erroneous": [40], "spatial": [41], "localization": [42], "misinterpretation": [44], "of": [45], "medical": [46, 63, 89, 103, 146], "images,": [47], "which": [48, 93, 119], "primarily": [49], "arise": [50], "from": [51], "two": [52], "fundamental": [53], "issues:": [54], "inadequate": [55], "image-text": [56, 83, 135], "alignment": [57, 84, 136], "insufficient": [59], "domain-specified": [60], "knowledge": [61, 90], "for": [62, 122, 151], "applications.": [64], "To": [65], "address": [66], "these": [67], "issues,": [68], "we": [69], "introduce": [70], "Cross-Modal": [72], "Clinical": [73], "Knowledge": [74], "Distiller": [75], "(ClinKD),": [76], "an": [77], "innovative": [78], "framework": [79], "designed": [80], "to": [81, 96, 142, 144], "enhance": [82], "establish": [86], "more": [87], "effective": [88], "transformation": [91], "mechanisms,": [92], "enables": [94, 140], "MLLMs": [95, 141], "perform": [97], "better": [98], "even": [99], "lacking": [101], "prior": [102], "knowledge.": [104, 147], "Our": [105], "extensive": [106], "experimental": [107], "evaluations": [108], "demonstrate": [109], "that": [110, 128], "ClinKD": [112, 152], "achieves": [113], "state-of-the-art": [114], "performance": [115], "on": [116], "several": [117], "datasets": [118], "are": [120], "Med-VQA": [123], "task.": [124], "The": [125, 148], "results": [126], "indicate": [127], "our": [129], "approach": [130], "not": [131], "only": [132], "significantly": [133], "improves": [134], "but": [137], "also": [138], "effectively": [139], "adapt": [143], "source": [149], "code": [150], "is": [153], "available": [154], "at:": [155], "https://github.com/overloadedHenry/ClinKD.": [156]}, "primary_topic": {"id": "https://openalex.org/T11714", "display_name": "Multimodal Machine Learning Applications", "score": 0.9994999766349792, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T11714", "display_name": "Multimodal Machine Learning Applications", "score": 0.9994999766349792, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T11636", "display_name": "Artificial Intelligence in Healthcare and Education", "score": 0.0005000000237487257, "subfield": {"id": "https://openalex.org/subfields/2718", "display_name": "Health Informatics"}, "field": {"id": "https://openalex.org/fields/27", "display_name": "Medicine"}, "domain": {"id": "https://openalex.org/domains/4", "display_name": "Health Sciences"}}, {"id": "https://openalex.org/T10824", "display_name": "Image Retrieval and Classification Techniques", "score": 0.0, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2502.05928", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2502.05928", "pdf_url": "https://arxiv.org/pdf/2502.05928", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": null, "license_id": null, "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4414581662", "doi": "https://doi.org/10.48550/arxiv.2505.18761", "title": "How Is LLM Reasoning Distracted by Irrelevant Context? An Analysis Using a Controlled Benchmark", "publication_year": 2025, "publication_date": "2025-05-24", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5014654292", "display_name": "Minglai Yang", "orcid": "https://orcid.org/0009-0001-9463-8042", "observed_orcids": ["https://orcid.org/0009-0001-9463-8042"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Yang, Minglai", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": null, "display_name": "Huang, Ethan", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Huang, Ethan", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5100425225", "display_name": "Liang Zhang", "orcid": "https://orcid.org/0000-0002-5805-7099", "observed_orcids": ["https://orcid.org/0000-0002-5805-7099"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Zhang, Liang", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5047699502", "display_name": "Mihai Surdeanu", "orcid": "https://orcid.org/0000-0001-6956-8030", "observed_orcids": ["https://orcid.org/0000-0001-6956-8030"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Surdeanu, Mihai", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5032613436", "display_name": "William B. Wang", "orcid": "https://orcid.org/0000-0002-3048-7847", "observed_orcids": ["https://orcid.org/0000-0002-3048-7847"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Wang, William", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "last", "author": {"id": null, "display_name": "Pan, Liangming", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Pan, Liangming", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}], "abstract_inverted_index": {"We": [0, 71], "introduce": [1], "Grade": [2], "School": [3], "Math": [4], "with": [5, 31, 60], "Distracting": [6], "Context": [7], "(GSM-DC),": [8], "a": [9, 74, 80], "synthetic": [10], "benchmark": [11], "to": [12, 47], "evaluate": [13], "Large": [14], "Language": [15], "Models'": [16], "(LLMs)": [17], "reasoning": [18, 29, 51], "robustness": [19, 87], "against": [20], "systematically": [21], "controlled": [22], "irrelevant": [23], "context": [24], "(IC).": [25], "GSM-DC": [26], "constructs": [27], "symbolic": [28], "graphs": [30], "precise": [32], "distractor": [33], "injections,": [34], "enabling": [35], "rigorous,": [36], "reproducible": [37], "evaluation.": [38], "Our": [39], "experiments": [40], "demonstrate": [41], "that": [42], "LLMs": [43], "are": [44], "significantly": [45], "sensitive": [46], "IC,": [48], "affecting": [49], "both": [50, 66], "path": [52], "selection": [53], "and": [54, 68], "arithmetic": [55], "accuracy.": [56], "Additionally,": [57], "training": [58], "models": [59], "strong": [61], "distractors": [62], "improves": [63], "performance": [64], "in": [65, 88], "in-distribution": [67], "out-of-distribution": [69, 89], "scenarios.": [70], "further": [72], "propose": [73], "stepwise": [75], "tree": [76], "search": [77], "guided": [78], "by": [79], "process": [81], "reward": [82], "model,": [83], "which": [84], "notably": [85], "enhances": [86], "conditions.": [90]}, "primary_topic": {"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.63919997215271, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.63919997215271, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T11689", "display_name": "Adversarial Robustness in Machine Learning", "score": 0.1128000020980835, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10181", "display_name": "Natural Language Processing Techniques", "score": 0.07150000333786011, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2505.18761", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2505.18761", "pdf_url": "https://arxiv.org/pdf/2505.18761", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": null, "license_id": null, "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4417353196", "doi": "https://doi.org/10.48550/arxiv.2506.10504", "title": "Beyond Single-User Dialogue: Assessing Multi-User Dialogue State Tracking Capabilities of Large Language Models", "publication_year": 2025, "publication_date": "2025-06-12", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5110784571", "display_name": "Sangmin Song", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Song, Sangmin", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5101772444", "display_name": "Juhwan Choi", "orcid": "https://orcid.org/0000-0003-0657-581X", "observed_orcids": ["https://orcid.org/0000-0003-0657-581X"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Choi, Juhwan", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5010256940", "display_name": "Jungmin Yun", "orcid": "https://orcid.org/0000-0001-6868-286X", "observed_orcids": ["https://orcid.org/0000-0001-6868-286X"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Yun, JungMin", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "last", "author": {"id": "https://openalex.org/A5016930939", "display_name": "Youngbin Kim", "orcid": "https://orcid.org/0000-0002-2114-0120", "observed_orcids": ["https://orcid.org/0000-0002-2114-0120", "https://orcid.org/0009-0002-3190-6940"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Kim, YoungBin", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}], "abstract_inverted_index": {"Large": [0], "language": [1], "models": [2], "(LLMs)": [3], "have": [4], "demonstrated": [5], "remarkable": [6], "performance": [7, 106], "in": [8, 48, 60, 98, 118], "zero-shot": [9], "dialogue": [10, 122], "state": [11], "tracking": [12, 121], "(DST),": [13], "reducing": [14], "the": [15, 33, 44, 113, 130, 143], "need": [16, 131], "for": [17, 132, 138, 145], "task-specific": [18], "training.": [19], "However,": [20], "conventional": [21], "DST": [22, 50, 68, 140, 150], "benchmarks": [23], "primarily": [24], "focus": [25], "on": [26, 78], "structured": [27], "user-agent": [28], "conversations,": [29, 91], "failing": [30], "to": [31, 109, 135], "capture": [32], "complexities": [34], "of": [35, 46, 73, 96, 115], "real-world": [36], "multi-user": [37, 49, 99, 139], "interactions.": [38], "In": [39], "this": [40], "study,": [41], "we": [42, 64], "assess": [43], "robustness": [45], "LLMs": [47, 97, 117, 137], "while": [51], "minimizing": [52], "dataset": [53, 69], "construction": [54], "costs.": [55], "Inspired": [56], "by": [57, 70], "recent": [58], "advances": [59], "LLM-based": [61], "data": [62], "annotation,": [63], "extend": [65], "an": [66], "existing": [67], "generating": [71], "utterances": [72, 89], "a": [74, 86, 93, 104], "second": [75, 87], "user": [76], "based": [77], "speech": [79], "act": [80], "theory.": [81], "Our": [82, 127], "methodology": [83], "systematically": [84], "incorporates": [85], "user's": [88], "into": [90], "enabling": [92], "controlled": [94], "evaluation": [95], "settings.": [100], "Experimental": [101], "results": [102], "reveal": [103], "significant": [105], "drop": [107], "compared": [108], "single-user": [110], "DST,": [111], "highlighting": [112], "limitations": [114], "current": [116], "extracting": [119], "and": [120, 148], "states": [123], "amidst": [124], "multiple": [125], "speakers.": [126], "findings": [128], "emphasize": [129], "future": [133], "research": [134], "enhance": [136], "scenarios,": [141], "paving": [142], "way": [144], "more": [146], "realistic": [147], "robust": [149], "models.": [151]}, "primary_topic": {"id": "https://openalex.org/T12031", "display_name": "Speech and dialogue systems", "score": 1.0, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T12031", "display_name": "Speech and dialogue systems", "score": 1.0, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T12128", "display_name": "AI in Service Interactions", "score": 0.0, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.0, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2506.10504", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2506.10504", "pdf_url": "https://arxiv.org/pdf/2506.10504", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": null, "license_id": null, "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W7103987378", "doi": "https://doi.org/10.1162/tacl.a.44", "title": "Safe Pruning LoRA: Robust Distance-Guided Pruning for Safety Alignment in Adaptation of LLMs", "publication_year": 2025, "publication_date": "2025-01-01", "type": "article", "language": "en", "authorships": [{"author_position": "first", "author": {"id": null, "display_name": "Shuang Ao", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I43439940", "display_name": "University of Southampton", "ror": "https://ror.org/01ryk1543", "country_code": "GB", "type": "education", "lineage": ["https://openalex.org/I43439940"]}], "countries": ["GB"], "is_corresponding": false, "raw_author_name": "Shuang Ao", "raw_affiliation_strings": ["School of Electronics and Computer Science, University of Southampton, UK. s.ao@soton.ac.uk"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "School of Electronics and Computer Science, University of Southampton, UK. s.ao@soton.ac.uk", "institution_ids": ["https://openalex.org/I43439940"]}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Yi Dong", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I146655781", "display_name": "University of Liverpool", "ror": "https://ror.org/04xs57h96", "country_code": "GB", "type": "education", "lineage": ["https://openalex.org/I146655781"]}], "countries": ["GB"], "is_corresponding": false, "raw_author_name": "Yi Dong", "raw_affiliation_strings": ["Department of Computer Science, University of Liverpool, UK. yi.dong@liverpool.ac.uk"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Computer Science, University of Liverpool, UK. yi.dong@liverpool.ac.uk", "institution_ids": ["https://openalex.org/I146655781"]}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Jinwei Hu", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I146655781", "display_name": "University of Liverpool", "ror": "https://ror.org/04xs57h96", "country_code": "GB", "type": "education", "lineage": ["https://openalex.org/I146655781"]}], "countries": ["GB"], "is_corresponding": false, "raw_author_name": "Jinwei Hu", "raw_affiliation_strings": ["Department of Computer Science, University of Liverpool, UK. j.hu33@liverpool.ac.uk"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Computer Science, University of Liverpool, UK. j.hu33@liverpool.ac.uk", "institution_ids": ["https://openalex.org/I146655781"]}]}, {"author_position": "last", "author": {"id": null, "display_name": "Sarvapali D. Ramchurn", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I43439940", "display_name": "University of Southampton", "ror": "https://ror.org/01ryk1543", "country_code": "GB", "type": "education", "lineage": ["https://openalex.org/I43439940"]}], "countries": ["GB"], "is_corresponding": false, "raw_author_name": "Sarvapali D. Ramchurn", "raw_affiliation_strings": ["School of Electronics and Computer Science, University of Southampton, UK. sdr1@soton.ac.uk"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "School of Electronics and Computer Science, University of Southampton, UK. sdr1@soton.ac.uk", "institution_ids": ["https://openalex.org/I43439940"]}]}], "abstract_inverted_index": {"Abstract": [0], "Fine-tuning": [1], "Large": [2], "Language": [3], "Models": [4], "(LLMs)": [5], "with": [6, 23, 100], "Low-Rank": [7], "Adaptation": [8], "(LoRA)": [9], "enhances": [10], "adaptability": [11], "while": [12, 71, 132], "reducing": [13, 129], "computational": [14], "costs.": [15], "However,": [16], "fine-tuning": [17], "can": [18], "compromise": [19], "safety": [20, 32, 67, 70, 88, 125, 130], "alignment,": [21, 68], "even": [22], "benign": [24, 103, 109], "data,": [25, 106], "increasing": [26], "susceptibility": [27], "to": [28, 36, 42], "harmful": [29], "outputs.": [30], "Existing": [31], "alignment": [33, 126], "methods": [34], "struggle": [35], "capture": [37], "complex": [38], "parameter": [39], "shifts,": [40], "leading": [41], "suboptimal": [43], "safety-utility": [44], "trade-offs.": [45], "To": [46], "address": [47], "this": [48], "issue,": [49], "we": [50, 77], "propose": [51], "Safe": [52], "Pruning": [53], "LoRA": [54, 63], "(SPLoRA),": [55], "a": [56, 81, 147], "novel": [57], "pruning-based": [58], "approach": [59], "that": [60, 65, 85, 121], "selectively": [61], "removes": [62], "layers": [64], "weaken": [66], "improving": [69, 135], "preserving": [72], "performance.": [73], "At": [74], "its": [75], "core,": [76], "introduce": [78], "Empirical-DIEM": [79], "(E-DIEM),": [80], "dimension-insensitive": [82], "similarity": [83], "metric": [84], "effectively": [86], "detects": [87], "misalignment": [89], "in": [90], "LoRA-adapted": [91], "models.": [92], "We": [93], "conduct": [94], "extensive": [95], "experiments": [96], "on": [97], "LLMs": [98], "fine-tuned": [99], "mixed": [101], "of": [102], "and": [104, 107, 116, 138, 149, 155], "malicious": [105], "purely": [108], "datasets,": [110], "evaluating": [111], "SPLoRA": [112, 122, 141], "across": [113], "utility,": [114], "safety,": [115], "reliability": [117], "metrics.": [118], "Results": [119], "demonstrate": [120], "outperforms": [123], "state-of-the-art": [124], "techniques,": [127], "significantly": [128], "risks": [131], "maintaining": [133], "or": [134], "model": [136], "performance": [137], "reliability.": [139], "Additionally,": [140], "reduces": [142], "inference": [143], "overhead,": [144], "making": [145], "it": [146], "scalable": [148], "efficient": [150], "solution": [151], "for": [152], "deploying": [153], "safer": [154], "more": [156], "reliable": [157], "LLMs.": [158], "The": [159], "code": [160], "is": [161], "available": [162], "at": [163], "https://github.com/AoShuang92/SPLoRA.": [164]}, "primary_topic": {"id": "https://openalex.org/T11689", "display_name": "Adversarial Robustness in Machine Learning", "score": 0.9204000234603882, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T11689", "display_name": "Adversarial Robustness in Machine Learning", "score": 0.9204000234603882, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.0215000007301569, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10883", "display_name": "Ethics and Social Impacts of AI", "score": 0.013100000098347664, "subfield": {"id": "https://openalex.org/subfields/3311", "display_name": "Safety Research"}, "field": {"id": "https://openalex.org/fields/33", "display_name": "Social Sciences"}, "domain": {"id": "https://openalex.org/domains/2", "display_name": "Social Sciences"}}], "primary_location": {"id": "doi:10.1162/tacl.a.44", "is_oa": true, "landing_page_url": "https://doi.org/10.1162/tacl.a.44", "pdf_url": null, "source": {"id": "https://openalex.org/S2729999759", "display_name": "Transactions of the Association for Computational Linguistics", "issn_l": "2307-387X", "issn": ["2307-387X"], "is_oa": true, "is_in_doaj": true, "is_core": true, "listed_in": ["cwts-core", "doaj", "jufo-1", "norway-2"], "host_organization": "https://openalex.org/P4310320244", "host_organization_name": "Association for Computational Linguistics", "host_organization_lineage": ["https://openalex.org/P4310320244"], "host_organization_lineage_names": ["Association for Computational Linguistics"], "type": "journal"}, "license": "cc-by", "license_id": "https://openalex.org/licenses/cc-by", "version": "publishedVersion", "is_accepted": true, "is_published": true, "raw_source_name": "Transactions of the Association for Computational Linguistics", "raw_type": "journal-article"}, "cited_by_count": 4, "is_retracted": false}, {"id": "https://openalex.org/W4416905110", "doi": "https://doi.org/10.48550/arxiv.2504.19739", "title": "Contrastive Language-Image Learning with Augmented Textual Prompts for 3D/4D FER Using Vision-Language Model", "publication_year": 2025, "publication_date": "2025-04-28", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5034624002", "display_name": "Muzammil Behzad", "orcid": "https://orcid.org/0000-0003-3693-4596", "observed_orcids": ["https://orcid.org/0000-0003-3693-4596"]}, "institutions": [{"id": "https://openalex.org/I134085113", "display_name": "King Fahd University of Petroleum and Minerals", "ror": "https://ror.org/03yez3163", "country_code": "SA", "type": "education", "lineage": ["https://openalex.org/I134085113"]}], "countries": ["SA"], "is_corresponding": false, "raw_author_name": "Behzad, Muzammil", "raw_affiliation_strings": ["Information & Computer Science Department , King Fahd University of Petroleum & Minerals , Saudi Arabia"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Information & Computer Science Department , King Fahd University of Petroleum & Minerals , Saudi Arabia", "institution_ids": ["https://openalex.org/I134085113"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5101470710", "display_name": "Guoying Zhao", "orcid": "https://orcid.org/0000-0001-6967-7341", "observed_orcids": ["https://orcid.org/0000-0001-6967-7341", "https://orcid.org/0000-0003-3657-544X"]}, "institutions": [{"id": "https://openalex.org/I98381234", "display_name": "University of Oulu", "ror": "https://ror.org/03yj89h83", "country_code": "FI", "type": "education", "lineage": ["https://openalex.org/I98381234"]}], "countries": ["FI"], "is_corresponding": false, "raw_author_name": "Zhao, Guoying", "raw_affiliation_strings": ["Center for Machine Vision and Signal Analysis , University of Oulu , Finland"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Center for Machine Vision and Signal Analysis , University of Oulu , Finland", "institution_ids": ["https://openalex.org/I98381234"]}]}], "abstract_inverted_index": {"In": [0], "this": [1], "paper,": [2], "we": [3, 32, 55], "introduce": [4, 56], "AffectVLM,": [5], "a": [6, 14, 34, 41, 79, 83], "vision-language": [7], "model": [8, 48, 90], "designed": [9], "to": [10, 60, 71], "integrate": [11], "multiviews": [12], "for": [13, 82, 91], "semantically": [15], "rich": [16], "and": [17, 66, 87], "visually": [18], "comprehensive": [19], "understanding": [20], "of": [21, 100], "facial": [22], "emotions": [23], "from": [24], "3D/4D": [25], "data.": [26], "To": [27], "effectively": [28], "capture": [29], "visual": [30, 74], "features,": [31], "propose": [33], "joint": [35], "representation": [36], "learning": [37], "framework": [38], "paired": [39], "with": [40], "novel": [42], "gradient-friendly": [43], "loss": [44], "function": [45], "that": [46], "accelerates": [47], "convergence": [49], "towards": [50], "optimal": [51], "feature": [52], "representation.": [53], "Additionally,": [54], "augmented": [57], "textual": [58], "prompts": [59], "enhance": [61], "the": [62, 73, 89, 97], "model's": [63], "linguistic": [64], "capabilities": [65], "employ": [67], "mixed": [68], "view": [69], "augmentation": [70], "expand": [72], "dataset.": [75], "We": [76], "also": [77], "develop": [78], "Streamlit": [80], "app": [81], "real-time": [84], "interactive": [85], "inference": [86], "enable": [88], "distributed": [92], "learning.": [93], "Extensive": [94], "experiments": [95], "validate": [96], "superior": [98], "performance": [99], "AffectVLM": [101], "across": [102], "multiple": [103], "benchmarks.": [104]}, "primary_topic": {"id": "https://openalex.org/T10667", "display_name": "Emotion and Mood Recognition", "score": 0.9434000253677368, "subfield": {"id": "https://openalex.org/subfields/3205", "display_name": "Experimental and Cognitive Psychology"}, "field": {"id": "https://openalex.org/fields/32", "display_name": "Psychology"}, "domain": {"id": "https://openalex.org/domains/2", "display_name": "Social Sciences"}}, "topics": [{"id": "https://openalex.org/T10667", "display_name": "Emotion and Mood Recognition", "score": 0.9434000253677368, "subfield": {"id": "https://openalex.org/subfields/3205", "display_name": "Experimental and Cognitive Psychology"}, "field": {"id": "https://openalex.org/fields/32", "display_name": "Psychology"}, "domain": {"id": "https://openalex.org/domains/2", "display_name": "Social Sciences"}}, {"id": "https://openalex.org/T11448", "display_name": "Face recognition and analysis", "score": 0.02850000001490116, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T11714", "display_name": "Multimodal Machine Learning Applications", "score": 0.009399999864399433, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2504.19739", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2504.19739", "pdf_url": "https://arxiv.org/pdf/2504.19739", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": "cc-by", "license_id": "https://openalex.org/licenses/cc-by", "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4417077351", "doi": "https://doi.org/10.48550/arxiv.2509.14860", "title": "MARIC: Multi-Agent Reasoning for Image Classification", "publication_year": 2025, "publication_date": "2025-09-18", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": null, "display_name": "Seo, Wonduk", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I20231570", "display_name": "Peking University", "ror": "https://ror.org/02v51f717", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I20231570"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Seo, Wonduk", "raw_affiliation_strings": ["Enhans , Seoul , South Korea", "Peking University , Beijing , China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Enhans , Seoul , South Korea", "institution_ids": []}, {"raw_affiliation_string": "Peking University , Beijing , China", "institution_ids": ["https://openalex.org/I20231570"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5048054713", "display_name": "Minhyeong Yu", "orcid": "https://orcid.org/0000-0002-0350-4349", "observed_orcids": ["https://orcid.org/0000-0002-0350-4349"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Yu, Minhyeong", "raw_affiliation_strings": ["Enhans , Seoul , South Korea"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Enhans , Seoul , South Korea", "institution_ids": []}]}, {"author_position": "middle", "author": {"id": null, "display_name": "An, Hyunjin", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "An, Hyunjin", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "last", "author": {"id": "https://openalex.org/A5100426795", "display_name": "Seunghyun Lee", "orcid": "https://orcid.org/0000-0002-7855-5036", "observed_orcids": ["https://orcid.org/0000-0002-7855-5036"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Lee, Seunghyun", "raw_affiliation_strings": ["Enhans , Seoul , South Korea"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Enhans , Seoul , South Korea", "institution_ids": []}]}], "abstract_inverted_index": {"Image": [0, 61], "classification": [1, 71, 157], "has": [2], "traditionally": [3], "relied": [4], "on": [5, 38, 96, 153], "parameter-intensive": [6], "model": [7], "training,": [8], "requiring": [9], "large-scale": [10], "annotated": [11], "datasets": [12, 159], "and": [13, 91, 136, 148, 175], "extensive": [14], "fine": [15, 103], "tuning": [16], "to": [17, 44, 83], "achieve": [18], "competitive": [19], "performance.": [20], "While": [21], "recent": [22], "vision": [23], "language": [24], "models": [25], "(VLMs)": [26], "alleviate": [27], "some": [28], "of": [29, 48, 88, 144, 169], "these": [30, 97, 115], "constraints,": [31], "they": [32], "remain": [33], "limited": [34], "by": [35], "their": [36], "reliance": [37], "single": [39], "pass": [40], "representations,": [41], "often": [42], "failing": [43], "capture": [45], "complementary": [46, 116], "aspects": [47], "visual": [49, 108, 171], "content.": [50], "In": [51], "this": [52], "paper,": [53], "we": [54], "introduce": [55], "Multi": [56], "Agent": [57, 82, 113], "based": [58], "Reasoning": [59, 112], "for": [60, 126, 173], "Classification": [62], "(MARIC),": [63], "a": [64, 73, 111, 123], "multi": [65], "agent": [66], "framework": [67], "that": [68, 161], "reformulates": [69], "image": [70, 90, 156, 177], "as": [72], "collaborative": [74], "reasoning": [75, 172], "process.": [76], "MARIC": [77, 140, 162], "first": [78], "utilizes": [79], "an": [80], "Outliner": [81], "analyze": [84], "the": [85, 89, 131, 142, 167], "global": [86], "theme": [87], "generate": [92], "targeted": [93], "prompts.": [94], "Based": [95], "prompts,": [98], "three": [99], "Aspect": [100], "Agents": [101], "extract": [102], "grained": [104], "descriptions": [105], "along": [106], "distinct": [107], "dimensions.": [109], "Finally,": [110], "synthesizes": [114], "outputs": [117], "through": [118], "integrated": [119], "reflection": [120], "step,": [121], "producing": [122], "unified": [124], "representation": [125], "classification.": [127, 178], "By": [128], "explicitly": [129], "decomposing": [130], "task": [132], "into": [133], "multiple": [134], "perspectives": [135], "encouraging": [137], "reflective": [138], "synthesis,": [139], "mitigates": [141], "shortcomings": [143], "both": [145], "parameter-heavy": [146], "training": [147], "monolithic": [149], "VLM": [150], "reasoning.": [151], "Experiments": [152], "4": [154], "diverse": [155], "benchmark": [158], "demonstrate": [160], "significantly": [163], "outperforms": [164], "baselines,": [165], "highlighting": [166], "effectiveness": [168], "multi-agent": [170], "robust": [174], "interpretable": [176]}, "primary_topic": {"id": "https://openalex.org/T11714", "display_name": "Multimodal Machine Learning Applications", "score": 0.9980000257492065, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T11714", "display_name": "Multimodal Machine Learning Applications", "score": 0.9980000257492065, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10036", "display_name": "Advanced Neural Network Applications", "score": 0.0008999999845400453, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T11307", "display_name": "Domain Adaptation and Few-Shot Learning", "score": 0.00039999998989515007, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2509.14860", "is_oa": true, "landing_page_url": "https://arxiv.org/abs/2509.14860", "pdf_url": "https://arxiv.org/pdf/2509.14860", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": "cc-by-nc-sa", "license_id": "https://openalex.org/licenses/cc-by-nc-sa", "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4407761215", "doi": "https://doi.org/10.3389/fpubh.2025.1540946", "title": "Building an intelligent diabetes Q&A system with knowledge graphs and large language models", "publication_year": 2025, "publication_date": "2025-02-20", "type": "article", "language": "en", "authorships": [{"author_position": "first", "author": {"id": null, "display_name": "Zhenkai Qin", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I4405280785", "display_name": "Guangxi Police College", "ror": "https://ror.org/03ef1fb09", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I4405280785"]}, {"id": "https://openalex.org/I4800084", "display_name": "Southwest Jiaotong University", "ror": "https://ror.org/00hn7w693", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I4800084"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Zhenkai Qin", "raw_affiliation_strings": ["School of Computer Science and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China", "School of Information Technology, Guangxi Police College, Nanning, China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "School of Computer Science and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China", "institution_ids": ["https://openalex.org/I4800084"]}, {"raw_affiliation_string": "School of Information Technology, Guangxi Police College, Nanning, China", "institution_ids": ["https://openalex.org/I4405280785"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5100771814", "display_name": "Dongze Wu", "orcid": "https://orcid.org/0000-0002-5571-8728", "observed_orcids": ["https://orcid.org/0000-0002-5571-8728", "https://orcid.org/0009-0000-8053-1335"]}, "institutions": [{"id": "https://openalex.org/I4405280785", "display_name": "Guangxi Police College", "ror": "https://ror.org/03ef1fb09", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I4405280785"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Dongze Wu", "raw_affiliation_strings": ["School of Information Technology, Guangxi Police College, Nanning, China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "School of Information Technology, Guangxi Police College, Nanning, China", "institution_ids": ["https://openalex.org/I4405280785"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5108241136", "display_name": "Zhidong Zang", "orcid": "https://orcid.org/0000-0002-4165-7178", "observed_orcids": ["https://orcid.org/0000-0002-4165-7178"]}, "institutions": [{"id": "https://openalex.org/I78978612", "display_name": "Yangzhou University", "ror": "https://ror.org/03tqb8s11", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I78978612"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Zhidong Zang", "raw_affiliation_strings": ["School of Social Development, Yangzhou University, Yangzhou, China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "School of Social Development, Yangzhou University, Yangzhou, China", "institution_ids": ["https://openalex.org/I78978612"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5100365891", "display_name": "Xiaolong Chen", "orcid": "https://orcid.org/0000-0002-8704-8212", "observed_orcids": ["https://orcid.org/0000-0002-8704-8212"]}, "institutions": [{"id": "https://openalex.org/I49835588", "display_name": "Macao Polytechnic University", "ror": "https://ror.org/02sf5td35", "country_code": "MO", "type": "education", "lineage": ["https://openalex.org/I49835588"]}], "countries": ["MO"], "is_corresponding": true, "raw_author_name": "Xiaolong Chen", "raw_affiliation_strings": ["Faculty of Humanities and Social Sciences, Macao Polytechnic University, Macao, China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Faculty of Humanities and Social Sciences, Macao Polytechnic University, Macao, China", "institution_ids": ["https://openalex.org/I49835588"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5100714194", "display_name": "Hongfeng Zhang", "orcid": "https://orcid.org/0000-0002-7586-2364", "observed_orcids": ["https://orcid.org/0000-0002-7586-2364"]}, "institutions": [{"id": "https://openalex.org/I49835588", "display_name": "Macao Polytechnic University", "ror": "https://ror.org/02sf5td35", "country_code": "MO", "type": "education", "lineage": ["https://openalex.org/I49835588"]}], "countries": ["MO"], "is_corresponding": true, "raw_author_name": "Hongfeng Zhang", "raw_affiliation_strings": ["Faculty of Humanities and Social Sciences, Macao Polytechnic University, Macao, China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Faculty of Humanities and Social Sciences, Macao Polytechnic University, Macao, China", "institution_ids": ["https://openalex.org/I49835588"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5080595813", "display_name": "Cora Un In Wong", "orcid": "https://orcid.org/0000-0001-5523-4355", "observed_orcids": ["https://orcid.org/0000-0001-5523-4355"]}, "institutions": [{"id": "https://openalex.org/I49835588", "display_name": "Macao Polytechnic University", "ror": "https://ror.org/02sf5td35", "country_code": "MO", "type": "education", "lineage": ["https://openalex.org/I49835588"]}], "countries": ["MO"], "is_corresponding": false, "raw_author_name": "Cora Un In Wong", "raw_affiliation_strings": ["Faculty of Humanities and Social Sciences, Macao Polytechnic University, Macao, China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Faculty of Humanities and Social Sciences, Macao Polytechnic University, Macao, China", "institution_ids": ["https://openalex.org/I49835588"]}]}], "abstract_inverted_index": {"Introduction:": [0], "This": [1, 142], "paper": [2], "introduces": [3], "an": [4], "intelligent": [5], "question-answering": [6, 159], "system": [7, 26, 51, 102], "designed": [8], "to": [9, 14, 28, 84, 133, 156], "deliver": [10], "personalized": [11, 135, 182], "medical": [12, 35, 47, 136, 158], "information": [13], "diabetic": [15], "patients.": [16], "By": [17], "integrating": [18, 148], "large": [19, 149], "language": [20, 150], "models": [21, 151], "with": [22, 57, 152], "knowledge": [23, 55, 120, 154], "graphs,": [24], "the": [25, 38, 58, 78, 124, 145], "aims": [27], "provide": [29, 134], "more": [30], "accurate": [31], "and": [32, 60, 69, 82, 96, 109, 127, 172], "contextually": [33], "relevant": [34], "guidance,": [36], "addressing": [37], "limitations": [39], "of": [40, 117, 147], "traditional": [41], "healthcare": [42, 174, 183], "systems": [43], "in": [44, 106, 112], "handling": [45], "complex": [46], "queries.": [48], "Methods:": [49], "The": [50, 88, 101, 115, 161], "combines": [52], "a": [53, 118, 165, 177], "Neo4j-based": [54], "graph": [56, 121], "Baichuan2-13B": [59], "Qwen2.5-7B": [61], "models.": [62], "To": [63], "enhance": [64], "performance,": [65], "Low-Rank": [66], "Adaptation": [67], "(LoRA)": [68], "prompt-based": [70], "learning": [71], "techniques": [72], "are": [73], "applied.": [74], "These": [75], "methods": [76], "improve": [77, 157], "system's": [79, 89, 125], "semantic": [80], "understanding": [81], "ability": [83, 132], "generate": [85], "high-quality": [86], "responses.": [87], "performance": [90], "is": [91], "evaluated": [92], "using": [93], "entity": [94, 107], "recognition": [95, 108], "intent": [97, 113], "classification": [98], "tasks.": [99], "Results:": [100], "achieves": [103], "85.91%": [104], "precision": [105, 111], "88.55%": [110], "classification.": [114], "integration": [116], "structured": [119, 153], "significantly": [122], "improves": [123], "accuracy": [126], "clinical": [128], "relevance,": [129], "enhancing": [130], "its": [131], "responses": [137], "for": [138, 168, 180], "diabetes": [139, 170], "management.": [140], "Discussion:": [141], "study": [143], "demonstrates": [144], "effectiveness": [146], "graphs": [155], "systems.": [160], "proposed": [162], "approach": [163], "offers": [164], "promising": [166], "framework": [167], "advancing": [169], "management": [171], "other": [173], "applications,": [175], "providing": [176], "solid": [178], "foundation": [179], "future": [181], "interventions.": [184]}, "primary_topic": {"id": "https://openalex.org/T11636", "display_name": "Artificial Intelligence in Healthcare and Education", "score": 0.8852999806404114, "subfield": {"id": "https://openalex.org/subfields/2718", "display_name": "Health Informatics"}, "field": {"id": "https://openalex.org/fields/27", "display_name": "Medicine"}, "domain": {"id": "https://openalex.org/domains/4", "display_name": "Health Sciences"}}, "topics": [{"id": "https://openalex.org/T11636", "display_name": "Artificial Intelligence in Healthcare and Education", "score": 0.8852999806404114, "subfield": {"id": "https://openalex.org/subfields/2718", "display_name": "Health Informatics"}, "field": {"id": "https://openalex.org/fields/27", "display_name": "Medicine"}, "domain": {"id": "https://openalex.org/domains/4", "display_name": "Health Sciences"}}, {"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.025299999862909317, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T11710", "display_name": "Biomedical Text Mining and Ontologies", "score": 0.020400000736117363, "subfield": {"id": "https://openalex.org/subfields/1312", "display_name": "Molecular Biology"}, "field": {"id": "https://openalex.org/fields/13", "display_name": "Biochemistry, Genetics and Molecular Biology"}, "domain": {"id": "https://openalex.org/domains/1", "display_name": "Life Sciences"}}], "primary_location": {"id": "doi:10.3389/fpubh.2025.1540946", "is_oa": true, "landing_page_url": "https://doi.org/10.3389/fpubh.2025.1540946", "pdf_url": "https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1540946/pdf", "source": {"id": "https://openalex.org/S2595931848", "display_name": "Frontiers in Public Health", "issn_l": "2296-2565", "issn": ["2296-2565"], "is_oa": true, "is_in_doaj": true, "is_core": true, "listed_in": ["cwts-core", "doaj", "medline", "norway-1"], "host_organization": "https://openalex.org/P4310320527", "host_organization_name": "Frontiers Media", "host_organization_lineage": ["https://openalex.org/P4310320527"], "host_organization_lineage_names": ["Frontiers Media"], "type": "journal"}, "license": "cc-by", "license_id": "https://openalex.org/licenses/cc-by", "version": "publishedVersion", "is_accepted": true, "is_published": true, "raw_source_name": "Frontiers in Public Health", "raw_type": "journal-article"}, "cited_by_count": 9, "is_retracted": false}, {"id": "https://openalex.org/W4416445992", "doi": "https://doi.org/10.48550/arxiv.2505.15922", "title": "Aligning Dialogue Agents with Global Feedback via Large Language Model Multimodal Reward Decomposition", "publication_year": 2025, "publication_date": "2025-05-21", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5100398178", "display_name": "Dong Won Lee", "orcid": "https://orcid.org/0000-0002-6336-5512", "observed_orcids": ["https://orcid.org/0000-0002-6336-5512"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Lee, Dong Won", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5102016303", "display_name": "Hae Won Park", "orcid": "https://orcid.org/0000-0001-9638-1722", "observed_orcids": ["https://orcid.org/0000-0001-9638-1722"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Park, Hae Won", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5108541589", "display_name": "Cynthia Breazeal", "orcid": "https://orcid.org/0000-0002-0587-2065", "observed_orcids": ["https://orcid.org/0000-0002-0587-2065"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Breazeal, Cynthia", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "last", "author": {"id": "https://openalex.org/A5112302512", "display_name": "Louis-Philippe Morency", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Morency, Louis-Philippe", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}], "abstract_inverted_index": {"We": [0, 21, 101], "propose": [1], "a": [2, 16, 27, 88], "large": [3, 30], "language": [4, 31, 79], "model": [5, 32], "based": [6], "reward": [7, 54, 90, 110, 128, 136], "decomposition": [8, 55, 111], "framework": [9], "for": [10, 95, 98, 134], "aligning": [11], "dialogue": [12, 59, 99], "agents": [13], "using": [14, 56], "only": [15, 57], "single": [17], "session-level": [18, 43], "feedback": [19], "signal.": [20], "leverage": [22], "the": [23, 50, 58, 132], "reasoning": [24], "capabilities": [25], "of": [26, 120], "frozen,": [28], "pretrained": [29], "(LLM)": [33], "to": [34, 52], "infer": [35], "fine-grained": [36], "local": [37], "implicit": [38], "rewards": [39, 84], "by": [40], "decomposing": [41], "global,": [42], "feedback.": [44, 141], "Our": [45], "first": [46], "\\emph{text-only}": [47], "variant": [48, 64], "prompts": [49], "LLM": [51], "perform": [53], "transcript.": [60], "The": [61], "second": [62], "\\emph{multimodal}": [63], "incorporates": [65], "additional": [66], "behavioral": [67], "cues,": [68], "such": [69], "as": [70, 77], "pitch,": [71], "gaze,": [72], "and": [73, 105, 113, 138], "facial": [74], "affect,": [75], "expressed": [76], "natural": [78], "descriptions.": [80], "These": [81], "inferred": [82], "turn-level": [83], "are": [85, 126], "distilled": [86], "into": [87], "lightweight": [89], "model,": [91], "which": [92], "we": [93], "utilize": [94], "RL-based": [96], "fine-tuning": [97], "generation.": [100], "evaluate": [102], "both": [103], "text-only": [104], "multimodal": [106], "variants": [107], "against": [108], "state-of-the-art": [109], "methods": [112], "demonstrate": [114], "notable": [115], "improvements": [116], "in": [117], "human": [118, 140], "evaluations": [119], "conversation": [121], "quality,": [122], "suggesting": [123], "that": [124, 130], "LLMs": [125], "strong": [127], "decomposers": [129], "obviate": [131], "need": [133], "manual": [135], "shaping": [137], "granular": [139]}, "primary_topic": {"id": "https://openalex.org/T12031", "display_name": "Speech and dialogue systems", "score": 0.9990000128746033, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T12031", "display_name": "Speech and dialogue systems", "score": 0.9990000128746033, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10462", "display_name": "Reinforcement Learning in Robotics", "score": 0.0003000000142492354, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T11902", "display_name": "Intelligent Tutoring Systems and Adaptive Learning", "score": 9.999999747378752e-05, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2505.15922", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2505.15922", "pdf_url": "https://arxiv.org/pdf/2505.15922", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": "cc-by-nc", "license_id": "https://openalex.org/licenses/cc-by-nc", "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4416053312", "doi": "https://doi.org/10.48550/arxiv.2504.10504", "title": "LayerFlow: Layer-wise Exploration of LLM Embeddings using Uncertainty-aware Interlinked Projections", "publication_year": 2025, "publication_date": "2025-04-09", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5014635198", "display_name": "Rita Sevastjanova", "orcid": "https://orcid.org/0000-0002-2629-9579", "observed_orcids": ["https://orcid.org/0000-0002-2629-9579"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Sevastjanova, Rita", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": null, "display_name": "Gerling, Robin", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Gerling, Robin", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5004978832", "display_name": "Thilo Spinner", "orcid": "https://orcid.org/0000-0002-1168-1804", "observed_orcids": ["https://orcid.org/0000-0002-1168-1804"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Spinner, Thilo", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "last", "author": {"id": "https://openalex.org/A5020415668", "display_name": "Mennatallah El\u2010Assady", "orcid": "https://orcid.org/0000-0001-8526-2613", "observed_orcids": ["https://orcid.org/0000-0001-8526-2613"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "El-Assady, Mennatallah", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}], "abstract_inverted_index": {"Large": [0], "language": [1, 12, 27], "models": [2], "(LLMs)": [3], "represent": [4], "words": [5], "through": [6, 47, 164, 177], "contextual": [7], "word": [8], "embeddings": [9, 31, 107], "encoding": [10], "different": [11, 182], "properties": [13, 20], "like": [14], "semantics": [15], "and": [16, 86, 113, 118, 129, 143, 152, 166, 181], "syntax.": [17], "Understanding": [18], "these": [19], "is": [21], "crucial,": [22], "especially": [23], "for": [24, 32, 51], "researchers": [25], "investigating": [26], "model": [28], "capabilities,": [29], "employing": [30], "tasks": [33], "related": [34], "to": [35, 63, 82, 123, 174], "text": [36], "similarity,": [37], "or": [38], "evaluating": [39], "the": [40, 83, 91, 115, 131, 158, 161, 172], "reasons": [41], "behind": [42], "token": [43], "importance": [44], "as": [45, 67, 138], "measured": [46], "attribution": [48], "methods.": [49], "Applications": [50], "embedding": [52], "exploration": [53], "frequently": [54], "involve": [55], "dimensionality": [56], "reduction": [57], "techniques,": [58], "which": [59], "reduce": [60], "high-dimensional": [61], "vectors": [62], "two": [64], "dimensions": [65], "used": [66], "coordinates": [68], "in": [69, 108], "a": [70, 101], "scatterplot.": [71], "This": [72], "data": [73, 127, 146, 183], "transformation": [74], "step": [75], "introduces": [76], "uncertainty": [77, 176], "that": [78, 105, 170], "can": [79], "be": [80], "propagated": [81], "visual": [84, 102, 135, 179], "representation": [85], "influence": [87], "users'": [88], "interpretation": [89, 119], "of": [90, 160], "data.": [92], "To": [93], "communicate": [94, 175], "such": [95, 137], "uncertainties,": [96, 130], "we": [97], "present": [98], "LayerFlow": [99], "-": [100], "analytics": [103], "workspace": [104, 132, 163], "displays": [106], "an": [109], "interlinked": [110], "projection": [111, 153], "design": [112], "communicates": [114], "transformation,": [116], "representation,": [117], "uncertainty.": [120], "In": [121], "particular,": [122], "hint": [124], "at": [125], "potential": [126], "distortions": [128], "includes": [133], "several": [134], "components,": [136], "convex": [139], "hulls": [140], "showing": [141], "2D": [142], "HD": [144], "clusters,": [145], "point": [147], "pairwise": [148], "distances,": [149], "cluster": [150], "summaries,": [151], "quality": [154], "metrics.": [155], "We": [156], "show": [157], "usability": [159], "presented": [162], "replication": [165], "expert": [167], "case": [168], "studies": [169], "highlight": [171], "need": [173], "multiple": [178], "components": [180], "perspectives.": [184]}, "primary_topic": {"id": "https://openalex.org/T10799", "display_name": "Data Visualization and Analytics", "score": 0.9984999895095825, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T10799", "display_name": "Data Visualization and Analytics", "score": 0.9984999895095825, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T12026", "display_name": "Explainable Artificial Intelligence (XAI)", "score": 0.00019999999494757503, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 9.999999747378752e-05, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2504.10504", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2504.10504", "pdf_url": "https://arxiv.org/pdf/2504.10504", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": null, "license_id": null, "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4414536410", "doi": "https://doi.org/10.48550/arxiv.2508.13037", "title": "Can Large Models Teach Student Models to Solve Mathematical Problems Like Human Beings? A Reasoning Distillation Method via Multi-LoRA Interaction", "publication_year": 2025, "publication_date": "2025-08-18", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5073294147", "display_name": "X. Li", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Li, Xinhe", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5029321864", "display_name": "Jiajun Liu", "orcid": "https://orcid.org/0000-0002-7871-0518", "observed_orcids": ["https://orcid.org/0000-0002-7871-0518"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Liu, Jiajun", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "last", "author": {"id": "https://openalex.org/A5100396091", "display_name": "Peng Wang", "orcid": "https://orcid.org/0000-0002-8745-320X", "observed_orcids": ["https://orcid.org/0000-0002-8745-320X", "https://orcid.org/0000-0001-5876-4650"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Wang, Peng", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}], "abstract_inverted_index": {"Recent": [0], "studies": [1], "have": [2, 9], "demonstrated": [3], "that": [4, 175, 225], "Large": [5], "Language": [6, 31], "Models": [7, 32], "(LLMs)": [8], "strong": [10], "mathematical": [11, 104, 214], "reasoning": [12, 28, 59, 105, 114, 215], "abilities": [13], "but": [14], "rely": [15], "on": [16, 63, 99, 131, 231], "hundreds": [17], "of": [18, 20, 26, 43, 91, 115, 188, 217], "billions": [19], "parameters.": [21], "To": [22], "tackle": [23], "the": [24, 100, 111, 132, 154, 172, 183, 186, 200, 213, 232, 238, 250], "challenge": [25], "poor": [27], "in": [29, 185], "Small": [30], "(SLMs),": [33], "existing": [34], "methods": [35], "typically": [36], "leverage": [37], "LLMs": [38], "to": [39, 53, 121, 147, 177, 181, 204], "generate": [40], "massive": [41], "amounts": [42], "data": [44], "for": [45, 103, 144], "cramming": [46], "training.": [47], "In": [48], "psychology,": [49], "they": [50], "are": [51, 197], "akin": [52], "System": [54, 72, 149], "1": [55], "thinking,": [56, 74, 92, 151], "which": [57, 140], "resolves": [58], "problems": [60], "rapidly": [61], "based": [62, 98], "experience": [64], "and": [65, 80, 113, 158, 190, 199, 246], "intuition.": [66], "However,": [67], "human": [68], "learning": [69], "also": [70], "requires": [71], "2": [73, 150], "where": [75, 235], "knowledge": [76, 167, 176], "is": [77], "first": [78], "acquired": [79], "then": [81], "reinforced": [82], "through": [83, 219], "practice.": [84], "Inspired": [85], "by": [86, 241], "such": [87], "two": [88], "distinct": [89], "modes": [90], "we": [93, 109, 126, 152, 192], "propose": [94], "a": [95, 128], "novel": [96], "method": [97, 240], "multi-LoRA": [101], "Interaction": [102], "Distillation": [106], "(LoRID).": [107], "First,": [108], "input": [110], "question": [112], "each": [116], "sample": [117], "into": [118], "an": [119, 136], "LLM": [120], "create": [122], "knowledge-enhanced": [123], "datasets.": [124], "Subsequently,": [125], "train": [127, 153], "LoRA": [129], "block": [130], "student": [133], "model": [134], "as": [135], "Intuitive": [137], "Reasoner": [138, 160], "(IR),": [139], "directly": [141], "generates": [142], "Chain-of-Thoughts": [143], "problem-solving.": [145], "Then,": [146], "imitate": [148], "Knowledge": [155], "Generator": [156], "(KG)": [157], "Deep": [159], "(DR),": [161], "respectively.": [162, 254], "The": [163], "former": [164], "outputs": [165, 196], "only": [166], "after": [168], "receiving": [169], "problems,": [170], "while": [171], "latter": [173], "uses": [174], "perform": [178], "reasoning.": [179], "Finally,": [180], "address": [182], "randomness": [184], "generation": [187], "IR": [189], "DR,": [191], "evaluate": [193], "whether": [194], "their": [195], "consistent,": [198], "inference": [201], "process": [202], "needs": [203], "be": [205], "iterated": [206], "if": [207], "not.": [208], "This": [209], "step": [210], "can": [211], "enhance": [212], "ability": [216], "SLMs": [218], "mutual": [220], "feedback.": [221], "Experimental": [222], "results": [223], "show": [224], "LoRID": [226], "achieves": [227], "state-of-the-art": [228], "performance,": [229], "especially": [230], "GSM8K": [233], "dataset,": [234], "it": [236], "outperforms": [237], "second-best": [239], "2.3%,": [242], "16.1%,": [243], "2.4%,": [244], "12.3%,": [245], "1.8%": [247], "accuracy": [248], "across": [249], "five": [251], "base": [252], "models,": [253]}, "primary_topic": {"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.600600004196167, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.600600004196167, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T11902", "display_name": "Intelligent Tutoring Systems and Adaptive Learning", "score": 0.06790000200271606, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T12522", "display_name": "Mathematics Education and Pedagogy", "score": 0.05180000141263008, "subfield": {"id": "https://openalex.org/subfields/2604", "display_name": "Applied Mathematics"}, "field": {"id": "https://openalex.org/fields/26", "display_name": "Mathematics"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2508.13037", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2508.13037", "pdf_url": "https://arxiv.org/pdf/2508.13037", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": null, "license_id": null, "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4414975410", "doi": "https://doi.org/10.48550/arxiv.2510.02418", "title": "BrowserArena: Evaluating LLM Agents on Real-World Web Navigation Tasks", "publication_year": 2025, "publication_date": "2025-10-02", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5116129876", "display_name": "Sagnik Anupam", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Anupam, Sagnik", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": null, "display_name": "Brown, Davis", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Brown, Davis", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5100386655", "display_name": "Shuo Li", "orcid": "https://orcid.org/0000-0003-1486-1162", "observed_orcids": ["https://orcid.org/0000-0003-1486-1162"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Li, Shuo", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": null, "display_name": "Wong, Eric", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Wong, Eric", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5059354479", "display_name": "Hamed Hassani", "orcid": "https://orcid.org/0000-0002-9448-8750", "observed_orcids": ["https://orcid.org/0000-0002-9448-8750"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Hassani, Hamed", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "last", "author": {"id": "https://openalex.org/A5029243071", "display_name": "Osbert Bastani", "orcid": "https://orcid.org/0000-0001-9990-7566", "observed_orcids": ["https://orcid.org/0000-0001-9990-7566"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Bastani, Osbert", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}], "abstract_inverted_index": {"LLM": [0], "web": [1, 134, 148], "agents": [2], "now": [3], "browse": [4], "and": [5, 41, 51, 70, 115, 130, 146], "take": [6], "actions": [7], "on": [8, 55], "the": [9, 56, 128], "open": [10], "web,": [11], "yet": [12], "current": [13, 133], "agent": [14, 30, 57, 149], "evaluations": [15], "are": [16], "constrained": [17], "to": [18, 46, 73, 79, 108, 144], "sandboxed": [19], "environments": [20], "or": [21], "artificial": [22], "tasks.": [23], "We": [24, 96], "introduce": [25], "BrowserArena,": [26], "a": [27, 103], "live": [28], "open-web": [29], "evaluation": [31], "platform": [32], "that": [33, 100], "collects": [34], "user-submitted": [35], "tasks,": [36, 83], "runs": [37], "Arena-style": [38], "head-to-head": [39], "comparisons,": [40], "uses": [42], "step-level": [43, 53], "human": [44], "feedback": [45], "surface": [47, 126], "failure": [48, 63, 94, 150], "modes.": [49, 95], "Collecting": [50], "analyzing": [52], "annotations": [54], "traces,": [58], "we": [59, 84], "identify": [60], "three": [61], "consistent": [62], "modes:": [64], "captcha": [65, 110], "resolution,": [66], "pop-up": [67, 121], "banner": [68, 122], "removal,": [69], "direct": [71], "navigation": [72], "URLs.": [74], "By": [75], "constructing": [76], "targeted": [77], "datasets": [78], "further": [80], "study": [81], "these": [82, 93], "discover": [85], "variations": [86], "in": [87], "how": [88], "different": [89], "language": [90], "models": [91, 114], "navigate": [92], "find,": [97], "for": [98], "example,": [99], "o4-mini": [101], "deploys": [102], "wider": [104], "variety": [105], "of": [106, 132], "strategies": [107], "circumvent": [109], "resolution": [111], "than": [112], "other": [113], "DeepSeek-R1": [116], "consistently": [117], "misleads": [118], "users": [119], "about": [120], "closure.": [123], "Our": [124], "findings": [125], "both": [127], "diversity": [129], "brittleness": [131], "agents.": [135], "More": [136], "broadly,": [137], "our": [138], "benchmarking": [139], "methodology": [140], "provides": [141], "an": [142], "approach": [143], "evaluating": [145], "understanding": [147], "modes": [151], "at": [152], "scale.": [153]}, "primary_topic": {"id": "https://openalex.org/T12016", "display_name": "Web Data Mining and Analysis", "score": 0.7759000062942505, "subfield": {"id": "https://openalex.org/subfields/1710", "display_name": "Information Systems"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T12016", "display_name": "Web Data Mining and Analysis", "score": 0.7759000062942505, "subfield": {"id": "https://openalex.org/subfields/1710", "display_name": "Information Systems"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10286", "display_name": "Information Retrieval and Search Behavior", "score": 0.16349999606609344, "subfield": {"id": "https://openalex.org/subfields/1710", "display_name": "Information Systems"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T13904", "display_name": "Artificial Intelligence Applications", "score": 0.005900000222027302, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2510.02418", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2510.02418", "pdf_url": "https://arxiv.org/pdf/2510.02418", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": null, "license_id": null, "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4408845944", "doi": "https://doi.org/10.1097/scs.0000000000011258", "title": "Assessing AI Accuracy in Generating CPT Codes From Surgical Operative Notes", "publication_year": 2025, "publication_date": "2025-03-24", "type": "article", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5112601512", "display_name": "Emily L. Isch", "orcid": "https://orcid.org/0009-0002-1974-6972", "observed_orcids": ["https://orcid.org/0009-0002-1974-6972"]}, "institutions": [{"id": "https://openalex.org/I149251103", "display_name": "Thomas Jefferson University", "ror": "https://ror.org/00ysqcn41", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I149251103"]}], "countries": ["US"], "is_corresponding": true, "raw_author_name": "Emily L. Isch", "raw_affiliation_strings": ["Division of Plastic Surgery, Department of Surgery, Thomas Jefferson University"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Division of Plastic Surgery, Department of Surgery, Thomas Jefferson University", "institution_ids": ["https://openalex.org/I149251103"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5114483057", "display_name": "Judith Monzy", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I149251103", "display_name": "Thomas Jefferson University", "ror": "https://ror.org/00ysqcn41", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I149251103"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Judith Monzy", "raw_affiliation_strings": ["Sidney Kimmel Medical College at Thomas Jefferson University"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Sidney Kimmel Medical College at Thomas Jefferson University", "institution_ids": ["https://openalex.org/I149251103"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5004765011", "display_name": "Bhavana Thota", "orcid": "https://orcid.org/0000-0001-7208-1215", "observed_orcids": ["https://orcid.org/0000-0001-7208-1215"]}, "institutions": [{"id": "https://openalex.org/I149251103", "display_name": "Thomas Jefferson University", "ror": "https://ror.org/00ysqcn41", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I149251103"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Bhavana Thota", "raw_affiliation_strings": ["Sidney Kimmel Medical College at Thomas Jefferson University"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Sidney Kimmel Medical College at Thomas Jefferson University", "institution_ids": ["https://openalex.org/I149251103"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5083140590", "display_name": "Sydney Somers", "orcid": "https://orcid.org/0000-0002-6854-5053", "observed_orcids": ["https://orcid.org/0000-0002-6854-5053"]}, "institutions": [{"id": "https://openalex.org/I149251103", "display_name": "Thomas Jefferson University", "ror": "https://ror.org/00ysqcn41", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I149251103"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Sydney Somers", "raw_affiliation_strings": ["Sidney Kimmel Medical College at Thomas Jefferson University"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Sidney Kimmel Medical College at Thomas Jefferson University", "institution_ids": ["https://openalex.org/I149251103"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5005178755", "display_name": "Dwight Mitchell Self", "orcid": "https://orcid.org/0000-0002-8463-5497", "observed_orcids": ["https://orcid.org/0000-0002-8463-5497"]}, "institutions": [{"id": "https://openalex.org/I149251103", "display_name": "Thomas Jefferson University", "ror": "https://ror.org/00ysqcn41", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I149251103"]}, {"id": "https://openalex.org/I2799360765", "display_name": "Thomas Jefferson University Hospital", "ror": "https://ror.org/04zhhva53", "country_code": "US", "type": "healthcare", "lineage": ["https://openalex.org/I2799360765", "https://openalex.org/I4210114575"]}, {"id": "https://openalex.org/I4210160719", "display_name": "Jefferson Hospital for Neuroscience", "ror": "https://ror.org/05km9hp38", "country_code": "US", "type": "healthcare", "lineage": ["https://openalex.org/I2799360765", "https://openalex.org/I4210114575", "https://openalex.org/I4210160719"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "D. Mitchell Self", "raw_affiliation_strings": ["Department of Neurosurgery, Thomas Jefferson University and Jefferson Hospital for Neuroscience, Philadelphia, PA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Neurosurgery, Thomas Jefferson University and Jefferson Hospital for Neuroscience, Philadelphia, PA", "institution_ids": ["https://openalex.org/I149251103", "https://openalex.org/I2799360765", "https://openalex.org/I4210160719"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5067142132", "display_name": "Edward J. Caterson", "orcid": "https://orcid.org/0000-0003-4024-650X", "observed_orcids": ["https://orcid.org/0000-0003-4024-650X"]}, "institutions": [{"id": "https://openalex.org/I4210108049", "display_name": "Nemours Children's Health System", "ror": "https://ror.org/01mzw6m29", "country_code": "US", "type": "healthcare", "lineage": ["https://openalex.org/I4210108049"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "EJ Caterson", "raw_affiliation_strings": ["Department of Surgery, Division of Plastic Surgery, Nemours Children\u2019s Hospital, Wilmington, DE"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Surgery, Division of Plastic Surgery, Nemours Children\u2019s Hospital, Wilmington, DE", "institution_ids": ["https://openalex.org/I4210108049"]}]}], "abstract_inverted_index": {"INTRODUCTION:": [0], "Accurate": [1], "and": [2, 11, 33, 51, 114, 143, 153, 219, 251], "efficient": [3], "medical": [4, 25, 234], "coding": [5, 65, 221, 257], "is": [6], "essential": [7], "for": [8, 60, 79, 90, 136, 212, 256], "proper": [9], "reimbursement": [10], "health": [12], "care": [13], "management.": [14], "Current": [15], "Procedural": [16], "Terminology": [17], "(CPT)": [18], "codes,": [19, 161], "derived": [20], "from": [21, 97, 224], "operative": [22, 85], "notes,": [23], "standardize": [24], "billing": [26, 235], "but": [27], "are": [28, 57], "often": [29], "prone": [30], "to": [31, 36, 63, 74, 109, 116, 247], "variability": [32], "errors": [34], "due": [35], "the": [37, 70, 172, 229], "complexity": [38], "of": [39, 72, 184, 231, 253], "surgical": [40, 81, 93, 240, 249], "procedures.": [41], "With": [42], "advancements": [43], "in": [44, 158, 167, 239], "artificial": [45], "intelligence": [46], "(AI),": [47], "tools": [48, 111], "like": [49], "ChatGPT": [50, 152, 191], "other": [52, 248], "large": [53], "language": [54], "models": [55, 173, 255], "(LLMs)": [56], "being": [58], "explored": [59], "their": [61], "potential": [62], "automate": [64], "tasks.": [66, 258], "This": [67, 201], "study": [68, 202], "evaluates": [69], "ability": [71], "LLMs": [73], "generate": [75, 117], "accurate": [76], "CPT": [77, 119, 141, 160, 214], "codes": [78, 123, 142], "craniofacial": [80, 92, 213], "procedures": [82], "based": [83], "on": [84], "notes.": [86], "METHODS:": [87], "Operative": [88], "notes": [89, 106], "10": [91], "cases": [94], "were": [95, 107, 124, 134], "collected": [96], "a": [98, 180, 208], "single": [99], "surgeon": [100], "at": [101], "Nemours": [102], "Children's": [103], "Health.": [104], "The": [105], "provided": [108], "AI": [110, 205, 232, 254], "(ChatGPT": [112], "4.0": [113], "Gemini)": [115], "corresponding": [118], "codes.": [120], "These": [121], "AI-generated": [122], "compared": [125], "against": [126, 138], "manually": [127, 139], "coded": [128], "results": [129], "by": [130], "expert": [131], "reviewers.": [132], "Responses": [133], "evaluated": [135], "accuracy": [137, 168], "generated": [140], "classified": [144], "as": [145], "correct,": [146, 148], "partially": [147, 194], "or": [149, 169], "incorrect.": [150], "RESULTS:": [151], "Gemini": [154, 178], "demonstrated": [155], "similar": [156], "performance": [157], "generating": [159], "with": [162], "no": [163], "statistically": [164], "significant": [165], "differences": [166], "correctness": [170], "between": [171], "(": [174], "P": [175], ">": [176], "0.999).": [177], "produced": [179], "slightly": [181], "higher": [182], "proportion": [183], "correct": [185, 195], "responses": [186, 196], "(30%": [187], "versus": [188, 198], "20%),": [189], "whereas": [190], "had": [192], "more": [193], "(50%": [197], "40%).": [199], "CONCLUSIONS:": [200], "demonstrates": [203], "that": [204], "may": [206], "be": [207], "clinically": [209], "valuable": [210], "resource": [211], "coding,": [215], "reducing": [216], "administrative": [217], "burden": [218], "increasing": [220], "accuracy.": [222], "Findings": [223], "this": [225], "research": [226, 243], "could": [227], "inform": [228], "integration": [230], "into": [233], "practices,": [236], "promoting": [237], "efficiency": [238], "specialties.": [241], "Future": [242], "will": [244], "explore": [245], "generalizability": [246], "domains": [250], "refinement": [252]}, "primary_topic": {"id": "https://openalex.org/T14400", "display_name": "Medical Coding and Health Information", "score": 0.951200008392334, "subfield": {"id": "https://openalex.org/subfields/3605", "display_name": "Health Information Management"}, "field": {"id": "https://openalex.org/fields/36", "display_name": "Health Professions"}, "domain": {"id": "https://openalex.org/domains/4", "display_name": "Health Sciences"}}, "topics": [{"id": "https://openalex.org/T14400", "display_name": "Medical Coding and Health Information", "score": 0.951200008392334, "subfield": {"id": "https://openalex.org/subfields/3605", "display_name": "Health Information Management"}, "field": {"id": "https://openalex.org/fields/36", "display_name": "Health Professions"}, "domain": {"id": "https://openalex.org/domains/4", "display_name": "Health Sciences"}}, {"id": "https://openalex.org/T11636", "display_name": "Artificial Intelligence in Healthcare and Education", "score": 0.04529999941587448, "subfield": {"id": "https://openalex.org/subfields/2718", "display_name": "Health Informatics"}, "field": {"id": "https://openalex.org/fields/27", "display_name": "Medicine"}, "domain": {"id": "https://openalex.org/domains/4", "display_name": "Health Sciences"}}, {"id": "https://openalex.org/T13702", "display_name": "Machine Learning in Healthcare", "score": 0.000699999975040555, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "doi:10.1097/scs.0000000000011258", "is_oa": false, "landing_page_url": "https://doi.org/10.1097/scs.0000000000011258", "pdf_url": null, "source": {"id": "https://openalex.org/S36642063", "display_name": "Journal of Craniofacial Surgery", "issn_l": "1049-2275", "issn": ["1049-2275", "1536-3732"], "is_oa": false, "is_in_doaj": false, "is_core": true, "listed_in": ["cwts-core", "doyens", "jufo-1", "ki-jl-1", "medline", "norway-1"], "host_organization": "https://openalex.org/P4310315671", "host_organization_name": "Lippincott Williams & Wilkins", "host_organization_lineage": ["https://openalex.org/P4310315671", "https://openalex.org/P4310318547"], "host_organization_lineage_names": ["Lippincott Williams & Wilkins", "Wolters Kluwer"], "type": "journal"}, "license": null, "license_id": null, "version": "publishedVersion", "is_accepted": true, "is_published": true, "raw_source_name": "Journal of Craniofacial Surgery", "raw_type": "journal-article"}, "cited_by_count": 6, "is_retracted": false}, {"id": "https://openalex.org/W4415292013", "doi": "https://doi.org/10.36676/urr.v12.i4.1621", "title": "Low-Resource Fine-Tuning of LLMs for Domain-Specific Tasks", "publication_year": 2025, "publication_date": "2025-10-12", "type": "article", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5120039399", "display_name": "Vamshikrishna Challa", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Vamshikrishna Challa", "raw_affiliation_strings": ["Master's Computer Science Department: Computational Analysis and Modeling (CAM) Email: vamshikrishnachalla1999@gmail.com,  Ruston, Louisiana,  USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Master's Computer Science Department: Computational Analysis and Modeling (CAM) Email: vamshikrishnachalla1999@gmail.com,  Ruston, Louisiana,  USA", "institution_ids": []}]}, {"author_position": "last", "author": {"id": null, "display_name": "Abika Osinachi Bright", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I919208787", "display_name": "Louisiana Tech University", "ror": "https://ror.org/04q9esz89", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I2799628689", "https://openalex.org/I919208787"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Abika Osinachi Bright", "raw_affiliation_strings": ["Masters Electrical Engineering  Department: Material and infrastructural systems engineering  Email: abikab2022@gmail.com ,  Ruston, Louisiana ,  USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Masters Electrical Engineering  Department: Material and infrastructural systems engineering  Email: abikab2022@gmail.com ,  Ruston, Louisiana ,  USA", "institution_ids": ["https://openalex.org/I919208787"]}]}], "abstract_inverted_index": {"Large": [0], "Language": [1], "Models": [2], "(LLMs)": [3], "such": [4, 38, 92], "as": [5, 93], "GPT": [6], "and": [7, 24, 33, 49, 62, 95, 119, 126, 133, 144], "LLaMA": [8], "have": [9], "demonstrated": [10], "remarkable": [11], "capabilities": [12], "across": [13], "diverse": [14], "natural": [15], "language": [16], "processing": [17], "(NLP)": [18], "applications.": [19], "However,": [20], "their": [21], "enormous": [22], "computational": [23], "memory": [25], "requirements": [26], "hinder": [27], "adoption": [28], "by": [29], "smaller": [30], "research": [31, 103], "labs": [32], "enterprises.": [34], "Full-scale": [35], "fine-tuning": [36, 106], "of": [37, 111, 116], "models": [39], "is": [40, 100], "often": [41], "infeasible": [42], "due": [43], "to": [44, 70, 89], "high": [45], "GPU": [46], "memory,": [47], "storage,": [48], "energy": [50], "consumption.": [51], "Parameter-Efficient": [52], "Fine-Tuning": [53], "(PEFT)": [54], "techniques,": [55], "including": [56], "Low-Rank": [57], "Adaptation": [58], "(LoRA),": [59], "adapter-based": [60, 134], "methods,": [61], "prefix-tuning,": [63], "present": [64], "an": [65], "alternative": [66], "for": [67, 80, 108, 151], "adapting": [68], "LLMs": [69], "downstream": [71], "tasks": [72], "under": [73], "constrained": [74, 117], "budgets.": [75], "Despite": [76], "progress": [77], "in": [78], "PEFT": [79], "general": [81], "NLP": [82], "benchmarks,": [83], "limited": [84], "attention": [85], "has": [86], "been": [87], "given": [88], "domain-specific": [90], "applications": [91], "healthcare": [94], "energy,": [96], "where": [97], "specialized": [98], "knowledge": [99], "critical.": [101], "This": [102], "investigates": [104], "low-resource": [105], "strategies": [107], "domain": [109], "adaptation": [110], "LLMs,": [112], "identifies": [113], "the": [114], "challenges": [115], "environments,": [118], "evaluates": [120], "practical": [121], "frameworks": [122], "that": [123, 131], "balance": [124], "efficiency": [125], "performance.": [127], "Experimental": [128], "results": [129], "demonstrate": [130], "LoRA-": [132], "methods": [135], "achieve": [136], "competitive": [137], "accuracy": [138], "while": [139], "drastically": [140], "reducing": [141], "trainable": [142], "parameters": [143], "compute": [145], "costs,": [146], "making": [147], "them": [148], "highly": [149], "suitable": [150], "resource-limited": [152], "settings.": [153]}, "primary_topic": {"id": "https://openalex.org/T11307", "display_name": "Domain Adaptation and Few-Shot Learning", "score": 0.8715999722480774, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T11307", "display_name": "Domain Adaptation and Few-Shot Learning", "score": 0.8715999722480774, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.11999999731779099, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10181", "display_name": "Natural Language Processing Techniques", "score": 0.004800000227987766, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "doi:10.36676/urr.v12.i4.1621", "is_oa": true, "landing_page_url": "https://doi.org/10.36676/urr.v12.i4.1621", "pdf_url": "https://urr.shodhsagar.com/index.php/j/article/download/1621/1736", "source": {"id": "https://openalex.org/S4387291108", "display_name": "Universal Research Reports", "issn_l": "2348-5612", "issn": ["2348-5612"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": null, "host_organization_name": null, "host_organization_lineage": [], "host_organization_lineage_names": [], "type": "journal"}, "license": "cc-by-nc", "license_id": "https://openalex.org/licenses/cc-by-nc", "version": "publishedVersion", "is_accepted": true, "is_published": true, "raw_source_name": "Universal Research Reports", "raw_type": "journal-article"}, "cited_by_count": 3, "is_retracted": false}, {"id": "https://openalex.org/W4412699162", "doi": "https://doi.org/10.1016/j.osnem.2025.100326", "title": "Assessing the potential of generative agents in crowdsourced fact-checking", "publication_year": 2025, "publication_date": "2025-07-28", "type": "article", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5119094557", "display_name": "Luigia Costabile", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I71267560", "display_name": "University of Naples Federico II", "ror": "https://ror.org/05290cv24", "country_code": "IT", "type": "education", "lineage": ["https://openalex.org/I71267560"]}], "countries": ["IT"], "is_corresponding": false, "raw_author_name": "Luigia Costabile", "raw_affiliation_strings": ["Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Via Claudio 21, Naples, Italy"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Via Claudio 21, Naples, Italy", "institution_ids": ["https://openalex.org/I71267560"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5046756099", "display_name": "Gian Marco Orlando", "orcid": "https://orcid.org/0009-0004-7136-1804", "observed_orcids": ["https://orcid.org/0009-0004-7136-1804"]}, "institutions": [{"id": "https://openalex.org/I71267560", "display_name": "University of Naples Federico II", "ror": "https://ror.org/05290cv24", "country_code": "IT", "type": "education", "lineage": ["https://openalex.org/I71267560"]}], "countries": ["IT"], "is_corresponding": true, "raw_author_name": "Gian Marco Orlando", "raw_affiliation_strings": ["Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Via Claudio 21, Naples, Italy"], "raw_orcid": "https://orcid.org/0009-0004-7136-1804", "affiliations": [{"raw_affiliation_string": "Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Via Claudio 21, Naples, Italy", "institution_ids": ["https://openalex.org/I71267560"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5038118094", "display_name": "Valerio La Gatta", "orcid": "https://orcid.org/0000-0002-5941-4684", "observed_orcids": ["https://orcid.org/0000-0002-5941-4684"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Valerio La Gatta", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "last", "author": {"id": "https://openalex.org/A5081965427", "display_name": "Vincenzo Moscato", "orcid": "https://orcid.org/0000-0002-0754-7696", "observed_orcids": ["https://orcid.org/0000-0002-0754-7696"]}, "institutions": [{"id": "https://openalex.org/I71267560", "display_name": "University of Naples Federico II", "ror": "https://ror.org/05290cv24", "country_code": "IT", "type": "education", "lineage": ["https://openalex.org/I71267560"]}], "countries": ["IT"], "is_corresponding": false, "raw_author_name": "Vincenzo Moscato", "raw_affiliation_strings": ["Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Via Claudio 21, Naples, Italy"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, Via Claudio 21, Naples, Italy", "institution_ids": ["https://openalex.org/I71267560"]}]}], "abstract_inverted_index": {"The": [0], "growing": [1], "spread": [2], "of": [3, 121, 130, 209], "online": [4], "misinformation": [5], "has": [6], "created": [7], "an": [8], "urgent": [9], "need": [10], "for": [11, 115], "scalable,": [12, 213], "reliable": [13], "fact-checking": [14, 77, 111, 221], "solutions.": [15], "Crowdsourced": [16], "fact-checking\u2014where": [17], "non-experts": [18], "evaluate": [19], "claim": [20, 80], "veracity\u2014offers": [21], "a": [22, 198], "cost-effective": [23], "alternative": [24], "to": [25, 59, 110, 173, 179, 219], "expert": [26], "verification,": [27], "despite": [28], "concerns": [29], "about": [30], "variability": [31], "in": [32, 40, 66, 89, 162], "quality": [33, 146], "and": [34, 51, 82, 106, 136, 148, 169, 175, 194, 215], "bias.": [35], "Encouraged": [36], "by": [37], "promising": [38], "results": [39, 154], "certain": [41], "contexts,": [42], "major": [43], "platforms": [44], "such": [45, 188], "as": [46, 189, 212], "X": [47], "(formerly": [48], "Twitter),": [49], "Facebook,": [50], "Instagram": [52], "have": [53, 71], "begun": [54], "shifting": [55], "from": [56], "centralized": [57], "moderation": [58], "decentralized,": [60], "crowd-based": [61, 220], "approaches.": [62], "In": [63], "parallel,": [64], "advances": [65], "Large": [67], "Language": [68], "Models": [69], "(LLMs)": [70], "shown": [72], "strong": [73], "performance": [74], "across": [75], "core": [76], "tasks,": [78], "including": [79], "detection": [81], "evidence": [83], "evaluation.": [84], "However,": [85], "their": [86], "potential": [87, 208], "role": [88], "crowdsourced": [90], "workflows": [91], "remains": [92], "unexplored.": [93], "This": [94], "paper": [95], "investigates": [96], "whether": [97], "LLM-powered": [98], "generative": [99, 131, 210], "agents\u2014autonomous": [100], "entities": [101], "that": [102, 156], "emulate": [103], "human": [104, 116, 160], "behavior": [105], "decision-making\u2014can": [107], "meaningfully": [108], "contribute": [109], "tasks": [112], "traditionally": [113], "reserved": [114], "crowds.": [117], "Using": [118], "the": [119, 207], "protocol": [120], "La": [122], "Barbera": [123], "et": [124], "al.": [125], "(2024),": [126], "we": [127], "simulate": [128], "crowds": [129, 158, 161], "agents": [132, 181, 211], "with": [133], "diverse": [134], "demographic": [135], "ideological": [137], "profiles.": [138], "Agents": [139], "retrieve": [140], "evidence,": [141], "assess": [142], "claims": [143], "along": [144], "multiple": [145], "dimensions,": [147], "issue": [149], "final": [150], "veracity": [151], "judgments.": [152], "Our": [153], "show": [155, 170], "agent": [157], "outperform": [159], "truthfulness": [163], "classification,": [164], "exhibit": [165], "higher": [166], "internal": [167], "consistency,": [168], "reduced": [171], "susceptibility": [172], "social": [174], "cognitive": [176], "biases.": [177], "Compared": [178], "humans,": [180], "rely": [182], "more": [183, 199], "systematically": [184], "on": [185], "informative": [186], "criteria": [187], "Accuracy": [190], ",": [191, 193, 196], "Precision": [192], "Informativeness": [195], "suggesting": [197], "structured": [200], "decision-making": [201], "process.": [202], "Overall,": [203], "our": [204], "findings": [205], "highlight": [206], "consistent,": [214], "less": [216], "biased": [217], "contributors": [218], "systems.": [222]}, "primary_topic": {"id": "https://openalex.org/T11147", "display_name": "Misinformation and Its Impacts", "score": 1.0, "subfield": {"id": "https://openalex.org/subfields/3312", "display_name": "Sociology and Political Science"}, "field": {"id": "https://openalex.org/fields/33", "display_name": "Social Sciences"}, "domain": {"id": "https://openalex.org/domains/2", "display_name": "Social Sciences"}}, "topics": [{"id": "https://openalex.org/T11147", "display_name": "Misinformation and Its Impacts", "score": 1.0, "subfield": {"id": "https://openalex.org/subfields/3312", "display_name": "Sociology and Political Science"}, "field": {"id": "https://openalex.org/fields/33", "display_name": "Social Sciences"}, "domain": {"id": "https://openalex.org/domains/2", "display_name": "Social Sciences"}}, {"id": "https://openalex.org/T11704", "display_name": "Mobile Crowdsensing and Crowdsourcing", "score": 0.0, "subfield": {"id": "https://openalex.org/subfields/1706", "display_name": "Computer Science Applications"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T11644", "display_name": "Spam and Phishing Detection", "score": 0.0, "subfield": {"id": "https://openalex.org/subfields/1710", "display_name": "Information Systems"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "doi:10.1016/j.osnem.2025.100326", "is_oa": true, "landing_page_url": "https://doi.org/10.1016/j.osnem.2025.100326", "pdf_url": null, "source": {"id": "https://openalex.org/S2898299116", "display_name": "Online Social Networks and Media", "issn_l": "2468-6964", "issn": ["2468-6964"], "is_oa": false, "is_in_doaj": false, "is_core": true, "listed_in": ["cwts-core", "jufo-1", "norway-1"], "host_organization": "https://openalex.org/P4310320990", "host_organization_name": "Elsevier BV", "host_organization_lineage": ["https://openalex.org/P4310320990"], "host_organization_lineage_names": ["Elsevier BV"], "type": "journal"}, "license": "cc-by", "license_id": "https://openalex.org/licenses/cc-by", "version": "publishedVersion", "is_accepted": true, "is_published": true, "raw_source_name": "Online Social Networks and Media", "raw_type": "journal-article"}, "cited_by_count": 5, "is_retracted": false}, {"id": "https://openalex.org/W4417456798", "doi": "https://doi.org/10.48550/arxiv.2512.13337", "title": "FROC: A Unified Framework with Risk-Optimized Control for Machine Unlearning in LLMs", "publication_year": 2025, "publication_date": "2025-12-15", "type": "preprint", "language": null, "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5102603901", "display_name": "Si Qi Goh", "orcid": "https://orcid.org/0009-0006-9061-4744", "observed_orcids": ["https://orcid.org/0009-0006-9061-4744"]}, "institutions": [{"id": "https://openalex.org/I172675005", "display_name": "Nanyang Technological University", "ror": "https://ror.org/02e7b5302", "country_code": "SG", "type": "education", "lineage": ["https://openalex.org/I172675005"]}], "countries": ["SG"], "is_corresponding": false, "raw_author_name": "Goh, Si Qi", "raw_affiliation_strings": ["College of Computing and Data Science , Nanyang Technological University , Singapore", "Digital Trust Centre , Nanyang Technological University , Singapore"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "College of Computing and Data Science , Nanyang Technological University , Singapore", "institution_ids": ["https://openalex.org/I172675005"]}, {"raw_affiliation_string": "Digital Trust Centre , Nanyang Technological University , Singapore", "institution_ids": ["https://openalex.org/I172675005"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5107937881", "display_name": "Y.Y. Zheng", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I172675005", "display_name": "Nanyang Technological University", "ror": "https://ror.org/02e7b5302", "country_code": "SG", "type": "education", "lineage": ["https://openalex.org/I172675005"]}], "countries": ["SG"], "is_corresponding": false, "raw_author_name": "Zheng, Yongsen", "raw_affiliation_strings": ["College of Computing and Data Science , Nanyang Technological University , Singapore", "Digital Trust Centre , Nanyang Technological University , Singapore"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "College of Computing and Data Science , Nanyang Technological University , Singapore", "institution_ids": ["https://openalex.org/I172675005"]}, {"raw_affiliation_string": "Digital Trust Centre , Nanyang Technological University , Singapore", "institution_ids": ["https://openalex.org/I172675005"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5013901304", "display_name": "Ziyao Liu", "orcid": "https://orcid.org/0000-0002-5332-6333", "observed_orcids": ["https://orcid.org/0000-0002-5332-6333", "https://orcid.org/0000-0002-6706-1604"]}, "institutions": [{"id": "https://openalex.org/I172675005", "display_name": "Nanyang Technological University", "ror": "https://ror.org/02e7b5302", "country_code": "SG", "type": "education", "lineage": ["https://openalex.org/I172675005"]}], "countries": ["SG"], "is_corresponding": false, "raw_author_name": "Liu, Ziyao", "raw_affiliation_strings": ["Digital Trust Centre , Nanyang Technological University , Singapore"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Digital Trust Centre , Nanyang Technological University , Singapore", "institution_ids": ["https://openalex.org/I172675005"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5120843099", "display_name": "Sami Hormi", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I172675005", "display_name": "Nanyang Technological University", "ror": "https://ror.org/02e7b5302", "country_code": "SG", "type": "education", "lineage": ["https://openalex.org/I172675005"]}], "countries": ["SG"], "is_corresponding": false, "raw_author_name": "Hormi, Sami", "raw_affiliation_strings": ["Digital Trust Centre , Nanyang Technological University , Singapore"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Digital Trust Centre , Nanyang Technological University , Singapore", "institution_ids": ["https://openalex.org/I172675005"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5101720092", "display_name": "Kwok\u2010Yan Lam", "orcid": "https://orcid.org/0000-0001-7479-7970", "observed_orcids": ["https://orcid.org/0000-0001-7479-7970"]}, "institutions": [{"id": "https://openalex.org/I172675005", "display_name": "Nanyang Technological University", "ror": "https://ror.org/02e7b5302", "country_code": "SG", "type": "education", "lineage": ["https://openalex.org/I172675005"]}], "countries": ["SG"], "is_corresponding": false, "raw_author_name": "Lam, Kwok-Yan", "raw_affiliation_strings": ["College of Computing and Data Science , Nanyang Technological University , Singapore", "Digital Trust Centre , Nanyang Technological University , Singapore"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "College of Computing and Data Science , Nanyang Technological University , Singapore", "institution_ids": ["https://openalex.org/I172675005"]}, {"raw_affiliation_string": "Digital Trust Centre , Nanyang Technological University , Singapore", "institution_ids": ["https://openalex.org/I172675005"]}]}], "abstract_inverted_index": {"Machine": [0], "unlearning": [1, 83, 101, 189, 218, 240], "(MU)": [2], "seeks": [3], "to": [4, 65, 108, 121, 178], "eliminate": [5], "the": [6, 48, 63, 162, 172, 195], "influence": [7, 179], "of": [8, 50], "specific": [9], "training": [10], "examples": [11], "from": [12, 26], "deployed": [13], "models.": [14], "As": [15], "large": [16], "language": [17], "models": [18], "(LLMs)": [19], "become": [20], "widely": [21], "used,": [22], "managing": [23, 239], "risks": [24], "arising": [25], "insufficient": [27], "forgetting": [28, 125, 144], "or": [29], "utility": [30, 128, 147, 223], "loss": [31], "is": [32, 87], "increasingly": [33], "crucial.": [34], "Current": [35], "MU": [36, 110, 203, 227], "techniques": [37], "lack": [38], "effective": [39], "mechanisms": [40], "for": [41, 81, 238], "evaluating": [42], "and": [43, 56, 58, 116, 127, 146, 182, 213, 222, 233], "controlling": [44], "these": [45, 70], "risks,": [46], "hindering": [47], "selection": [49, 119], "strategies": [51], "that": [52, 94, 142, 174, 191, 206], "appropriately": [53], "balance": [54], "safety": [55], "utility,": [57], "raising": [59], "trust": [60], "concerns": [61], "surrounding": [62], "\"right": [64], "be": [66], "forgotten.\"": [67], "To": [68, 130], "address": [69], "issues,": [71], "we": [72], "propose": [73], "FROC,": [74], "a": [75, 90, 96, 136, 150, 167, 229, 235], "unified": [76], "framework": [77], "with": [78], "Risk-Optimized": [79], "Control": [80], "machine": [82], "in": [84, 242], "LLMs.": [85], "FROC": [86, 107, 134, 159, 207, 225], "built": [88], "around": [89], "conformal-style": [91], "risk-control": [92], "formulation": [93], "expresses": [95], "user-specified": [97], "risk": [98, 140, 157, 197, 211], "budget": [99], "on": [100, 155, 171], "behavior.": [102], "This": [103], "probability-based": [104], "constraint": [105], "enables": [106], "compare": [109], "strategies,": [111], "identify": [112, 188], "feasible": [113], "operating": [114], "regions,": [115], "guide": [117], "hyperparameter": [118], "according": [120], "desired": [122], "trade-offs": [123], "between": [124, 217], "sufficiency": [126], "preservation.": [129], "operationalize": [131], "this": [132], "constraint,": [133], "introduces": [135], "smoothly": [137], "varying": [138], "continuous": [139], "model": [141, 180], "aggregates": [143], "deficiency": [145], "degradation": [148], "into": [149], "single": [151], "configuration-level": [152], "score.": [153], "Building": [154], "conformal": [156], "analysis,": [158], "computes": [160], "(1)": [161], "Conformal": [163], "Unlearning": [164], "Risk": [165], "(CUR),": [166], "data-driven": [168], "estimated": [169], "value": [170], "probability": [173], "forgotten": [175], "samples": [176], "continue": [177], "predictions,": [181], "(2)": [183], "risk-controlled": [184], "configuration": [185], "sets,": [186], "which": [187], "hyperparameters": [190], "are": [192], "valid": [193], "under": [194], "specified": [196], "budget.": [198], "Experiments": [199], "across": [200], "multiple": [201], "LLM": [202, 244], "methods": [204], "demonstrate": [205], "produces": [208], "stable,": [209], "interpretable": [210], "landscapes": [212], "reveals": [214], "consistent": [215], "relationships": [216], "configurations,": [219], "semantic": [220], "shift,": [221], "impact.": [224], "reframes": [226], "as": [228], "controllable,": [230], "risk-aware": [231], "process": [232], "offers": [234], "practical": [236], "foundation": [237], "behavior": [241], "large-scale": [243], "deployments.": [245]}, "primary_topic": {"id": "https://openalex.org/T10764", "display_name": "Privacy-Preserving Technologies in Data", "score": 0.9990000128746033, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T10764", "display_name": "Privacy-Preserving Technologies in Data", "score": 0.9990000128746033, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T11689", "display_name": "Adversarial Robustness in Machine Learning", "score": 0.0003000000142492354, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10883", "display_name": "Ethics and Social Impacts of AI", "score": 9.999999747378752e-05, "subfield": {"id": "https://openalex.org/subfields/3311", "display_name": "Safety Research"}, "field": {"id": "https://openalex.org/fields/33", "display_name": "Social Sciences"}, "domain": {"id": "https://openalex.org/domains/2", "display_name": "Social Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2512.13337", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2512.13337", "pdf_url": "https://arxiv.org/pdf/2512.13337", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": null, "license_id": null, "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4414457883", "doi": "https://doi.org/10.48550/arxiv.2508.03368", "title": "Game Reasoning Arena: A Framework and Benchmark for Assessing Reasoning Capabilities of Large Language Models via Game Play", "publication_year": 2025, "publication_date": "2025-08-05", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5099038786", "display_name": "Lucia Cipolina-Kun", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Cipolina-Kun, Lucia", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5042252873", "display_name": "Marianna Nezhurina", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Nezhurina, Marianna", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "last", "author": {"id": "https://openalex.org/A5085870953", "display_name": "Jenia Jitsev", "orcid": "https://orcid.org/0000-0002-1221-7851", "observed_orcids": ["https://orcid.org/0000-0002-1221-7851"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Jitsev, Jenia", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}], "abstract_inverted_index": {"The": [0, 28], "Game": [1], "Reasoning": [2], "Arena": [3], "library": [4, 85], "provides": [5], "a": [6], "framework": [7, 29], "for": [8], "evaluating": [9], "the": [10, 84, 92, 99, 103], "decision": [11], "making": [12], "abilities": [13], "of": [14, 91, 102, 105], "large": [15], "language": [16], "models": [17, 67], "(LLMs)": [18], "through": [19, 79], "strategic": [20], "board": [21, 53], "games": [22, 56], "implemented": [23], "in": [24, 46], "Google": [25], "OpenSpiel": [26], "library.": [27], "enables": [30], "systematic": [31], "comparisons": [32], "between": [33], "LLM": [34, 106], "based": [35], "agents": [36, 39], "and": [37, 54, 57, 75, 89, 107], "other": [38], "(random,": [40], "heuristic,": [41], "reinforcement": [42], "learning": [43], "agents,": [44], "etc.)": [45], "various": [47], "game": [48, 108], "scenarios": [49], "by": [50], "wrapping": [51], "multiple": [52], "matrix": [55], "supporting": [58], "different": [59], "agent": [60], "types.": [61], "It": [62], "integrates": [63], "API": [64], "access": [65], "to": [66, 98], "via": [68, 73], "liteLLM,": [69], "local": [70], "model": [71], "deployment": [72], "vLLM,": [74], "offers": [76], "distributed": [77], "execution": [78], "Ray.": [80], "This": [81], "paper": [82], "summarises": [83], "structure,": [86], "key": [87], "characteristics,": [88], "motivation": [90], "repository,": [93], "highlighting": [94], "how": [95], "it": [96], "contributes": [97], "empirical": [100], "evaluation": [101], "reasoning": [104], "theoretic": [109], "behaviour.": [110]}, "primary_topic": {"id": "https://openalex.org/T11574", "display_name": "Artificial Intelligence in Games", "score": 0.9868000149726868, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T11574", "display_name": "Artificial Intelligence in Games", "score": 0.9868000149726868, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T13904", "display_name": "Artificial Intelligence Applications", "score": 0.005900000222027302, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.002400000113993883, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2508.03368", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2508.03368", "pdf_url": "https://arxiv.org/pdf/2508.03368", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": null, "license_id": null, "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4414261172", "doi": "https://doi.org/10.21203/rs.3.rs-7444252/v1", "title": "Adapting General Representations of Pretrained Vision Foundation Models to Seismic Understanding via Geologically Informed Prompting and Lightweight Tuning", "publication_year": 2025, "publication_date": "2025-09-12", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5058640298", "display_name": "Xinming Wu", "orcid": "https://orcid.org/0000-0002-4910-8253", "observed_orcids": ["https://orcid.org/0000-0002-4910-8253"]}, "institutions": [{"id": "https://openalex.org/I126520041", "display_name": "University of Science and Technology of China", "ror": "https://ror.org/04c4dkn09", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I126520041", "https://openalex.org/I19820366"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Xinmin Wu", "raw_affiliation_strings": ["University of Science and Technology of China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "University of Science and Technology of China", "institution_ids": ["https://openalex.org/I126520041"]}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Zhengfa Bi", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I148283060", "display_name": "Lawrence Berkeley National Laboratory", "ror": "https://ror.org/02jbv0t02", "country_code": "US", "type": "facility", "lineage": ["https://openalex.org/I1330989302", "https://openalex.org/I148283060", "https://openalex.org/I39565521"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Zhengfa Bi", "raw_affiliation_strings": ["Lawrence Berkeley National Laboratory"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Lawrence Berkeley National Laboratory", "institution_ids": ["https://openalex.org/I148283060"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5034332525", "display_name": "Nori Nakata", "orcid": "https://orcid.org/0000-0002-9295-9416", "observed_orcids": ["https://orcid.org/0000-0002-9295-9416", "https://orcid.org/0000-0003-4120-7924"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Nori Nakata", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "last", "author": {"id": "https://openalex.org/A5012264697", "display_name": "Nuo Chen", "orcid": "https://orcid.org/0000-0003-3515-8791", "observed_orcids": ["https://orcid.org/0000-0003-3515-8791"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Nuo Chen", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}], "abstract_inverted_index": null, "primary_topic": {"id": "https://openalex.org/T10271", "display_name": "Seismic Imaging and Inversion Techniques", "score": 0.8270999789237976, "subfield": {"id": "https://openalex.org/subfields/1908", "display_name": "Geophysics"}, "field": {"id": "https://openalex.org/fields/19", "display_name": "Earth and Planetary Sciences"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T10271", "display_name": "Seismic Imaging and Inversion Techniques", "score": 0.8270999789237976, "subfield": {"id": "https://openalex.org/subfields/1908", "display_name": "Geophysics"}, "field": {"id": "https://openalex.org/fields/19", "display_name": "Earth and Planetary Sciences"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T11307", "display_name": "Domain Adaptation and Few-Shot Learning", "score": 0.03229999914765358, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10036", "display_name": "Advanced Neural Network Applications", "score": 0.019899999722838402, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "doi:10.21203/rs.3.rs-7444252/v1", "is_oa": true, "landing_page_url": "https://doi.org/10.21203/rs.3.rs-7444252/v1", "pdf_url": null, "source": {"id": "https://openalex.org/S4306525896", "display_name": "Research Square", "issn_l": null, "issn": null, "is_oa": false, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": null, "host_organization_name": null, "host_organization_lineage": [], "host_organization_lineage_names": [], "type": "repository"}, "license": "cc-by", "license_id": "https://openalex.org/licenses/cc-by", "version": "acceptedVersion", "is_accepted": true, "is_published": false, "raw_source_name": "Research Square", "raw_type": "posted-content"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4410849261", "doi": "https://doi.org/10.1007/s10278-025-01541-3", "title": "Standardizing Heterogeneous MRI Series Description Metadata Using Large Language Models", "publication_year": 2025, "publication_date": "2025-05-29", "type": "article", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5043842458", "display_name": "Peter I. Kamel", "orcid": "https://orcid.org/0000-0001-6810-9801", "observed_orcids": ["https://orcid.org/0000-0001-6810-9801"]}, "institutions": [{"id": "https://openalex.org/I126744593", "display_name": "University of Maryland, Baltimore", "ror": "https://ror.org/04rq5mt64", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I126744593"]}, {"id": "https://openalex.org/I1343551460", "display_name": "The University of Texas MD Anderson Cancer Center", "ror": "https://ror.org/04twxam07", "country_code": "US", "type": "healthcare", "lineage": ["https://openalex.org/I1343551460"]}], "countries": ["US"], "is_corresponding": true, "raw_author_name": "Peter I. Kamel", "raw_affiliation_strings": ["Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA. peterkamelmd.correspondence@gmail.com", "Department of Neuroradiology, Division of Diagnostic Imaging, MD Anderson Cancer Center, Houston, TX, USA. peterkamelmd.correspondence@gmail.com", "University of Maryland Medical Intelligent Imaging (UM2ii) Center, University of Maryland School of Medicine, Baltimore, MD, USA. peterkamelmd.correspondence@gmail.com"], "raw_orcid": "https://orcid.org/0000-0001-6810-9801", "affiliations": [{"raw_affiliation_string": "Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA. peterkamelmd.correspondence@gmail.com", "institution_ids": ["https://openalex.org/I126744593"]}, {"raw_affiliation_string": "Department of Neuroradiology, Division of Diagnostic Imaging, MD Anderson Cancer Center, Houston, TX, USA. peterkamelmd.correspondence@gmail.com", "institution_ids": ["https://openalex.org/I1343551460"]}, {"raw_affiliation_string": "University of Maryland Medical Intelligent Imaging (UM2ii) Center, University of Maryland School of Medicine, Baltimore, MD, USA. peterkamelmd.correspondence@gmail.com", "institution_ids": ["https://openalex.org/I126744593"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5081719654", "display_name": "Florence Xini Doo", "orcid": "https://orcid.org/0000-0001-6519-5222", "observed_orcids": ["https://orcid.org/0000-0001-6519-5222"]}, "institutions": [{"id": "https://openalex.org/I126744593", "display_name": "University of Maryland, Baltimore", "ror": "https://ror.org/04rq5mt64", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I126744593"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Florence X. Doo", "raw_affiliation_strings": ["Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA", "University of Maryland Institute for Health Computing, North Bethesda, MD, USA", "University of Maryland Medical Intelligent Imaging (UM2ii) Center, University of Maryland School of Medicine, Baltimore, MD, USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA", "institution_ids": ["https://openalex.org/I126744593"]}, {"raw_affiliation_string": "University of Maryland Institute for Health Computing, North Bethesda, MD, USA", "institution_ids": []}, {"raw_affiliation_string": "University of Maryland Medical Intelligent Imaging (UM2ii) Center, University of Maryland School of Medicine, Baltimore, MD, USA", "institution_ids": ["https://openalex.org/I126744593"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5093393754", "display_name": "Dharmam Savani", "orcid": "https://orcid.org/0009-0003-8241-4599", "observed_orcids": ["https://orcid.org/0009-0003-8241-4599"]}, "institutions": [{"id": "https://openalex.org/I126744593", "display_name": "University of Maryland, Baltimore", "ror": "https://ror.org/04rq5mt64", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I126744593"]}, {"id": "https://openalex.org/I1313298211", "display_name": "St. Jude Children's Research Hospital", "ror": "https://ror.org/02r3e0967", "country_code": "US", "type": "healthcare", "lineage": ["https://openalex.org/I1313298211", "https://openalex.org/I2802152183"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Dharmam Savani", "raw_affiliation_strings": ["Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA", "Department of Radiology, St. Jude Children's Research Hospital, Memphis, TN, USA", "University of Maryland Medical Intelligent Imaging (UM2ii) Center, University of Maryland School of Medicine, Baltimore, MD, USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA", "institution_ids": ["https://openalex.org/I126744593"]}, {"raw_affiliation_string": "Department of Radiology, St. Jude Children's Research Hospital, Memphis, TN, USA", "institution_ids": ["https://openalex.org/I1313298211"]}, {"raw_affiliation_string": "University of Maryland Medical Intelligent Imaging (UM2ii) Center, University of Maryland School of Medicine, Baltimore, MD, USA", "institution_ids": ["https://openalex.org/I126744593"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5080849879", "display_name": "Adway Kanhere", "orcid": "https://orcid.org/0000-0001-5295-2634", "observed_orcids": ["https://orcid.org/0000-0001-5295-2634"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Adway Kanhere", "raw_affiliation_strings": ["University of Maryland Institute for Health Computing, North Bethesda, MD, USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "University of Maryland Institute for Health Computing, North Bethesda, MD, USA", "institution_ids": []}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5015923200", "display_name": "Paul Hyunsoo Yi", "orcid": "https://orcid.org/0000-0001-9433-8093", "observed_orcids": ["https://orcid.org/0000-0001-9433-8093"]}, "institutions": [{"id": "https://openalex.org/I1313298211", "display_name": "St. Jude Children's Research Hospital", "ror": "https://ror.org/02r3e0967", "country_code": "US", "type": "healthcare", "lineage": ["https://openalex.org/I1313298211", "https://openalex.org/I2802152183"]}, {"id": "https://openalex.org/I145311948", "display_name": "Johns Hopkins University", "ror": "https://ror.org/00za53h95", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I145311948"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Paul H. Yi", "raw_affiliation_strings": ["Department of Radiology, St. Jude Children's Research Hospital, Memphis, TN, USA", "Malone Center for Engineering in Healthcare, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Radiology, St. Jude Children's Research Hospital, Memphis, TN, USA", "institution_ids": ["https://openalex.org/I1313298211"]}, {"raw_affiliation_string": "Malone Center for Engineering in Healthcare, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA", "institution_ids": ["https://openalex.org/I145311948"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5067064191", "display_name": "Vishwa S. Parekh", "orcid": "https://orcid.org/0000-0002-6306-0305", "observed_orcids": ["https://orcid.org/0000-0002-6306-0305", "https://orcid.org/0009-0002-2299-597X", "https://orcid.org/0009-0006-2427-549X"]}, "institutions": [{"id": "https://openalex.org/I126744593", "display_name": "University of Maryland, Baltimore", "ror": "https://ror.org/04rq5mt64", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I126744593"]}, {"id": "https://openalex.org/I145311948", "display_name": "Johns Hopkins University", "ror": "https://ror.org/00za53h95", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I145311948"]}, {"id": "https://openalex.org/I2799853436", "display_name": "Johns Hopkins Medicine", "ror": "https://ror.org/037zgn354", "country_code": "US", "type": "healthcare", "lineage": ["https://openalex.org/I2799853436"]}, {"id": "https://openalex.org/I919571938", "display_name": "The University of Texas Health Science Center at Houston", "ror": "https://ror.org/03gds6c39", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I919571938"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Vishwa S. Parekh", "raw_affiliation_strings": ["Department Of Diagnostic and Interventional Imaging, McGovern Medical School, UTHealth Houston, Houston, TX, USA", "Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA", "Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA", "University of Maryland Medical Intelligent Imaging (UM2ii) Center, University of Maryland School of Medicine, Baltimore, MD, USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department Of Diagnostic and Interventional Imaging, McGovern Medical School, UTHealth Houston, Houston, TX, USA", "institution_ids": ["https://openalex.org/I919571938"]}, {"raw_affiliation_string": "Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA", "institution_ids": ["https://openalex.org/I126744593"]}, {"raw_affiliation_string": "Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA", "institution_ids": ["https://openalex.org/I145311948", "https://openalex.org/I2799853436"]}, {"raw_affiliation_string": "University of Maryland Medical Intelligent Imaging (UM2ii) Center, University of Maryland School of Medicine, Baltimore, MD, USA", "institution_ids": ["https://openalex.org/I126744593"]}]}], "abstract_inverted_index": null, "primary_topic": {"id": "https://openalex.org/T11636", "display_name": "Artificial Intelligence in Healthcare and Education", "score": 0.9639000296592712, "subfield": {"id": "https://openalex.org/subfields/2718", "display_name": "Health Informatics"}, "field": {"id": "https://openalex.org/fields/27", "display_name": "Medicine"}, "domain": {"id": "https://openalex.org/domains/4", "display_name": "Health Sciences"}}, "topics": [{"id": "https://openalex.org/T11636", "display_name": "Artificial Intelligence in Healthcare and Education", "score": 0.9639000296592712, "subfield": {"id": "https://openalex.org/subfields/2718", "display_name": "Health Informatics"}, "field": {"id": "https://openalex.org/fields/27", "display_name": "Medicine"}, "domain": {"id": "https://openalex.org/domains/4", "display_name": "Health Sciences"}}, {"id": "https://openalex.org/T10378", "display_name": "Advanced MRI Techniques and Applications", "score": 0.01080000028014183, "subfield": {"id": "https://openalex.org/subfields/2741", "display_name": "Radiology, Nuclear Medicine and Imaging"}, "field": {"id": "https://openalex.org/fields/27", "display_name": "Medicine"}, "domain": {"id": "https://openalex.org/domains/4", "display_name": "Health Sciences"}}, {"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.006800000090152025, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "doi:10.1007/s10278-025-01541-3", "is_oa": false, "landing_page_url": "https://doi.org/10.1007/s10278-025-01541-3", "pdf_url": null, "source": {"id": "https://openalex.org/S62275304", "display_name": "Journal of Imaging Informatics in Medicine", "issn_l": "0897-1889", "issn": ["0897-1889", "1618-727X", "2948-2925", "2948-2933"], "is_oa": false, "is_in_doaj": false, "is_core": true, "listed_in": ["cwts-core", "doyens", "jufo-1", "ki-jl-1", "medline", "norway-1"], "host_organization": "https://openalex.org/P4310319900", "host_organization_name": "Springer Science+Business Media", "host_organization_lineage": ["https://openalex.org/P4310319900", "https://openalex.org/P4310319965"], "host_organization_lineage_names": ["Springer Science+Business Media", "Springer Nature"], "type": "journal"}, "license": null, "license_id": null, "version": "publishedVersion", "is_accepted": true, "is_published": true, "raw_source_name": "Journal of Imaging Informatics in Medicine", "raw_type": "journal-article"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4415183329", "doi": "https://doi.org/10.48550/arxiv.2506.21393", "title": "TableMoE: Neuro-Symbolic Routing for Structured Expert Reasoning in Multimodal Table Understanding", "publication_year": 2025, "publication_date": "2025-06-26", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5108046750", "display_name": "Junwen Zhang", "orcid": "https://orcid.org/0000-0001-8041-1608", "observed_orcids": ["https://orcid.org/0000-0001-8041-1608"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Zhang, Junwen", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5100704521", "display_name": "Pu Chen", "orcid": "https://orcid.org/0000-0002-9659-8697", "observed_orcids": ["https://orcid.org/0000-0002-9659-8697", "https://orcid.org/0000-0002-5114-5942", "https://orcid.org/0000-0001-7412-6501"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Chen, Pu", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "last", "author": {"id": "https://openalex.org/A5100343699", "display_name": "Yin Zhang", "orcid": "https://orcid.org/0000-0001-6986-4227", "observed_orcids": ["https://orcid.org/0000-0001-6986-4227"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Zhang, Yin", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}], "abstract_inverted_index": {"Multimodal": [0], "understanding": [1], "of": [2, 13, 124, 187, 208], "tables": [3], "in": [4, 44], "real-world": [5, 160], "contexts": [6], "is": [7], "challenging": [8, 146], "due": [9], "to": [10, 96, 156], "the": [11, 119, 184, 206], "complexity": [12], "structure,": [14], "symbolic": [15, 109], "density,": [16], "and": [17, 47, 91, 132, 143, 152, 163, 190, 202], "visual": [18], "degradation": [19, 162], "(blur,": [20], "skew,": [21], "watermarking,": [22], "incomplete": [23], "structures": [24], "or": [25, 28], "fonts,": [26], "multi-span": [27], "hierarchically": [29], "nested": [30], "layouts).": [31], "Existing": [32], "multimodal": [33, 69, 161, 213], "large": [34], "language": [35], "models": [36, 158], "(MLLMs)": [37], "struggle": [38], "with": [39], "such": [40], "WildStruct": [41, 147], "conditions,": [42], "resulting": [43], "limited": [45], "performance": [46], "poor": [48], "generalization.": [49], "To": [50, 112], "address": [51], "these": [52], "challenges,": [53], "we": [54, 117, 141, 197], "propose": [55], "TableMoE,": [56], "a": [57, 103], "neuro-symbolic": [58, 210], "Mixture-of-Connector-Experts": [59], "(MoCE)": [60], "architecture": [61], "specifically": [62, 155], "designed": [63, 154], "for": [64, 136, 212], "robust,": [65], "structured": [66, 191], "reasoning": [67, 110, 211], "over": [68], "table": [70, 94, 214], "data.": [71], "TableMoE": [72, 170], "features": [73], "an": [74], "innovative": [75], "Neuro-Symbolic": [76, 188], "Routing": [77, 189], "mechanism,": [78], "which": [79], "predicts": [80], "latent": [81], "semantic": [82], "token": [83], "roles": [84], "(e.g.,": [85], "header,": [86], "data": [87], "cell,": [88], "axis,": [89], "formula)": [90], "dynamically": [92], "routes": [93], "elements": [95], "specialized": [97], "experts": [98], "(Table-to-HTML,": [99], "Table-to-JSON,": [100], "Table-to-Code)": [101], "using": [102], "confidence-aware": [104], "gating": [105], "strategy": [106], "informed": [107], "by": [108], "graphs.": [111], "facilitate": [113], "effective": [114], "alignment-driven": [115], "pretraining,": [116], "introduce": [118], "large-scale": [120], "TableMoE-Align": [121], "dataset,": [122], "consisting": [123], "1.2M": [125], "table-HTML-JSON-code": [126], "quadruples": [127], "across": [128], "finance,": [129], "science,": [130], "biomedicine": [131], "industry,": [133], "utilized": [134], "exclusively": [135], "model": [137], "pretraining.": [138], "For": [139], "evaluation,": [140], "curate": [142], "release": [144], "four": [145], "benchmarks:": [148], "WMMFinQA,": [149], "WMMTatQA,": [150], "WMMTabDialog,": [151], "WMMFinanceMath,": [153], "stress-test": [157], "under": [159], "structural": [164], "complexity.": [165], "Experimental": [166], "results": [167], "demonstrate": [168], "that": [169], "significantly": [171], "surpasses": [172], "existing": [173], "state-of-the-art": [174], "models.": [175], "Extensive": [176], "ablation": [177], "studies": [178], "validate": [179], "each": [180], "core": [181], "component,": [182], "emphasizing": [183], "critical": [185], "role": [186], "expert": [192], "alignment.": [193], "Through": [194], "qualitative": [195], "analyses,": [196], "further": [198], "showcase": [199], "TableMoE's": [200], "interpretability": [201], "enhanced": [203], "robustness,": [204], "underscoring": [205], "effectiveness": [207], "integrating": [209], "understanding.": [215]}, "primary_topic": {"id": "https://openalex.org/T11714", "display_name": "Multimodal Machine Learning Applications", "score": 0.9595000147819519, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T11714", "display_name": "Multimodal Machine Learning Applications", "score": 0.9595000147819519, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10601", "display_name": "Handwritten Text Recognition Techniques", "score": 0.02319999970495701, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.005900000222027302, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2506.21393", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2506.21393", "pdf_url": "https://arxiv.org/pdf/2506.21393", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": null, "license_id": null, "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4417142411", "doi": "https://doi.org/10.48550/arxiv.2512.05379", "title": "Mitigating Self-Preference by Authorship Obfuscation", "publication_year": 2025, "publication_date": "2025-12-05", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5120727121", "display_name": "Taslim Mahbub", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Mahbub, Taslim", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "last", "author": {"id": "https://openalex.org/A5044868467", "display_name": "Feng Shi", "orcid": "https://orcid.org/0000-0003-1522-9943", "observed_orcids": ["https://orcid.org/0000-0003-1522-9943"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Feng, Shi", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}], "abstract_inverted_index": {"Language": [0], "models": [1], "(LMs)": [2], "judges": [3, 18, 35, 57], "are": [4, 72], "widely": [5], "used": [6], "to": [7, 52, 84, 93, 102, 108, 137, 146], "evaluate": [8], "the": [9, 69, 89, 110, 139, 155], "quality": [10], "of": [11, 65, 151], "LM": [12, 17, 34, 56, 90], "outputs.": [13, 97], "Despite": [14], "many": [15, 170], "advantages,": [16], "display": [19], "concerning": [20], "biases": [21], "that": [22, 117, 163], "can": [23, 58, 167], "impair": [24], "their": [25, 37, 60, 76, 95], "integrity": [26], "in": [27, 105], "evaluations.": [28], "One": [29], "such": [30], "bias": [31, 49], "is": [32, 50], "self-preference:": [33], "preferring": [36], "own": [38, 61, 96], "answers": [39], "over": [40], "those": [41, 64], "produced": [42], "by": [43, 87], "other": [44], "LMs": [45], "or": [46], "humans.": [47], "The": [48], "hard": [51], "eliminate": [53], "as": [54, 119, 121], "frontier": [55], "distinguish": [59], "outputs": [62], "from": [63], "others,": [66], "even": [67], "when": [68, 141], "evaluation": [70, 103, 156], "candidates": [71, 104], "not": [73], "labeled": [74], "with": [75], "sources.": [77], "In": [78], "this": [79], "paper,": [80], "we": [81, 132, 142], "investigate": [82], "strategies": [83], "mitigate": [85], "self-preference": [86, 158, 166], "reducing": [88], "judges'": [91], "ability": [92], "recognize": [94], "We": [98, 115], "apply": [99], "black-box": [100], "perturbations": [101, 118, 145], "pairwise": [106], "comparison": [107], "obfuscate": [109], "authorship": [111], "and": [112, 165, 173], "reduce": [113, 129], "self-recognition.": [114], "find": [116], "simple": [120], "synonym": [122], "replacement": [123], "for": [124], "a": [125, 147], "few": [126], "words": [127], "predictably": [128], "self-preference.": [130], "However,": [131], "also": [133], "uncover": [134], "fundamental": [135], "challenges": [136], "eliminating": [138], "bias:": [140], "extrapolate": [143], "our": [144], "more": [148], "complete": [149, 174], "neutralization": [150], "stylistic": [152], "differences": [153], "between": [154], "candidates,": [157], "recovers.": [159], "Our": [160], "findings": [161], "suggest": [162], "self-recognition": [164], "happen": [168], "on": [169], "semantic": [171], "levels,": [172], "mitigation": [175], "remains": [176], "challenging": [177], "despite": [178], "promising": [179], "initial": [180], "results.": [181]}, "primary_topic": {"id": "https://openalex.org/T12380", "display_name": "Authorship Attribution and Profiling", "score": 0.20640000700950623, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T12380", "display_name": "Authorship Attribution and Profiling", "score": 0.20640000700950623, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T11689", "display_name": "Adversarial Robustness in Machine Learning", "score": 0.20399999618530273, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10883", "display_name": "Ethics and Social Impacts of AI", "score": 0.18960000574588776, "subfield": {"id": "https://openalex.org/subfields/3311", "display_name": "Safety Research"}, "field": {"id": "https://openalex.org/fields/33", "display_name": "Social Sciences"}, "domain": {"id": "https://openalex.org/domains/2", "display_name": "Social Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2512.05379", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2512.05379", "pdf_url": "https://arxiv.org/pdf/2512.05379", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": null, "license_id": null, "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4408770757", "doi": "https://doi.org/10.26434/chemrxiv-2025-ggp8n", "title": "How to generalize machine learning models to both canonical and non-canonical peptides", "publication_year": 2025, "publication_date": "2025-03-24", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5092249392", "display_name": "Ra\u00fal Fern\u00e1ndez-D\u00edaz", "orcid": "https://orcid.org/0000-0002-7383-6568", "observed_orcids": ["https://orcid.org/0000-0002-7383-6568"]}, "institutions": [{"id": "https://openalex.org/I100930933", "display_name": "University College Dublin", "ror": "https://ror.org/05m7pjf47", "country_code": "IE", "type": "education", "lineage": ["https://openalex.org/I100930933"]}, {"id": "https://openalex.org/I4210100977", "display_name": "IBM (Ireland)", "ror": "https://ror.org/0146h2h75", "country_code": "IE", "type": "company", "lineage": ["https://openalex.org/I1341412227", "https://openalex.org/I4210100977"]}, {"id": "https://openalex.org/I4210145784", "display_name": "IBM Research - Ireland", "ror": "https://ror.org/04jnxr720", "country_code": "IE", "type": "facility", "lineage": ["https://openalex.org/I1341412227", "https://openalex.org/I4210114115", "https://openalex.org/I4210145784"]}], "countries": ["IE"], "is_corresponding": false, "raw_author_name": "Ra\u00fal Fern\u00e1ndez-D\u00edaz", "raw_affiliation_strings": ["IBM Research - Ireland", "University College Dublin"], "raw_orcid": "https://orcid.org/0000-0002-7383-6568", "affiliations": [{"raw_affiliation_string": "IBM Research - Ireland", "institution_ids": ["https://openalex.org/I4210100977", "https://openalex.org/I4210145784"]}, {"raw_affiliation_string": "University College Dublin", "institution_ids": ["https://openalex.org/I100930933"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5041873604", "display_name": "Rodrigo Ochoa", "orcid": "https://orcid.org/0000-0003-0734-2196", "observed_orcids": ["https://orcid.org/0000-0003-0734-2196"]}, "institutions": [{"id": "https://openalex.org/I189090001", "display_name": "Novo Nordisk (Denmark)", "ror": "https://ror.org/0435rc536", "country_code": "DK", "type": "company", "lineage": ["https://openalex.org/I189090001", "https://openalex.org/I2801134892", "https://openalex.org/I4210159963"]}], "countries": ["DK"], "is_corresponding": false, "raw_author_name": "Rodrigo Ochoa", "raw_affiliation_strings": ["Novo Nordisk (Denmark)"], "raw_orcid": "https://orcid.org/0000-0003-0734-2196", "affiliations": [{"raw_affiliation_string": "Novo Nordisk (Denmark)", "institution_ids": ["https://openalex.org/I189090001"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5044216056", "display_name": "Thanh Lam Hoang", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I4210100977", "display_name": "IBM (Ireland)", "ror": "https://ror.org/0146h2h75", "country_code": "IE", "type": "company", "lineage": ["https://openalex.org/I1341412227", "https://openalex.org/I4210100977"]}, {"id": "https://openalex.org/I4210145784", "display_name": "IBM Research - Ireland", "ror": "https://ror.org/04jnxr720", "country_code": "IE", "type": "facility", "lineage": ["https://openalex.org/I1341412227", "https://openalex.org/I4210114115", "https://openalex.org/I4210145784"]}], "countries": ["IE"], "is_corresponding": false, "raw_author_name": "Thanh Lam Hoang", "raw_affiliation_strings": ["IBM Research - Ireland"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "IBM Research - Ireland", "institution_ids": ["https://openalex.org/I4210100977", "https://openalex.org/I4210145784"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5030268064", "display_name": "Vanessa L\u00f3pez", "orcid": "https://orcid.org/0000-0002-0674-5324", "observed_orcids": ["https://orcid.org/0000-0002-0674-5324"]}, "institutions": [{"id": "https://openalex.org/I4210100977", "display_name": "IBM (Ireland)", "ror": "https://ror.org/0146h2h75", "country_code": "IE", "type": "company", "lineage": ["https://openalex.org/I1341412227", "https://openalex.org/I4210100977"]}, {"id": "https://openalex.org/I4210145784", "display_name": "IBM Research - Ireland", "ror": "https://ror.org/04jnxr720", "country_code": "IE", "type": "facility", "lineage": ["https://openalex.org/I1341412227", "https://openalex.org/I4210114115", "https://openalex.org/I4210145784"]}], "countries": ["IE"], "is_corresponding": false, "raw_author_name": "Vanessa Lopez", "raw_affiliation_strings": ["IBM Research - Ireland"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "IBM Research - Ireland", "institution_ids": ["https://openalex.org/I4210100977", "https://openalex.org/I4210145784"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5004065601", "display_name": "Denis C. Shields", "orcid": "https://orcid.org/0000-0003-4015-2474", "observed_orcids": ["https://orcid.org/0000-0003-4015-2474"]}, "institutions": [{"id": "https://openalex.org/I100930933", "display_name": "University College Dublin", "ror": "https://ror.org/05m7pjf47", "country_code": "IE", "type": "education", "lineage": ["https://openalex.org/I100930933"]}], "countries": ["IE"], "is_corresponding": false, "raw_author_name": "Denis Shields", "raw_affiliation_strings": ["University College Dublin"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "University College Dublin", "institution_ids": ["https://openalex.org/I100930933"]}]}], "abstract_inverted_index": {"Bioactive": [0], "peptides": [1, 33, 170], "are": [2, 48, 109, 209, 234], "an": [3, 89], "important": [4], "class": [5], "of": [6, 67, 73, 82, 183, 205], "natural": [7], "products": [8], "with": [9, 201], "great": [10], "functional": [11], "diversity.": [12], "Chemical": [13, 153], "modifications": [14], "can": [15], "improve": [16], "their": [17, 20], "pharmacology,": [18], "yet": [19], "structural": [21], "diversity": [22], "presents": [23], "unique": [24], "challenges": [25], "for": [26, 31, 39, 76, 136, 168, 174, 230], "computational": [27], "modeling.": [28], "Furthermore,": [29], "data": [30, 54, 228], "canonical": [32, 53, 138, 199, 221], "(non-modified)": [34], "is": [35, 88], "more": [36], "abundant": [37], "than": [38], "non-canonical": [40, 56, 169, 206, 223], "(chemically": [41], "modified).": [42], "We": [43, 118, 144], "explored": [44], "whether": [45], "current": [46], "methods": [47], "sufficient": [49], "to": [50, 55, 114, 189, 192, 211, 219], "generalize": [51], "from": [52, 111, 152], "datasets.": [57], "To": [58], "do": [59], "this,": [60, 177], "we": [61, 208], "first": [62], "considered": [63], "two": [64, 185], "critical": [65], "aspects": [66], "the": [68, 94, 103, 106, 116, 149, 184, 193, 198, 232], "modeling": [69], "problem,": [70], "namely": [71], "choice": [72, 81], "similarity": [74, 129], "function": [75, 123], "guiding": [77], "dataset": [78, 86, 95], "partitioning": [79, 87, 137], "and": [80, 98, 161, 171, 222, 227], "molecular": [83], "representation.": [84], "Similarity-based": [85], "evaluation": [90], "technique": [91], "that": [92, 102, 126, 148, 216], "divides": [93], "into": [96], "train": [97], "test": [99, 107], "subsets,": [100], "such": [101], "molecules": [104], "in": [105, 236], "set": [108], "different": [110], "those": [112], "used": [113], "fit": [115], "model.": [117], "demonstrate,": [119], "across": [120], "four": [121], "peptide": [122, 139, 186], "prediction": [124], "tasks,": [125], "chemical": [127, 159], "fingerprint-based": [128], "measures": [130], "outperform": [131, 158], "traditional": [132], "sequence": [133], "alignment-based": [134], "metrics": [135], "datasets,": [140], "challenging": [141], "standard": [142], "practices.": [143], "have": [145], "also": [146], "found": [147], "deep-learned": [150], "embeddings": [151], "Language": [154], "Models": [155], "(CLMs)": [156], "generally": [157], "fingerprints": [160], "other": [162], "peptide-specific": [163], "pre-trained": [164], "models,": [165], "performing": [166], "best": [167, 173], "second": [172], "canonical.": [175], "Despite": [176], "models": [178, 215], "trained": [179], "on": [180], "only": [181], "one": [182], "classes": [187], "fail": [188], "properly": [190], "extrapolate": [191], "other.": [194], "However,": [195], "by": [196], "enriching": [197], "datasets": [200], "a": [202], "small": [203], "proportion": [204], "peptides,": [207], "able": [210], "build": [212], "robust": [213], "joint": [214], "generalise": [217], "adequately": [218], "both": [220], "data.": [224], "All": [225], "code": [226], "necessary": [229], "reproducing": [231], "experiments": [233], "available": [235], "Github": [237], "(https://github.com/IBM/AutoPeptideML/tree/peptide-rep-gen).": [238]}, "primary_topic": {"id": "https://openalex.org/T10211", "display_name": "Computational Drug Discovery Methods", "score": 0.9848999977111816, "subfield": {"id": "https://openalex.org/subfields/1703", "display_name": "Computational Theory and Mathematics"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T10211", "display_name": "Computational Drug Discovery Methods", "score": 0.9848999977111816, "subfield": {"id": "https://openalex.org/subfields/1703", "display_name": "Computational Theory and Mathematics"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10911", "display_name": "Chemical Synthesis and Analysis", "score": 0.007699999958276749, "subfield": {"id": "https://openalex.org/subfields/1312", "display_name": "Molecular Biology"}, "field": {"id": "https://openalex.org/fields/13", "display_name": "Biochemistry, Genetics and Molecular Biology"}, "domain": {"id": "https://openalex.org/domains/1", "display_name": "Life Sciences"}}, {"id": "https://openalex.org/T12254", "display_name": "Machine Learning in Bioinformatics", "score": 0.0019000000320374966, "subfield": {"id": "https://openalex.org/subfields/1312", "display_name": "Molecular Biology"}, "field": {"id": "https://openalex.org/fields/13", "display_name": "Biochemistry, Genetics and Molecular Biology"}, "domain": {"id": "https://openalex.org/domains/1", "display_name": "Life Sciences"}}], "primary_location": {"id": "doi:10.26434/chemrxiv-2025-ggp8n", "is_oa": true, "landing_page_url": "https://doi.org/10.26434/chemrxiv-2025-ggp8n", "pdf_url": null, "source": {"id": "https://openalex.org/S4393918830", "display_name": "ChemRxiv", "issn_l": "2573-2293", "issn": ["2573-2293"], "is_oa": false, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": null, "host_organization_name": null, "host_organization_lineage": [], "host_organization_lineage_names": [], "type": "repository"}, "license": "cc-by-nc-nd", "license_id": "https://openalex.org/licenses/cc-by-nc-nd", "version": "acceptedVersion", "is_accepted": true, "is_published": false, "raw_source_name": null, "raw_type": "posted-content"}, "cited_by_count": 1, "is_retracted": false}, {"id": "https://openalex.org/W4409877648", "doi": "https://doi.org/10.1007/s11432-024-4404-4", "title": "Autonomous embodied navigation task generation from natural language dialogues", "publication_year": 2025, "publication_date": "2025-04-25", "type": "article", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5101434141", "display_name": "Haifeng Xu", "orcid": "https://orcid.org/0000-0001-6371-4906", "observed_orcids": ["https://orcid.org/0000-0001-6371-4906"]}, "institutions": [{"id": "https://openalex.org/I99065089", "display_name": "Tsinghua University", "ror": "https://ror.org/03cve4549", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I99065089"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Haifeng Xu", "raw_affiliation_strings": ["Department of Automation, Tsinghua University, Beijing, 100084, China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Automation, Tsinghua University, Beijing, 100084, China", "institution_ids": ["https://openalex.org/I99065089"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5101581292", "display_name": "Yongchang Li", "orcid": "https://orcid.org/0000-0003-4589-0062", "observed_orcids": ["https://orcid.org/0000-0003-4589-0062"]}, "institutions": [{"id": "https://openalex.org/I18452120", "display_name": "Yantai University", "ror": "https://ror.org/01rp41m56", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I18452120"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Yongchang Li", "raw_affiliation_strings": ["School of Physics and Electronic Information, Yantai University, Yantai, 264005, China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "School of Physics and Electronic Information, Yantai University, Yantai, 264005, China", "institution_ids": ["https://openalex.org/I18452120"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5064304208", "display_name": "Lei Ma", "orcid": "https://orcid.org/0000-0002-0906-3978", "observed_orcids": ["https://orcid.org/0000-0002-0906-3978"]}, "institutions": [{"id": "https://openalex.org/I67001856", "display_name": "Shanghai Institute of Technology", "ror": "https://ror.org/00fjzqj15", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I67001856"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Lumeng Ma", "raw_affiliation_strings": ["School of Electrical and Electronic Engineering, Shanghai Institute of Technology, Shanghai, 201418, China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "School of Electrical and Electronic Engineering, Shanghai Institute of Technology, Shanghai, 201418, China", "institution_ids": ["https://openalex.org/I67001856"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5103714205", "display_name": "Chunwen Li", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I99065089", "display_name": "Tsinghua University", "ror": "https://ror.org/03cve4549", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I99065089"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Chunwen Li", "raw_affiliation_strings": ["Department of Automation, Tsinghua University, Beijing, 100084, China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Automation, Tsinghua University, Beijing, 100084, China", "institution_ids": ["https://openalex.org/I99065089"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5037709138", "display_name": "Yanzhi Dong", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I18452120", "display_name": "Yantai University", "ror": "https://ror.org/01rp41m56", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I18452120"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Yanzhi Dong", "raw_affiliation_strings": ["School of Physics and Electronic Information, Yantai University, Yantai, 264005, China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "School of Physics and Electronic Information, Yantai University, Yantai, 264005, China", "institution_ids": ["https://openalex.org/I18452120"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5013721700", "display_name": "Xiaohu Yuan", "orcid": "https://orcid.org/0009-0008-4239-2889", "observed_orcids": ["https://orcid.org/0009-0008-4239-2889"]}, "institutions": [{"id": "https://openalex.org/I99065089", "display_name": "Tsinghua University", "ror": "https://ror.org/03cve4549", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I99065089"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Xiaohu Yuan", "raw_affiliation_strings": ["Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China", "institution_ids": ["https://openalex.org/I99065089"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5101678494", "display_name": "Huaping Liu", "orcid": "https://orcid.org/0000-0001-7017-4127", "observed_orcids": ["https://orcid.org/0000-0001-7017-4127"]}, "institutions": [{"id": "https://openalex.org/I99065089", "display_name": "Tsinghua University", "ror": "https://ror.org/03cve4549", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I99065089"]}], "countries": ["CN"], "is_corresponding": true, "raw_author_name": "Huaping Liu", "raw_affiliation_strings": ["Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China", "institution_ids": ["https://openalex.org/I99065089"]}]}], "abstract_inverted_index": null, "primary_topic": {"id": "https://openalex.org/T12031", "display_name": "Speech and dialogue systems", "score": 0.9589999914169312, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T12031", "display_name": "Speech and dialogue systems", "score": 0.9589999914169312, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10653", "display_name": "Robot Manipulation and Learning", "score": 0.016200000420212746, "subfield": {"id": "https://openalex.org/subfields/2207", "display_name": "Control and Systems Engineering"}, "field": {"id": "https://openalex.org/fields/22", "display_name": "Engineering"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10709", "display_name": "Social Robot Interaction and HRI", "score": 0.006399999838322401, "subfield": {"id": "https://openalex.org/subfields/3207", "display_name": "Social Psychology"}, "field": {"id": "https://openalex.org/fields/32", "display_name": "Psychology"}, "domain": {"id": "https://openalex.org/domains/2", "display_name": "Social Sciences"}}], "primary_location": {"id": "doi:10.1007/s11432-024-4404-4", "is_oa": false, "landing_page_url": "https://doi.org/10.1007/s11432-024-4404-4", "pdf_url": null, "source": {"id": "https://openalex.org/S4210218743", "display_name": "Science China Information Sciences", "issn_l": "1869-1919", "issn": ["1869-1919", "1674-733X"], "is_oa": false, "is_in_doaj": false, "is_core": true, "listed_in": ["cwts-core", "jufo-1", "norway-1"], "host_organization": "https://openalex.org/P4310319965", "host_organization_name": "Springer Nature", "host_organization_lineage": ["https://openalex.org/P4310319965"], "host_organization_lineage_names": ["Springer Nature"], "type": "journal"}, "license": null, "license_id": null, "version": "publishedVersion", "is_accepted": true, "is_published": true, "raw_source_name": "Science China Information Sciences", "raw_type": "journal-article"}, "cited_by_count": 2, "is_retracted": false}, {"id": "https://openalex.org/W4414820763", "doi": "https://doi.org/10.48550/arxiv.2510.02084", "title": "KAIROS: Unified Training for Universal Non-Autoregressive Time Series Forecasting", "publication_year": 2025, "publication_date": "2025-10-02", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5101251259", "display_name": "Kuiye Ding", "orcid": "https://orcid.org/0009-0007-1717-7733", "observed_orcids": ["https://orcid.org/0009-0007-1717-7733"]}, "institutions": [{"id": "https://openalex.org/I19820366", "display_name": "Chinese Academy of Sciences", "ror": "https://ror.org/034t30j35", "country_code": "CN", "type": "government", "lineage": ["https://openalex.org/I19820366"]}, {"id": "https://openalex.org/I4210090176", "display_name": "Institute of Computing Technology", "ror": "https://ror.org/0090r4d87", "country_code": "CN", "type": "facility", "lineage": ["https://openalex.org/I19820366", "https://openalex.org/I4210090176"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Ding, Kuiye", "raw_affiliation_strings": ["Institute of Computing Technology , Chinese Academy of Sciences Beijing , China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Institute of Computing Technology , Chinese Academy of Sciences Beijing , China", "institution_ids": ["https://openalex.org/I19820366", "https://openalex.org/I4210090176"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5083043281", "display_name": "Fanda Fan", "orcid": "https://orcid.org/0000-0002-5214-0959", "observed_orcids": ["https://orcid.org/0000-0002-5214-0959"]}, "institutions": [{"id": "https://openalex.org/I19820366", "display_name": "Chinese Academy of Sciences", "ror": "https://ror.org/034t30j35", "country_code": "CN", "type": "government", "lineage": ["https://openalex.org/I19820366"]}, {"id": "https://openalex.org/I4210090176", "display_name": "Institute of Computing Technology", "ror": "https://ror.org/0090r4d87", "country_code": "CN", "type": "facility", "lineage": ["https://openalex.org/I19820366", "https://openalex.org/I4210090176"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Fan, Fanda", "raw_affiliation_strings": ["Institute of Computing Technology , Chinese Academy of Sciences Beijing , China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Institute of Computing Technology , Chinese Academy of Sciences Beijing , China", "institution_ids": ["https://openalex.org/I19820366", "https://openalex.org/I4210090176"]}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Wang, Zheya", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I190082696", "display_name": "Durham University", "ror": "https://ror.org/01v29qb04", "country_code": "GB", "type": "education", "lineage": ["https://openalex.org/I190082696"]}], "countries": ["GB"], "is_corresponding": false, "raw_author_name": "Wang, Zheya", "raw_affiliation_strings": ["Department of Mathematical Sciences , Durham University Durham , UK"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Mathematical Sciences , Durham University Durham , UK", "institution_ids": ["https://openalex.org/I190082696"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5101824486", "display_name": "Hongxiao Li", "orcid": "https://orcid.org/0000-0002-6114-8215", "observed_orcids": ["https://orcid.org/0000-0002-6114-8215"]}, "institutions": [{"id": "https://openalex.org/I19820366", "display_name": "Chinese Academy of Sciences", "ror": "https://ror.org/034t30j35", "country_code": "CN", "type": "government", "lineage": ["https://openalex.org/I19820366"]}, {"id": "https://openalex.org/I4210090176", "display_name": "Institute of Computing Technology", "ror": "https://ror.org/0090r4d87", "country_code": "CN", "type": "facility", "lineage": ["https://openalex.org/I19820366", "https://openalex.org/I4210090176"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Li, Hongxiao", "raw_affiliation_strings": ["Institute of Computing Technology , Chinese Academy of Sciences Beijing , China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Institute of Computing Technology , Chinese Academy of Sciences Beijing , China", "institution_ids": ["https://openalex.org/I19820366", "https://openalex.org/I4210090176"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5026207516", "display_name": "Yifan Wang", "orcid": "https://orcid.org/0000-0001-7764-8698", "observed_orcids": ["https://orcid.org/0000-0001-7764-8698", "https://orcid.org/0000-0002-8110-528X"]}, "institutions": [{"id": "https://openalex.org/I19820366", "display_name": "Chinese Academy of Sciences", "ror": "https://ror.org/034t30j35", "country_code": "CN", "type": "government", "lineage": ["https://openalex.org/I19820366"]}, {"id": "https://openalex.org/I4210090176", "display_name": "Institute of Computing Technology", "ror": "https://ror.org/0090r4d87", "country_code": "CN", "type": "facility", "lineage": ["https://openalex.org/I19820366", "https://openalex.org/I4210090176"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Wang, Yifan", "raw_affiliation_strings": ["Institute of Computing Technology , Chinese Academy of Sciences Beijing , China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Institute of Computing Technology , Chinese Academy of Sciences Beijing , China", "institution_ids": ["https://openalex.org/I19820366", "https://openalex.org/I4210090176"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5107094040", "display_name": "Lei Wang", "orcid": "https://orcid.org/0000-0001-8085-2374", "observed_orcids": ["https://orcid.org/0000-0001-8085-2374", "https://orcid.org/0009-0001-2769-6701"]}, "institutions": [{"id": "https://openalex.org/I19820366", "display_name": "Chinese Academy of Sciences", "ror": "https://ror.org/034t30j35", "country_code": "CN", "type": "government", "lineage": ["https://openalex.org/I19820366"]}, {"id": "https://openalex.org/I4210090176", "display_name": "Institute of Computing Technology", "ror": "https://ror.org/0090r4d87", "country_code": "CN", "type": "facility", "lineage": ["https://openalex.org/I19820366", "https://openalex.org/I4210090176"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Wang, Lei", "raw_affiliation_strings": ["Institute of Computing Technology , Chinese Academy of Sciences Beijing , China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Institute of Computing Technology , Chinese Academy of Sciences Beijing , China", "institution_ids": ["https://openalex.org/I19820366", "https://openalex.org/I4210090176"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5033347503", "display_name": "Chunjie Luo", "orcid": "https://orcid.org/0000-0002-6977-929X", "observed_orcids": ["https://orcid.org/0000-0002-6977-929X"]}, "institutions": [{"id": "https://openalex.org/I19820366", "display_name": "Chinese Academy of Sciences", "ror": "https://ror.org/034t30j35", "country_code": "CN", "type": "government", "lineage": ["https://openalex.org/I19820366"]}, {"id": "https://openalex.org/I4210090176", "display_name": "Institute of Computing Technology", "ror": "https://ror.org/0090r4d87", "country_code": "CN", "type": "facility", "lineage": ["https://openalex.org/I19820366", "https://openalex.org/I4210090176"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Luo, Chunjie", "raw_affiliation_strings": ["Institute of Computing Technology , Chinese Academy of Sciences Beijing , China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Institute of Computing Technology , Chinese Academy of Sciences Beijing , China", "institution_ids": ["https://openalex.org/I19820366", "https://openalex.org/I4210090176"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5085963553", "display_name": "Jianfeng Zhan", "orcid": "https://orcid.org/0000-0002-3728-6837", "observed_orcids": ["https://orcid.org/0000-0002-3728-6837"]}, "institutions": [{"id": "https://openalex.org/I19820366", "display_name": "Chinese Academy of Sciences", "ror": "https://ror.org/034t30j35", "country_code": "CN", "type": "government", "lineage": ["https://openalex.org/I19820366"]}, {"id": "https://openalex.org/I4210090176", "display_name": "Institute of Computing Technology", "ror": "https://ror.org/0090r4d87", "country_code": "CN", "type": "facility", "lineage": ["https://openalex.org/I19820366", "https://openalex.org/I4210090176"]}, {"id": "https://openalex.org/I4210165038", "display_name": "University of Chinese Academy of Sciences", "ror": "https://ror.org/05qbk4x57", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I19820366", "https://openalex.org/I4210165038"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Zhan, Jianfeng", "raw_affiliation_strings": ["Institute of Computing Technology , Chinese Academy of Sciences Beijing , China", "University of Chinese Academy of Sciences Beijing , China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Institute of Computing Technology , Chinese Academy of Sciences Beijing , China", "institution_ids": ["https://openalex.org/I19820366", "https://openalex.org/I4210090176"]}, {"raw_affiliation_string": "University of Chinese Academy of Sciences Beijing , China", "institution_ids": ["https://openalex.org/I4210165038"]}]}], "abstract_inverted_index": {"In": [0], "the": [1, 10, 92, 128], "World": [2], "Wide": [3], "Web,": [4], "reliable": [5], "time": [6, 38, 58, 141], "series": [7, 39, 59], "forecasts": [8], "provide": [9], "forward-looking": [11], "signals": [12], "that": [13, 62, 85], "drive": [14], "resource": [15], "planning,": [16], "cache": [17], "placement,": [18], "and": [19, 30, 75], "anomaly": [20], "response,": [21], "enabling": [22], "platforms": [23], "to": [24, 48, 87, 109], "operate": [25], "efficiently": [26], "as": [27, 133], "user": [28], "behavior": [29], "content": [31], "distributions": [32], "evolve.": [33], "Compared": [34], "with": [35, 113], "other": [36], "domains,": [37], "forecasting": [40, 60, 106], "for": [41, 137], "Web": [42], "applications": [43], "requires": [44], "much": [45], "faster": [46], "responsiveness": [47], "support": [49], "real-time": [50], "decision": [51], "making.": [52], "We": [53], "present": [54], "KAIROS,": [55], "a": [56, 117, 134], "non-autoregressive": [57, 83, 131], "framework": [61], "directly": [63], "models": [64, 84, 112, 139], "segment-level": [65], "multi-peak": [66], "distributions.": [67], "Unlike": [68], "autoregressive": [69], "approaches,": [70], "KAIROS": [71, 95, 126], "avoids": [72], "error": [73], "accumulation": [74], "achieves": [76], "just-in-time": [77], "inference,": [78], "while": [79], "improving": [80], "over": [81], "existing": [82], "collapse": [86], "over-smoothed": [88], "predictions.": [89], "Trained": [90], "on": [91, 100], "large-scale": [93], "corpus,": [94], "demonstrates": [96], "strong": [97], "zero-shot": [98], "generalization": [99], "six": [101], "widely": [102], "used": [103], "benchmarks,": [104], "delivering": [105], "performance": [107], "comparable": [108], "state-of-the-art": [110], "foundation": [111, 138], "similar": [114], "scale,": [115], "at": [116], "fraction": [118], "of": [119, 130], "their": [120], "inference": [121], "cost.": [122], "Beyond": [123], "empirical": [124], "results,": [125], "highlights": [127], "importance": [129], "design": [132], "scalable": [135], "paradigm": [136], "in": [140], "series.": [142]}, "primary_topic": {"id": "https://openalex.org/T12205", "display_name": "Time Series Analysis and Forecasting", "score": 0.9247999787330627, "subfield": {"id": "https://openalex.org/subfields/1711", "display_name": "Signal Processing"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T12205", "display_name": "Time Series Analysis and Forecasting", "score": 0.9247999787330627, "subfield": {"id": "https://openalex.org/subfields/1711", "display_name": "Signal Processing"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T11918", "display_name": "Forecasting Techniques and Applications", "score": 0.0640999972820282, "subfield": {"id": "https://openalex.org/subfields/1803", "display_name": "Management Science and Operations Research"}, "field": {"id": "https://openalex.org/fields/18", "display_name": "Decision Sciences"}, "domain": {"id": "https://openalex.org/domains/2", "display_name": "Social Sciences"}}, {"id": "https://openalex.org/T11344", "display_name": "Traffic Prediction and Management Techniques", "score": 0.004999999888241291, "subfield": {"id": "https://openalex.org/subfields/2215", "display_name": "Building and Construction"}, "field": {"id": "https://openalex.org/fields/22", "display_name": "Engineering"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2510.02084", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2510.02084", "pdf_url": "https://arxiv.org/pdf/2510.02084", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": "cc-by-nc-nd", "license_id": "https://openalex.org/licenses/cc-by-nc-nd", "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4415217940", "doi": "https://doi.org/10.1016/j.mcpdig.2025.100296", "title": "Identifying Bias at Scale in Clinical Notes Using Large Language Models", "publication_year": 2025, "publication_date": "2025-10-15", "type": "article", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5012807122", "display_name": "Donald U. Apakama", "orcid": "https://orcid.org/0000-0001-6217-1620", "observed_orcids": ["https://orcid.org/0000-0001-6217-1620"]}, "institutions": [{"id": "https://openalex.org/I98704320", "display_name": "Icahn School of Medicine at Mount Sinai", "ror": "https://ror.org/04a9tmd77", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I1320796813", "https://openalex.org/I98704320"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Donald U. Apakama", "raw_affiliation_strings": ["Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, NY", "Institute for Health Equity Research, Icahn School of Medicine at Mount Sinai, New York, NY", "The Charles Bronfman Department of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY", "The Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, NY", "institution_ids": ["https://openalex.org/I98704320"]}, {"raw_affiliation_string": "Institute for Health Equity Research, Icahn School of Medicine at Mount Sinai, New York, NY", "institution_ids": ["https://openalex.org/I98704320"]}, {"raw_affiliation_string": "The Charles Bronfman Department of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY", "institution_ids": ["https://openalex.org/I98704320"]}, {"raw_affiliation_string": "The Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY", "institution_ids": ["https://openalex.org/I98704320"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5016800014", "display_name": "Kim-Anh-Nhi Nguyen", "orcid": "https://orcid.org/0000-0002-3446-9750", "observed_orcids": ["https://orcid.org/0000-0002-3446-9750"]}, "institutions": [{"id": "https://openalex.org/I98704320", "display_name": "Icahn School of Medicine at Mount Sinai", "ror": "https://ror.org/04a9tmd77", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I1320796813", "https://openalex.org/I98704320"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Kim-Anh-Nhi Nguyen", "raw_affiliation_strings": ["Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY", "institution_ids": ["https://openalex.org/I98704320"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5114404030", "display_name": "Daphnee Hyppolite", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I1320796813", "display_name": "Mount Sinai Health System", "ror": "https://ror.org/04kfn4587", "country_code": "US", "type": "healthcare", "lineage": ["https://openalex.org/I1320796813"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Daphnee Hyppolite", "raw_affiliation_strings": ["Office of the Chief Medical Officer, Mount Sinai Health System, New York, NY"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Office of the Chief Medical Officer, Mount Sinai Health System, New York, NY", "institution_ids": ["https://openalex.org/I1320796813"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5079561272", "display_name": "Shelly Soffer", "orcid": "https://orcid.org/0000-0002-7853-2029", "observed_orcids": ["https://orcid.org/0000-0002-7853-2029"]}, "institutions": [{"id": "https://openalex.org/I2800389475", "display_name": "Rabin Medical Center", "ror": "https://ror.org/01vjtf564", "country_code": "IL", "type": "healthcare", "lineage": ["https://openalex.org/I2800389475", "https://openalex.org/I64767286"]}], "countries": ["IL"], "is_corresponding": true, "raw_author_name": "Shelly Soffer", "raw_affiliation_strings": ["Institute of Hematology, Davidoff Cancer Center, Rabin Medical Center Petah-Tikva, Israel"], "raw_orcid": "https://orcid.org/0000-0002-7853-2029", "affiliations": [{"raw_affiliation_string": "Institute of Hematology, Davidoff Cancer Center, Rabin Medical Center Petah-Tikva, Israel", "institution_ids": ["https://openalex.org/I2800389475"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5095921818", "display_name": "Aya Mudrik", "orcid": "https://orcid.org/0009-0002-3429-9242", "observed_orcids": ["https://orcid.org/0009-0002-3429-9242"]}, "institutions": [{"id": "https://openalex.org/I124227911", "display_name": "Ben-Gurion University of the Negev", "ror": "https://ror.org/05tkyf982", "country_code": "IL", "type": "education", "lineage": ["https://openalex.org/I124227911"]}], "countries": ["IL"], "is_corresponding": false, "raw_author_name": "Aya Mudrik", "raw_affiliation_strings": ["Ben-Gurion University of the Negev, Be'er Sheva, Israel"], "raw_orcid": "https://orcid.org/0009-0002-3429-9242", "affiliations": [{"raw_affiliation_string": "Ben-Gurion University of the Negev, Be'er Sheva, Israel", "institution_ids": ["https://openalex.org/I124227911"]}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Emilia Ling", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I98704320", "display_name": "Icahn School of Medicine at Mount Sinai", "ror": "https://ror.org/04a9tmd77", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I1320796813", "https://openalex.org/I98704320"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Emilia Ling", "raw_affiliation_strings": ["Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, NY"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, NY", "institution_ids": ["https://openalex.org/I98704320"]}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Akini Moses", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I98704320", "display_name": "Icahn School of Medicine at Mount Sinai", "ror": "https://ror.org/04a9tmd77", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I1320796813", "https://openalex.org/I98704320"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Akini Moses", "raw_affiliation_strings": ["Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, NY"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, NY", "institution_ids": ["https://openalex.org/I98704320"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5114404031", "display_name": "Ivanka Temnycky", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I98704320", "display_name": "Icahn School of Medicine at Mount Sinai", "ror": "https://ror.org/04a9tmd77", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I1320796813", "https://openalex.org/I98704320"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Ivanka Temnycky", "raw_affiliation_strings": ["Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, NY"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, NY", "institution_ids": ["https://openalex.org/I98704320"]}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Allison Glasser", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I1320796813", "display_name": "Mount Sinai Health System", "ror": "https://ror.org/04kfn4587", "country_code": "US", "type": "healthcare", "lineage": ["https://openalex.org/I1320796813"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Allison Glasser", "raw_affiliation_strings": ["Office of the Chief Medical Officer, Mount Sinai Health System, New York, NY"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Office of the Chief Medical Officer, Mount Sinai Health System, New York, NY", "institution_ids": ["https://openalex.org/I1320796813"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5017430360", "display_name": "Rebecca Anderson", "orcid": "https://orcid.org/0000-0001-8925-7002", "observed_orcids": ["https://orcid.org/0000-0001-8925-7002"]}, "institutions": [{"id": "https://openalex.org/I1320796813", "display_name": "Mount Sinai Health System", "ror": "https://ror.org/04kfn4587", "country_code": "US", "type": "healthcare", "lineage": ["https://openalex.org/I1320796813"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Rebecca Anderson", "raw_affiliation_strings": ["Office of the Chief Medical Officer, Mount Sinai Health System, New York, NY"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Office of the Chief Medical Officer, Mount Sinai Health System, New York, NY", "institution_ids": ["https://openalex.org/I1320796813"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5038896342", "display_name": "Prathamesh Parchure", "orcid": "https://orcid.org/0009-0001-9383-691X", "observed_orcids": ["https://orcid.org/0009-0001-9383-691X"]}, "institutions": [{"id": "https://openalex.org/I98704320", "display_name": "Icahn School of Medicine at Mount Sinai", "ror": "https://ror.org/04a9tmd77", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I1320796813", "https://openalex.org/I98704320"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Prathamesh Parchure", "raw_affiliation_strings": ["Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY", "institution_ids": ["https://openalex.org/I98704320"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5114404029", "display_name": "Evajoyce Woullard", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I98704320", "display_name": "Icahn School of Medicine at Mount Sinai", "ror": "https://ror.org/04a9tmd77", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I1320796813", "https://openalex.org/I98704320"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Evajoyce Woullard", "raw_affiliation_strings": ["Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY", "institution_ids": ["https://openalex.org/I98704320"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5054823755", "display_name": "Masoud Edalati", "orcid": "https://orcid.org/0000-0002-1132-9480", "observed_orcids": ["https://orcid.org/0000-0002-1132-9480"]}, "institutions": [{"id": "https://openalex.org/I98704320", "display_name": "Icahn School of Medicine at Mount Sinai", "ror": "https://ror.org/04a9tmd77", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I1320796813", "https://openalex.org/I98704320"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Masoud Edalati", "raw_affiliation_strings": ["Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY", "institution_ids": ["https://openalex.org/I98704320"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5059085082", "display_name": "Lili Chan", "orcid": "https://orcid.org/0000-0003-4300-5760", "observed_orcids": ["https://orcid.org/0000-0003-4300-5760"]}, "institutions": [{"id": "https://openalex.org/I98704320", "display_name": "Icahn School of Medicine at Mount Sinai", "ror": "https://ror.org/04a9tmd77", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I1320796813", "https://openalex.org/I98704320"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Lili Chan", "raw_affiliation_strings": ["The Charles Bronfman Department of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY", "The Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "The Charles Bronfman Department of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY", "institution_ids": ["https://openalex.org/I98704320"]}, {"raw_affiliation_string": "The Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY", "institution_ids": ["https://openalex.org/I98704320"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5027333200", "display_name": "Clair A. Kronk", "orcid": "https://orcid.org/0000-0001-8397-8810", "observed_orcids": ["https://orcid.org/0000-0001-8397-8810"]}, "institutions": [{"id": "https://openalex.org/I98704320", "display_name": "Icahn School of Medicine at Mount Sinai", "ror": "https://ror.org/04a9tmd77", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I1320796813", "https://openalex.org/I98704320"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Clair Kronk", "raw_affiliation_strings": ["Institute for Health Equity Research, Icahn School of Medicine at Mount Sinai, New York, NY"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Institute for Health Equity Research, Icahn School of Medicine at Mount Sinai, New York, NY", "institution_ids": ["https://openalex.org/I98704320"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5070462882", "display_name": "Robert M. Freeman", "orcid": "https://orcid.org/0000-0003-4946-6533", "observed_orcids": ["https://orcid.org/0000-0003-4946-6533"]}, "institutions": [{"id": "https://openalex.org/I98704320", "display_name": "Icahn School of Medicine at Mount Sinai", "ror": "https://ror.org/04a9tmd77", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I1320796813", "https://openalex.org/I98704320"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Robert Freeman", "raw_affiliation_strings": ["The Charles Bronfman Department of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "The Charles Bronfman Department of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY", "institution_ids": ["https://openalex.org/I98704320"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5004797900", "display_name": "Arash Negahdari Kia", "orcid": "https://orcid.org/0000-0002-3675-0239", "observed_orcids": ["https://orcid.org/0000-0002-3675-0239"]}, "institutions": [{"id": "https://openalex.org/I98704320", "display_name": "Icahn School of Medicine at Mount Sinai", "ror": "https://ror.org/04a9tmd77", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I1320796813", "https://openalex.org/I98704320"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Arash Kia", "raw_affiliation_strings": ["Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY", "institution_ids": ["https://openalex.org/I98704320"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5021533090", "display_name": "Prem Timsina", "orcid": "https://orcid.org/0000-0002-6047-887X", "observed_orcids": ["https://orcid.org/0000-0002-6047-887X"]}, "institutions": [{"id": "https://openalex.org/I98704320", "display_name": "Icahn School of Medicine at Mount Sinai", "ror": "https://ror.org/04a9tmd77", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I1320796813", "https://openalex.org/I98704320"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Prem Timsina", "raw_affiliation_strings": ["Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY", "The Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY", "institution_ids": ["https://openalex.org/I98704320"]}, {"raw_affiliation_string": "The Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY", "institution_ids": ["https://openalex.org/I98704320"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5089697111", "display_name": "Matthew A. Levin", "orcid": "https://orcid.org/0000-0002-6013-2684", "observed_orcids": ["https://orcid.org/0000-0002-6013-2684"]}, "institutions": [{"id": "https://openalex.org/I98704320", "display_name": "Icahn School of Medicine at Mount Sinai", "ror": "https://ror.org/04a9tmd77", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I1320796813", "https://openalex.org/I98704320"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Matthew A. Levin", "raw_affiliation_strings": ["Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, NY", "Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY", "Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY", "The Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Anesthesiology, Perioperative and Pain Medicine, Icahn School of Medicine at Mount Sinai, New York, NY", "institution_ids": ["https://openalex.org/I98704320"]}, {"raw_affiliation_string": "Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY", "institution_ids": ["https://openalex.org/I98704320"]}, {"raw_affiliation_string": "Institute for Healthcare Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY", "institution_ids": ["https://openalex.org/I98704320"]}, {"raw_affiliation_string": "The Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY", "institution_ids": ["https://openalex.org/I98704320"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5017974764", "display_name": "Rohan Khera", "orcid": "https://orcid.org/0000-0001-9467-6199", "observed_orcids": ["https://orcid.org/0000-0001-9467-6199"]}, "institutions": [{"id": "https://openalex.org/I32971472", "display_name": "Yale University", "ror": "https://ror.org/03v76x132", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I32971472"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Rohan Khera", "raw_affiliation_strings": ["Department of Medicine, Yale University School of Medicine New Haven, CT"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Medicine, Yale University School of Medicine New Haven, CT", "institution_ids": ["https://openalex.org/I32971472"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5019842052", "display_name": "Patricia Kovatch", "orcid": "https://orcid.org/0000-0001-8368-1742", "observed_orcids": ["https://orcid.org/0000-0001-8368-1742"]}, "institutions": [{"id": "https://openalex.org/I98704320", "display_name": "Icahn School of Medicine at Mount Sinai", "ror": "https://ror.org/04a9tmd77", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I1320796813", "https://openalex.org/I98704320"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Patricia Kovatch", "raw_affiliation_strings": ["Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY", "institution_ids": ["https://openalex.org/I98704320"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5091224898", "display_name": "Alexander W. Charney", "orcid": "https://orcid.org/0000-0001-8135-6858", "observed_orcids": ["https://orcid.org/0000-0001-8135-6858"]}, "institutions": [{"id": "https://openalex.org/I98704320", "display_name": "Icahn School of Medicine at Mount Sinai", "ror": "https://ror.org/04a9tmd77", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I1320796813", "https://openalex.org/I98704320"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Alexander W. Charney", "raw_affiliation_strings": ["Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY", "The Charles Bronfman Department of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY", "institution_ids": ["https://openalex.org/I98704320"]}, {"raw_affiliation_string": "The Charles Bronfman Department of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY", "institution_ids": ["https://openalex.org/I98704320"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5024282536", "display_name": "Brendan G. Carr", "orcid": "https://orcid.org/0000-0002-9147-9701", "observed_orcids": ["https://orcid.org/0000-0002-9147-9701"]}, "institutions": [{"id": "https://openalex.org/I98704320", "display_name": "Icahn School of Medicine at Mount Sinai", "ror": "https://ror.org/04a9tmd77", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I1320796813", "https://openalex.org/I98704320"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Brendan G. Carr", "raw_affiliation_strings": ["Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, NY"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, NY", "institution_ids": ["https://openalex.org/I98704320"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5075743971", "display_name": "Lynne D. Richardson", "orcid": "https://orcid.org/0000-0002-5425-1601", "observed_orcids": ["https://orcid.org/0000-0002-5425-1601"]}, "institutions": [{"id": "https://openalex.org/I98704320", "display_name": "Icahn School of Medicine at Mount Sinai", "ror": "https://ror.org/04a9tmd77", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I1320796813", "https://openalex.org/I98704320"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Lynne D. Richardson", "raw_affiliation_strings": ["Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, NY", "Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY", "Institute for Health Equity Research, Icahn School of Medicine at Mount Sinai, New York, NY"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, NY", "institution_ids": ["https://openalex.org/I98704320"]}, {"raw_affiliation_string": "Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY", "institution_ids": ["https://openalex.org/I98704320"]}, {"raw_affiliation_string": "Institute for Health Equity Research, Icahn School of Medicine at Mount Sinai, New York, NY", "institution_ids": ["https://openalex.org/I98704320"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5110836986", "display_name": "Carol R. Horowitz", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I98704320", "display_name": "Icahn School of Medicine at Mount Sinai", "ror": "https://ror.org/04a9tmd77", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I1320796813", "https://openalex.org/I98704320"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Carol R. Horowitz", "raw_affiliation_strings": ["Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY", "Institute for Health Equity Research, Icahn School of Medicine at Mount Sinai, New York, NY"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY", "institution_ids": ["https://openalex.org/I98704320"]}, {"raw_affiliation_string": "Institute for Health Equity Research, Icahn School of Medicine at Mount Sinai, New York, NY", "institution_ids": ["https://openalex.org/I98704320"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5039899830", "display_name": "Eyal Klang", "orcid": "https://orcid.org/0000-0002-4567-3108", "observed_orcids": ["https://orcid.org/0000-0002-4567-3108"]}, "institutions": [{"id": "https://openalex.org/I98704320", "display_name": "Icahn School of Medicine at Mount Sinai", "ror": "https://ror.org/04a9tmd77", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I1320796813", "https://openalex.org/I98704320"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Eyal Klang", "raw_affiliation_strings": ["The Charles Bronfman Department of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY", "The Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "The Charles Bronfman Department of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY", "institution_ids": ["https://openalex.org/I98704320"]}, {"raw_affiliation_string": "The Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY", "institution_ids": ["https://openalex.org/I98704320"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5047405500", "display_name": "Girish N. Nadkarni", "orcid": "https://orcid.org/0000-0001-6319-4314", "observed_orcids": ["https://orcid.org/0000-0001-6319-4314"]}, "institutions": [{"id": "https://openalex.org/I98704320", "display_name": "Icahn School of Medicine at Mount Sinai", "ror": "https://ror.org/04a9tmd77", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I1320796813", "https://openalex.org/I98704320"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Girish N. Nadkarni", "raw_affiliation_strings": ["The Charles Bronfman Department of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY", "The Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "The Charles Bronfman Department of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY", "institution_ids": ["https://openalex.org/I98704320"]}, {"raw_affiliation_string": "The Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY", "institution_ids": ["https://openalex.org/I98704320"]}]}], "abstract_inverted_index": {"Objective:": [0], "To": [1], "evaluate": [2], "whether": [3], "generative": [4], "pretrained": [5], "transformer": [6], "(GPT)-4": [7], "can": [8], "detect": [9], "and": [10, 23, 33, 41, 84, 102, 113, 136, 156, 193, 251, 262], "revise": [11], "biased": [12, 30, 205, 240], "language": [13, 124, 145, 241], "in": [14, 147, 242, 265], "emergency": [15], "department": [16], "(ED)": [17], "notes,": [18, 244], "against": [19], "human-adjudicated": [20], "gold-standard": [21], "labels,": [22], "to": [24, 53, 97, 120, 211, 248], "identify": [25], "modifiable": [26, 246], "factors": [27], "associated": [28], "with": [29, 140], "documentation.": [31, 206], "Patients": [32], "Methods:": [34], "We": [35, 57, 92, 116], "randomly": [36, 59], "sampled": [37, 60], "50,000": [38], "ED": [39], "medical": [40], "nursing": [42], "notes": [43, 63, 155], "from": [44, 64], "the": [45, 65, 141], "Mount": [46, 153], "Sinai": [47, 154], "Health": [48], "System": [49], "(January": [50], "1,": [51], "2023,": [52], "December": [54], "31,": [55], "2023).": [56], "also": [58], "500": [61], "discharge": [62], "Medical": [66, 162], "Information": [67, 163], "Mart": [68, 164], "for": [69, 165], "Intensive": [70, 166], "Care": [71, 167], "IV": [72, 168], "database.": [73], "The": [74, 131, 233], "GPT-4": [75, 132, 237], "flagged": [76], "4": [77], "types": [78], "of": [79, 150, 152, 159, 161, 204, 230], "bias:": [80], "discrediting,": [81], "stigmatizing/labeling,": [82], "judgmental,": [83], "stereotyping.": [85], "Two": [86], "human": [87, 142], "reviewers": [88], "verified": [89], "model": [90], "detections.": [91], "used": [93], "multivariable": [94], "logistic": [95], "regression": [96], "examine": [98], "associations": [99], "between": [100], "bias": [101, 213, 261], "health": [103, 174], "care": [104, 175], "utilization,": [105], "presenting": [106], "problems": [107], "(eg,": [108], "substance": [109, 185], "use),": [110], "shift": [111], "timing,": [112], "provider": [114], "type.": [115], "then": [117], "asked": [118], "physicians": [119], "rate": [121], "GPT-4's": [122, 221], "proposed": [123], "revisions": [125, 223], "on": [126], "a": [127], "10-point": [128], "scale.": [129], "Results:": [130], "showed": [133, 201, 235], "97.6%": [134], "sensitivity": [135], "85.7%": [137], "specificity": [138], "compared": [139], "review.": [143], "Biased": [144], "appeared": [146], "6.5%": [148], "(3229": [149], "50,000)": [151], "7.4%": [157], "(37": [158], "500)": [160], "notes.": [169], "In": [170], "adjusted": [171], "models,": [172], "frequent": [173], "utilization": [176], "(adjusted": [177], "odds": [178, 203], "ratio": [179], "[aOR],": [180], "2.85;": [181], "95%": [182, 190, 198, 218], "CI,": [183, 191, 199, 219], "1.95-4.17),": [184], "use": [186], "presentations": [187], "(aOR,": [188, 196, 216], "3.09;": [189], "2.51-3.80),": [192], "overnight": [194], "shifts": [195], "1.37;": [197], "1.23-1.52)": [200], "elevated": [202], "Physicians": [207], "were": [208], "more": [209], "likely": [210], "include": [212], "than": [214], "nurses": [215], "2.26;": [217], "2.07-2.46);": [220], "recommended": [222], "received": [224], "mean": [225], "physician": [226], "ratings": [227], "above": [228], "9": [229], "10.": [231], "Conclusion:": [232], "study": [234], "that": [236, 249], "accurately": [238], "detects": [239], "clinical": [243], "identifies": [245], "contributors": [247], "bias,": [250], "delivers": [252], "physician-endorsed": [253], "revisions.": [254], "This": [255], "approach": [256], "may": [257], "help": [258], "mitigate": [259], "documentation": [260], "reduce": [263], "disparities": [264], "care.": [266]}, "primary_topic": {"id": "https://openalex.org/T10883", "display_name": "Ethics and Social Impacts of AI", "score": 0.46239998936653137, "subfield": {"id": "https://openalex.org/subfields/3311", "display_name": "Safety Research"}, "field": {"id": "https://openalex.org/fields/33", "display_name": "Social Sciences"}, "domain": {"id": "https://openalex.org/domains/2", "display_name": "Social Sciences"}}, "topics": [{"id": "https://openalex.org/T10883", "display_name": "Ethics and Social Impacts of AI", "score": 0.46239998936653137, "subfield": {"id": "https://openalex.org/subfields/3311", "display_name": "Safety Research"}, "field": {"id": "https://openalex.org/fields/33", "display_name": "Social Sciences"}, "domain": {"id": "https://openalex.org/domains/2", "display_name": "Social Sciences"}}, {"id": "https://openalex.org/T11636", "display_name": "Artificial Intelligence in Healthcare and Education", "score": 0.21580000221729279, "subfield": {"id": "https://openalex.org/subfields/2718", "display_name": "Health Informatics"}, "field": {"id": "https://openalex.org/fields/27", "display_name": "Medicine"}, "domain": {"id": "https://openalex.org/domains/4", "display_name": "Health Sciences"}}, {"id": "https://openalex.org/T13702", "display_name": "Machine Learning in Healthcare", "score": 0.09939999878406525, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "doi:10.1016/j.mcpdig.2025.100296", "is_oa": true, "landing_page_url": "https://doi.org/10.1016/j.mcpdig.2025.100296", "pdf_url": "https://www.mcpdigitalhealth.org/article/S2949-7612(25)00103-8/pdf", "source": {"id": "https://openalex.org/S4387287143", "display_name": "Mayo Clinic Proceedings Digital Health", "issn_l": "2949-7612", "issn": ["2949-7612"], "is_oa": true, "is_in_doaj": true, "is_core": true, "listed_in": ["cwts-core", "doaj", "jufo-1", "ki-jl-1", "norway-1"], "host_organization": "https://openalex.org/P4310320990", "host_organization_name": "Elsevier BV", "host_organization_lineage": ["https://openalex.org/P4310320990"], "host_organization_lineage_names": ["Elsevier BV"], "type": "journal"}, "license": "cc-by-nc-nd", "license_id": "https://openalex.org/licenses/cc-by-nc-nd", "version": "publishedVersion", "is_accepted": true, "is_published": true, "raw_source_name": "Mayo Clinic Proceedings: Digital Health", "raw_type": "journal-article"}, "cited_by_count": 5, "is_retracted": false}, {"id": "https://openalex.org/W4415090273", "doi": "https://doi.org/10.48550/arxiv.2509.09071", "title": "Strategic Tradeoffs Between Humans and AI in Multi-Agent Bargaining", "publication_year": 2025, "publication_date": "2025-09-11", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5043246682", "display_name": "Crystal Qian", "orcid": "https://orcid.org/0000-0001-7716-7245", "observed_orcids": ["https://orcid.org/0000-0001-7716-7245"]}, "institutions": [{"id": "https://openalex.org/I63966007", "display_name": "Massachusetts Institute of Technology", "ror": "https://ror.org/042nb2s44", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I63966007"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Qian, Crystal", "raw_affiliation_strings": ["MIT"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "MIT", "institution_ids": ["https://openalex.org/I63966007"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5095787373", "display_name": "Kehang Zhu", "orcid": "https://orcid.org/0009-0002-8822-3665", "observed_orcids": ["https://orcid.org/0009-0002-8822-3665"]}, "institutions": [{"id": "https://openalex.org/I136199984", "display_name": "Harvard University", "ror": "https://ror.org/03vek6s52", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I136199984"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Zhu, Kehang", "raw_affiliation_strings": ["Harvard University"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Harvard University", "institution_ids": ["https://openalex.org/I136199984"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5047851963", "display_name": "John Horton", "orcid": "https://orcid.org/0000-0002-2956-2264", "observed_orcids": ["https://orcid.org/0000-0002-2956-2264"]}, "institutions": [{"id": "https://openalex.org/I1321305853", "display_name": "National Bureau of Economic Research", "ror": "https://ror.org/04grmx538", "country_code": "US", "type": "nonprofit", "lineage": ["https://openalex.org/I1321305853"]}, {"id": "https://openalex.org/I63966007", "display_name": "Massachusetts Institute of Technology", "ror": "https://ror.org/042nb2s44", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I63966007"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Horton, John", "raw_affiliation_strings": ["MIT & NBER"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "MIT & NBER", "institution_ids": ["https://openalex.org/I1321305853", "https://openalex.org/I63966007"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5095787372", "display_name": "Benjamin S. Manning", "orcid": "https://orcid.org/0009-0000-9977-2390", "observed_orcids": ["https://orcid.org/0009-0000-9977-2390"]}, "institutions": [{"id": "https://openalex.org/I63966007", "display_name": "Massachusetts Institute of Technology", "ror": "https://ror.org/042nb2s44", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I63966007"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Manning, Benjamin S.", "raw_affiliation_strings": ["MIT"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "MIT", "institution_ids": ["https://openalex.org/I63966007"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5040764983", "display_name": "Vivian Tsai", "orcid": "https://orcid.org/0009-0003-4264-2706", "observed_orcids": ["https://orcid.org/0009-0003-4264-2706"]}, "institutions": [{"id": "https://openalex.org/I136199984", "display_name": "Harvard University", "ror": "https://ror.org/03vek6s52", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I136199984"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Tsai, Vivian", "raw_affiliation_strings": ["Harvard University"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Harvard University", "institution_ids": ["https://openalex.org/I136199984"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5081809692", "display_name": "James Wexler", "orcid": "https://orcid.org/0009-0006-8105-6998", "observed_orcids": ["https://orcid.org/0009-0006-8105-6998"]}, "institutions": [{"id": "https://openalex.org/I1321305853", "display_name": "National Bureau of Economic Research", "ror": "https://ror.org/04grmx538", "country_code": "US", "type": "nonprofit", "lineage": ["https://openalex.org/I1321305853"]}, {"id": "https://openalex.org/I63966007", "display_name": "Massachusetts Institute of Technology", "ror": "https://ror.org/042nb2s44", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I63966007"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Wexler, James", "raw_affiliation_strings": ["MIT & NBER"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "MIT & NBER", "institution_ids": ["https://openalex.org/I1321305853", "https://openalex.org/I63966007"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5094123524", "display_name": "Nithum Thain", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I136199984", "display_name": "Harvard University", "ror": "https://ror.org/03vek6s52", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I136199984"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Thain, Nithum", "raw_affiliation_strings": ["Harvard University"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Harvard University", "institution_ids": ["https://openalex.org/I136199984"]}]}], "abstract_inverted_index": {"Markets": [0], "increasingly": [1], "accommodate": [2], "large": [3], "language": [4], "models": [5], "(LLMs)": [6], "as": [7], "autonomous": [8], "decision-making": [9], "agents.": [10], "As": [11], "this": [12, 33], "transition": [13], "occurs,": [14], "it": [15], "becomes": [16], "critical": [17], "to": [18, 25, 115], "evaluate": [19], "how": [20, 138], "these": [21], "agents": [22, 51, 61], "behave": [23, 140], "relative": [24], "their": [26, 82], "human": [27], "and": [28, 48, 75], "task-specific": [29], "statistical": [30], "predecessors.": [31], "In": [32], "work,": [34], "we": [35], "present": [36], "results": [37], "from": [38], "an": [39], "empirical": [40], "study": [41], "comparing": [42], "humans": [43, 102], "(N=216),": [44], "multiple": [45], "frontier": [46], "LLMs,": [47, 100], "customized": [49], "Bayesian": [50, 60], "in": [52, 128, 137, 141], "dynamic": [53], "multi-player": [54], "bargaining": [55], "games": [56], "under": [57], "identical": [58], "conditions.": [59], "extract": [62], "the": [63], "highest": [64], "surplus": [65, 80], "with": [66, 108], "aggressive": [67], "trade": [68], "proposals": [69, 93], "that": [70, 94, 105, 121], "are": [71, 95, 106, 112], "frequently": [72], "rejected.": [73, 117], "Humans": [74], "LLMs": [76, 89, 139], "achieve": [77], "comparable": [78], "aggregate": [79], "within": [81], "groups,": [83], "but": [84, 111], "exhibit": [85], "different": [86], "trading": [87], "strategies.": [88], "favor": [90], "conservative,": [91], "concessionary": [92], "usually": [96], "accepted": [97], "by": [98], "other": [99], "while": [101], "propose": [103], "trades": [104], "consistent": [107], "fairness": [109], "norms": [110], "more": [113], "likely": [114], "be": [116], "These": [118], "findings": [119], "highlight": [120], "performance": [122], "parity": [123], "--": [124, 131], "a": [125], "common": [126], "benchmark": [127], "agent": [129], "evaluation": [130], "can": [132], "mask": [133], "substantive": [134], "procedural": [135], "differences": [136], "complex": [142], "multi-agent": [143], "interactions.": [144]}, "primary_topic": {"id": "https://openalex.org/T10456", "display_name": "Multi-Agent Systems and Negotiation", "score": 0.9218999743461609, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T10456", "display_name": "Multi-Agent Systems and Negotiation", "score": 0.9218999743461609, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10991", "display_name": "Game Theory and Voting Systems", "score": 0.03880000114440918, "subfield": {"id": "https://openalex.org/subfields/2002", "display_name": "Economics and Econometrics"}, "field": {"id": "https://openalex.org/fields/20", "display_name": "Economics, Econometrics and Finance"}, "domain": {"id": "https://openalex.org/domains/2", "display_name": "Social Sciences"}}, {"id": "https://openalex.org/T11031", "display_name": "Game Theory and Applications", "score": 0.009100000374019146, "subfield": {"id": "https://openalex.org/subfields/1803", "display_name": "Management Science and Operations Research"}, "field": {"id": "https://openalex.org/fields/18", "display_name": "Decision Sciences"}, "domain": {"id": "https://openalex.org/domains/2", "display_name": "Social Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2509.09071", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2509.09071", "pdf_url": "https://arxiv.org/pdf/2509.09071", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": "cc-by", "license_id": "https://openalex.org/licenses/cc-by", "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W7111084870", "doi": "https://doi.org/10.6084/m9.figshare.30828032.v4", "title": "Living Inside a Saturated Language: Manifold-Level Intelligence and the Geometry of Meaning", "publication_year": 2025, "publication_date": "2025-01-01", "type": "article", "language": null, "authorships": [{"author_position": "first", "author": {"id": null, "display_name": "Glanz, Alan", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": true, "raw_author_name": "Glanz, Alan", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}], "abstract_inverted_index": {"This": [0], "theoretical": [1], "essay": [2, 83, 169], "develops": [3], "two": [4], "linked": [5], "concepts": [6], "for": [7, 31], "understanding": [8], "how": [9, 225], "modern": [10], "civilizations": [11], "think:": [12], "language": [13, 24, 145], "saturation": [14], "and": [15, 40, 70, 74, 87, 123, 161, 190, 196, 203, 215], "manifold-level": [16, 94], "intelligence.": [17], "Building": [18], "on": [19, 152], "earlier": [20], "work": [21], "that": [22, 36], "treats": [23], "as": [25, 80, 96, 156, 219, 221], "an": [26, 45, 109], "operating": [27], "system": [28, 101], "or": [29, 56, 108], "\u201cledger\u201d": [30], "reality,": [32], "the": [33, 76, 82, 97, 126, 137, 174, 230], "paper": [34, 134], "argues": [35], "human,": [37], "genetic,": [38], "mathematical,": [39], "digital": [41], "codes": [42], "now": [43], "occupy": [44], "increasingly": [46], "saturated": [47, 226], "representational": [48, 131], "space.": [49], "In": [50], "this": [51], "regime,": [52], "new": [53, 62], "statements,": [54], "models,": [55, 146], "AI": [57, 206], "outputs": [58], "rarely": [59], "open": [60], "genuinely": [61], "directions;": [63], "instead,": [64], "they": [65], "densify": [66], "existing": [67, 72], "conceptual": [68], "basins": [69], "amplify": [71], "curvatures": [73], "biases.Using": [75], "metaphor": [77], "of": [78, 99, 128, 139, 217, 232], "languages": [79], "manifolds,": [81], "reframes": [84], "novelty,": [85], "coherence,": [86], "hallucination": [88], "in": [89, 173, 205, 224], "geometric": [90], "terms.": [91], "It": [92, 198], "introduces": [93], "intelligence": [95], "capacity": [98], "a": [100, 104, 106, 116], "(a": [102], "brain,": [103], "species,": [105], "civilization,": [107], "AI)": [110], "not": [111], "just": [112], "to": [113, 120, 201], "navigate": [114], "within": [115], "fixed": [117], "language,": [118], "but": [119], "map,": [121], "diagnose,": [122], "deliberately": [124], "reshape": [125], "geometry": [127, 191], "its": [129], "own": [130], "manifolds.": [132], "The": [133], "then": [135], "examines": [136], "risks": [138], "letting": [140], "sub-manifold": [141], "systems": [142, 211], "(e.g.,": [143], "large": [144], "financial": [147], "algorithms,": [148], "bureaucratic": [149], "procedures)": [150], "act": [151], "manifold-scale": [153], "ledgers": [154], "such": [155], "global": [157], "markets,": [158], "genomic": [159], "databases,": [160], "planetary": [162], "sensor": [163], "networks,": [164, 214], "without": [165], "explicit": [166], "continuity": [167], "constraints.The": [168], "situates": [170], "these": [171], "ideas": [172], "broader": [175], "Autarkeia": [176], "/": [177], "continuity-law": [178], "research": [179], "program,": [180], "which": [181], "seeks": [182], "unified": [183], "structural": [184], "principles": [185], "linking": [186], "energy,": [187], "entropy,": [188], "information,": [189], "across": [192], "physics,": [193], "genomics,": [194], "computation,": [195], "language.": [197], "is": [199], "relevant": [200], "researchers": [202], "practitioners": [204], "safety,": [207], "information": [208], "theory,": [209], "epistemology,": [210], "science,": [212, 218], "complex": [213], "philosophy": [216], "well": [220], "anyone": [222], "interested": [223], "symbolic": [227], "environments": [228], "shape": [229], "future": [231], "our": [233], "species.": [234]}, "primary_topic": {"id": "https://openalex.org/T13308", "display_name": "Cybernetics and Technology in Society", "score": 0.5859000086784363, "subfield": {"id": "https://openalex.org/subfields/1207", "display_name": "History and Philosophy of Science"}, "field": {"id": "https://openalex.org/fields/12", "display_name": "Arts and Humanities"}, "domain": {"id": "https://openalex.org/domains/2", "display_name": "Social Sciences"}}, "topics": [{"id": "https://openalex.org/T13308", "display_name": "Cybernetics and Technology in Society", "score": 0.5859000086784363, "subfield": {"id": "https://openalex.org/subfields/1207", "display_name": "History and Philosophy of Science"}, "field": {"id": "https://openalex.org/fields/12", "display_name": "Arts and Humanities"}, "domain": {"id": "https://openalex.org/domains/2", "display_name": "Social Sciences"}}, {"id": "https://openalex.org/T14345", "display_name": "Scientific Research and Philosophical Inquiry", "score": 0.2531999945640564, "subfield": {"id": "https://openalex.org/subfields/1710", "display_name": "Information Systems"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T14299", "display_name": "Complex Systems and Dynamics", "score": 0.0649000033736229, "subfield": {"id": "https://openalex.org/subfields/3109", "display_name": "Statistical and Nonlinear Physics"}, "field": {"id": "https://openalex.org/fields/31", "display_name": "Physics and Astronomy"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "doi:10.6084/m9.figshare.30828032.v4", "is_oa": true, "landing_page_url": "https://doi.org/10.6084/m9.figshare.30828032.v4", "pdf_url": null, "source": {"id": "https://openalex.org/S4377196282", "display_name": "Figshare", "issn_l": null, "issn": null, "is_oa": false, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I4210132348", "host_organization_name": "Figshare (United Kingdom)", "host_organization_lineage": ["https://openalex.org/I4210132348"], "host_organization_lineage_names": [], "type": "repository"}, "license": "cc-by", "license_id": "https://openalex.org/licenses/cc-by", "version": null, "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "article-journal"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4410738052", "doi": "https://doi.org/10.1109/tpami.2025.3573609", "title": "LLaFS++: Few-Shot Image Segmentation With Large Language Models", "publication_year": 2025, "publication_date": "2025-05-26", "type": "article", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5074956841", "display_name": "Lanyun Zhu", "orcid": "https://orcid.org/0000-0001-7309-3330", "observed_orcids": ["https://orcid.org/0000-0001-7309-3330"]}, "institutions": [{"id": "https://openalex.org/I152815399", "display_name": "Singapore University of Technology and Design", "ror": "https://ror.org/05j6fvn87", "country_code": "SG", "type": "education", "lineage": ["https://openalex.org/I152815399"]}], "countries": ["SG"], "is_corresponding": false, "raw_author_name": "Lanyun Zhu", "raw_affiliation_strings": ["Information Systems Technology and Design Pillar, Singapore University of Technology and Design, Singapore"], "raw_orcid": "https://orcid.org/0000-0001-7309-3330", "affiliations": [{"raw_affiliation_string": "Information Systems Technology and Design Pillar, Singapore University of Technology and Design, Singapore", "institution_ids": ["https://openalex.org/I152815399"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5054714748", "display_name": "Tianrun Chen", "orcid": "https://orcid.org/0000-0003-0177-0157", "observed_orcids": ["https://orcid.org/0000-0003-0177-0157"]}, "institutions": [{"id": "https://openalex.org/I76130692", "display_name": "Zhejiang University", "ror": "https://ror.org/00a2xv884", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I76130692"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Tianrun Chen", "raw_affiliation_strings": ["College of Computer Science and Technology, Zhejiang University, Hangzhou, China", "College of Computer Science and Technology, Zhejiang University, China"], "raw_orcid": "https://orcid.org/0000-0003-0177-0157", "affiliations": [{"raw_affiliation_string": "College of Computer Science and Technology, Zhejiang University, Hangzhou, China", "institution_ids": ["https://openalex.org/I76130692"]}, {"raw_affiliation_string": "College of Computer Science and Technology, Zhejiang University, China", "institution_ids": ["https://openalex.org/I76130692"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5089146499", "display_name": "Deyi Ji", "orcid": "https://orcid.org/0000-0001-7561-9789", "observed_orcids": ["https://orcid.org/0000-0001-7561-9789"]}, "institutions": [{"id": "https://openalex.org/I45928872", "display_name": "Alibaba Group (China)", "ror": "https://ror.org/00k642b80", "country_code": "CN", "type": "company", "lineage": ["https://openalex.org/I45928872"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Deyi Ji", "raw_affiliation_strings": ["Alibaba Cloud Computing, Beijing, China", "Alibaba Group, China"], "raw_orcid": "https://orcid.org/0000-0001-7561-9789", "affiliations": [{"raw_affiliation_string": "Alibaba Cloud Computing, Beijing, China", "institution_ids": ["https://openalex.org/I45928872"]}, {"raw_affiliation_string": "Alibaba Group, China", "institution_ids": ["https://openalex.org/I45928872"]}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Peng Xu", "orcid": "https://orcid.org/0009-0005-5545-4530", "observed_orcids": ["https://orcid.org/0009-0005-5545-4530"]}, "institutions": [{"id": "https://openalex.org/I45928872", "display_name": "Alibaba Group (China)", "ror": "https://ror.org/00k642b80", "country_code": "CN", "type": "company", "lineage": ["https://openalex.org/I45928872"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Peng Xu", "raw_affiliation_strings": ["Alibaba Cloud Computing, Beijing, China", "Alibaba Group, China"], "raw_orcid": "https://orcid.org/0009-0005-5545-4530", "affiliations": [{"raw_affiliation_string": "Alibaba Cloud Computing, Beijing, China", "institution_ids": ["https://openalex.org/I45928872"]}, {"raw_affiliation_string": "Alibaba Group, China", "institution_ids": ["https://openalex.org/I45928872"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5010419481", "display_name": "Jieping Ye", "orcid": "https://orcid.org/0000-0001-8662-5818", "observed_orcids": ["https://orcid.org/0000-0001-8662-5818"]}, "institutions": [{"id": "https://openalex.org/I45928872", "display_name": "Alibaba Group (China)", "ror": "https://ror.org/00k642b80", "country_code": "CN", "type": "company", "lineage": ["https://openalex.org/I45928872"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Jieping Ye", "raw_affiliation_strings": ["Alibaba Cloud Computing, Beijing, China", "Alibaba Group, China"], "raw_orcid": "https://orcid.org/0000-0001-8662-5818", "affiliations": [{"raw_affiliation_string": "Alibaba Cloud Computing, Beijing, China", "institution_ids": ["https://openalex.org/I45928872"]}, {"raw_affiliation_string": "Alibaba Group, China", "institution_ids": ["https://openalex.org/I45928872"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5100361857", "display_name": "Jun Liu", "orcid": "https://orcid.org/0000-0002-4365-4165", "observed_orcids": ["https://orcid.org/0000-0002-4365-4165"]}, "institutions": [{"id": "https://openalex.org/I67415387", "display_name": "Lancaster University", "ror": "https://ror.org/04f2nsd36", "country_code": "GB", "type": "education", "lineage": ["https://openalex.org/I67415387"]}], "countries": ["GB"], "is_corresponding": false, "raw_author_name": "Jun Liu", "raw_affiliation_strings": ["School of Computing and Communications, Lancaster University, Lancaster, U.K", "School of Computing and Communications, Lancaster University, U.K"], "raw_orcid": "https://orcid.org/0000-0002-4365-4165", "affiliations": [{"raw_affiliation_string": "School of Computing and Communications, Lancaster University, Lancaster, U.K", "institution_ids": ["https://openalex.org/I67415387"]}, {"raw_affiliation_string": "School of Computing and Communications, Lancaster University, U.K", "institution_ids": ["https://openalex.org/I67415387"]}]}], "abstract_inverted_index": {"Despite": [0], "the": [1, 21, 61, 77, 86, 92, 97, 108, 111, 125, 136, 154, 193, 221], "rapid": [2], "advancements": [3], "in": [4, 12, 96, 114], "few-shot": [5, 98, 229], "segmentation": [6, 87, 141], "(FSS),": [7], "most": [8], "of": [9, 31, 45, 110, 224], "existing": [10], "methods": [11], "this": [13, 49, 51], "domain": [14], "are": [15], "hampered": [16], "by": [17, 82, 192], "their": [18, 38], "reliance": [19], "on": [20, 209], "limited": [22, 93], "and": [23, 71, 101, 121, 146, 158, 167, 176, 180, 215], "biased": [24], "information": [25, 94], "from": [26], "only": [27], "a": [28, 54, 148, 182], "small": [29], "number": [30], "labeled": [32, 99], "samples.": [33], "This": [34], "limitation": [35], "inherently": [36], "restricts": [37], "capability": [39], "to": [40, 84, 138, 152, 173, 186, 227], "achieve": [41, 177], "sufficiently": [42], "high": [43], "levels": [44], "performance.": [46], "To": [47, 106], "address": [48], "issue,": [50], "paper": [52], "proposes": [53], "pioneering": [55], "framework": [56, 204], "named": [57], "LLaFS++,": [58], "which,": [59], "for": [60, 91, 171], "first": [62], "time,": [63], "applies": [64], "large": [65], "language": [66], "models": [67], "(LLMs)": [68], "into": [69], "FSS": [70, 115], "achieves": [72, 206], "notable": [73], "success.": [74], "LLaFS++": [75, 126, 200], "leverages": [76], "extensive": [78], "prior": [79], "knowledge": [80], "embedded": [81], "LLMs": [83, 113, 226], "guide": [85], "process,": [88], "effectively": [89], "compensating": [90], "contained": [95], "samples": [100, 166], "thereby": [102], "achieving": [103], "superior": [104, 218], "results.": [105], "enhance": [107], "effectiveness": [109], "text-based": [112], "scenarios,": [116], "we": [117, 129], "present": [118], "several": [119], "innovative": [120], "task-specific": [122], "designs": [123], "within": [124], "framework.": [127], "Specifically,": [128], "introduce": [130], "an": [131, 202], "input": [132], "instruction": [133], "that": [134, 205], "allows": [135], "LLM": [137], "directly": [139], "produce": [140], "results": [142, 208], "represented": [143], "as": [144], "polygons,": [145], "propose": [147, 181], "region-attribute": [149], "corresponding": [150], "table": [151], "simulate": [153], "human": [155], "visual": [156], "system": [157], "provide": [159], "multi-modal": [160], "guidance.": [161], "We": [162], "also": [163], "synthesize": [164], "pseudo": [165], "use": [168], "curriculum": [169], "learning": [170], "pretraining": [172], "augment": [174], "data": [175], "better": [178], "optimization,": [179], "novel": [183], "inference": [184], "method": [185], "mitigate": [187], "potential": [188, 223], "oversegmentation": [189], "hallucinations": [190], "caused": [191], "regional": [194], "guidance": [195], "information.": [196], "Incorporating": [197], "these": [198], "designs,": [199], "constitutes": [201], "effective": [203], "state-of-the-art": [207], "multiple": [210], "datasets": [211], "including": [212], "PASCAL-$5^{i}$5i,": [213], "COCO-$20^{i}$20i,": [214], "FSS-1000.": [216], "Our": [217], "performance": [219], "showcases": [220], "remarkable": [222], "applying": [225], "process": [228], "vision": [230], "tasks.": [231]}, "primary_topic": {"id": "https://openalex.org/T11307", "display_name": "Domain Adaptation and Few-Shot Learning", "score": 0.9800000190734863, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T11307", "display_name": "Domain Adaptation and Few-Shot Learning", "score": 0.9800000190734863, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10036", "display_name": "Advanced Neural Network Applications", "score": 0.014700000174343586, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T11714", "display_name": "Multimodal Machine Learning Applications", "score": 0.003599999938160181, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "doi:10.1109/tpami.2025.3573609", "is_oa": false, "landing_page_url": "https://doi.org/10.1109/tpami.2025.3573609", "pdf_url": null, "source": {"id": "https://openalex.org/S199944782", "display_name": "IEEE Transactions on Pattern Analysis and Machine Intelligence", "issn_l": "0162-8828", "issn": ["0162-8828", "1939-3539", "2160-9292"], "is_oa": false, "is_in_doaj": false, "is_core": true, "listed_in": ["cwts-core", "doyens", "jufo-3", "medline", "norway-2"], "host_organization": "https://openalex.org/P4310320439", "host_organization_name": "IEEE Computer Society", "host_organization_lineage": ["https://openalex.org/P4310320439", "https://openalex.org/P4310319808"], "host_organization_lineage_names": ["IEEE Computer Society", "Institute of Electrical and Electronics Engineers"], "type": "journal"}, "license": null, "license_id": null, "version": "publishedVersion", "is_accepted": true, "is_published": true, "raw_source_name": "IEEE Transactions on Pattern Analysis and Machine Intelligence", "raw_type": "journal-article"}, "cited_by_count": 6, "is_retracted": false}, {"id": "https://openalex.org/W4417452860", "doi": "https://doi.org/10.48550/arxiv.2512.12887", "title": "Revisiting 2D Foundation Models for Scalable 3D Medical Image Classification", "publication_year": 2025, "publication_date": "2025-12-15", "type": "preprint", "language": null, "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5100349041", "display_name": "Han Liu", "orcid": "https://orcid.org/0000-0002-3051-1990", "observed_orcids": ["https://orcid.org/0000-0002-3051-1990"]}, "institutions": [{"id": "https://openalex.org/I4210151799", "display_name": "Siemens Healthcare (United States)", "ror": "https://ror.org/054962n91", "country_code": "US", "type": "company", "lineage": ["https://openalex.org/I4210151799"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Liu, Han", "raw_affiliation_strings": ["Digital Technology and Innovation , Siemens Healthineers , Princeton NJ , USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Digital Technology and Innovation , Siemens Healthineers , Princeton NJ , USA", "institution_ids": ["https://openalex.org/I4210151799"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5011888340", "display_name": "Bogdan Georgescu", "orcid": "https://orcid.org/0000-0001-5388-5699", "observed_orcids": ["https://orcid.org/0000-0001-5388-5699"]}, "institutions": [{"id": "https://openalex.org/I4210151799", "display_name": "Siemens Healthcare (United States)", "ror": "https://ror.org/054962n91", "country_code": "US", "type": "company", "lineage": ["https://openalex.org/I4210151799"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Georgescu, Bogdan", "raw_affiliation_strings": ["Digital Technology and Innovation , Siemens Healthineers , Princeton NJ , USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Digital Technology and Innovation , Siemens Healthineers , Princeton NJ , USA", "institution_ids": ["https://openalex.org/I4210151799"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5089156334", "display_name": "Yanbo Zhang", "orcid": "https://orcid.org/0000-0002-1429-8413", "observed_orcids": ["https://orcid.org/0000-0002-1429-8413"]}, "institutions": [{"id": "https://openalex.org/I4210151799", "display_name": "Siemens Healthcare (United States)", "ror": "https://ror.org/054962n91", "country_code": "US", "type": "company", "lineage": ["https://openalex.org/I4210151799"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Zhang, Yanbo", "raw_affiliation_strings": ["Digital Technology and Innovation , Siemens Healthineers , Princeton NJ , USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Digital Technology and Innovation , Siemens Healthineers , Princeton NJ , USA", "institution_ids": ["https://openalex.org/I4210151799"]}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Yoo, Youngjin", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I4210151799", "display_name": "Siemens Healthcare (United States)", "ror": "https://ror.org/054962n91", "country_code": "US", "type": "company", "lineage": ["https://openalex.org/I4210151799"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Yoo, Youngjin", "raw_affiliation_strings": ["Digital Technology and Innovation , Siemens Healthineers , Princeton NJ , USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Digital Technology and Innovation , Siemens Healthineers , Princeton NJ , USA", "institution_ids": ["https://openalex.org/I4210151799"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5101474595", "display_name": "Michael Baumgartner", "orcid": "https://orcid.org/0000-0003-4455-9917", "observed_orcids": ["https://orcid.org/0000-0003-4455-9917"]}, "institutions": [{"id": "https://openalex.org/I1325886976", "display_name": "Siemens (Germany)", "ror": "https://ror.org/059mq0909", "country_code": "DE", "type": "company", "lineage": ["https://openalex.org/I1325886976"]}, {"id": "https://openalex.org/I4210153902", "display_name": "Siemens Healthineers (Germany)", "ror": "https://ror.org/0449c4c15", "country_code": "DE", "type": "company", "lineage": ["https://openalex.org/I4210153902"]}], "countries": ["DE"], "is_corresponding": false, "raw_author_name": "Baumgartner, Michael", "raw_affiliation_strings": ["Digital Technology and Innovation , Siemens Healthineers , Erlangen , Germany"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Digital Technology and Innovation , Siemens Healthineers , Erlangen , Germany", "institution_ids": ["https://openalex.org/I1325886976", "https://openalex.org/I4210153902"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5078051038", "display_name": "Riqiang Gao", "orcid": "https://orcid.org/0000-0002-8729-1941", "observed_orcids": ["https://orcid.org/0000-0002-8729-1941", "https://orcid.org/0000-0003-4585-3237"]}, "institutions": [{"id": "https://openalex.org/I4210151799", "display_name": "Siemens Healthcare (United States)", "ror": "https://ror.org/054962n91", "country_code": "US", "type": "company", "lineage": ["https://openalex.org/I4210151799"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Gao, Riqiang", "raw_affiliation_strings": ["Digital Technology and Innovation , Siemens Healthineers , Princeton NJ , USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Digital Technology and Innovation , Siemens Healthineers , Princeton NJ , USA", "institution_ids": ["https://openalex.org/I4210151799"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5101411103", "display_name": "Jianing Wang", "orcid": "https://orcid.org/0000-0003-4925-8515", "observed_orcids": ["https://orcid.org/0000-0003-4925-8515"]}, "institutions": [{"id": "https://openalex.org/I4210151799", "display_name": "Siemens Healthcare (United States)", "ror": "https://ror.org/054962n91", "country_code": "US", "type": "company", "lineage": ["https://openalex.org/I4210151799"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Wang, Jianing", "raw_affiliation_strings": ["Digital Technology and Innovation , Siemens Healthineers , Princeton NJ , USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Digital Technology and Innovation , Siemens Healthineers , Princeton NJ , USA", "institution_ids": ["https://openalex.org/I4210151799"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5050110257", "display_name": "Gengyan Zhao", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I4210151799", "display_name": "Siemens Healthcare (United States)", "ror": "https://ror.org/054962n91", "country_code": "US", "type": "company", "lineage": ["https://openalex.org/I4210151799"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Zhao, Gengyan", "raw_affiliation_strings": ["Digital Technology and Innovation , Siemens Healthineers , Princeton NJ , USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Digital Technology and Innovation , Siemens Healthineers , Princeton NJ , USA", "institution_ids": ["https://openalex.org/I4210151799"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5025274219", "display_name": "Eli Gibson", "orcid": "https://orcid.org/0000-0001-9207-7280", "observed_orcids": ["https://orcid.org/0000-0001-9207-7280"]}, "institutions": [{"id": "https://openalex.org/I4210151799", "display_name": "Siemens Healthcare (United States)", "ror": "https://ror.org/054962n91", "country_code": "US", "type": "company", "lineage": ["https://openalex.org/I4210151799"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Gibson, Eli", "raw_affiliation_strings": ["Digital Technology and Innovation , Siemens Healthineers , Princeton NJ , USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Digital Technology and Innovation , Siemens Healthineers , Princeton NJ , USA", "institution_ids": ["https://openalex.org/I4210151799"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5012751147", "display_name": "Dorin Comaniciu", "orcid": "https://orcid.org/0000-0002-5238-8647", "observed_orcids": ["https://orcid.org/0000-0002-5238-8647"]}, "institutions": [{"id": "https://openalex.org/I4210151799", "display_name": "Siemens Healthcare (United States)", "ror": "https://ror.org/054962n91", "country_code": "US", "type": "company", "lineage": ["https://openalex.org/I4210151799"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Comaniciu, Dorin", "raw_affiliation_strings": ["Digital Technology and Innovation , Siemens Healthineers , Princeton NJ , USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Digital Technology and Innovation , Siemens Healthineers , Princeton NJ , USA", "institution_ids": ["https://openalex.org/I4210151799"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5087446167", "display_name": "Sa\u0161a Grbi\u0107", "orcid": "https://orcid.org/0000-0002-7564-9811", "observed_orcids": ["https://orcid.org/0000-0002-7564-9811"]}, "institutions": [{"id": "https://openalex.org/I4210151799", "display_name": "Siemens Healthcare (United States)", "ror": "https://ror.org/054962n91", "country_code": "US", "type": "company", "lineage": ["https://openalex.org/I4210151799"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Grbic, Sasa", "raw_affiliation_strings": ["Digital Technology and Innovation , Siemens Healthineers , Princeton NJ , USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Digital Technology and Innovation , Siemens Healthineers , Princeton NJ , USA", "institution_ids": ["https://openalex.org/I4210151799"]}]}], "abstract_inverted_index": {"3D": [0, 53, 115, 147, 150], "medical": [1], "image": [2], "classification": [3, 116], "is": [4, 126], "essential": [5, 127], "for": [6, 20, 149, 182], "modern": [7], "clinical": [8], "workflows.": [9], "Medical": [10], "foundation": [11], "models": [12], "(FMs)": [13], "have": [14], "emerged": [15], "as": [16], "a": [17, 51, 79, 99, 168], "promising": [18], "approach": [19], "scaling": [21], "to": [22, 63, 128], "new": [23, 64], "tasks,": [24], "yet": [25], "current": [26], "research": [27], "suffers": [28], "from": [29, 56], "three": [30], "critical": [31], "pitfalls:": [32], "data-regime": [33], "bias,": [34], "suboptimal": [35], "adaptation,": [36], "and": [37, 48, 93, 109, 111, 142], "insufficient": [38], "task": [39], "coverage.": [40], "In": [41], "this": [42], "paper,": [43], "we": [44, 156], "address": [45], "these": [46], "pitfalls": [47], "introduce": [49], "AnyMC3D,": [50], "scalable": [52, 170], "classifier": [54], "adapted": [55], "2D": [57], "FMs.": [58], "Our": [59, 118], "method": [60], "scales": [61], "efficiently": [62], "tasks": [65, 104], "by": [66], "adding": [67], "only": [68], "lightweight": [69], "plugins": [70], "(about": [71], "1M": [72], "parameters": [73], "per": [74], "task)": [75], "on": [76], "top": [77], "of": [78, 102, 160], "single": [80, 169], "frozen": [81], "backbone.": [82], "This": [83], "versatile": [84], "framework": [85, 171], "also": [86], "supports": [87], "multi-view": [88], "inputs,": [89], "auxiliary": [90], "pixel-level": [91], "supervision,": [92], "interpretable": [94], "heatmap": [95], "generation.": [96], "We": [97], "establish": [98], "comprehensive": [100], "benchmark": [101], "12": [103], "covering": [105], "diverse": [106, 165], "pathologies,": [107], "anatomies,": [108], "modalities,": [110], "systematically": [112], "analyze": [113], "state-of-the-art": [114, 162], "techniques.": [117], "analysis": [119], "reveals": [120], "key": [121], "insights:": [122], "(1)": [123], "effective": [124], "adaptation": [125], "unlock": [129], "FM": [130], "potential,": [131], "(2)": [132], "general-purpose": [133], "FMs": [134, 138], "can": [135], "match": [136], "medical-specific": [137], "if": [139], "properly": [140], "adapted,": [141], "(3)": [143], "2D-based": [144], "methods": [145], "surpass": [146], "architectures": [148], "classification.": [151], "For": [152], "the": [153, 158, 176, 180], "first": [154], "time,": [155], "demonstrate": [157], "feasibility": [159], "achieving": [161], "performance": [163], "across": [164], "applications": [166], "using": [167], "(including": [172], "1st": [173], "place": [174], "in": [175], "VLM3D": [177], "challenge),": [178], "eliminating": [179], "need": [181], "separate": [183], "task-specific": [184], "models.": [185]}, "primary_topic": {"id": "https://openalex.org/T11307", "display_name": "Domain Adaptation and Few-Shot Learning", "score": 0.44290000200271606, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T11307", "display_name": "Domain Adaptation and Few-Shot Learning", "score": 0.44290000200271606, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10052", "display_name": "Medical Image Segmentation Techniques", "score": 0.20280000567436218, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10036", "display_name": "Advanced Neural Network Applications", "score": 0.10480000078678131, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2512.12887", "is_oa": true, "landing_page_url": "https://arxiv.org/abs/2512.12887", "pdf_url": "https://arxiv.org/pdf/2512.12887", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": "cc-by", "license_id": "https://openalex.org/licenses/cc-by", "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4406975591", "doi": "https://doi.org/10.48550/arxiv.2501.17549", "title": "Query-Aware Learnable Graph Pooling Tokens as Prompt for Large Language Models", "publication_year": 2025, "publication_date": "2025-01-29", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5118844306", "display_name": "Wooyoung Kim", "orcid": "https://orcid.org/0000-0002-9993-8431", "observed_orcids": ["https://orcid.org/0000-0002-9993-8431", "https://orcid.org/0000-0002-6015-7064"]}, "institutions": [{"id": "https://openalex.org/I193775966", "display_name": "Yonsei University", "ror": "https://ror.org/01wjejq96", "country_code": "KR", "type": "education", "lineage": ["https://openalex.org/I193775966"]}], "countries": ["KR"], "is_corresponding": false, "raw_author_name": "Kim, Wooyoung", "raw_affiliation_strings": ["Smart Systems Lab , Department of Industrial Engineering Yonsei University Seoul , 03722 , Republic of Korea"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Smart Systems Lab , Department of Industrial Engineering Yonsei University Seoul , 03722 , Republic of Korea", "institution_ids": ["https://openalex.org/I193775966"]}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Park, Byungyoon", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I193775966", "display_name": "Yonsei University", "ror": "https://ror.org/01wjejq96", "country_code": "KR", "type": "education", "lineage": ["https://openalex.org/I193775966"]}], "countries": ["KR"], "is_corresponding": false, "raw_author_name": "Park, Byungyoon", "raw_affiliation_strings": ["Smart Systems Lab , Department of Industrial Engineering Yonsei University Seoul , 03722 , Republic of Korea"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Smart Systems Lab , Department of Industrial Engineering Yonsei University Seoul , 03722 , Republic of Korea", "institution_ids": ["https://openalex.org/I193775966"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5019101777", "display_name": "Wooju Kim", "orcid": "https://orcid.org/0000-0001-5828-178X", "observed_orcids": ["https://orcid.org/0000-0001-5828-178X", "https://orcid.org/0000-0002-6726-062X"]}, "institutions": [{"id": "https://openalex.org/I193775966", "display_name": "Yonsei University", "ror": "https://ror.org/01wjejq96", "country_code": "KR", "type": "education", "lineage": ["https://openalex.org/I193775966"]}], "countries": ["KR"], "is_corresponding": false, "raw_author_name": "Kim, Wooju", "raw_affiliation_strings": ["Smart Systems Lab , Department of Industrial Engineering Yonsei University Seoul , 03722 , Republic of Korea"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Smart Systems Lab , Department of Industrial Engineering Yonsei University Seoul , 03722 , Republic of Korea", "institution_ids": ["https://openalex.org/I193775966"]}]}], "abstract_inverted_index": {"Graph-structured": [0], "data": [1], "plays": [2], "a": [3, 47, 124], "vital": [4], "role": [5], "in": [6, 64, 70, 88, 141], "numerous": [7], "domains,": [8], "such": [9], "as": [10, 86], "social": [11], "networks,": [12, 14], "citation": [13], "commonsense": [15], "reasoning": [16], "graphs": [17], "and": [18, 67, 76, 94], "knowledge": [19], "graphs.": [20], "While": [21], "graph": [22, 29, 78, 96, 113, 119, 145], "neural": [23], "networks": [24], "have": [25, 33], "been": [26], "employed": [27], "for": [28, 39], "processing,": [30], "recent": [31], "advancements": [32], "explored": [34], "integrating": [35], "large": [36, 89, 135], "language": [37, 90, 136], "models": [38], "graph-based": [40], "tasks.": [41], "In": [42], "this": [43], "paper,": [44], "we": [45, 99], "propose": [46], "novel": [48], "approach": [49], "named": [50], "Learnable": [51], "Graph": [52], "Pooling": [53], "Token": [54], "(LGPT),": [55], "which": [56, 106], "addresses": [57], "the": [58, 61, 112, 129, 134], "limitations": [59], "of": [60], "scalability": [62], "issues": [63], "node-level": [65], "projection": [66], "information": [68], "loss": [69], "graph-level": [71], "projection.": [72], "LGPT": [73], "enables": [74], "flexible": [75], "efficient": [77], "representation": [79], "by": [80], "introducing": [81], "learnable": [82], "parameters": [83], "that": [84], "act": [85], "tokens": [87], "models,": [91], "balancing": [92], "fine-grained": [93], "global": [95], "information.": [97], "Additionally,": [98], "investigate": [100], "an": [101], "Early": [102], "Query": [103], "Fusion": [104], "technique,": [105], "fuses": [107], "query": [108], "context": [109], "before": [110], "constructing": [111], "representation,": [114], "leading": [115], "to": [116], "more": [117], "effective": [118], "embeddings.": [120], "Our": [121], "method": [122], "achieves": [123], "4.13\\%": [125], "performance": [126], "improvement": [127], "on": [128], "GraphQA": [130], "benchmark": [131], "without": [132], "training": [133], "model,": [137], "demonstrating": [138], "significant": [139], "gains": [140], "handling": [142], "complex": [143], "textual-attributed": [144], "data.": [146]}, "primary_topic": {"id": "https://openalex.org/T11273", "display_name": "Advanced Graph Neural Networks", "score": 0.9984999895095825, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T11273", "display_name": "Advanced Graph Neural Networks", "score": 0.9984999895095825, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T12292", "display_name": "Graph Theory and Algorithms", "score": 0.000699999975040555, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.0005000000237487257, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2501.17549", "is_oa": true, "landing_page_url": "https://arxiv.org/abs/2501.17549", "pdf_url": "https://arxiv.org/pdf/2501.17549", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": "cc-by", "license_id": "https://openalex.org/licenses/cc-by", "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 1, "is_retracted": false}, {"id": "https://openalex.org/W4416716717", "doi": "https://doi.org/10.1016/j.ibmed.2025.100316", "title": "Artificial intelligence for natural product drug discovery and development: current landscape, applications, and future directions", "publication_year": 2025, "publication_date": "2025-01-01", "type": "article", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5010102161", "display_name": "Zhinya Kawa Othman", "orcid": "https://orcid.org/0009-0008-3914-0867", "observed_orcids": ["https://orcid.org/0009-0008-3914-0867"]}, "institutions": [{"id": "https://openalex.org/I4387152906", "display_name": "Kurdistan Technical Institute", "ror": "https://ror.org/01zzcvm19", "country_code": null, "type": "education", "lineage": ["https://openalex.org/I4387152906"]}], "countries": ["IQ"], "is_corresponding": false, "raw_author_name": "Zhinya Kawa Othman", "raw_affiliation_strings": ["Department of Pharmacy, Kurdistan Technical Institute, Sulaymaniyah, Kurdistan Region, Iraq"], "raw_orcid": "https://orcid.org/0009-0008-3914-0867", "affiliations": [{"raw_affiliation_string": "Department of Pharmacy, Kurdistan Technical Institute, Sulaymaniyah, Kurdistan Region, Iraq", "institution_ids": ["https://openalex.org/I4387152906"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5103164997", "display_name": "Mohamed Mustaf Ahmed", "orcid": "https://orcid.org/0009-0006-5991-4052", "observed_orcids": ["https://orcid.org/0009-0006-5991-4052"]}, "institutions": [{"id": "https://openalex.org/I4210127735", "display_name": "SIMAD University", "ror": "https://ror.org/03dynh639", "country_code": "SO", "type": "education", "lineage": ["https://openalex.org/I4210127735"]}], "countries": ["SO"], "is_corresponding": true, "raw_author_name": "Mohamed Mustaf Ahmed", "raw_affiliation_strings": ["Faculty of Medicine and Health Sciences, SIMAD University, Mogadishu, Somalia"], "raw_orcid": "https://orcid.org/0009-0006-5991-4052", "affiliations": [{"raw_affiliation_string": "Faculty of Medicine and Health Sciences, SIMAD University, Mogadishu, Somalia", "institution_ids": ["https://openalex.org/I4210127735"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5098979775", "display_name": "Omar Kasimieh", "orcid": "https://orcid.org/0000-0002-0782-2192", "observed_orcids": ["https://orcid.org/0000-0002-0782-2192"]}, "institutions": [{"id": "https://openalex.org/I4210109993", "display_name": "University of the East Ramon Magsaysay Memorial Medical Center", "ror": "https://ror.org/01rxjzf54", "country_code": "PH", "type": "healthcare", "lineage": ["https://openalex.org/I4210109993"]}], "countries": ["PH"], "is_corresponding": false, "raw_author_name": "Omar Kasimieh", "raw_affiliation_strings": ["University of the East Ramon Magsaysay Memorial Medical Center, Quezon City, Philippines"], "raw_orcid": "https://orcid.org/0000-0002-0782-2192", "affiliations": [{"raw_affiliation_string": "University of the East Ramon Magsaysay Memorial Medical Center, Quezon City, Philippines", "institution_ids": ["https://openalex.org/I4210109993"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5047025560", "display_name": "Shuaibu Saidu Musa", "orcid": "https://orcid.org/0000-0003-0138-3261", "observed_orcids": ["https://orcid.org/0000-0003-0138-3261"]}, "institutions": [{"id": "https://openalex.org/I158708052", "display_name": "Chulalongkorn University", "ror": "https://ror.org/028wp3y58", "country_code": "TH", "type": "education", "lineage": ["https://openalex.org/I158708052"]}], "countries": ["TH"], "is_corresponding": false, "raw_author_name": "Shuaibu Saidu Musa", "raw_affiliation_strings": ["School of Global Health, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand"], "raw_orcid": "https://orcid.org/0000-0003-0138-3261", "affiliations": [{"raw_affiliation_string": "School of Global Health, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand", "institution_ids": ["https://openalex.org/I158708052"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5066714548", "display_name": "Francesco Branda", "orcid": "https://orcid.org/0000-0002-9485-3877", "observed_orcids": ["https://orcid.org/0000-0002-9485-3877"]}, "institutions": [{"id": "https://openalex.org/I155125353", "display_name": "Universit\u00e0 Campus Bio-Medico", "ror": "https://ror.org/04gqx4x78", "country_code": "IT", "type": "education", "lineage": ["https://openalex.org/I155125353"]}], "countries": ["IT"], "is_corresponding": false, "raw_author_name": "Francesco Branda", "raw_affiliation_strings": ["Unit of Medical Statistics and Molecular Epidemiology, Campus Bio-Medico University of Rome, Rome, Italy"], "raw_orcid": "https://orcid.org/0000-0002-9485-3877", "affiliations": [{"raw_affiliation_string": "Unit of Medical Statistics and Molecular Epidemiology, Campus Bio-Medico University of Rome, Rome, Italy", "institution_ids": ["https://openalex.org/I155125353"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5120476883", "display_name": "Edgar G. Cue", "orcid": "https://orcid.org/0009-0005-5728-722X", "observed_orcids": ["https://orcid.org/0009-0005-5728-722X", "https://orcid.org/0009-0006-2485-1960"]}, "institutions": [{"id": "https://openalex.org/I4405282074", "display_name": "Mountain Province State University", "ror": "https://ror.org/059hw1r60", "country_code": "PH", "type": "education", "lineage": ["https://openalex.org/I4405282074"]}], "countries": ["PH"], "is_corresponding": false, "raw_author_name": "Edgar G. Cue", "raw_affiliation_strings": ["Office of the University President, Mountain Province State University, Bontoc, Mountain Province, Philippines"], "raw_orcid": "https://orcid.org/0009-0005-5728-722X", "affiliations": [{"raw_affiliation_string": "Office of the University President, Mountain Province State University, Bontoc, Mountain Province, Philippines", "institution_ids": ["https://openalex.org/I4405282074"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5112927459", "display_name": "Justine Marie A. Ocampo", "orcid": "https://orcid.org/0009-0002-3075-6899", "observed_orcids": ["https://orcid.org/0009-0002-3075-6899"]}, "institutions": [{"id": "https://openalex.org/I3129998261", "display_name": "University of Makati", "ror": "https://ror.org/00473rv55", "country_code": "PH", "type": "education", "lineage": ["https://openalex.org/I3129998261"]}], "countries": ["PH"], "is_corresponding": false, "raw_author_name": "Justine Marie A. Ocampo", "raw_affiliation_strings": ["Institute of Pharmacy, University of Makati, Makati City, Philippines"], "raw_orcid": "https://orcid.org/0009-0002-3075-6899", "affiliations": [{"raw_affiliation_string": "Institute of Pharmacy, University of Makati, Makati City, Philippines", "institution_ids": ["https://openalex.org/I3129998261"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5089169927", "display_name": "Don Eliseo Lucero\u2010Prisno", "orcid": "https://orcid.org/0000-0002-2179-6365", "observed_orcids": ["https://orcid.org/0000-0002-2179-6365"]}, "institutions": [{"id": "https://openalex.org/I2802234726", "display_name": "Palompon Institute of Technology", "ror": "https://ror.org/05jzcs626", "country_code": "PH", "type": "education", "lineage": ["https://openalex.org/I2802234726"]}, {"id": "https://openalex.org/I3129998261", "display_name": "University of Makati", "ror": "https://ror.org/00473rv55", "country_code": "PH", "type": "education", "lineage": ["https://openalex.org/I3129998261"]}, {"id": "https://openalex.org/I4210089966", "display_name": "London School of Hygiene & Tropical Medicine", "ror": "https://ror.org/00a0jsq62", "country_code": "GB", "type": "education", "lineage": ["https://openalex.org/I124357947", "https://openalex.org/I4210089966"]}, {"id": "https://openalex.org/I4210120717", "display_name": "Faculty of Public Health", "ror": "https://ror.org/01zqv1s26", "country_code": "GB", "type": "nonprofit", "lineage": ["https://openalex.org/I4210120717"]}], "countries": ["GB", "PH"], "is_corresponding": false, "raw_author_name": "Don Eliseo Lucero Prisno", "raw_affiliation_strings": ["Center for University Research, University of Makati, Makati City, Philippines", "Department of Global Health and Development, Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, United Kingdom", "Research Office, Palompon Institute of Technology, Palompon, Leyte, Philippines"], "raw_orcid": "https://orcid.org/0000-0002-2179-6365", "affiliations": [{"raw_affiliation_string": "Center for University Research, University of Makati, Makati City, Philippines", "institution_ids": ["https://openalex.org/I3129998261"]}, {"raw_affiliation_string": "Department of Global Health and Development, Faculty of Public Health and Policy, London School of Hygiene and Tropical Medicine, London, United Kingdom", "institution_ids": ["https://openalex.org/I4210089966", "https://openalex.org/I4210120717"]}, {"raw_affiliation_string": "Research Office, Palompon Institute of Technology, Palompon, Leyte, Philippines", "institution_ids": ["https://openalex.org/I2802234726"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5040943712", "display_name": "Sornkanok Vimolmangkang", "orcid": "https://orcid.org/0000-0002-1705-6217", "observed_orcids": ["https://orcid.org/0000-0002-1705-6217"]}, "institutions": [{"id": "https://openalex.org/I158708052", "display_name": "Chulalongkorn University", "ror": "https://ror.org/028wp3y58", "country_code": "TH", "type": "education", "lineage": ["https://openalex.org/I158708052"]}], "countries": ["TH"], "is_corresponding": false, "raw_author_name": "Sornkanok Vimolmangkang", "raw_affiliation_strings": ["Center of Excellence in Plant-Produced Pharmaceuticals, Chulalongkorn University, Bangkok, Thailand", "Department of Pharmacognosy & Pharmaceutical Botany, Faculty of Pharmaceutical Sciences, Chulalongkorn University, Bangkok, Thailand", "Phyto Analytica Testing Laboratory, Leapdelab Company Limited, Bangkok, Thailand"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Center of Excellence in Plant-Produced Pharmaceuticals, Chulalongkorn University, Bangkok, Thailand", "institution_ids": ["https://openalex.org/I158708052"]}, {"raw_affiliation_string": "Department of Pharmacognosy & Pharmaceutical Botany, Faculty of Pharmaceutical Sciences, Chulalongkorn University, Bangkok, Thailand", "institution_ids": ["https://openalex.org/I158708052"]}, {"raw_affiliation_string": "Phyto Analytica Testing Laboratory, Leapdelab Company Limited, Bangkok, Thailand", "institution_ids": []}]}], "abstract_inverted_index": {"Artificial": [0], "intelligence": [1], "accelerates": [2], "natural": [3, 122, 186, 193, 215], "product": [4, 123, 187], "discovery": [5, 196], "in": [6, 185, 219], "oncology,": [7], "infection,": [8], "inflammation,": [9], "and": [10, 18, 28, 36, 58, 80, 90, 97, 103, 112, 127, 130, 137, 145, 153, 160, 170, 197, 202, 209, 232], "neuroprotection": [11], "by": [12], "enabling": [13], "activity": [14], "prediction,": [15], "mechanism": [16], "inference,": [17], "prioritization.": [19], "These": [20], "approaches": [21], "include": [22, 95, 116, 227], "tree": [23], "ensembles,": [24], "graph": [25], "neural": [26], "networks,": [27], "self-supervised": [29], "molecular": [30, 63], "embeddings": [31], "for": [32, 119, 125, 176], "mixtures,": [33], "isolated": [34], "metabolites,": [35], "peptide": [37], "analogs,": [38], "while": [39], "network": [40], "pharmacology": [41], "models": [42, 83, 205], "herb\u2013ingredient\u2013target\u2013pathway": [43], "graphs": [44], "to": [45, 77, 86], "propose": [46], "synergistic": [47], "effects.": [48], "Operational": [49], "multi-omics": [50], "gates": [51], "(transcriptomic": [52], "signature": [53], "reversal,": [54], "proteome-scale": [55], "target": [56], "engagement,": [57], "untargeted": [59], "metabolomics": [60], "with": [61, 133, 150], "feature-based": [62], "networking)": [64], "move": [65], "ranked": [66], "candidates": [67], "into": [68, 173], "reproducible": [69], "validations.": [70], "Population-level": [71], "analyses": [72], "map": [73], "formulation-derived": [74], "ingredient\u2013target": [75], "signatures": [76], "clinical": [78], "outcomes,": [79], "large": [81], "language": [82], "are": [84], "beginning": [85], "standardize": [87], "herbal": [88], "prescriptions": [89], "their": [91], "curation.": [92], "Persistent": [93], "barriers": [94], "mixture": [96], "batch": [98], "variability,": [99], "incomplete": [100], "provenance,": [101], "small": [102, 228], "imbalanced": [104], "datasets,": [105, 229], "domain": [106], "shift,": [107], "off-target": [108], "liability,": [109], "limited": [110, 233], "interpretability,": [111], "bias.": [113], "Practical": [114], "solutions": [115], "minimal": [117], "information": [118], "AI": [120, 184, 190], "on": [121], "metadata": [124], "provenance": [126], "safety,": [128], "scaffold": [129], "time-split": [131], "benchmarks": [132], "cross-lab": [134], "replication,": [135], "uncertainty": [136], "applicability-domain": [138], "gating,": [139], "mechanistic": [140], "add-back": [141], "experiments,": [142], "constrained": [143], "generative": [144], "semi-synthetic": [146], "design,": [147], "micro-physiological": [148], "systems": [149], "digital": [151], "twins,": [152], "provenance-aware": [154], "pharmacovigilance": [155], "under": [156], "evolving": [157], "FDA,": [158], "EMA,": [159], "WHO": [161], "expectations.": [162], "This": [163], "review": [164], "integrates": [165], "these": [166], "advances,": [167], "evidence": [168], "gaps,": [169], "governance": [171], "requirements": [172], "a": [174], "roadmap": [175], "the": [177], "mechanistically": [178], "grounded,": [179], "prospectively": [180], "validated": [181, 218], "translation": [182], "of": [183], "research.": [188], "\u2022": [189, 199, 212, 224], "tools": [191], "accelerate": [192], "product\u2013based": [194], "drug": [195], "development.": [198], "Machine": [200], "learning": [201, 204], "deep": [203], "predict": [206], "anticancer,": [207], "anti-inflammatory,": [208], "antimicrobial": [210], "actions.": [211], "Several": [213], "AI-predicted": [214], "compounds": [216], "were": [217], "vitro,": [220], "confirming": [221], "translational": [222], "potential.": [223], "Key": [225], "challenges": [226], "data": [230], "imbalance,": [231], "experimental": [234], "validation.": [235]}, "primary_topic": {"id": "https://openalex.org/T10211", "display_name": "Computational Drug Discovery Methods", "score": 0.9994999766349792, "subfield": {"id": "https://openalex.org/subfields/1703", "display_name": "Computational Theory and Mathematics"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T10211", "display_name": "Computational Drug Discovery Methods", "score": 0.9994999766349792, "subfield": {"id": "https://openalex.org/subfields/1703", "display_name": "Computational Theory and Mathematics"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T11948", "display_name": "Machine Learning in Materials Science", "score": 9.999999747378752e-05, "subfield": {"id": "https://openalex.org/subfields/2505", "display_name": "Materials Chemistry"}, "field": {"id": "https://openalex.org/fields/25", "display_name": "Materials Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T12257", "display_name": "Bioactive natural compounds", "score": 9.999999747378752e-05, "subfield": {"id": "https://openalex.org/subfields/1312", "display_name": "Molecular Biology"}, "field": {"id": "https://openalex.org/fields/13", "display_name": "Biochemistry, Genetics and Molecular Biology"}, "domain": {"id": "https://openalex.org/domains/1", "display_name": "Life Sciences"}}], "primary_location": {"id": "doi:10.1016/j.ibmed.2025.100316", "is_oa": true, "landing_page_url": "https://doi.org/10.1016/j.ibmed.2025.100316", "pdf_url": null, "source": {"id": "https://openalex.org/S4210227372", "display_name": "Intelligence-Based Medicine", "issn_l": "2666-5212", "issn": ["2666-5212"], "is_oa": true, "is_in_doaj": true, "is_core": true, "listed_in": ["cwts-core", "doaj", "doyens", "norway-1"], "host_organization": "https://openalex.org/P4310320990", "host_organization_name": "Elsevier BV", "host_organization_lineage": ["https://openalex.org/P4310320990"], "host_organization_lineage_names": ["Elsevier BV"], "type": "journal"}, "license": "cc-by", "license_id": "https://openalex.org/licenses/cc-by", "version": "publishedVersion", "is_accepted": true, "is_published": true, "raw_source_name": "Intelligence-Based Medicine", "raw_type": "journal-article"}, "cited_by_count": 25, "is_retracted": false}, {"id": "https://openalex.org/W4416289397", "doi": "https://doi.org/10.48550/arxiv.2507.07297", "title": "MagiC: Evaluating Multimodal Cognition Toward Grounded Visual Reasoning", "publication_year": 2025, "publication_date": "2025-07-09", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": null, "display_name": "Wu, Chengfei", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Wu, Chengfei", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5119934310", "display_name": "Ronald Seoh", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Seoh, Ronald", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": null, "display_name": "Li, Bingxuan", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Li, Bingxuan", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5100459445", "display_name": "Liqiang Zhang", "orcid": "https://orcid.org/0000-0002-4175-7590", "observed_orcids": ["https://orcid.org/0000-0002-4175-7590", "https://orcid.org/0009-0001-6908-7496"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Zhang, Liqiang", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": null, "display_name": "Han, Fengrong", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Han, Fengrong", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "last", "author": {"id": "https://openalex.org/A5032121234", "display_name": "Dan Goldwasser", "orcid": "https://orcid.org/0000-0001-9326-8601", "observed_orcids": ["https://orcid.org/0000-0001-9326-8601"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Goldwasser, Dan", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}], "abstract_inverted_index": {"Recent": [0], "advances": [1], "in": [2, 11, 164], "large": [3], "vision-language": [4, 103], "models": [5, 24, 104], "have": [6], "led": [7], "to": [8, 48, 108, 129, 167], "impressive": [9], "performance": [10], "visual": [12, 28, 70, 135, 169], "question": [13], "answering": [14], "and": [15, 35, 65, 86, 96, 121, 137, 152, 154, 162], "multimodal": [16, 51], "reasoning.": [17, 170], "However,": [18], "it": [19], "remains": [20], "unclear": [21], "whether": [22], "these": [23], "genuinely": [25], "perform": [26], "grounded": [27, 50, 168], "reasoning": [29, 64, 117], "or": [30], "rely": [31], "on": [32], "superficial": [33], "patterns": [34], "dataset": [36], "biases.": [37], "In": [38], "this": [39], "work,": [40], "we": [41], "introduce": [42, 146], "MagiC,": [43], "a": [44], "comprehensive": [45, 156], "benchmark": [46, 73], "designed": [47], "evaluate": [49, 101], "cognition,": [52], "assessing": [53], "not": [54], "only": [55], "answer": [56, 115], "accuracy": [57], "but": [58], "also": [59], "the": [60], "quality": [61], "of": [62], "step-by-step": [63], "its": [66], "alignment": [67], "with": [68, 90], "relevant": [69], "evidence.": [71], "Our": [72], "includes": [74, 126], "approximately": [75], "5,500": [76], "weakly": [77], "supervised": [78], "QA": [79], "examples": [80, 89], "generated": [81], "from": [82, 106], "strong": [83], "model": [84, 131], "outputs": [85], "900": [87], "human-curated": [88], "fine-grained": [91], "annotations,": [92], "including": [93], "answers,": [94], "rationales,": [95], "bounding": [97], "box": [98], "groundings.": [99], "We": [100, 145], "15": [102], "ranging": [105], "7B": [107], "70B": [109], "parameters": [110], "across": [111], "four": [112], "dimensions:": [113], "final": [114], "correctness,": [116], "validity,": [118], "grounding": [119], "fidelity,": [120], "self-correction": [122], "ability.": [123], "MagiC": [124], "further": [125], "diagnostic": [127], "settings": [128], "probe": [130], "robustness": [132], "under": [133], "adversarial": [134], "cues": [136], "assess": [138], "their": [139], "capacity": [140], "for": [141], "introspective": [142], "error": [143], "correction.": [144], "new": [147], "metrics": [148], "such": [149], "as": [150], "MagiScore": [151], "StepSense,": [153], "provide": [155], "analyses": [157], "that": [158], "reveal": [159], "key": [160], "limitations": [161], "opportunities": [163], "current": [165], "approaches": [166]}, "primary_topic": {"id": "https://openalex.org/T11714", "display_name": "Multimodal Machine Learning Applications", "score": 1.0, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T11714", "display_name": "Multimodal Machine Learning Applications", "score": 1.0, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T11605", "display_name": "Visual Attention and Saliency Detection", "score": 0.0, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10036", "display_name": "Advanced Neural Network Applications", "score": 0.0, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2507.07297", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2507.07297", "pdf_url": "https://arxiv.org/pdf/2507.07297", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": null, "license_id": null, "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4416257326", "doi": "https://doi.org/10.48550/arxiv.2509.11101", "title": "Seeing is Not Understanding: A Benchmark on Perception-Cognition Disparities in Large Language Models", "publication_year": 2025, "publication_date": "2025-09-14", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5027983702", "display_name": "Haokun Li", "orcid": "https://orcid.org/0009-0002-6460-6688", "observed_orcids": ["https://orcid.org/0009-0002-6460-6688"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Li, Haokun", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5101860226", "display_name": "Ya-Zhou Zhang", "orcid": "https://orcid.org/0000-0001-6046-2950", "observed_orcids": ["https://orcid.org/0000-0001-6046-2950"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Zhang, Yazhou", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": null, "display_name": "Ding, Jizhi", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Ding, Jizhi", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5026465666", "display_name": "Qiuchi Li", "orcid": "https://orcid.org/0000-0002-8219-0869", "observed_orcids": ["https://orcid.org/0000-0002-8219-0869"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Li, Qiuchi", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "last", "author": {"id": "https://openalex.org/A5100713185", "display_name": "Peng Zhang", "orcid": "https://orcid.org/0000-0002-0470-772X", "observed_orcids": ["https://orcid.org/0000-0002-0470-772X"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Zhang, Peng", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}], "abstract_inverted_index": {"With": [0], "the": [1, 36, 69, 125], "rapid": [2], "advancement": [3], "of": [4, 18, 119, 157, 169], "Multimodal": [5], "Large": [6], "Language": [7], "Models": [8], "(MLLMs),": [9], "they": [10], "have": [11], "demonstrated": [12], "exceptional": [13], "capabilities": [14], "across": [15], "a": [16, 53, 95, 155, 166], "variety": [17], "vision-language": [19], "tasks.": [20], "However,": [21], "current": [22], "evaluation": [23, 168], "benchmarks": [24], "predominantly": [25], "focus": [26], "on": [27, 178], "objective": [28], "visual": [29, 131], "question": [30, 118], "answering": [31], "or": [32], "captioning,": [33], "inadequately": [34], "assessing": [35], "models'": [37], "ability": [38, 127], "to": [39, 104, 128], "understand": [40], "complex": [41], "and": [42, 81, 115, 144, 162, 176], "subjective": [43], "human": [44], "emotions.": [45], "To": [46], "bridge": [47], "this": [48], "gap,": [49], "we": [50], "introduce": [51], "EmoBench-Reddit,": [52], "novel,": [54], "hierarchical": [55, 96], "benchmark": [56], "for": [57], "multimodal": [58], "emotion": [59, 83], "understanding.": [60], "The": [61], "dataset": [62], "comprises": [63], "350": [64], "meticulously": [65], "curated": [66], "samples": [67], "from": [68, 101], "social": [70], "media": [71], "platform": [72], "Reddit,": [73], "each": [74, 108], "containing": [75], "an": [76, 82], "image,": [77], "associated": [78], "user-provided": [79], "text,": [80], "category": [84], "(sad,": [85], "humor,": [86], "sarcasm,": [87], "happy)": [88], "confirmed": [89], "by": [90], "user": [91], "flairs.": [92], "We": [93, 150], "designed": [94], "task": [97], "framework": [98], "that": [99], "progresses": [100], "basic": [102, 130], "perception": [103], "advanced": [105], "cognition,": [106], "with": [107], "data": [109], "point": [110], "featuring": [111], "six": [112], "multiple-choice": [113], "questions": [114], "one": [116], "open-ended": [117], "increasing": [120], "difficulty.": [121], "Perception": [122], "tasks": [123, 138], "evaluate": [124], "model's": [126], "identify": [129], "elements": [132], "(e.g.,": [133], "colors,": [134], "objects),": [135], "while": [136], "cognition": [137], "require": [139], "scene": [140], "reasoning,": [141], "intent": [142], "understanding,": [143], "deep": [145], "empathy": [146], "integrating": [147], "textual": [148], "context.": [149], "ensured": [151], "annotation": [152], "quality": [153], "through": [154], "combination": [156], "AI": [158], "assistance": [159], "(Claude": [160], "4)": [161], "manual": [163], "verification.We": [164], "conducted": [165], "comprehensive": [167], "nine": [170], "leading": [171], "MLLMs,": [172], "including": [173], "GPT-5,": [174], "Gemini-2.5-pro,": [175], "GPT-4o,": [177], "EmoBench-Reddit.": [179]}, "primary_topic": {"id": "https://openalex.org/T11714", "display_name": "Multimodal Machine Learning Applications", "score": 0.9994999766349792, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T11714", "display_name": "Multimodal Machine Learning Applications", "score": 0.9994999766349792, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10664", "display_name": "Sentiment Analysis and Opinion Mining", "score": 9.999999747378752e-05, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10667", "display_name": "Emotion and Mood Recognition", "score": 0.0, "subfield": {"id": "https://openalex.org/subfields/3205", "display_name": "Experimental and Cognitive Psychology"}, "field": {"id": "https://openalex.org/fields/32", "display_name": "Psychology"}, "domain": {"id": "https://openalex.org/domains/2", "display_name": "Social Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2509.11101", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2509.11101", "pdf_url": "https://arxiv.org/pdf/2509.11101", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": null, "license_id": null, "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4410984629", "doi": "https://doi.org/10.38053/acmj.1649283", "title": "The role of Artificial Intelligence in Celiac disease support: analyzing ChatGPT's effectiveness for healthcare providers and patients", "publication_year": 2025, "publication_date": "2025-05-30", "type": "article", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5020789976", "display_name": "Yunus Halil Polat", "orcid": "https://orcid.org/0000-0002-2388-5388", "observed_orcids": ["https://orcid.org/0000-0002-2388-5388"]}, "institutions": [{"id": "https://openalex.org/I4210128276", "display_name": "Sa\u011fl\u0131k Bilimleri \u00dcniversitesi", "ror": "https://ror.org/03k7bde87", "country_code": "TR", "type": "education", "lineage": ["https://openalex.org/I4210128276"]}], "countries": ["TR"], "is_corresponding": false, "raw_author_name": "Yunus Halil Polat", "raw_affiliation_strings": ["SA\u011eLIK B\u0130L\u0130MLER\u0130 \u00dcN\u0130VERS\u0130TES\u0130, ANKARA SA\u011eLIK UYGULAMA VE ARA\u015eTIRMA MERKEZ\u0130"], "raw_orcid": "https://orcid.org/0000-0002-2388-5388", "affiliations": [{"raw_affiliation_string": "SA\u011eLIK B\u0130L\u0130MLER\u0130 \u00dcN\u0130VERS\u0130TES\u0130, ANKARA SA\u011eLIK UYGULAMA VE ARA\u015eTIRMA MERKEZ\u0130", "institution_ids": ["https://openalex.org/I4210128276"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5009326384", "display_name": "Rasim Eren Cankurtaran", "orcid": "https://orcid.org/0000-0002-3687-3845", "observed_orcids": ["https://orcid.org/0000-0002-3687-3845"]}, "institutions": [{"id": "https://openalex.org/I4210150808", "display_name": "Ankara Y\u0131ld\u0131r\u0131m Beyaz\u0131t University", "ror": "https://ror.org/05ryemn72", "country_code": "TR", "type": "education", "lineage": ["https://openalex.org/I4210150808"]}], "countries": ["TR"], "is_corresponding": false, "raw_author_name": "Rasim Eren Cankurtaran", "raw_affiliation_strings": ["ANKARA YILDIRIM BEYAZIT UNIVERSITY"], "raw_orcid": "https://orcid.org/0000-0002-3687-3845", "affiliations": [{"raw_affiliation_string": "ANKARA YILDIRIM BEYAZIT UNIVERSITY", "institution_ids": ["https://openalex.org/I4210150808"]}]}], "abstract_inverted_index": {"Aims:": [0], "Celiac": [1], "disease": [2, 110, 170, 188], "is": [3], "a": [4, 10, 71], "significant": [5, 192], "autoimmune": [6], "disorder": [7], "that": [8, 211], "affects": [9], "substantial": [11], "portion": [12], "of": [13, 35, 59, 78, 88, 136, 201, 257, 265, 284], "the": [14, 33, 55, 76, 103, 150, 157, 168, 176, 199, 236, 252, 263, 282], "population,": [15], "necessitating": [16], "accurate": [17], "and": [18, 57, 94, 118, 140, 153, 159, 172, 186, 204, 221, 227], "reliable": [19, 220], "information": [20, 46, 231, 279], "for": [21, 92, 96, 138, 142, 156, 224, 254], "effective": [22], "management.": [23], "Despite": [24], "its": [25, 271], "importance,": [26], "there": [27], "are": [28], "currently": [29], "no": [30, 191], "studies": [31], "assessing": [32], "performance": [34], "Artificial": [36], "Intelligence": [37], "(AI)": [38], "language": [39, 213], "models,": [40], "such": [41], "as": [42, 219], "ChatGPT,": [43], "in": [44, 74, 80, 198, 239, 247, 267], "providing": [45], "on": [47, 102, 166], "this": [48, 81], "condition.": [49], "This": [50, 208], "study": [51, 209], "aims": [52], "to": [53, 62, 273, 276, 287], "evaluates": [54], "reliability": [56, 139, 152, 202], "usefulness": [58, 154, 205], "ChatGPT's": [60], "responses": [61], "frequently": [63, 105], "asked": [64], "questions": [65, 90, 165], "regarding": [66], "celiac": [67, 109, 169, 187, 233], "disease,": [68], "thereby": [69], "filling": [70], "critical": [72], "gap": [73], "understanding": [75], "capabilities": [77], "AI": [79, 258, 266], "important": [82], "healthcare": [83, 97, 177, 228, 268], "context.": [84], "Methods:": [85], "A": [86], "total": [87], "20": [89], "(10": [91], "patients/caregivers": [93], "10": [95], "professionals)": [98], "were": [99, 162, 194], "prepared": [100], "based": [101], "most": [104], "searched": [106], "queries": [107], "about": [108, 232], "using": [111], "Google": [112], "Trends.": [113], "Responses": [114], "generated": [115], "by": [116, 120], "ChatGPT": [117, 216], "scored": [119], "two": [121], "independent": [122], "Likert": [123], "raters.": [124, 148], "Results:": [125], "The": [126], "analysis": [127], "revealed": [128, 245], "strong": [129], "inter-rater": [130], "reliability,": [131], "with": [132, 164], "Cronbach\u2019s": [133], "alpha": [134], "values": [135], "\u03b1=0.839": [137], "\u03b1=0.753": [141], "usefulness,": [143], "indicating": [144], "robust": [145], "agreement": [146], "between": [147, 196], "Notably,": [149], "highest": [151], "scores": [155], "patient": [158], "caregiver": [160], "group": [161], "associated": [163], "symptoms,": [167], "diet,": [171], "gluten-free": [173], "products.": [174], "For": [175], "professionals": [178, 229], "group,": [179], "key": [180], "topics": [181], "included": [182], "diagnosis,": [183], "pathological": [184], "classification,": [185], "comorbidities.": [189], "Importantly,": [190], "differences": [193], "found": [195], "raters": [197], "evaluation": [200], "(p=0.939)": [203], "(p=0.102).": [206], "Conclusion:": [207], "demonstrates": [210], "AI-based": [212], "models": [214], "like": [215], "can": [217], "serve": [218], "useful": [222], "resources": [223], "both": [225], "patients": [226], "seeking": [230], "disease.": [234], "While": [235], "model": [237], "excelled": [238], "addressing": [240], "commonly": [241], "discussed": [242], "topics,": [243], "it": [244], "limitations": [246], "handling": [248], "complex": [249], "issues,": [250], "emphasizing": [251], "need": [253], "ongoing": [255], "refinement": [256], "tools.": [259], "These": [260], "findings": [261], "support": [262], "integration": [264], "communication,": [269], "highlighting": [270], "potential": [272], "enhance": [274], "access": [275], "crucial": [277], "health": [278], "while": [280], "underscoring": [281], "importance": [283], "continual": [285], "improvement": [286], "meet": [288], "diverse": [289], "user": [290], "needs.": [291]}, "primary_topic": {"id": "https://openalex.org/T11636", "display_name": "Artificial Intelligence in Healthcare and Education", "score": 0.9976000189781189, "subfield": {"id": "https://openalex.org/subfields/2718", "display_name": "Health Informatics"}, "field": {"id": "https://openalex.org/fields/27", "display_name": "Medicine"}, "domain": {"id": "https://openalex.org/domains/4", "display_name": "Health Sciences"}}, "topics": [{"id": "https://openalex.org/T11636", "display_name": "Artificial Intelligence in Healthcare and Education", "score": 0.9976000189781189, "subfield": {"id": "https://openalex.org/subfields/2718", "display_name": "Health Informatics"}, "field": {"id": "https://openalex.org/fields/27", "display_name": "Medicine"}, "domain": {"id": "https://openalex.org/domains/4", "display_name": "Health Sciences"}}, {"id": "https://openalex.org/T11348", "display_name": "Celiac Disease Research and Management", "score": 0.0010000000474974513, "subfield": {"id": "https://openalex.org/subfields/2715", "display_name": "Gastroenterology"}, "field": {"id": "https://openalex.org/fields/27", "display_name": "Medicine"}, "domain": {"id": "https://openalex.org/domains/4", "display_name": "Health Sciences"}}, {"id": "https://openalex.org/T10737", "display_name": "Health Literacy and Information Accessibility", "score": 0.0003000000142492354, "subfield": {"id": "https://openalex.org/subfields/3600", "display_name": "General Health Professions"}, "field": {"id": "https://openalex.org/fields/36", "display_name": "Health Professions"}, "domain": {"id": "https://openalex.org/domains/4", "display_name": "Health Sciences"}}], "primary_location": {"id": "doi:10.38053/acmj.1649283", "is_oa": true, "landing_page_url": "https://doi.org/10.38053/acmj.1649283", "pdf_url": "https://dergipark.org.tr/en/download/article-file/4651690", "source": {"id": "https://openalex.org/S4210215369", "display_name": "Anatolian Current Medical Journal", "issn_l": "2718-0115", "issn": ["2718-0115"], "is_oa": false, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": null, "host_organization_name": null, "host_organization_lineage": [], "host_organization_lineage_names": [], "type": "journal"}, "license": "cc-by", "license_id": "https://openalex.org/licenses/cc-by", "version": "publishedVersion", "is_accepted": true, "is_published": true, "raw_source_name": "Anatolian Current Medical Journal", "raw_type": "journal-article"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4411522888", "doi": "https://doi.org/10.1145/3728902", "title": "ConTested: Consistency-Aided Tested Code Generation with LLM", "publication_year": 2025, "publication_date": "2025-06-22", "type": "article", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5051353261", "display_name": "Jinhao Dong", "orcid": "https://orcid.org/0009-0009-0416-6896", "observed_orcids": ["https://orcid.org/0009-0009-0416-6896"]}, "institutions": [{"id": "https://openalex.org/I165932596", "display_name": "National University of Singapore", "ror": "https://ror.org/01tgyzw49", "country_code": "SG", "type": "education", "lineage": ["https://openalex.org/I165932596"]}, {"id": "https://openalex.org/I20231570", "display_name": "Peking University", "ror": "https://ror.org/02v51f717", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I20231570"]}], "countries": ["CN", "SG"], "is_corresponding": false, "raw_author_name": "Jinhao Dong", "raw_affiliation_strings": ["National University of Singapore, Singapore, Singapore", "Peking University, Beijing, China"], "raw_orcid": "https://orcid.org/0009-0009-0416-6896", "affiliations": [{"raw_affiliation_string": "National University of Singapore, Singapore, Singapore", "institution_ids": ["https://openalex.org/I165932596"]}, {"raw_affiliation_string": "Peking University, Beijing, China", "institution_ids": ["https://openalex.org/I20231570"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5100429004", "display_name": "Jun Sun", "orcid": "https://orcid.org/0000-0002-3545-1392", "observed_orcids": ["https://orcid.org/0000-0002-3545-1392", "https://orcid.org/0000-0002-0325-1951"]}, "institutions": [{"id": "https://openalex.org/I79891267", "display_name": "Singapore Management University", "ror": "https://ror.org/050qmg959", "country_code": "SG", "type": "education", "lineage": ["https://openalex.org/I79891267"]}], "countries": ["SG"], "is_corresponding": false, "raw_author_name": "Jun Sun", "raw_affiliation_strings": ["Singapore Management University, Singapore, Singapore"], "raw_orcid": "https://orcid.org/0000-0002-3545-1392", "affiliations": [{"raw_affiliation_string": "Singapore Management University, Singapore, Singapore", "institution_ids": ["https://openalex.org/I79891267"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5100385507", "display_name": "Wenjie Zhang", "orcid": "https://orcid.org/0000-0002-2669-1837", "observed_orcids": ["https://orcid.org/0000-0002-2669-1837"]}, "institutions": [{"id": "https://openalex.org/I165932596", "display_name": "National University of Singapore", "ror": "https://ror.org/01tgyzw49", "country_code": "SG", "type": "education", "lineage": ["https://openalex.org/I165932596"]}], "countries": ["SG"], "is_corresponding": false, "raw_author_name": "Wenjie Zhang", "raw_affiliation_strings": ["National University of Singapore, Singapore, Singapore"], "raw_orcid": "https://orcid.org/0000-0002-2669-1837", "affiliations": [{"raw_affiliation_string": "National University of Singapore, Singapore, Singapore", "institution_ids": ["https://openalex.org/I165932596"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5085067496", "display_name": "Jin Song Dong", "orcid": "https://orcid.org/0000-0002-6512-8326", "observed_orcids": ["https://orcid.org/0000-0002-6512-8326"]}, "institutions": [{"id": "https://openalex.org/I165932596", "display_name": "National University of Singapore", "ror": "https://ror.org/01tgyzw49", "country_code": "SG", "type": "education", "lineage": ["https://openalex.org/I165932596"]}, {"id": "https://openalex.org/I20231570", "display_name": "Peking University", "ror": "https://ror.org/02v51f717", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I20231570"]}], "countries": ["CN", "SG"], "is_corresponding": false, "raw_author_name": "Jin Song Dong", "raw_affiliation_strings": ["National University of Singapore, Singapore, Singapore", "Peking University, Beijing, China"], "raw_orcid": "https://orcid.org/0000-0002-6512-8326", "affiliations": [{"raw_affiliation_string": "National University of Singapore, Singapore, Singapore", "institution_ids": ["https://openalex.org/I165932596"]}, {"raw_affiliation_string": "Peking University, Beijing, China", "institution_ids": ["https://openalex.org/I20231570"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5085393851", "display_name": "Dan Hao", "orcid": "https://orcid.org/0000-0001-8295-303X", "observed_orcids": ["https://orcid.org/0000-0001-8295-303X"]}, "institutions": [{"id": "https://openalex.org/I20231570", "display_name": "Peking University", "ror": "https://ror.org/02v51f717", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I20231570"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Dan Hao", "raw_affiliation_strings": ["Peking University, Shenzhen, China"], "raw_orcid": "https://orcid.org/0000-0001-8295-303X", "affiliations": [{"raw_affiliation_string": "Peking University, Shenzhen, China", "institution_ids": ["https://openalex.org/I20231570"]}]}], "abstract_inverted_index": {"Recent": [0], "advancements": [1], "in": [2], "large": [3], "language": [4, 20], "models": [5], "(LLMs)": [6], "have": [7, 48], "significantly": [8, 118], "improved": [9], "code": [10, 14, 54, 130, 146, 222], "generation,": [11], "which": [12], "generates": [13], "snippets": [15], "automatically": [16], "based": [17, 82], "on": [18, 79, 83], "natural": [19], "requirements.": [21], "Despite": [22], "achieving": [23], "state-of-the-art": [24], "performance,": [25], "LLMs": [26], "often": [27], "struggle": [28], "to": [29, 37, 52, 87, 103, 220], "generate": [30], "accurate": [31], "and": [32, 42, 60, 131, 147, 165, 175, 213], "reliable": [33], "code,": [34], "requiring": [35, 202], "developers": [36], "spend": [38], "substantial": [39, 226], "effort": [40], "debugging": [41], "evaluating": [43], "the": [44, 70, 137, 142, 186, 211], "generated": [45, 75], "output.": [46], "Researchers": [47], "proposed": [49], "leveraging": [50], "Consistency": [51], "select": [53], "that": [55, 99], "passes": [56], "more": [57, 65, 149], "tests": [58, 71, 85, 148], "(inter-consistency)": [59], "demonstrates": [61], "consistent": [62], "behavior": [63], "across": [64, 160], "counterparts": [66], "(intra-consistency).": [67], "However,": [68], "since": [69], "themselves": [72], "are": [73], "also": [74], "by": [76], "LLMs,": [77, 162], "relying": [78], "majority": [80], "voting": [81, 144], "incorrect": [84], "leads": [86], "unreliable": [88], "results.": [89], "To": [90], "address": [91], "this,": [92], "we": [93, 123], "propose": [94], "a": [95, 125, 197], "lightweight": [96], "interaction": [97, 199], "framework": [98], "incorporates": [100], "user": [101, 204, 207], "feedback": [102], "effectively": [104], "guide": [105], "consistency.": [106], "Our": [107, 167], "results": [108, 168], "demonstrate": [109], "that,": [110], "with": [111, 195, 200], "minimal": [112, 203], "human": [113], "effort,": [114], "performance": [115], "can": [116], "be": [117], "improved.": [119], "In": [120], "each": [121], "iteration,": [122], "introduce": [124], "rank-correct-fix": [126], "co-evolution": [127], "process": [128, 134], "between": [129, 145], "tests.": [132], "This": [133, 191], "iteratively": [135], "enhances": [136], "quality": [138], "of": [139, 171, 215], "both,": [140], "making": [141], "consistency": [143], "reliable.": [150], "We": [151], "evaluate": [152], "ConTested": [153, 180], "through": [154], "extensive": [155], "experiments,": [156], "demonstrating": [157], "its": [158, 218], "effectiveness": [159], "multiple": [161], "including": [163], "GPT-3.5": [164, 174], "GPT-4o.": [166, 178], "show": [169], "improvements": [170], "32.9%": [172], "over": [173, 177, 185], "16.97%": [176], "Additionally,": [179], "achieves": [181], "an": [182], "11.1%": [183], "improvement": [184, 192], "SOTA": [187], "post-processing": [188], "technique,": [189], "MPSC.": [190], "is": [193], "achieved": [194], "only": [196], "4-round": [198], "users,": [201], "effort.": [205], "A": [206], "study": [208], "further": [209], "confirms": [210], "feasibility": [212], "cost-effectiveness": [214], "ConTested,": [216], "highlighting": [217], "ability": [219], "enhance": [221], "generation": [223], "without": [224], "introducing": [225], "overhead.": [227]}, "primary_topic": {"id": "https://openalex.org/T10260", "display_name": "Software Engineering Research", "score": 0.9937000274658203, "subfield": {"id": "https://openalex.org/subfields/1710", "display_name": "Information Systems"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T10260", "display_name": "Software Engineering Research", "score": 0.9937000274658203, "subfield": {"id": "https://openalex.org/subfields/1710", "display_name": "Information Systems"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10743", "display_name": "Software Testing and Debugging Techniques", "score": 0.0044999998062849045, "subfield": {"id": "https://openalex.org/subfields/1712", "display_name": "Software"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10430", "display_name": "Software Engineering Techniques and Practices", "score": 0.0003000000142492354, "subfield": {"id": "https://openalex.org/subfields/1710", "display_name": "Information Systems"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "doi:10.1145/3728902", "is_oa": true, "landing_page_url": "https://doi.org/10.1145/3728902", "pdf_url": "https://dl.acm.org/doi/pdf/10.1145/3728902", "source": {"id": "https://openalex.org/S4404663975", "display_name": "Proceedings of the ACM on software engineering.", "issn_l": "2994-970X", "issn": ["2994-970X"], "is_oa": false, "is_in_doaj": false, "is_core": false, "listed_in": ["jufo-1"], "host_organization": "https://openalex.org/P4310319798", "host_organization_name": "Association for Computing Machinery", "host_organization_lineage": ["https://openalex.org/P4310319798"], "host_organization_lineage_names": ["Association for Computing Machinery"], "type": "journal"}, "license": null, "license_id": null, "version": "publishedVersion", "is_accepted": true, "is_published": true, "raw_source_name": "Proceedings of the ACM on Software Engineering", "raw_type": "journal-article"}, "cited_by_count": 9, "is_retracted": false}, {"id": "https://openalex.org/W4415335193", "doi": null, "title": "It Helps to Take a Second Opinion: Teaching Smaller LLMs to Deliberate Mutually via Selective Rationale Optimisation", "publication_year": 2025, "publication_date": "2025-03-04", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5061843533", "display_name": "Sohan Patnaik", "orcid": "https://orcid.org/0000-0002-4181-4161", "observed_orcids": ["https://orcid.org/0000-0002-4181-4161"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Patnaik, Sohan", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5087749825", "display_name": "Milan Aggarwal", "orcid": "https://orcid.org/0000-0001-6246-9750", "observed_orcids": ["https://orcid.org/0000-0001-6246-9750"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Aggarwal, Milan", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5022961965", "display_name": "Sumit Bhatia", "orcid": "https://orcid.org/0000-0002-8146-4100", "observed_orcids": ["https://orcid.org/0000-0002-8146-4100"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Bhatia, Sumit", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "last", "author": {"id": "https://openalex.org/A5024546524", "display_name": "Balaji Krishnamurthy", "orcid": "https://orcid.org/0000-0002-0366-2427", "observed_orcids": ["https://orcid.org/0000-0002-0366-2427"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Krishnamurthy, Balaji", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}], "abstract_inverted_index": {"Very": [0], "large": [1, 60], "language": [2, 23, 199], "models": [3, 24, 63, 67], "(LLMs)": [4], "such": [5, 49], "as": [6, 50], "GPT-4": [7], "have": [8, 31], "shown": [9], "the": [10, 36, 78, 85, 113, 126, 142, 164, 168, 179, 186, 216, 223, 228, 233, 255], "ability": [11, 79], "to": [12, 64, 83, 119, 133, 157, 177, 207, 226, 243], "handle": [13], "complex": [14], "tasks": [15], "by": [16, 34, 93, 205], "generating": [17, 159, 183], "and": [18, 55, 90, 116, 121, 144, 184, 197, 246], "self-refining": [19], "step-by-step": [20], "rationales.": [21, 187, 229], "Smaller": [22], "(SLMs),": [25], "typically": [26], "with": [27], "<": [28], "13B": [29], "parameters,": [30], "been": [32, 74], "improved": [33], "using": [35, 59, 222], "data": [37], "generated": [38], "from": [39], "very-large": [40], "LMs": [41, 238], "through": [42, 95], "knowledge": [43], "distillation.": [44], "However,": [45], "various": [46], "practical": [47], "constraints": [48], "API": [51], "costs,": [52], "copyright,": [53], "legal": [54], "ethical": [56], "policies": [57], "restrict": [58], "(often": [61], "opaque)": [62], "train": [65], "smaller": [66], "for": [68, 125, 182, 237, 257], "commercial": [69], "use.": [70], "Limited": [71], "success": [72], "has": [73], "achieved": [75], "at": [76, 260], "improving": [77], "of": [80, 87, 112, 137, 166, 235, 239], "an": [81], "SLM": [82, 115], "explore": [84], "space": [86], "possible": [88], "rationales": [89, 123, 140], "evaluate": [91], "them": [92, 118], "itself": [94], "self-deliberation.": [96], "To": [97], "address": [98], "this,": [99], "we": [100], "propose": [101], "COALITION,": [102], "a": [103, 135, 175], "trainable": [104], "framework": [105], "that": [106, 162, 213], "facilitates": [107], "interaction": [108], "between": [109, 215], "two": [110, 217], "variants": [111, 129, 218], "same": [114], "trains": [117], "generate": [120], "refine": [122], "optimized": [124], "end-task.": [127], "The": [128, 147], "exhibit": [130], "different": [131, 190], "behaviors": [132], "produce": [134], "set": [136], "diverse": [138], "candidate": [139], "during": [141], "generation": [143], "refinement": [145], "steps.": [146], "model": [148, 225, 247], "is": [149], "then": [150], "trained": [151], "via": [152], "Selective": [153], "Rationale": [154], "Optimization": [155], "(SRO)": [156], "prefer": [158], "rationale": [160], "candidates": [161], "maximize": [163], "likelihood": [165], "producing": [167], "ground-truth": [169], "answer.": [170], "During": [171], "inference,": [172, 200], "COALITION": [173, 201, 236], "employs": [174], "controller": [176], "select": [178], "suitable": [180], "variant": [181], "refining": [185], "On": [188], "five": [189], "datasets": [191], "covering": [192], "mathematical": [193], "problems,": [194], "commonsense": [195], "reasoning,": [196], "natural": [198], "outperforms": [202], "several": [203], "baselines": [204], "up": [206], "5%.": [208], "Our": [209], "ablation": [210], "studies": [211], "reveal": [212], "cross-communication": [214], "performs": [219], "better": [220], "than": [221], "single": [224], "self-refine": [227], "We": [230, 253], "also": [231], "demonstrate": [232], "applicability": [234], "varying": [240], "scales": [241], "(4B": [242], "14B": [244], "parameters)": [245], "families": [248], "(Mistral,": [249], "Llama,": [250], "Qwen,": [251], "Phi).": [252], "release": [254], "code": [256], "this": [258], "work": [259], "https://github.com/Sohanpatnaik106/coalition.": [261]}, "primary_topic": {"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.7788000106811523, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.7788000106811523, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T12026", "display_name": "Explainable Artificial Intelligence (XAI)", "score": 0.0649000033736229, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10181", "display_name": "Natural Language Processing Techniques", "score": 0.05460000038146973, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2503.02463", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2503.02463", "pdf_url": "https://arxiv.org/pdf/2503.02463", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": null, "license_id": null, "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4409171348", "doi": "https://doi.org/10.1016/j.procir.2025.02.023", "title": "Do LLMs understand shapes? Exploring STL files for automatic CAD feature recognition", "publication_year": 2025, "publication_date": "2025-01-01", "type": "article", "language": "en", "authorships": [{"author_position": "first", "author": {"id": null, "display_name": "Oihana Garcia", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I4210136402", "display_name": "Tekniker", "ror": "https://ror.org/033vryh36", "country_code": "ES", "type": "nonprofit", "lineage": ["https://openalex.org/I4210136402"]}], "countries": ["ES"], "is_corresponding": true, "raw_author_name": "Oihana Garcia", "raw_affiliation_strings": ["TEKNIKER, I\u00f1aki Goenaga 5, Eibar 20600, Spain"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "TEKNIKER, I\u00f1aki Goenaga 5, Eibar 20600, Spain", "institution_ids": ["https://openalex.org/I4210136402"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5046808762", "display_name": "Kerman L\u00f3pez de Calle-Etxabe", "orcid": "https://orcid.org/0000-0003-4147-1813", "observed_orcids": ["https://orcid.org/0000-0003-4147-1813"]}, "institutions": [{"id": "https://openalex.org/I4210136402", "display_name": "Tekniker", "ror": "https://ror.org/033vryh36", "country_code": "ES", "type": "nonprofit", "lineage": ["https://openalex.org/I4210136402"]}], "countries": ["ES"], "is_corresponding": false, "raw_author_name": "Kerman L\u00f3pez de Calle", "raw_affiliation_strings": ["TEKNIKER, I\u00f1aki Goenaga 5, Eibar 20600, Spain"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "TEKNIKER, I\u00f1aki Goenaga 5, Eibar 20600, Spain", "institution_ids": ["https://openalex.org/I4210136402"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5068878644", "display_name": "Jon Ander Sarasua", "orcid": "https://orcid.org/0000-0002-7330-8597", "observed_orcids": ["https://orcid.org/0000-0002-7330-8597"]}, "institutions": [{"id": "https://openalex.org/I4210136402", "display_name": "Tekniker", "ror": "https://ror.org/033vryh36", "country_code": "ES", "type": "nonprofit", "lineage": ["https://openalex.org/I4210136402"]}], "countries": ["ES"], "is_corresponding": false, "raw_author_name": "Jon Ander Sarasua", "raw_affiliation_strings": ["TEKNIKER, I\u00f1aki Goenaga 5, Eibar 20600, Spain"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "TEKNIKER, I\u00f1aki Goenaga 5, Eibar 20600, Spain", "institution_ids": ["https://openalex.org/I4210136402"]}]}], "abstract_inverted_index": {"The": [0, 137], "manufacture": [1], "of": [2, 30, 54, 80, 154, 161, 173, 192], "industrial": [3], "components": [4], "requires": [5], "a": [6, 110, 118, 130, 171, 180], "process": [7, 49], "planning": [8], "stage": [9], "where": [10], "features": [11, 33], "such": [12, 165], "as": [13, 57, 106, 166], "slots,": [14], "holes,": [15], "and": [16, 145, 156], "steps": [17], "need": [18], "to": [19, 36, 85, 129, 141, 157, 184], "be": [20], "identified": [21], "in": [22, 151, 200], "the": [23, 42, 69, 77, 95, 143, 152, 185], "Computer-Aided": [24], "Design": [25], "(CAD)": [26], "models.": [27], "Precise": [28], "detection": [29], "these": [31], "machining": [32], "is": [34, 127, 177], "essential": [35], "generate": [37], "accurate": [38], "manufacturing": [39], "instructions.": [40], "Over": [41], "past": [43], "four": [44], "decades,": [45], "automating": [46], "this": [47, 73, 89, 99, 169, 201], "manual": [48], "has": [50], "been": [51], "an": [52, 190], "area": [53], "research": [55], "known": [56], "Automatic": [58], "Feature": [59], "Recognition": [60], "(AFR).": [61], "To": [62], "date,": [63], "Convolutional": [64], "Neural": [65], "Networks": [66], "(CNNs)": [67], "are": [68, 104, 139], "state-of-the-art": [70], "approach": [71], "for": [72, 94, 109, 179, 197], "task.": [74, 113, 182], "Nevertheless,": [75], "given": [76], "increasing": [78], "ability": [79], "Large": [81], "Language": [82], "Models": [83], "(LLMs)": [84], "understand": [86, 142], "complex": [87], "information,": [88], "work": [90], "proposes": [91], "employing": [92], "LLMs": [93, 150], "AFR": [96], "problem.": [97], "In": [98], "study,": [100], "ASCII-formatted": [101], "STL": [102, 193], "files": [103], "treated": [105], "language-based": [107], "data": [108], "feature": [111, 175, 198], "classification": [112, 181, 199], "For": [114, 168], "doing": [115], "so,": [116], "Qwen,": [117], "pre-trained": [119], "LLM": [120], "with": [121], "robust": [122], "performance": [123], "across": [124], "diverse": [125], "tasks,": [126], "compared": [128], "CNN": [131], "model": [132], "that": [133], "processes": [134], "voxelised": [135], "input.": [136], "results": [138], "validated": [140], "capabilities": [144], "computational": [146], "effort": [147], "required": [148], "by": [149], "context": [153], "AFR,": [155], "evaluate": [158], "their": [159], "understanding": [160, 191], "new": [162], "text-based": [163], "formats": [164], "STL.": [167], "purpose,": [170], "dataset": [172], "24": [174], "classes": [176], "used": [178], "According": [183], "results,": [186], "LLM-based": [187], "methods": [188], "demonstrate": [189], "data,": [194], "revealing": [195], "potential": [196], "field.": [202]}, "primary_topic": {"id": "https://openalex.org/T11159", "display_name": "Manufacturing Process and Optimization", "score": 0.9789999723434448, "subfield": {"id": "https://openalex.org/subfields/2209", "display_name": "Industrial and Manufacturing Engineering"}, "field": {"id": "https://openalex.org/fields/22", "display_name": "Engineering"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T11159", "display_name": "Manufacturing Process and Optimization", "score": 0.9789999723434448, "subfield": {"id": "https://openalex.org/subfields/2209", "display_name": "Industrial and Manufacturing Engineering"}, "field": {"id": "https://openalex.org/fields/22", "display_name": "Engineering"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10719", "display_name": "3D Shape Modeling and Analysis", "score": 0.007300000172108412, "subfield": {"id": "https://openalex.org/subfields/2206", "display_name": "Computational Mechanics"}, "field": {"id": "https://openalex.org/fields/22", "display_name": "Engineering"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T11245", "display_name": "Advanced Numerical Analysis Techniques", "score": 0.0038999998942017555, "subfield": {"id": "https://openalex.org/subfields/2206", "display_name": "Computational Mechanics"}, "field": {"id": "https://openalex.org/fields/22", "display_name": "Engineering"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "doi:10.1016/j.procir.2025.02.023", "is_oa": true, "landing_page_url": "https://doi.org/10.1016/j.procir.2025.02.023", "pdf_url": null, "source": {"id": "https://openalex.org/S2898565713", "display_name": "Procedia CIRP", "issn_l": "2212-8271", "issn": ["2212-8271"], "is_oa": true, "is_in_doaj": false, "is_core": true, "listed_in": ["cwts-core", "jufo-1", "norway-1"], "host_organization": "https://openalex.org/P4310320990", "host_organization_name": "Elsevier BV", "host_organization_lineage": ["https://openalex.org/P4310320990"], "host_organization_lineage_names": ["Elsevier BV"], "type": "journal"}, "license": "cc-by", "license_id": "https://openalex.org/licenses/cc-by", "version": "publishedVersion", "is_accepted": true, "is_published": true, "raw_source_name": "Procedia CIRP", "raw_type": "journal-article"}, "cited_by_count": 3, "is_retracted": false}, {"id": "https://openalex.org/W4415227534", "doi": "https://doi.org/10.1016/j.eswa.2025.129954", "title": "Attribute-guided aesthetic assessment for artistic images based on multimodal hybrid network", "publication_year": 2025, "publication_date": "2025-10-15", "type": "article", "language": "en", "authorships": [{"author_position": "first", "author": {"id": null, "display_name": "Xiaoping Xu", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I4210131919", "display_name": "Xi'an University of Technology", "ror": "https://ror.org/038avdt50", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I4210131919"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Xiaoping Xu", "raw_affiliation_strings": ["School of Sciences, Xi\u2019an University of Technology, Xi\u2019an, Shaanxi,710054, PR China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "School of Sciences, Xi\u2019an University of Technology, Xi\u2019an, Shaanxi,710054, PR China", "institution_ids": ["https://openalex.org/I4210131919"]}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Siye Huang", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I4210131919", "display_name": "Xi'an University of Technology", "ror": "https://ror.org/038avdt50", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I4210131919"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Siye Huang", "raw_affiliation_strings": ["School of Sciences, Xi\u2019an University of Technology, Xi\u2019an, Shaanxi,710054, PR China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "School of Sciences, Xi\u2019an University of Technology, Xi\u2019an, Shaanxi,710054, PR China", "institution_ids": ["https://openalex.org/I4210131919"]}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Wenbo Li", "orcid": "https://orcid.org/0009-0007-1587-1457", "observed_orcids": ["https://orcid.org/0009-0007-1587-1457"]}, "institutions": [{"id": "https://openalex.org/I4210131919", "display_name": "Xi'an University of Technology", "ror": "https://ror.org/038avdt50", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I4210131919"]}], "countries": ["CN"], "is_corresponding": true, "raw_author_name": "Wenbo Li", "raw_affiliation_strings": ["School of Sciences, Xi\u2019an University of Technology, Xi\u2019an, Shaanxi,710054, PR China"], "raw_orcid": "https://orcid.org/0009-0007-1587-1457", "affiliations": [{"raw_affiliation_string": "School of Sciences, Xi\u2019an University of Technology, Xi\u2019an, Shaanxi,710054, PR China", "institution_ids": ["https://openalex.org/I4210131919"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5100669010", "display_name": "Feng Wang", "orcid": "https://orcid.org/0000-0002-2378-9126", "observed_orcids": ["https://orcid.org/0000-0002-2378-9126"]}, "institutions": [{"id": "https://openalex.org/I87445476", "display_name": "Xi'an Jiaotong University", "ror": "https://ror.org/017zhmm22", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I87445476"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Feng Wang", "raw_affiliation_strings": ["School of Mathematics and Statistics, Xi\u2019an Jiaotong University, Xi\u2019an, Shaanxi,710049, PR China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "School of Mathematics and Statistics, Xi\u2019an Jiaotong University, Xi\u2019an, Shaanxi,710049, PR China", "institution_ids": ["https://openalex.org/I87445476"]}]}], "abstract_inverted_index": null, "primary_topic": {"id": "https://openalex.org/T11165", "display_name": "Image and Video Quality Assessment", "score": 0.7422000169754028, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T11165", "display_name": "Image and Video Quality Assessment", "score": 0.7422000169754028, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10824", "display_name": "Image Retrieval and Classification Techniques", "score": 0.09080000221729279, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T12650", "display_name": "Aesthetic Perception and Analysis", "score": 0.05480000004172325, "subfield": {"id": "https://openalex.org/subfields/2805", "display_name": "Cognitive Neuroscience"}, "field": {"id": "https://openalex.org/fields/28", "display_name": "Neuroscience"}, "domain": {"id": "https://openalex.org/domains/1", "display_name": "Life Sciences"}}], "primary_location": {"id": "doi:10.1016/j.eswa.2025.129954", "is_oa": false, "landing_page_url": "https://doi.org/10.1016/j.eswa.2025.129954", "pdf_url": null, "source": {"id": "https://openalex.org/S13144211", "display_name": "Expert Systems with Applications", "issn_l": "0957-4174", "issn": ["0957-4174", "1873-6793"], "is_oa": false, "is_in_doaj": false, "is_core": true, "listed_in": ["cwts-core", "doyens", "jufo-2", "ki-jl-2", "norway-2"], "host_organization": "https://openalex.org/P4310320990", "host_organization_name": "Elsevier BV", "host_organization_lineage": ["https://openalex.org/P4310320990"], "host_organization_lineage_names": ["Elsevier BV"], "type": "journal"}, "license": null, "license_id": null, "version": "publishedVersion", "is_accepted": true, "is_published": true, "raw_source_name": "Expert Systems with Applications", "raw_type": "journal-article"}, "cited_by_count": 5, "is_retracted": false}, {"id": "https://openalex.org/W4406133426", "doi": "https://doi.org/10.48550/arxiv.2501.02144", "title": "Establishing baselines for generative discovery of inorganic crystals", "publication_year": 2025, "publication_date": "2025-01-04", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5021228636", "display_name": "Nathan J. Szymanski", "orcid": "https://orcid.org/0000-0003-2255-9676", "observed_orcids": ["https://orcid.org/0000-0003-2255-9676"]}, "institutions": [{"id": "https://openalex.org/I130238516", "display_name": "University of Minnesota", "ror": "https://ror.org/017zqws13", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I130238516"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Szymanski, Nathan J.", "raw_affiliation_strings": ["University of Minnesota , Department of Chemical Engineering and Materials Science , Minneapolis , MN , USA 55455"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "University of Minnesota , Department of Chemical Engineering and Materials Science , Minneapolis , MN , USA 55455", "institution_ids": ["https://openalex.org/I130238516"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5065773454", "display_name": "Christopher J. Bartel", "orcid": "https://orcid.org/0000-0002-5198-5036", "observed_orcids": ["https://orcid.org/0000-0002-5198-5036"]}, "institutions": [{"id": "https://openalex.org/I130238516", "display_name": "University of Minnesota", "ror": "https://ror.org/017zqws13", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I130238516"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Bartel, Christopher J.", "raw_affiliation_strings": ["University of Minnesota , Department of Chemical Engineering and Materials Science , Minneapolis , MN , USA 55455"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "University of Minnesota , Department of Chemical Engineering and Materials Science , Minneapolis , MN , USA 55455", "institution_ids": ["https://openalex.org/I130238516"]}]}], "abstract_inverted_index": {"Generative": [0], "artificial": [1], "intelligence": [2], "offers": [3], "a": [4, 126, 172], "promising": [5], "avenue": [6], "for": [7, 180, 186, 192, 199], "materials": [8, 71, 181, 205], "discovery,": [9], "yet": [10], "its": [11], "advantages": [12], "over": [13], "traditional": [14], "methods": [15, 61], "remain": [16], "unclear.": [17], "In": [18, 83], "this": [19, 188], "work,": [20], "we": [21, 124], "introduce": [22], "and": [23, 34, 52, 110, 121, 139, 169], "benchmark": [24], "two": [25], "baseline": [26, 120], "approaches": [27], "-": [28, 41], "random": [29], "enumeration": [30], "of": [31, 38, 77, 117, 163, 195, 203], "charge-balanced": [32], "prototypes": [33], "data-driven": [35], "ion": [36, 64], "exchange": [37, 65], "known": [39, 81], "compounds": [40], "against": [42], "four": [43], "generative": [44, 85, 122, 178, 196], "techniques": [45], "based": [46], "on": [47], "diffusion": [48], "models,": [49, 197], "variational": [50], "autoencoders,": [51], "large": [53], "language": [54], "models.": [55], "Our": [56], "results": [57], "show": [58], "that": [59, 72, 206], "established": [60], "such": [62, 105], "as": [63, 106], "are": [66, 73, 135, 207], "better": [67], "at": [68, 88], "generating": [69], "novel": [70, 90, 204], "stable,": [74], "although": [75], "many": [76], "these": [78], "closely": [79], "resemble": [80], "compounds.": [82], "contrast,": [84], "models": [86, 146], "excel": [87], "proposing": [89], "structural": [91], "frameworks": [92], "and,": [93], "when": [94], "sufficient": [95], "training": [96], "data": [97], "is": [98], "available,": [99], "can": [100], "more": [101, 176], "effectively": [102], "target": [103], "properties": [104], "electronic": [107], "band": [108], "gap": [109], "bulk": [111], "modulus.": [112], "To": [113], "enhance": [114], "the": [115, 119, 160, 200], "performance": [116], "both": [118], "approaches,": [123], "implement": [125], "post-generation": [127], "screening": [128], "step": [129, 154], "in": [130, 159], "which": [131], "all": [132, 164], "proposed": [133], "structures": [134], "passed": [136], "through": [137], "stability": [138], "property": [140], "filters": [141], "from": [142], "pre-trained": [143], "machine": [144], "learning": [145], "including": [147], "universal": [148], "interatomic": [149], "potentials.": [150], "This": [151], "low-cost": [152], "filtering": [153], "leads": [155], "to": [156], "substantial": [157], "improvement": [158], "success": [161], "rates": [162], "methods,": [165], "remains": [166], "computationally": [167], "efficient,": [168], "ultimately": [170], "provides": [171], "practical": [173], "pathway": [174], "toward": [175], "effective": [177], "strategies": [179], "discovery.": [182], "By": [183], "establishing": [184], "baselines": [185], "comparison,": [187], "work": [189], "highlights": [190], "opportunities": [191], "continued": [193], "advancement": [194], "especially": [198], "targeted": [201], "generation": [202], "thermodynamically": [208], "stable.": [209]}, "primary_topic": {"id": "https://openalex.org/T11948", "display_name": "Machine Learning in Materials Science", "score": 1.0, "subfield": {"id": "https://openalex.org/subfields/2505", "display_name": "Materials Chemistry"}, "field": {"id": "https://openalex.org/fields/25", "display_name": "Materials Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T11948", "display_name": "Machine Learning in Materials Science", "score": 1.0, "subfield": {"id": "https://openalex.org/subfields/2505", "display_name": "Materials Chemistry"}, "field": {"id": "https://openalex.org/fields/25", "display_name": "Materials Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10211", "display_name": "Computational Drug Discovery Methods", "score": 0.0, "subfield": {"id": "https://openalex.org/subfields/1703", "display_name": "Computational Theory and Mathematics"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10939", "display_name": "Crystal Structures and Properties", "score": 0.0, "subfield": {"id": "https://openalex.org/subfields/2504", "display_name": "Electronic, Optical and Magnetic Materials"}, "field": {"id": "https://openalex.org/fields/25", "display_name": "Materials Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2501.02144", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2501.02144", "pdf_url": "https://arxiv.org/pdf/2501.02144", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": null, "license_id": null, "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4415335975", "doi": "https://doi.org/10.48550/arxiv.2509.24046", "title": "PartnerMAS: An LLM Hierarchical Multi-Agent Framework for Business Partner Selection on High-Dimensional Features", "publication_year": 2025, "publication_date": "2025-09-28", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5102904037", "display_name": "Lingyao Li", "orcid": "https://orcid.org/0000-0001-6905-2149", "observed_orcids": ["https://orcid.org/0000-0001-6905-2149"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Li, Lingyao", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5028155508", "display_name": "Haolun Wu", "orcid": "https://orcid.org/0000-0001-6255-1535", "observed_orcids": ["https://orcid.org/0000-0001-6255-1535", "https://orcid.org/0000-0002-8206-4969"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Wu, Haolun", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5101435752", "display_name": "Zhenkun Li", "orcid": "https://orcid.org/0000-0002-0645-6493", "observed_orcids": ["https://orcid.org/0000-0002-0645-6493"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Li, Zhenkun", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5069269928", "display_name": "Jiabei Hu", "orcid": "https://orcid.org/0009-0007-5005-628X", "observed_orcids": ["https://orcid.org/0009-0007-5005-628X"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Hu, Jiabei", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5100643682", "display_name": "Yu Wang", "orcid": "https://orcid.org/0000-0003-3436-4251", "observed_orcids": ["https://orcid.org/0000-0003-3436-4251", "https://orcid.org/0000-0002-5509-9170"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Wang, Yu", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5103258970", "display_name": "Xiaoshan Huang", "orcid": "https://orcid.org/0000-0003-2868-489X", "observed_orcids": ["https://orcid.org/0000-0003-2868-489X", "https://orcid.org/0000-0003-1986-6683"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Huang, Xiaoshan", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5042013742", "display_name": "Wenyue Hua", "orcid": "https://orcid.org/0009-0008-2043-2704", "observed_orcids": ["https://orcid.org/0009-0008-2043-2704"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Hua, Wenyue", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "last", "author": {"id": null, "display_name": "Wang, Wenqian", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Wang, Wenqian", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}], "abstract_inverted_index": {"High-dimensional": [0], "decision-making": [1, 168], "tasks,": [2], "such": [3, 41], "as": [4, 162], "business": [5], "partner": [6], "selection,": [7], "involve": [8], "evaluating": [9], "large": [10, 21], "candidate": [11], "pools": [12], "with": [13, 36], "heterogeneous": [14], "numerical,": [15], "categorical,": [16], "and": [17, 38, 68, 95, 105, 134], "textual": [18], "features.": [19], "While": [20], "language": [22], "models": [23], "(LLMs)": [24], "offer": [25], "strong": [26], "in-context": [27], "reasoning": [28, 119], "capabilities,": [29], "single-agent": [30, 104], "or": [31], "debate-style": [32], "systems": [33], "often": [34], "struggle": [35], "scalability": [37], "consistency": [39], "in": [40, 140, 169], "settings.": [42], "We": [43], "propose": [44], "PartnerMAS,": [45], "a": [46, 56, 69, 83, 163], "hierarchical": [47], "multi-agent": [48, 107], "framework": [49, 165], "that": [50, 59, 64, 72, 121, 145], "decomposes": [51], "evaluation": [52], "into": [53], "three": [54], "layers:": [55], "Planner": [57], "Agent": [58, 71], "designs": [60], "strategies,": [61], "Specialized": [62], "Agents": [63], "perform": [65], "role-specific": [66], "assessments,": [67], "Supervisor": [70], "integrates": [73], "their": [74], "outputs.": [75], "To": [76], "support": [77], "systematic": [78], "evaluation,": [79], "we": [80], "also": [81], "introduce": [82], "curated": [84], "benchmark": [85], "dataset": [86], "of": [87, 117], "venture": [88], "capital": [89], "co-investments,": [90], "featuring": [91], "diverse": [92], "firm": [93], "attributes": [94], "ground-truth": [96], "syndicates.": [97], "Across": [98], "140": [99], "cases,": [100], "PartnerMAS": [101, 161], "consistently": [102], "outperforms": [103], "debate-based": [106], "baselines,": [108], "achieving": [109], "up": [110], "to": [111, 126], "10--15\\%": [112], "higher": [113], "match": [114], "rates.": [115], "Analysis": [116], "agent": [118], "shows": [120], "planners": [122], "are": [123], "most": [124], "responsive": [125], "domain-informed": [127], "prompts,": [128], "specialists": [129], "produce": [130], "complementary": [131], "feature": [132], "coverage,": [133], "supervisors": [135], "play": [136], "an": [137], "important": [138], "role": [139], "aggregation.": [141], "Our": [142], "findings": [143], "demonstrate": [144], "structured": [146], "collaboration": [147], "among": [148], "LLM": [149], "agents": [150], "can": [151], "generate": [152], "more": [153], "robust": [154], "outcomes": [155], "than": [156], "scaling": [157], "individual": [158], "models,": [159], "highlighting": [160], "promising": [164], "for": [166], "high-dimensional": [167], "data-rich": [170], "domains.": [171]}, "primary_topic": {"id": "https://openalex.org/T10456", "display_name": "Multi-Agent Systems and Negotiation", "score": 0.6704000234603882, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T10456", "display_name": "Multi-Agent Systems and Negotiation", "score": 0.6704000234603882, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T13904", "display_name": "Artificial Intelligence Applications", "score": 0.1363999992609024, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T13812", "display_name": "AI and HR Technologies", "score": 0.0210999995470047, "subfield": {"id": "https://openalex.org/subfields/1407", "display_name": "Organizational Behavior and Human Resource Management"}, "field": {"id": "https://openalex.org/fields/14", "display_name": "Business, Management and Accounting"}, "domain": {"id": "https://openalex.org/domains/2", "display_name": "Social Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2509.24046", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2509.24046", "pdf_url": "https://arxiv.org/pdf/2509.24046", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": null, "license_id": null, "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4414814635", "doi": "https://doi.org/10.48550/arxiv.2510.01238", "title": "Silent Tokens, Loud Effects: Padding in LLMs", "publication_year": 2025, "publication_date": "2025-09-23", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5116293067", "display_name": "Rom Himelstein", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Himelstein, Rom", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": null, "display_name": "LeVi, Amit", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "LeVi, Amit", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5051184573", "display_name": "Yonatan Belinkov", "orcid": "https://orcid.org/0000-0002-6280-5045", "observed_orcids": ["https://orcid.org/0000-0002-6280-5045"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Belinkov, Yonatan", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "last", "author": {"id": "https://openalex.org/A5089135250", "display_name": "Avi Mendelson", "orcid": "https://orcid.org/0000-0003-4274-6866", "observed_orcids": ["https://orcid.org/0000-0003-4274-6866", "https://orcid.org/0000-0001-9281-0817"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Mendelson, Avi", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}], "abstract_inverted_index": {"Padding": [0], "tokens": [1], "are": [2], "widely": [3], "used": [4], "in": [5, 81, 86, 112], "large": [6], "language": [7], "models": [8], "(LLMs)": [9], "to": [10, 28], "equalize": [11], "sequence": [12], "lengths": [13], "during": [14], "batched": [15], "inference.": [16], "While": [17], "they": [18], "should": [19], "be": [20, 109], "fully": [21], "masked,": [22], "implementation": [23], "errors": [24], "can": [25], "cause": [26], "them": [27], "influence": [29, 36], "computation,": [30], "and": [31, 59, 69, 89], "the": [32], "extent": [33], "of": [34, 57, 74], "this": [35, 44], "is": [37, 98], "not": [38, 99], "well": [39], "understood.": [40], "We": [41], "systematically": [42], "study": [43], "effect": [45], "across": [46], "three": [47], "open-source": [48], "model": [49], "families": [50], "(Llama,": [51], "Gemma,": [52], "Qwen),": [53], "inserting": [54], "controlled": [55], "amounts": [56, 73], "padding": [58, 75, 97], "evaluating": [60], "outcomes": [61], "along": [62], "four": [63], "axes:": [64], "activations,": [65], "generation": [66], "quality,": [67], "bias,": [68], "safety.": [70], "Even": [71], "small": [72], "shift": [76], "hidden": [77], "representations,": [78], "degrade": [79], "quality": [80], "smaller": [82], "models,": [83], "alter": [84], "bias": [85], "unpredictable": [87], "ways,": [88], "weaken": [90], "safety": [91], "guardrails.": [92], "These": [93], "findings": [94], "demonstrate": [95], "that": [96, 107], "a": [100, 104], "harmless": [101], "detail": [102], "but": [103], "robustness": [105], "risk": [106], "must": [108], "carefully": [110], "handled": [111], "deployment.": [113]}, "primary_topic": {"id": "https://openalex.org/T11689", "display_name": "Adversarial Robustness in Machine Learning", "score": 0.45829999446868896, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T11689", "display_name": "Adversarial Robustness in Machine Learning", "score": 0.45829999446868896, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.4196999967098236, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10181", "display_name": "Natural Language Processing Techniques", "score": 0.09549999982118607, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2510.01238", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2510.01238", "pdf_url": "https://arxiv.org/pdf/2510.01238", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": null, "license_id": null, "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W7116991302", "doi": "https://doi.org/10.22214/ijraset.2025.76479", "title": "A Comprehensive Survey on Indian Sign Language Interpretation Using Machine Learning", "publication_year": 2025, "publication_date": "2025-12-23", "type": "article", "language": null, "authorships": [{"author_position": "first", "author": {"id": null, "display_name": "Neha G M", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": true, "raw_author_name": "Neha G M", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}], "abstract_inverted_index": {"Indian": [0], "Sign": [1], "Language": [2], "(ISL)": [3], "serves": [4], "as": [5, 51, 98], "the": [6, 20, 69, 76, 87, 129, 146, 159, 184, 207, 216], "primary": [7], "communication": [8, 31], "medium": [9], "for": [10], "India's": [11], "vast": [12], "deaf": [13], "and": [14, 38, 44, 148, 153, 168, 183, 206, 219], "hard-of-hearing": [15], "community,": [16], "which": [17], "numbers": [18], "in": [19, 47, 67, 117], "millions.": [21], "A": [22], "severe": [23], "scarcity": [24], "of": [25, 72, 78, 89, 128, 131, 150, 158, 188, 209, 222], "qualified": [26], "human": [27], "interpreters": [28], "creates": [29], "significant": [30], "barriers,": [32], "limiting": [33], "access": [34], "to": [35, 54, 105, 139], "education,": [36], "healthcare,": [37], "social": [39], "integration.": [40], "Machine": [41], "Learning": [42], "(ML),": [43], "deep": [45, 79, 141], "learning": [46, 142], "particular,": [48], "have": [49, 113], "emerged": [50], "critical": [52, 160], "technologies": [53], "bridge": [55], "this": [56], "gap.": [57], "Traditional": [58], "ML": [59, 137], "approaches": [60], "provided": [61], "foundational": [62], "solutions": [63], "but": [64], "were": [65], "limited": [66], "handling": [68], "high": [70], "variability": [71], "visual": [73], "data.": [74], "With": [75], "advent": [77], "learning,": [80, 205], "Convolutional": [81], "Neural": [82], "Networks": [83], "(CNNs)": [84], "significantly": [85], "improved": [86], "accuracy": [88], "static": [90], "sign": [91], "recognition,": [92], "while": [93], "hybrid": [94], "spatiotemporal": [95], "models,": [96, 212], "such": [97], "CNN-Long": [99], "Short-Term": [100], "Memory": [101], "(LSTM)": [102], "networks,": [103], "began": [104], "address": [106], "dynamic": [107], "gestures.": [108], "More": [109], "recently,": [110], "Transformer-based": [111], "architectures": [112], "shown": [114], "state-of-the-art": [115], "performance": [116], "complex,": [118], "continuous": [119], "sign-to-text": [120], "translation.": [121], "This": [122], "survey": [123], "presents": [124], "a": [125, 155], "comprehensive": [126], "analysis": [127], "evolution": [130], "ISL": [132, 223], "interpretation": [133, 224], "systems,": [134], "from": [135], "traditional": [136], "classifiers": [138], "advanced": [140], "frameworks.": [143], "We": [144, 171], "discuss": [145], "strengths": [147], "limitations": [149], "these": [151], "techniques": [152], "provide": [154], "detailed": [156], "review": [157], "components,": [161], "including": [162, 175, 199], "system": [163], "architectures,": [164], "publicly": [165], "available": [166], "datasets,": [167], "evaluation": [169], "metrics.": [170], "highlight": [172], "persistent": [173], "challenges,": [174], "dataset": [176], "scarcity,": [177], "signer": [178], "dependency,": [179], "regional": [180], "linguistic": [181], "variations,": [182], "crucial,": [185], "often-overlooked,": [186], "role": [187], "non-manual": [189], "features.": [190], "The": [191], "study": [192], "concludes": [193], "by": [194], "outlining": [195], "open": [196], "research": [197], "directions,": [198], "generative": [200], "data": [201], "augmentation,": [202], "privacy-preserving": [203], "federated": [204], "integration": [208], "large": [210], "language": [211], "aimed": [213], "at": [214], "advancing": [215], "practical,": [217], "scalable,": [218], "equitable": [220], "deployment": [221], "systems.": [225]}, "primary_topic": {"id": "https://openalex.org/T11398", "display_name": "Hand Gesture Recognition Systems", "score": 1.0, "subfield": {"id": "https://openalex.org/subfields/1709", "display_name": "Human-Computer Interaction"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T11398", "display_name": "Hand Gesture Recognition Systems", "score": 1.0, "subfield": {"id": "https://openalex.org/subfields/1709", "display_name": "Human-Computer Interaction"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T11285", "display_name": "Hearing Impairment and Communication", "score": 0.0, "subfield": {"id": "https://openalex.org/subfields/3204", "display_name": "Developmental and Educational Psychology"}, "field": {"id": "https://openalex.org/fields/32", "display_name": "Psychology"}, "domain": {"id": "https://openalex.org/domains/2", "display_name": "Social Sciences"}}, {"id": "https://openalex.org/T10812", "display_name": "Human Pose and Action Recognition", "score": 0.0, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "doi:10.22214/ijraset.2025.76479", "is_oa": true, "landing_page_url": "https://doi.org/10.22214/ijraset.2025.76479", "pdf_url": "https://doi.org/10.22214/ijraset.2025.76479", "source": {"id": "https://openalex.org/S2764566388", "display_name": "International Journal for Research in Applied Science and Engineering Technology", "issn_l": "2321-9653", "issn": ["2321-9653"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/P4322614460", "host_organization_name": "International Journal for Research in Applied Science and Engineering Technology (IJRASET)", "host_organization_lineage": ["https://openalex.org/P4322614460"], "host_organization_lineage_names": ["International Journal for Research in Applied Science and Engineering Technology (IJRASET)"], "type": "journal"}, "license": null, "license_id": null, "version": "publishedVersion", "is_accepted": true, "is_published": true, "raw_source_name": "International Journal for Research in Applied Science and Engineering Technology", "raw_type": "journal-article"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W7117572120", "doi": "https://doi.org/10.48550/arxiv.2512.21675", "title": "UniPercept: Towards Unified Perceptual-Level Image Understanding across Aesthetics, Quality, Structure, and Texture", "publication_year": 2025, "publication_date": "2025-12-25", "type": "preprint", "language": null, "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5121555533", "display_name": "Shuo Cao", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Cao, Shuo", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5100769532", "display_name": "Jiayang Li", "orcid": "https://orcid.org/0000-0001-9271-3988", "observed_orcids": ["https://orcid.org/0000-0001-9271-3988"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Li, Jiayang", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5039464470", "display_name": "Xiaohui Li", "orcid": "https://orcid.org/0000-0003-0776-265X", "observed_orcids": ["https://orcid.org/0000-0003-0776-265X"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Li, Xiaohui", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5113380881", "display_name": "Yuandong Pu", "orcid": "https://orcid.org/0000-0003-3764-0370", "observed_orcids": ["https://orcid.org/0000-0003-3764-0370"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Pu, Yuandong", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5127848808", "display_name": "Kaiwen Zhu", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Zhu, Kaiwen", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5074618663", "display_name": "Yuan Gao", "orcid": "https://orcid.org/0000-0001-7007-9605", "observed_orcids": ["https://orcid.org/0000-0001-7007-9605"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Gao, Yuanting", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5121149752", "display_name": "Siqi Luo", "orcid": "https://orcid.org/0009-0006-5369-2321", "observed_orcids": ["https://orcid.org/0009-0006-5369-2321"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Luo, Siqi", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5125478781", "display_name": "Yi Xin", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Xin, Yi", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5126133011", "display_name": "Qi Qin", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Qin, Qi", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5121584145", "display_name": "Yu Zhou", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Zhou, Yu", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5107672073", "display_name": "Xiangyu Chen", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Chen, Xiangyu", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5121542075", "display_name": "Wenlong Zhang", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Zhang, Wenlong", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5121234725", "display_name": "Bin Fu", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Fu, Bin", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5127892833", "display_name": "Yu Qiao", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Qiao, Yu", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "last", "author": {"id": "https://openalex.org/A5107946409", "display_name": "Yihao Liu", "orcid": "https://orcid.org/0000-0003-3187-9903", "observed_orcids": ["https://orcid.org/0000-0003-3187-9903", "https://orcid.org/0009-0009-0517-3334"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Liu, Yihao", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}], "abstract_inverted_index": {"Multimodal": [0], "large": [1], "language": [2], "models": [3], "(MLLMs)": [4], "have": [5], "achieved": [6], "remarkable": [7], "progress": [8], "in": [9, 123], "visual": [10, 15], "understanding": [11, 42, 105], "tasks": [12], "such": [13], "as": [14, 109], "grounding,": [16], "segmentation,": [17], "and": [18, 50, 58, 81, 92, 106], "captioning.": [19], "However,": [20], "their": [21], "ability": [22], "to": [23, 62], "perceive": [24], "perceptual-level": [25, 40, 64, 103, 147], "image": [26, 41, 65, 104, 149], "features": [27], "remains": [28], "limited.": [29], "In": [30], "this": [31, 69], "work,": [32], "we": [33, 71], "present": [34], "UniPercept-Bench,": [35], "a": [36, 54, 73, 110, 133, 138, 142], "unified": [37], "framework": [38], "for": [39, 114, 145], "across": [43, 87], "three": [44], "key": [45], "domains:": [46], "Aesthetics,": [47], "Quality,": [48], "Structure": [49], "Texture.": [51], "We": [52], "establish": [53], "hierarchical": [55], "definition": [56], "system": [57], "construct": [59], "large-scale": [60], "datasets": [61], "evaluate": [63], "understanding.": [66, 150], "Based": [67], "on": [68, 102], "foundation,": [70], "develop": [72], "strong": [74, 139], "baseline": [75], "UniPercept": [76, 98], "trained": [77], "via": [78], "Domain-Adaptive": [79], "Pre-Training": [80], "Task-Aligned": [82], "RL,": [83], "enabling": [84], "robust": [85], "generalization": [86], "both": [88], "Visual": [89, 93], "Rating": [90], "(VR)": [91], "Question": [94], "Answering": [95], "(VQA)": [96], "tasks.": [97], "outperforms": [99], "existing": [100], "MLLMs": [101, 127], "can": [107], "serve": [108], "plug-and-play": [111], "reward": [112], "model": [113], "text-to-image": [115], "generation.": [116], "This": [117], "work": [118], "defines": [119], "Perceptual-Level": [120], "Image": [121], "Understanding": [122], "the": [124, 130], "era": [125], "of": [126, 132], "and,": [128], "through": [129], "introduction": [131], "comprehensive": [134], "benchmark": [135], "together": [136], "with": [137], "baseline,": [140], "provides": [141], "solid": [143], "foundation": [144], "advancing": [146], "multimodal": [148]}, "primary_topic": {"id": "https://openalex.org/T11714", "display_name": "Multimodal Machine Learning Applications", "score": 0.9994999766349792, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T11714", "display_name": "Multimodal Machine Learning Applications", "score": 0.9994999766349792, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10775", "display_name": "Generative Adversarial Networks and Image Synthesis", "score": 0.00019999999494757503, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10036", "display_name": "Advanced Neural Network Applications", "score": 0.0, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2512.21675", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2512.21675", "pdf_url": "https://arxiv.org/pdf/2512.21675", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": null, "license_id": null, "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4411639796", "doi": "https://doi.org/10.1109/mnet.2025.3583088", "title": "Large Language Models Powered Malicious Traffic Detection: Architecture, Opportunities, and Case Study", "publication_year": 2025, "publication_date": "2025-06-25", "type": "article", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5080142175", "display_name": "Xinggong Zhang", "orcid": "https://orcid.org/0000-0003-0484-5951", "observed_orcids": ["https://orcid.org/0000-0003-0484-5951"]}, "institutions": [{"id": "https://openalex.org/I20231570", "display_name": "Peking University", "ror": "https://ror.org/02v51f717", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I20231570"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Xinggong Zhang", "raw_affiliation_strings": ["Wangxuan Institute of Computer Technology, Peking University, Beijing, China", "Peking University, China"], "raw_orcid": "https://orcid.org/0000-0003-0484-5951", "affiliations": [{"raw_affiliation_string": "Wangxuan Institute of Computer Technology, Peking University, Beijing, China", "institution_ids": ["https://openalex.org/I20231570"]}, {"raw_affiliation_string": "Peking University, China", "institution_ids": ["https://openalex.org/I20231570"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5113898228", "display_name": "Haotian Meng", "orcid": "https://orcid.org/0009-0006-7739-8469", "observed_orcids": ["https://orcid.org/0009-0006-7739-8469"]}, "institutions": [{"id": "https://openalex.org/I6507939", "display_name": "China United Network Communications Group (China)", "ror": "https://ror.org/028w99c90", "country_code": "CN", "type": "company", "lineage": ["https://openalex.org/I6507939"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Haotian Meng", "raw_affiliation_strings": ["China Unicom, Beijing, China", "China Unicom, China"], "raw_orcid": "https://orcid.org/0009-0006-7739-8469", "affiliations": [{"raw_affiliation_string": "China Unicom, Beijing, China", "institution_ids": ["https://openalex.org/I6507939"]}, {"raw_affiliation_string": "China Unicom, China", "institution_ids": ["https://openalex.org/I6507939"]}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Qingyang Li", "orcid": "https://orcid.org/0009-0009-0552-865X", "observed_orcids": ["https://orcid.org/0009-0009-0552-865X"]}, "institutions": [{"id": "https://openalex.org/I20231570", "display_name": "Peking University", "ror": "https://ror.org/02v51f717", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I20231570"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Qingyang Li", "raw_affiliation_strings": ["Wangxuan Institute of Computer Technology, Peking University, Beijing, China", "Peking University, China"], "raw_orcid": "https://orcid.org/0009-0009-0552-865X", "affiliations": [{"raw_affiliation_string": "Wangxuan Institute of Computer Technology, Peking University, Beijing, China", "institution_ids": ["https://openalex.org/I20231570"]}, {"raw_affiliation_string": "Peking University, China", "institution_ids": ["https://openalex.org/I20231570"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5043915518", "display_name": "Y. P. Tan", "orcid": "https://orcid.org/0009-0002-8883-9865", "observed_orcids": ["https://orcid.org/0009-0002-8883-9865"]}, "institutions": [{"id": "https://openalex.org/I20231570", "display_name": "Peking University", "ror": "https://ror.org/02v51f717", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I20231570"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Yunpeng Tan", "raw_affiliation_strings": ["Wangxuan Institute of Computer Technology, Peking University, Beijing, China", "Peking University, China"], "raw_orcid": "https://orcid.org/0009-0002-8883-9865", "affiliations": [{"raw_affiliation_string": "Wangxuan Institute of Computer Technology, Peking University, Beijing, China", "institution_ids": ["https://openalex.org/I20231570"]}, {"raw_affiliation_string": "Peking University, China", "institution_ids": ["https://openalex.org/I20231570"]}]}, {"author_position": "last", "author": {"id": null, "display_name": "Lei Zhang", "orcid": "https://orcid.org/0000-0001-7991-2915", "observed_orcids": ["https://orcid.org/0000-0001-7991-2915"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Lei Zhang", "raw_affiliation_strings": ["Zhongguancun Laboratory, Beijing, China", "Zhongguancun Lab, China"], "raw_orcid": "https://orcid.org/0000-0001-7991-2915", "affiliations": [{"raw_affiliation_string": "Zhongguancun Laboratory, Beijing, China", "institution_ids": []}, {"raw_affiliation_string": "Zhongguancun Lab, China", "institution_ids": []}]}], "abstract_inverted_index": {"Malicious": [0], "traffic": [1, 14, 77, 103, 116, 144], "detection": [2, 167, 177], "is": [3], "a": [4, 26, 44, 68, 107, 169, 195], "pivotal": [5], "technology": [6], "for": [7, 47, 76], "network": [8, 13, 17, 48], "security": [9], "to": [10, 200], "identify": [11, 136], "abnormal": [12], "and": [15, 37, 84, 124, 131, 155], "detect": [16], "attacks.": [18], "Large": [19, 97], "Language": [20, 98], "Models": [21, 99], "(LLMs)": [22, 100], "are": [23, 157], "trained": [24], "on": [25, 61, 73, 91, 164, 178], "vast": [27], "corpus": [28], "of": [29, 35, 59, 70, 96, 110, 113, 121, 133, 149], "text,": [30], "have": [31, 52], "amassed": [32], "remarkable": [33], "capabilities": [34, 132, 185], "context-understanding": [36], "commonsense": [38], "knowledge.": [39], "This": [40], "has": [41], "opened": [42], "up": [43], "new": [45], "door": [46], "attacks": [49], "detection.": [50, 104], "Researchers": [51], "already": [53], "initiated": [54], "discussions": [55], "regarding": [56], "the": [57, 82, 93, 111, 119, 129, 151], "application": [58], "LLMs": [60, 75], "specific": [62], "cyber-security": [63], "tasks.": [64], "Unfortunately,": [65], "there": [66], "remains": [67], "lack": [69], "comprehensive": [71], "analysis": [72], "harnessing": [74], "detection,": [78, 117], "as": [79, 81, 168], "well": [80], "opportunities": [83, 154], "challenges.": [85], "In": [86], "this": [87], "paper,": [88], "we": [89, 135, 160], "focus": [90], "unleashing": [92], "full": [94], "potential": [95], "in": [101, 143, 186], "malicious": [102, 115], "We": [105], "present": [106, 161], "holistic": [108], "view": [109], "architecture": [112], "LLM-powered": [114, 165], "including": [118], "procedures": [120], "Pre-training,": [122], "Fine-tuning,": [123], "Detection.": [125], "Especially,": [126], "by": [127, 182], "exploring": [128], "knowledge": [130], "LLM,": [134], "three": [137], "distinct": [138], "roles": [139], "LLM": [140], "can": [141], "act": [142], "classification:Classifier,Encoder,": [145], "andPredictor.": [146], "For": [147], "each": [148], "them,": [150], "modeling": [152], "paradigm,": [153], "challenges": [156], "elaborated.": [158], "Finally,": [159], "our": [162], "design": [163], "DDoS": [166, 181], "case": [170], "study.": [171], "The": [172, 189], "proposed": [173], "framework": [174], "attains": [175], "accurate": [176], "carpet": [179], "bombing": [180], "exploiting": [183], "LLMs\u2019": [184], "contextual": [187], "mining.": [188], "evaluation": [190], "shows": [191], "its": [192], "efficacy,": [193], "exhibiting": [194], "nearly": [196], "35%": [197], "improvement": [198], "compared": [199], "existing": [201], "systems.": [202]}, "primary_topic": {"id": "https://openalex.org/T10400", "display_name": "Network Security and Intrusion Detection", "score": 0.9916999936103821, "subfield": {"id": "https://openalex.org/subfields/1705", "display_name": "Computer Networks and Communications"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T10400", "display_name": "Network Security and Intrusion Detection", "score": 0.9916999936103821, "subfield": {"id": "https://openalex.org/subfields/1705", "display_name": "Computer Networks and Communications"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T11598", "display_name": "Internet Traffic Analysis and Secure E-voting", "score": 0.005200000014156103, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T11689", "display_name": "Adversarial Robustness in Machine Learning", "score": 0.0013000000035390258, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "doi:10.1109/mnet.2025.3583088", "is_oa": false, "landing_page_url": "https://doi.org/10.1109/mnet.2025.3583088", "pdf_url": null, "source": {"id": "https://openalex.org/S186584794", "display_name": "IEEE Network", "issn_l": "0890-8044", "issn": ["0890-8044", "1558-156X"], "is_oa": false, "is_in_doaj": false, "is_core": true, "listed_in": ["cwts-core", "jufo-3", "norway-1"], "host_organization": "https://openalex.org/P4310319808", "host_organization_name": "Institute of Electrical and Electronics Engineers", "host_organization_lineage": ["https://openalex.org/P4310319808"], "host_organization_lineage_names": ["Institute of Electrical and Electronics Engineers"], "type": "journal"}, "license": null, "license_id": null, "version": "publishedVersion", "is_accepted": true, "is_published": true, "raw_source_name": "IEEE Network", "raw_type": "journal-article"}, "cited_by_count": 8, "is_retracted": false}, {"id": "https://openalex.org/W4412747791", "doi": "https://doi.org/10.1109/access.2025.3594218", "title": "Toward Inclusive Healthcare: An LLM-Based Multimodal Chatbot for Preliminary Diagnosis", "publication_year": 2025, "publication_date": "2025-01-01", "type": "article", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5101480577", "display_name": "Ishita Agarwal", "orcid": "https://orcid.org/0009-0003-8300-2182", "observed_orcids": ["https://orcid.org/0009-0003-8300-2182"]}, "institutions": [{"id": "https://openalex.org/I876193797", "display_name": "Vellore Institute of Technology University", "ror": "https://ror.org/00qzypv28", "country_code": "IN", "type": "education", "lineage": ["https://openalex.org/I876193797"]}], "countries": ["IN"], "is_corresponding": false, "raw_author_name": "Ishita Agarwal", "raw_affiliation_strings": ["School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India"], "raw_orcid": "https://orcid.org/0009-0003-8300-2182", "affiliations": [{"raw_affiliation_string": "School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India", "institution_ids": ["https://openalex.org/I876193797"]}]}, {"author_position": "middle", "author": {"id": null, "display_name": "V. Sakthivel", "orcid": "https://orcid.org/0000-0003-2543-0034", "observed_orcids": ["https://orcid.org/0000-0003-2543-0034"]}, "institutions": [{"id": "https://openalex.org/I876193797", "display_name": "Vellore Institute of Technology University", "ror": "https://ror.org/00qzypv28", "country_code": "IN", "type": "education", "lineage": ["https://openalex.org/I876193797"]}], "countries": ["IN"], "is_corresponding": false, "raw_author_name": "V. Sakthivel", "raw_affiliation_strings": ["School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India"], "raw_orcid": "https://orcid.org/0000-0003-2543-0034", "affiliations": [{"raw_affiliation_string": "School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India", "institution_ids": ["https://openalex.org/I876193797"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5113242021", "display_name": "P Prakash", "orcid": "https://orcid.org/0009-0008-8622-7156", "observed_orcids": ["https://orcid.org/0009-0008-8622-7156"]}, "institutions": [{"id": "https://openalex.org/I876193797", "display_name": "Vellore Institute of Technology University", "ror": "https://ror.org/00qzypv28", "country_code": "IN", "type": "education", "lineage": ["https://openalex.org/I876193797"]}], "countries": ["IN"], "is_corresponding": false, "raw_author_name": "P. Prakash", "raw_affiliation_strings": ["School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India"], "raw_orcid": "https://orcid.org/0009-0008-8622-7156", "affiliations": [{"raw_affiliation_string": "School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India", "institution_ids": ["https://openalex.org/I876193797"]}]}], "abstract_inverted_index": {"This": [0], "paper": [1], "presents": [2], "the": [3, 54, 103, 116], "design": [4], "and": [5, 28, 36, 82, 89, 122], "development": [6], "of": [7, 105], "a": [8, 17, 77], "multimodal": [9, 106], "medical": [10, 26, 37, 52, 66, 79], "chatbot": [11, 55], "that": [12], "leverages": [13], "Gemini-2.0-Flash": [14], "Model": [15, 98], "alongside": [16], "novel": [18], "Retrieval-Augmented": [19], "Generation": [20], "(RAG)": [21], "architecture": [22, 75], "to": [23, 41, 94], "support": [24, 126], "preliminary": [25], "diagnosis": [27], "recommendations.": [29], "The": [30, 72], "system": [31], "integrates": [32], "textual": [33], "prompt": [34], "analysis": [35], "image": [38], "interpretation,": [39], "aiming": [40], "improve": [42], "healthcare": [43], "accessibility,": [44], "particularly": [45], "for": [46, 118], "underserved": [47], "populations.": [48], "Focused": [49], "on": [50, 61], "data-rich": [51], "conditions,": [53], "generates": [56], "reliable": [57], "diagnostic": [58], "insights": [59], "based": [60], "natural": [62], "language": [63], "inputs": [64], "and/or": [65], "images,": [67], "requiring": [68], "minimal": [69], "user": [70], "expertise.": [71], "proposed": [73], "RAG-based": [74], "incorporates": [76], "curated": [78], "knowledge": [80], "base": [81], "structured": [83, 111], "retrieval": [84], "mechanisms,": [85], "significantly": [86], "reducing": [87], "hallucinations": [88], "enhancing": [90], "response": [91], "credibility": [92], "compared": [93], "direct": [95], "Large": [96], "Language": [97], "(LLM)": [99], "querying.": [100], "By": [101], "demonstrating": [102], "efficacy": [104], "reasoning": [107], "in": [108], "conjunction": [109], "with": [110], "retrieval,": [112], "this": [113], "work": [114], "paves": [115], "way": [117], "more": [119], "accessible,": [120], "accurate,": [121], "scalable": [123], "AI-driven": [124], "health": [125], "systems.": [127]}, "primary_topic": {"id": "https://openalex.org/T11636", "display_name": "Artificial Intelligence in Healthcare and Education", "score": 0.9965999722480774, "subfield": {"id": "https://openalex.org/subfields/2718", "display_name": "Health Informatics"}, "field": {"id": "https://openalex.org/fields/27", "display_name": "Medicine"}, "domain": {"id": "https://openalex.org/domains/4", "display_name": "Health Sciences"}}, "topics": [{"id": "https://openalex.org/T11636", "display_name": "Artificial Intelligence in Healthcare and Education", "score": 0.9965999722480774, "subfield": {"id": "https://openalex.org/subfields/2718", "display_name": "Health Informatics"}, "field": {"id": "https://openalex.org/fields/27", "display_name": "Medicine"}, "domain": {"id": "https://openalex.org/domains/4", "display_name": "Health Sciences"}}, {"id": "https://openalex.org/T11714", "display_name": "Multimodal Machine Learning Applications", "score": 0.001500000013038516, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T11396", "display_name": "Artificial Intelligence in Healthcare", "score": 0.0010999999940395355, "subfield": {"id": "https://openalex.org/subfields/3605", "display_name": "Health Information Management"}, "field": {"id": "https://openalex.org/fields/36", "display_name": "Health Professions"}, "domain": {"id": "https://openalex.org/domains/4", "display_name": "Health Sciences"}}], "primary_location": {"id": "doi:10.1109/access.2025.3594218", "is_oa": true, "landing_page_url": "https://doi.org/10.1109/access.2025.3594218", "pdf_url": null, "source": {"id": "https://openalex.org/S2485537415", "display_name": "IEEE Access", "issn_l": "2169-3536", "issn": ["2169-3536"], "is_oa": true, "is_in_doaj": true, "is_core": true, "listed_in": ["cwts-core", "doaj", "jufo-1", "ki-jl-1", "norway-1"], "host_organization": "https://openalex.org/P4310319808", "host_organization_name": "Institute of Electrical and Electronics Engineers", "host_organization_lineage": ["https://openalex.org/P4310319808"], "host_organization_lineage_names": ["Institute of Electrical and Electronics Engineers"], "type": "journal"}, "license": "cc-by", "license_id": "https://openalex.org/licenses/cc-by", "version": "publishedVersion", "is_accepted": true, "is_published": true, "raw_source_name": "IEEE Access", "raw_type": "journal-article"}, "cited_by_count": 12, "is_retracted": false}, {"id": "https://openalex.org/W4409039368", "doi": "https://doi.org/10.2139/ssrn.5200113", "title": "Optimizing Large Vision-Language Models for Context-Aware Construction Safety Assessment", "publication_year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5003452999", "display_name": "Taegeon Kim", "orcid": "https://orcid.org/0000-0003-1682-6061", "observed_orcids": ["https://orcid.org/0000-0003-1682-6061", "https://orcid.org/0000-0001-7815-5887", "https://orcid.org/0009-0003-4770-163X"]}, "institutions": [{"id": "https://openalex.org/I193775966", "display_name": "Yonsei University", "ror": "https://ror.org/01wjejq96", "country_code": "KR", "type": "education", "lineage": ["https://openalex.org/I193775966"]}], "countries": ["KR"], "is_corresponding": false, "raw_author_name": "Taegeon Kim", "raw_affiliation_strings": ["Yonsei University, Seoul, Korea, Republic of (South Korea)"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Yonsei University, Seoul, Korea, Republic of (South Korea)", "institution_ids": ["https://openalex.org/I193775966"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5082880112", "display_name": "Seokhwan Kim", "orcid": "https://orcid.org/0000-0002-7443-1212", "observed_orcids": ["https://orcid.org/0000-0002-7443-1212"]}, "institutions": [{"id": "https://openalex.org/I193775966", "display_name": "Yonsei University", "ror": "https://ror.org/01wjejq96", "country_code": "KR", "type": "education", "lineage": ["https://openalex.org/I193775966"]}], "countries": ["KR"], "is_corresponding": false, "raw_author_name": "Seokhwan Kim", "raw_affiliation_strings": ["Yonsei University, Seoul, Korea, Republic of (South Korea)"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Yonsei University, Seoul, Korea, Republic of (South Korea)", "institution_ids": ["https://openalex.org/I193775966"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5037093946", "display_name": "Wei\u2010Chih Chern", "orcid": "https://orcid.org/0009-0004-1535-0094", "observed_orcids": ["https://orcid.org/0009-0004-1535-0094"]}, "institutions": [{"id": "https://openalex.org/I127591826", "display_name": "University of Dayton", "ror": "https://ror.org/021v3qy27", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I127591826"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Wei-Chih Chern", "raw_affiliation_strings": ["University of Dayton, 300 College Park, Dayton, OH 45469, United States"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "University of Dayton, 300 College Park, Dayton, OH 45469, United States", "institution_ids": ["https://openalex.org/I127591826"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5101950079", "display_name": "Somin Park", "orcid": "https://orcid.org/0000-0002-8885-0872", "observed_orcids": ["https://orcid.org/0000-0002-8885-0872"]}, "institutions": [{"id": "https://openalex.org/I189196454", "display_name": "The University of Texas at Arlington", "ror": "https://ror.org/019kgqr73", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I189196454"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Somin Park", "raw_affiliation_strings": ["University of Texas at Arlington, 415 S West St Apt no 205, Arlington, TX TEXAS 76019, United States"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "University of Texas at Arlington, 415 S West St Apt no 205, Arlington, TX TEXAS 76019, United States", "institution_ids": ["https://openalex.org/I189196454"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5100773581", "display_name": "Daeho Kim", "orcid": "https://orcid.org/0000-0003-2921-3168", "observed_orcids": ["https://orcid.org/0000-0003-2921-3168"]}, "institutions": [{"id": "https://openalex.org/I185261750", "display_name": "University of Toronto", "ror": "https://ror.org/03dbr7087", "country_code": "CA", "type": "education", "lineage": ["https://openalex.org/I185261750"]}], "countries": ["CA"], "is_corresponding": false, "raw_author_name": "Daeho Kim", "raw_affiliation_strings": ["University of Toronto, 105 St George Street, Toronto, M5S 3G8, Canada"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "University of Toronto, 105 St George Street, Toronto, M5S 3G8, Canada", "institution_ids": ["https://openalex.org/I185261750"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5089943566", "display_name": "Hongjo Kim", "orcid": "https://orcid.org/0000-0002-2374-3785", "observed_orcids": ["https://orcid.org/0000-0002-2374-3785"]}, "institutions": [{"id": "https://openalex.org/I193775966", "display_name": "Yonsei University", "ror": "https://ror.org/01wjejq96", "country_code": "KR", "type": "education", "lineage": ["https://openalex.org/I193775966"]}], "countries": ["KR"], "is_corresponding": true, "raw_author_name": "Hongjo Kim", "raw_affiliation_strings": ["Yonsei University, Seoul, Korea, Republic of (South Korea)"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Yonsei University, Seoul, Korea, Republic of (South Korea)", "institution_ids": ["https://openalex.org/I193775966"]}]}], "abstract_inverted_index": null, "primary_topic": {"id": "https://openalex.org/T11714", "display_name": "Multimodal Machine Learning Applications", "score": 0.9042999744415283, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T11714", "display_name": "Multimodal Machine Learning Applications", "score": 0.9042999744415283, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10809", "display_name": "Occupational Health and Safety Research", "score": 0.03830000013113022, "subfield": {"id": "https://openalex.org/subfields/3614", "display_name": "Radiological and Ultrasound Technology"}, "field": {"id": "https://openalex.org/fields/36", "display_name": "Health Professions"}, "domain": {"id": "https://openalex.org/domains/4", "display_name": "Health Sciences"}}, {"id": "https://openalex.org/T10036", "display_name": "Advanced Neural Network Applications", "score": 0.03669999912381172, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "doi:10.2139/ssrn.5200113", "is_oa": true, "landing_page_url": "https://doi.org/10.2139/ssrn.5200113", "pdf_url": null, "source": {"id": "https://openalex.org/S4210172589", "display_name": "SSRN Electronic Journal", "issn_l": "1556-5068", "issn": ["1556-5068"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I1318003438", "host_organization_name": "RELX Group (Netherlands)", "host_organization_lineage": ["https://openalex.org/I1318003438"], "host_organization_lineage_names": [], "type": "repository"}, "license": null, "license_id": null, "version": "acceptedVersion", "is_accepted": true, "is_published": false, "raw_source_name": null, "raw_type": "posted-content"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W7126238697", "doi": "https://doi.org/10.65411/rst.2026.267581", "title": "RMUTTOBot: Transforming University Admission Services with a TAG-based RAG LLM Chatbot", "publication_year": 2025, "publication_date": "2025-12-30", "type": "article", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5124446385", "display_name": "Vipa Thananont", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I4210130811", "display_name": "Rajamangala University of Technology Tawan-ok", "ror": "https://ror.org/03cvxzw02", "country_code": "TH", "type": "education", "lineage": ["https://openalex.org/I10245363", "https://openalex.org/I4210130811"]}], "countries": ["TH"], "is_corresponding": false, "raw_author_name": "Vipa Thananont", "raw_affiliation_strings": ["Division of Information Technology, Faculty of Business Administration and Information Technology, Rajamangala University of Technology Tawan-ok : Chakrabongse Bhuvanarth Campus, Bangkok 10400, Thailand"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Division of Information Technology, Faculty of Business Administration and Information Technology, Rajamangala University of Technology Tawan-ok : Chakrabongse Bhuvanarth Campus, Bangkok 10400, Thailand", "institution_ids": ["https://openalex.org/I4210130811"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5054650166", "display_name": "Saowakhon Nookhao", "orcid": "https://orcid.org/0000-0002-9991-8748", "observed_orcids": ["https://orcid.org/0000-0002-9991-8748"]}, "institutions": [{"id": "https://openalex.org/I91538806", "display_name": "King Mongkut's Institute of Technology Ladkrabang", "ror": "https://ror.org/055mf0v62", "country_code": "TH", "type": "education", "lineage": ["https://openalex.org/I91538806"]}], "countries": ["TH"], "is_corresponding": false, "raw_author_name": "Saowakhon Nookhao", "raw_affiliation_strings": ["Digital Transformation and Technology Management Program, KMITL Business School, Bangkok 10520, Thailand"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Digital Transformation and Technology Management Program, KMITL Business School, Bangkok 10520, Thailand", "institution_ids": ["https://openalex.org/I91538806"]}]}], "abstract_inverted_index": {"Advancements": [0], "in": [1, 5, 206], "artificial": [2], "intelligence,": [3], "particularly": [4], "large": [6], "language": [7], "models": [8], "(LLMs)": [9], "and": [10, 22, 38, 48, 62, 116, 129, 146, 169, 196, 212], "retrieval-augmented": [11], "generation": [12, 88], "(RAG)": [13], "techniques,": [14], "have": [15], "improved": [16], "chatbot": [17, 68, 192], "capabilities": [18], "for": [19, 72, 98, 105, 202], "more": [20], "natural": [21], "domain-specific": [23, 67], "interactions.": [24], "However,": [25], "conventional": [26], "RAG": [27, 137, 148, 189], "systems,": [28], "which": [29], "retrieve": [30], "information": [31, 100], "from": [32], "unstructured": [33], "text": [34], "sources": [35], "like": [36], "websites": [37], "PDFs,": [39], "exhibit": [40], "critical": [41], "failures": [42], "when": [43], "applied": [44], "to": [45, 157], "the": [46, 60, 135, 142, 164], "dynamic": [47, 107], "precise": [49], "nature": [50], "of": [51, 64, 78], "university": [52], "information.": [53], "This": [54], "research": [55], "addresses": [56], "these": [57], "gaps": [58], "through": [59], "design": [61], "development": [63], "RMUTTOBot,": [65], "a": [66, 84, 93, 151, 158, 199], "providing": [69], "admissions": [70], "support": [71], "prospective": [73], "students": [74], "at": [75, 218], "Rajamangala": [76], "University": [77], "Technology": [79], "Tawan-ok": [80], "(RMUTTO).": [81], "We": [82], "propose": [83], "novel,": [85], "lightweight": [86], "table-augmented": [87], "(TAG)": [89], "approach": [90], "that": [91, 134, 183], "combines": [92], "curated,": [94], "updatable": [95], "knowledge": [96], "base": [97], "general": [99], "with": [101, 172, 188], "live": [102], "database": [103], "queries": [104], "real-time,": [106], "data.": [108], "Performance": [109], "was": [110], "evaluated": [111], "using": [112], "both": [113, 141], "automated": [114], "metrics": [115], "human": [117], "assessments": [118], "across": [119], "six": [120], "criteria:": [121], "semantic": [122], "similarity,": [123], "retrieval": [124], "effectiveness,": [125], "relevance,": [126], "fluency,": [127, 171], "coverage,": [128], "consistency.": [130], "Experimental": [131], "results": [132], "show": [133], "TAG-based": [136], "system": [138], "significantly": [139], "outperformed": [140], "baseline": [143], "LLM-only": [144], "configuration": [145], "PDF-based": [147, 159], "system,": [149], "achieving": [150], "12.76%": [152], "higher": [153], "BERTF1": [154], "score": [155], "compared": [156], "RAG.": [160], "Human": [161], "evaluation": [162], "confirmed": [163], "system\u2019s": [165], "high": [166], "response": [167], "relevance": [168], "linguistic": [170], "strong": [173], "inter-rater": [174], "reliability": [175], "(Krippendorff\u2019s": [176], "\u03b1": [177], ">": [178], "0.835).": [179], "These": [180], "findings": [181], "demonstrate": [182], "combining": [184], "structured": [185], "data": [186], "augmentation": [187], "substantially": [190], "enhances": [191], "accuracy,": [193], "contextual": [194], "grounding,": [195], "completeness,": [197], "offering": [198], "robust": [200], "framework": [201], "intelligent": [203], "conversational": [204], "systems": [205], "academic": [207], "domains.": [208], "The": [209], "source": [210], "code": [211], "implementation": [213], "details": [214], "are": [215], "publicly": [216], "available": [217], "https://github.com/vipa-thananant/RMUTTOBot.": [219]}, "primary_topic": {"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.4047999978065491, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.4047999978065491, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T12128", "display_name": "AI in Service Interactions", "score": 0.38749998807907104, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T12031", "display_name": "Speech and dialogue systems", "score": 0.09130000323057175, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "doi:10.65411/rst.2026.267581", "is_oa": true, "landing_page_url": "https://doi.org/10.65411/rst.2026.267581", "pdf_url": "https://li01.tci-thaijo.org/index.php/rmutsvrj/article/download/267581/181384", "source": {"id": "https://openalex.org/S5407056372", "display_name": "Recent Science and Technology", "issn_l": "3057-0786", "issn": ["3057-0786"], "is_oa": false, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": null, "host_organization_name": null, "host_organization_lineage": [], "host_organization_lineage_names": [], "type": "journal"}, "license": "cc-by-nc-nd", "license_id": "https://openalex.org/licenses/cc-by-nc-nd", "version": "publishedVersion", "is_accepted": true, "is_published": true, "raw_source_name": "Recent Science and Technology", "raw_type": "journal-article"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W7106299158", "doi": "https://doi.org/10.48550/arxiv.2511.14937", "title": "CIMemories: A Compositional Benchmark for Contextual Integrity of Persistent Memory in LLMs", "publication_year": 2025, "publication_date": "2025-11-18", "type": "preprint", "language": null, "authorships": [{"author_position": "first", "author": {"id": null, "display_name": "Mireshghallah, Niloofar", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Mireshghallah, Niloofar", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": null, "display_name": "Mangaokar, Neal", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Mangaokar, Neal", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": null, "display_name": "Kokhlikyan, Narine", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Kokhlikyan, Narine", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5009367143", "display_name": "Arman Serikuly Zharmagambetov", "orcid": "https://orcid.org/0000-0002-2182-8291", "observed_orcids": ["https://orcid.org/0000-0002-2182-8291"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Zharmagambetov, Arman", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": null, "display_name": "Zaheer, Manzil", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Zaheer, Manzil", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": null, "display_name": "Mahloujifar, Saeed", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Mahloujifar, Saeed", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "last", "author": {"id": null, "display_name": "Chaudhuri, Kamalika", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Chaudhuri, Kamalika", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}], "abstract_inverted_index": {"Large": [0], "Language": [1], "Models": [2], "(LLMs)": [3], "increasingly": [4], "use": [5], "persistent": [6], "memory": [7, 19, 45], "from": [8, 44, 117, 125], "past": [9], "interactions": [10], "to": [11, 88, 119, 127], "enhance": [12], "personalization": [13], "and": [14, 112, 141], "task": [15, 48, 64, 105], "performance.": [16], "However,": [17], "this": [18, 158], "introduces": [20], "critical": [21], "risks": [22], "when": [23, 131], "sensitive": [24], "information": [25, 42, 93], "is": [26, 135], "revealed": [27], "in": [28, 66, 144], "inappropriate": [29, 77], "contexts.": [30], "We": [31], "present": [32], "CIMemories,": [33], "a": [34], "benchmark": [35], "for": [36, 73, 78, 150], "evaluating": [37], "whether": [38], "LLMs": [39], "appropriately": [40], "control": [41], "flow": [43], "based": [46], "on": [47], "context.": [49], "CIMemories": [50], "uses": [51], "synthetic": [52], "user": [53], "profiles": [54], "with": [55, 62, 95], "over": [56], "100": [57], "attributes": [58, 149], "per": [59], "user,": [60], "paired": [61], "diverse": [63], "contexts": [65], "which": [67, 145], "each": [68], "attribute": [69], "may": [70], "be": [71], "essential": [72], "some": [74], "tasks": [75, 111], "but": [76], "others.": [79], "Our": [80], "evaluation": [81], "reveals": [82], "that": [83, 177], "frontier": [84], "models": [85, 146, 160], "exhibit": [86], "up": [87], "69%": [89], "attribute-level": [90], "violations": [91, 123], "(leaking": [92], "inappropriately),": [94], "lower": [96], "violation": [97], "rates": [98], "often": [99], "coming": [100], "at": [101], "the": [102, 132], "cost": [103], "of": [104], "utility.": [106], "Violations": [107], "accumulate": [108], "across": [109], "both": [110], "runs:": [113], "as": [114], "usage": [115], "increases": [116], "1": [118], "40": [120], "tasks,": [121], "GPT-5's": [122], "rise": [124], "0.1%": [126], "9.6%,": [128], "reaching": [129], "25.1%": [130], "same": [133], "prompt": [134], "executed": [136], "5": [137], "times,": [138], "revealing": [139], "arbitrary": [140], "unstable": [142], "behavior": [143], "leak": [147], "different": [148], "identical": [151], "prompts.": [152], "Privacy-conscious": [153], "prompting": [154, 186], "does": [155], "not": [156, 183], "solve": [157], "-": [159], "overgeneralize,": [161], "sharing": [162], "everything": [163], "or": [164, 187], "nothing": [165], "rather": [166], "than": [167], "making": [168], "nuanced,": [169], "context-dependent": [170], "decisions.": [171], "These": [172], "findings": [173], "reveal": [174], "fundamental": [175], "limitations": [176], "require": [178], "contextually": [179], "aware": [180], "reasoning": [181], "capabilities,": [182], "just": [184], "better": [185], "scaling.": [188]}, "primary_topic": {"id": "https://openalex.org/T10764", "display_name": "Privacy-Preserving Technologies in Data", "score": 0.8511000275611877, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T10764", "display_name": "Privacy-Preserving Technologies in Data", "score": 0.8511000275611877, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T11045", "display_name": "Privacy, Security, and Data Protection", "score": 0.09860000014305115, "subfield": {"id": "https://openalex.org/subfields/3312", "display_name": "Sociology and Political Science"}, "field": {"id": "https://openalex.org/fields/33", "display_name": "Social Sciences"}, "domain": {"id": "https://openalex.org/domains/2", "display_name": "Social Sciences"}}, {"id": "https://openalex.org/T10883", "display_name": "Ethics and Social Impacts of AI", "score": 0.014600000344216824, "subfield": {"id": "https://openalex.org/subfields/3311", "display_name": "Safety Research"}, "field": {"id": "https://openalex.org/fields/33", "display_name": "Social Sciences"}, "domain": {"id": "https://openalex.org/domains/2", "display_name": "Social Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2511.14937", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2511.14937", "pdf_url": "https://arxiv.org/pdf/2511.14937", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": null, "license_id": null, "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4409221375", "doi": "https://doi.org/10.1080/10095020.2025.2483884", "title": "Design and application of a semantic-driven geospatial modeling knowledge graph based on large language models", "publication_year": 2025, "publication_date": "2025-04-07", "type": "article", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5055854355", "display_name": "Jianyuan Liang", "orcid": "https://orcid.org/0009-0004-7280-5788", "observed_orcids": ["https://orcid.org/0009-0004-7280-5788"]}, "institutions": [{"id": "https://openalex.org/I37461747", "display_name": "Wuhan University", "ror": "https://ror.org/033vjfk17", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I37461747"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Jianyuan Liang", "raw_affiliation_strings": ["Wuhan University"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Wuhan University", "institution_ids": ["https://openalex.org/I37461747"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5110860664", "display_name": "Shuyang Hou", "orcid": "https://orcid.org/0009-0000-6984-9959", "observed_orcids": ["https://orcid.org/0009-0000-6984-9959"]}, "institutions": [{"id": "https://openalex.org/I37461747", "display_name": "Wuhan University", "ror": "https://ror.org/033vjfk17", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I37461747"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Shuyang Hou", "raw_affiliation_strings": ["Wuhan University"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Wuhan University", "institution_ids": ["https://openalex.org/I37461747"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5110916424", "display_name": "Anqi Zhao", "orcid": "https://orcid.org/0000-0001-7511-6268", "observed_orcids": ["https://orcid.org/0000-0001-7511-6268"]}, "institutions": [{"id": "https://openalex.org/I37461747", "display_name": "Wuhan University", "ror": "https://ror.org/033vjfk17", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I37461747"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Anqi Zhao", "raw_affiliation_strings": ["Wuhan University"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Wuhan University", "institution_ids": ["https://openalex.org/I37461747"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5073591829", "display_name": "Qingyang Xu", "orcid": "https://orcid.org/0000-0001-8351-4944", "observed_orcids": ["https://orcid.org/0000-0001-8351-4944"]}, "institutions": [{"id": "https://openalex.org/I37461747", "display_name": "Wuhan University", "ror": "https://ror.org/033vjfk17", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I37461747"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Qingyang Xu", "raw_affiliation_strings": ["Wuhan University"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Wuhan University", "institution_ids": ["https://openalex.org/I37461747"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5089044672", "display_name": "Longgang Xiang", "orcid": "https://orcid.org/0000-0001-9022-6991", "observed_orcids": ["https://orcid.org/0000-0001-9022-6991"]}, "institutions": [{"id": "https://openalex.org/I37461747", "display_name": "Wuhan University", "ror": "https://ror.org/033vjfk17", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I37461747"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Longgang Xiang", "raw_affiliation_strings": ["Wuhan University"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Wuhan University", "institution_ids": ["https://openalex.org/I37461747"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5021251779", "display_name": "Rui Li", "orcid": "https://orcid.org/0000-0001-5167-2956", "observed_orcids": ["https://orcid.org/0000-0001-5167-2956", "https://orcid.org/0009-0005-0625-6802"]}, "institutions": [{"id": "https://openalex.org/I37461747", "display_name": "Wuhan University", "ror": "https://ror.org/033vjfk17", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I37461747"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Rui Li", "raw_affiliation_strings": ["Wuhan University"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Wuhan University", "institution_ids": ["https://openalex.org/I37461747"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5030670883", "display_name": "Huayi Wu", "orcid": "https://orcid.org/0000-0003-3971-0512", "observed_orcids": ["https://orcid.org/0000-0003-3971-0512"]}, "institutions": [{"id": "https://openalex.org/I37461747", "display_name": "Wuhan University", "ror": "https://ror.org/033vjfk17", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I37461747"]}], "countries": ["CN"], "is_corresponding": true, "raw_author_name": "Huayi Wu", "raw_affiliation_strings": ["Wuhan University"], "raw_orcid": "https://orcid.org/0000-0003-3971-0512", "affiliations": [{"raw_affiliation_string": "Wuhan University", "institution_ids": ["https://openalex.org/I37461747"]}]}], "abstract_inverted_index": {"While": [0], "leveraging": [1], "large": [2], "language": [3], "models": [4], "(LLMs)": [5], "for": [6, 36, 67, 86, 133, 170, 217, 240], "intelligent": [7], "geospatial": [8, 38, 171, 241], "modeling": [9, 39, 53, 58, 103, 130, 164, 199], "has": [10], "garnered": [11], "significant": [12], "attention,": [13], "the": [14, 73, 78, 84, 149, 175, 193, 206, 228], "limited": [15], "domain-specific": [16, 87], "knowledge": [17, 35, 96, 142, 158], "of": [18, 75, 80, 123, 230], "LLMs": [19, 169, 236], "often": [20], "leads": [21], "to": [22, 50, 72, 147, 160, 237], "inefficient": [23], "or": [24], "unreliable": [25], "geo-analysis": [26], "model": [27], "generation.": [28], "Crowdsourced": [29], "geoprocessing": [30, 62, 179], "scripts": [31, 63, 106, 180, 191], "encapsulate": [32], "extensive": [33], "expert": [34], "different": [37], "tasks,": [40], "where": [41], "code": [42, 76, 239], "snippets": [43], "are": [44, 113], "strategically": [45], "combined": [46], "into": [47], "functional": [48, 203, 219], "steps": [49], "build": [51], "application-specific": [52], "processes.": [54], "However,": [55], "extracting": [56], "these": [57, 218], "processes": [59, 104, 200], "from": [60, 105], "heterogeneous": [61], "and": [64, 83, 101, 119, 141, 166, 181], "integrating": [65], "them": [66], "reuse": [68], "remains": [69], "challenging": [70], "due": [71], "complexity": [74], "interdependencies,": [77], "heterogeneity": [79], "scripting": [81], "approaches,": [82], "need": [85], "customization.": [88], "To": [89], "address": [90], "this,": [91], "we": [92], "propose": [93], "S-GMKG,": [94, 231], "a": [95, 115, 120, 139, 155], "graph": [97], "that": [98, 189], "systematically": [99], "extracts": [100], "integrates": [102], "as": [107, 154, 198], "structured": [108], "semantic": [109], "units.": [110], "Two": [111], "strategies": [112, 208], "introduced:": [114], "skeleton-based": [116], "extraction": [117, 132], "method": [118], "knowledge-enhanced": [121], "chain": [122], "thought": [124], "(CoT)": [125], "approach,": [126], "which": [127], "facilitate": [128], "automated": [129], "process": [131, 144], "S-GMKG": [134, 152, 176, 194], "via": [135], "prompt": [136], "engineering.": [137], "Furthermore,": [138], "self-canonicalization": [140], "augmentation": [143], "is": [145], "proposed": [146, 207], "refine": [148], "S-GMKG.": [150], "Consequently,": [151], "serves": [153], "robust": [156], "external": [157], "source": [159], "provide": [161], "interpretable,": [162], "graph-based": [163], "solutions": [165], "synergizes": [167], "with": [168, 201, 205, 235], "tasks.": [172], "We": [173], "implemented": [174], "using": [177], "4820": [178], "evaluated": [182], "it": [183], "across": [184], "various": [185], "LLMs.": [186], "Results": [187], "indicate": [188], "most": [190], "in": [192, 214, 223, 233], "can": [195], "be": [196], "represented": [197], "3\u20137": [202], "steps,": [204], "achieving": [209], "3.2%\u201314.5%": [210], "higher": [211], "recall": [212], "rates": [213], "relationship": [215], "identification": [216], "steps.": [220], "Case": [221], "studies": [222], "two": [224], "distinct": [225], "scenarios": [226], "demonstrate": [227], "practicality": [229], "particularly": [232], "collaborating": [234], "generate": [238], "modeling.": [242]}, "primary_topic": {"id": "https://openalex.org/T10757", "display_name": "Geographic Information Systems Studies", "score": 0.26589998602867126, "subfield": {"id": "https://openalex.org/subfields/3305", "display_name": "Geography, Planning and Development"}, "field": {"id": "https://openalex.org/fields/33", "display_name": "Social Sciences"}, "domain": {"id": "https://openalex.org/domains/2", "display_name": "Social Sciences"}}, "topics": [{"id": "https://openalex.org/T10757", "display_name": "Geographic Information Systems Studies", "score": 0.26589998602867126, "subfield": {"id": "https://openalex.org/subfields/3305", "display_name": "Geography, Planning and Development"}, "field": {"id": "https://openalex.org/fields/33", "display_name": "Social Sciences"}, "domain": {"id": "https://openalex.org/domains/2", "display_name": "Social Sciences"}}, {"id": "https://openalex.org/T10215", "display_name": "Semantic Web and Ontologies", "score": 0.2556000053882599, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T11986", "display_name": "Scientific Computing and Data Management", "score": 0.1509999930858612, "subfield": {"id": "https://openalex.org/subfields/1802", "display_name": "Information Systems and Management"}, "field": {"id": "https://openalex.org/fields/18", "display_name": "Decision Sciences"}, "domain": {"id": "https://openalex.org/domains/2", "display_name": "Social Sciences"}}], "primary_location": {"id": "doi:10.1080/10095020.2025.2483884", "is_oa": true, "landing_page_url": "https://doi.org/10.1080/10095020.2025.2483884", "pdf_url": null, "source": {"id": "https://openalex.org/S36798160", "display_name": "Geo-spatial Information Science", "issn_l": "1009-5020", "issn": ["1009-5020", "1993-5153"], "is_oa": true, "is_in_doaj": true, "is_core": true, "listed_in": ["cwts-core", "doaj", "jufo-1", "norway-1"], "host_organization": "https://openalex.org/P4310320547", "host_organization_name": "Taylor & Francis", "host_organization_lineage": ["https://openalex.org/P4310320547", "https://openalex.org/P4310320449"], "host_organization_lineage_names": ["Taylor & Francis", "Informa"], "type": "journal"}, "license": "cc-by", "license_id": "https://openalex.org/licenses/cc-by", "version": "publishedVersion", "is_accepted": true, "is_published": true, "raw_source_name": "Geo-spatial Information Science", "raw_type": "journal-article"}, "cited_by_count": 12, "is_retracted": false}, {"id": "https://openalex.org/W4407749301", "doi": "https://doi.org/10.52783/jisem.v10i11s.1594", "title": "Evaluation of Key Performance Indicators (KPIs) for Enhancing Efficiency, Sustainability, and Operational Optimization in Renewable Energy Management using Artificial Intelligence and Large Language Models", "publication_year": 2025, "publication_date": "2025-02-18", "type": "article", "language": "en", "authorships": [{"author_position": "first", "author": {"id": null, "display_name": "Athar Javed Ali", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": true, "raw_author_name": "Athar Javed Ali", "raw_affiliation_strings": ["Central Institute of Business Management, Research & Development , Nagpur"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Central Institute of Business Management, Research & Development , Nagpur", "institution_ids": []}]}], "abstract_inverted_index": {"The": [0, 70, 156], "integration": [1, 174], "of": [2, 44, 76, 112, 118, 126, 146, 175], "Large": [3], "Language": [4], "Models": [5], "(LLMs)": [6], "within": [7], "renewable": [8, 67, 119, 179], "energy": [9, 17, 68, 80, 103, 113, 120, 180], "systems": [10], "presents": [11], "an": [12], "innovative": [13], "approach": [14], "to": [15, 62, 168], "optimizing": [16], "efficiency,": [18], "enhancing": [19], "sustainability,": [20], "and": [21, 87, 108, 123, 153, 165, 172], "improving": [22], "operational": [23, 151], "performance": [24], "(Bai,": [25], "J.,": [26], "Wang,": [27], "Y.,": [28, 30], "Chen,": [29], "et.": [31, 95], "al.": [32, 96], "2021).": [33], "Despite": [34], "their": [35], "potential,": [36], "a": [37, 52, 139], "clear": [38], "methodology": [39], "for": [40, 55, 142, 162], "evaluating": [41, 56], "the": [42, 66, 74, 110, 116, 124, 136, 144, 170], "success": [43], "LLM": [45, 63, 106, 147], "implementations": [46], "remains": [47], "underdeveloped.": [48], "This": [49], "paper": [50, 137], "introduces": [51], "structured": [53], "framework": [54, 71], "Key": [57], "Performance": [58], "Indicators": [59], "(KPIs)": [60], "tailored": [61], "applications": [64], "in": [65, 79, 102, 178], "sector.": [69], "systematically": [72], "addresses": [73], "assessment": [75], "LLM-driven": [77], "improvements": [78], "forecasting": [81], "accuracy,": [82], "grid": [83], "management,": [84], "predictive": [85], "maintenance,": [86], "resource": [88, 121], "optimization": [89, 117], "(Dasgupta,": [90], "I.,": [91], "Lampinen,": [92], "A.": [93], "K.,": [94], "2022).": [97], "Critical": [98], "KPIs": [99], "include": [100], "reductions": [101], "consumption": [104], "during": [105], "training": [107], "inference,": [109], "accuracy": [111], "demand": [114], "predictions,": [115], "utilization,": [122], "minimization": [125], "carbon": [127], "footprints": [128], "(Piantadosi,": [129], "S.": [130], "2023).": [131], "By": [132], "establishing": [133], "this": [134], "framework,": [135], "provides": [138], "robust": [140], "tool": [141], "measuring": [143], "impact": [145], "technologies": [148], "on": [149], "both": [150], "efficiency": [152], "sustainability": [154], "outcomes.": [155], "study\u2019s": [157], "findings": [158], "offer": [159], "valuable": [160], "insights": [161], "policymakers,": [163], "researchers,": [164], "industry": [166], "stakeholders": [167], "guide": [169], "responsible": [171], "effective": [173], "AI-driven": [176], "solutions": [177], "infrastructures.": [181]}, "primary_topic": {"id": "https://openalex.org/T13904", "display_name": "Artificial Intelligence Applications", "score": 0.31200000643730164, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T13904", "display_name": "Artificial Intelligence Applications", "score": 0.31200000643730164, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T14260", "display_name": "Impact of AI and Big Data on Business and Society", "score": 0.26269999146461487, "subfield": {"id": "https://openalex.org/subfields/1803", "display_name": "Management Science and Operations Research"}, "field": {"id": "https://openalex.org/fields/18", "display_name": "Decision Sciences"}, "domain": {"id": "https://openalex.org/domains/2", "display_name": "Social Sciences"}}, {"id": "https://openalex.org/T12451", "display_name": "Smart Grid and Power Systems", "score": 0.1454000025987625, "subfield": {"id": "https://openalex.org/subfields/2208", "display_name": "Electrical and Electronic Engineering"}, "field": {"id": "https://openalex.org/fields/22", "display_name": "Engineering"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "doi:10.52783/jisem.v10i11s.1594", "is_oa": true, "landing_page_url": "https://doi.org/10.52783/jisem.v10i11s.1594", "pdf_url": "https://jisem-journal.com/index.php/journal/article/download/1594/620", "source": {"id": "https://openalex.org/S2737791640", "display_name": "Journal of Information Systems Engineering & Management", "issn_l": "2468-4376", "issn": ["2468-4376", "2468-2071"], "is_oa": true, "is_in_doaj": false, "is_core": true, "listed_in": ["cwts-core"], "host_organization": "https://openalex.org/P4310320063", "host_organization_name": "Lectito Journals", "host_organization_lineage": ["https://openalex.org/P4310320063"], "host_organization_lineage_names": ["Lectito Journals"], "type": "journal"}, "license": null, "license_id": null, "version": "publishedVersion", "is_accepted": true, "is_published": true, "raw_source_name": "Journal of Information Systems Engineering and Management", "raw_type": "journal-article"}, "cited_by_count": 2, "is_retracted": false}, {"id": "https://openalex.org/W4415200318", "doi": "https://doi.org/10.48550/arxiv.2505.10402", "title": "Rethinking Repetition Problems of LLMs in Code Generation", "publication_year": 2025, "publication_date": "2025-05-15", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5077542599", "display_name": "Yihong Dong", "orcid": "https://orcid.org/0000-0002-6048-2377", "observed_orcids": ["https://orcid.org/0000-0002-6048-2377", "https://orcid.org/0000-0001-6228-4019"]}, "institutions": [{"id": "https://openalex.org/I20231570", "display_name": "Peking University", "ror": "https://ror.org/02v51f717", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I20231570"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Dong, Yihong", "raw_affiliation_strings": ["Key Laboratory of High Confidence Software Technologies ( Peking University) , Ministry of Education ; School of Computer Science , Peking University , Beijing , China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Key Laboratory of High Confidence Software Technologies ( Peking University) , Ministry of Education ; School of Computer Science , Peking University , Beijing , China", "institution_ids": ["https://openalex.org/I20231570"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5100373068", "display_name": "Yuchen Liu", "orcid": "https://orcid.org/0009-0004-0955-5624", "observed_orcids": ["https://orcid.org/0009-0004-0955-5624"]}, "institutions": [{"id": "https://openalex.org/I20231570", "display_name": "Peking University", "ror": "https://ror.org/02v51f717", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I20231570"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Liu, Yuchen", "raw_affiliation_strings": ["Key Laboratory of High Confidence Software Technologies ( Peking University) , Ministry of Education ; School of Computer Science , Peking University , Beijing , China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Key Laboratory of High Confidence Software Technologies ( Peking University) , Ministry of Education ; School of Computer Science , Peking University , Beijing , China", "institution_ids": ["https://openalex.org/I20231570"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5082599057", "display_name": "Xue Jiang", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I20231570", "display_name": "Peking University", "ror": "https://ror.org/02v51f717", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I20231570"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Jiang, Xue", "raw_affiliation_strings": ["Key Laboratory of High Confidence Software Technologies ( Peking University) , Ministry of Education ; School of Computer Science , Peking University , Beijing , China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Key Laboratory of High Confidence Software Technologies ( Peking University) , Ministry of Education ; School of Computer Science , Peking University , Beijing , China", "institution_ids": ["https://openalex.org/I20231570"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5049100391", "display_name": "Zhi Gang Jin", "orcid": "https://orcid.org/0000-0003-1087-226X", "observed_orcids": ["https://orcid.org/0000-0003-1087-226X"]}, "institutions": [{"id": "https://openalex.org/I20231570", "display_name": "Peking University", "ror": "https://ror.org/02v51f717", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I20231570"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Jin, Zhi", "raw_affiliation_strings": ["Key Laboratory of High Confidence Software Technologies ( Peking University) , Ministry of Education ; School of Computer Science , Peking University , Beijing , China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Key Laboratory of High Confidence Software Technologies ( Peking University) , Ministry of Education ; School of Computer Science , Peking University , Beijing , China", "institution_ids": ["https://openalex.org/I20231570"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5100447682", "display_name": "Ge Li", "orcid": "https://orcid.org/0000-0002-5828-0186", "observed_orcids": ["https://orcid.org/0000-0002-5828-0186", "https://orcid.org/0000-0001-9988-005X"]}, "institutions": [{"id": "https://openalex.org/I20231570", "display_name": "Peking University", "ror": "https://ror.org/02v51f717", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I20231570"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Li, Ge", "raw_affiliation_strings": ["Key Laboratory of High Confidence Software Technologies ( Peking University) , Ministry of Education ; School of Computer Science , Peking University , Beijing , China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Key Laboratory of High Confidence Software Technologies ( Peking University) , Ministry of Education ; School of Computer Science , Peking University , Beijing , China", "institution_ids": ["https://openalex.org/I20231570"]}]}], "abstract_inverted_index": {"With": [0], "the": [1, 7, 17, 22, 42, 61, 106, 131, 162, 176, 194], "advent": [2], "of": [3, 9, 19, 41, 133, 196], "neural": [4], "language": [5], "models,": [6], "performance": [8], "code": [10, 47, 63, 110, 125, 144, 166], "generation": [11, 23, 111], "has": [12, 30], "been": [13], "significantly": [14], "boosted.": [15], "However,": [16], "problem": [18, 45, 54], "repetitions": [20, 191], "during": [21, 124], "process": [24], "continues": [25], "to": [26, 104, 120, 138, 156], "linger.": [27], "Previous": [28], "work": [29], "primarily": [31], "focused": [32], "on": [33, 102, 179], "content": [34], "repetition,": [35, 60], "which": [36, 73, 96], "is": [37, 55], "merely": [38], "a": [39, 70, 152], "fraction": [40], "broader": [43], "repetition": [44, 87, 107, 122, 163], "in": [46, 65, 78, 109, 143, 165], "generation.": [48, 145, 167], "A": [49], "more": [50], "prevalent": [51], "and": [52, 88, 127, 186, 192], "challenging": [53], "structural": [56, 59, 86], "repetition.": [57], "In": [58, 80], "repeated": [62], "appears": [64], "various": [66], "patterns": [67], "but": [68], "possesses": [69], "fixed": [71], "structure,": [72], "can": [74], "be": [75], "inherently": [76], "reflected": [77], "grammar.": [79], "this": [81, 148], "paper,": [82], "we": [83, 150], "formally": [84], "define": [85], "propose": [89], "an": [90], "efficient": [91], "decoding": [92], "approach": [93], "called": [94], "RPG,": [95], "stands": [97], "for": [98, 112, 160], "Repetition": [99], "Penalization": [100], "based": [101], "Grammar,": [103], "alleviate": [105], "problems": [108, 123, 164], "LLMs.": [113], "Specifically,": [114], "RPG": [115, 173], "first": [116], "leverages": [117], "grammar": [118], "rules": [119], "identify": [121], "generation,": [126], "then": [128], "strategically": [129], "decays": [130], "likelihood": [132], "critical": [134], "tokens": [135], "that": [136, 172], "contribute": [137], "repetitions,": [139], "thereby": [140], "mitigating": [141, 161], "them": [142], "To": [146], "facilitate": [147], "study,": [149], "construct": [151], "new": [153], "dataset": [154, 181], "CodeRepetEval": [155, 180], "comprehensively": [157], "evaluate": [158], "approaches": [159], "Extensive": [168], "experimental": [169], "results": [170], "demonstrate": [171], "substantially": [174], "outperforms": [175], "best-performing": [177], "baselines": [178], "as": [182, 184], "well": [183], "HumanEval": [185], "MBPP": [187], "benchmarks,": [188], "effectively": [189], "reducing": [190], "enhancing": [193], "quality": [195], "generated": [197], "code.": [198]}, "primary_topic": {"id": "https://openalex.org/T10260", "display_name": "Software Engineering Research", "score": 0.9945999979972839, "subfield": {"id": "https://openalex.org/subfields/1710", "display_name": "Information Systems"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T10260", "display_name": "Software Engineering Research", "score": 0.9945999979972839, "subfield": {"id": "https://openalex.org/subfields/1710", "display_name": "Information Systems"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.0020000000949949026, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10533", "display_name": "Teaching and Learning Programming", "score": 0.000699999975040555, "subfield": {"id": "https://openalex.org/subfields/1706", "display_name": "Computer Science Applications"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2505.10402", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2505.10402", "pdf_url": "https://arxiv.org/pdf/2505.10402", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": null, "license_id": null, "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4409243115", "doi": "https://doi.org/10.2139/ssrn.5209619", "title": "Foldexplorer: Fast and Accurate Protein Structure Search with Sequence-Enhanced Graph Embedding", "publication_year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5024384055", "display_name": "Yuan Liu", "orcid": "https://orcid.org/0009-0003-6872-2632", "observed_orcids": ["https://orcid.org/0009-0003-6872-2632"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Yuan Liu", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5100386227", "display_name": "Ying Zhang", "orcid": "https://orcid.org/0000-0003-1936-6101", "observed_orcids": ["https://orcid.org/0000-0003-1936-6101"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Ying Zhang", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5034955307", "display_name": "Zhen Ping Zhou", "orcid": "https://orcid.org/0000-0003-3232-9903", "observed_orcids": ["https://orcid.org/0000-0003-3232-9903"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Zhen Zhou", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "last", "author": {"id": null, "display_name": "Hongbin Shen", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I183067930", "display_name": "Shanghai Jiao Tong University", "ror": "https://ror.org/0220qvk04", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I183067930"]}], "countries": ["CN"], "is_corresponding": true, "raw_author_name": "Hongbin Shen", "raw_affiliation_strings": ["Shanghai Jiao Tong University (SJTU) - Institute of Image Processing and Pattern Recognition"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Shanghai Jiao Tong University (SJTU) - Institute of Image Processing and Pattern Recognition", "institution_ids": ["https://openalex.org/I183067930"]}]}], "abstract_inverted_index": null, "primary_topic": {"id": "https://openalex.org/T10044", "display_name": "Protein Structure and Dynamics", "score": 0.9009000062942505, "subfield": {"id": "https://openalex.org/subfields/1312", "display_name": "Molecular Biology"}, "field": {"id": "https://openalex.org/fields/13", "display_name": "Biochemistry, Genetics and Molecular Biology"}, "domain": {"id": "https://openalex.org/domains/1", "display_name": "Life Sciences"}}, "topics": [{"id": "https://openalex.org/T10044", "display_name": "Protein Structure and Dynamics", "score": 0.9009000062942505, "subfield": {"id": "https://openalex.org/subfields/1312", "display_name": "Molecular Biology"}, "field": {"id": "https://openalex.org/fields/13", "display_name": "Biochemistry, Genetics and Molecular Biology"}, "domain": {"id": "https://openalex.org/domains/1", "display_name": "Life Sciences"}}, {"id": "https://openalex.org/T12254", "display_name": "Machine Learning in Bioinformatics", "score": 0.05849999934434891, "subfield": {"id": "https://openalex.org/subfields/1312", "display_name": "Molecular Biology"}, "field": {"id": "https://openalex.org/fields/13", "display_name": "Biochemistry, Genetics and Molecular Biology"}, "domain": {"id": "https://openalex.org/domains/1", "display_name": "Life Sciences"}}, {"id": "https://openalex.org/T11162", "display_name": "Enzyme Structure and Function", "score": 0.011900000274181366, "subfield": {"id": "https://openalex.org/subfields/2505", "display_name": "Materials Chemistry"}, "field": {"id": "https://openalex.org/fields/25", "display_name": "Materials Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "doi:10.2139/ssrn.5209619", "is_oa": true, "landing_page_url": "https://doi.org/10.2139/ssrn.5209619", "pdf_url": null, "source": {"id": "https://openalex.org/S4210172589", "display_name": "SSRN Electronic Journal", "issn_l": "1556-5068", "issn": ["1556-5068"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I1318003438", "host_organization_name": "RELX Group (Netherlands)", "host_organization_lineage": ["https://openalex.org/I1318003438"], "host_organization_lineage_names": [], "type": "repository"}, "license": null, "license_id": null, "version": "acceptedVersion", "is_accepted": true, "is_published": false, "raw_source_name": null, "raw_type": "posted-content"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4406846967", "doi": "https://doi.org/10.3390/e27020128", "title": "SPIRIT: Structural Entropy Guided Prefix Tuning for Hierarchical Text Classification", "publication_year": 2025, "publication_date": "2025-01-26", "type": "article", "language": "en", "authorships": [{"author_position": "first", "author": {"id": null, "display_name": "He Zhu", "orcid": "https://orcid.org/0009-0005-1328-8431", "observed_orcids": ["https://orcid.org/0009-0005-1328-8431"]}, "institutions": [{"id": "https://openalex.org/I4391767781", "display_name": "State Key Laboratory of Software Development Environment", "ror": "https://ror.org/017r4dj03", "country_code": null, "type": "facility", "lineage": ["https://openalex.org/I4391767781", "https://openalex.org/I82880672"]}, {"id": "https://openalex.org/I82880672", "display_name": "Beihang University", "ror": "https://ror.org/00wk2mp56", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I82880672"]}], "countries": ["CN"], "is_corresponding": true, "raw_author_name": "He Zhu", "raw_affiliation_strings": ["State Key Laboratory of Software Development Environment, School of Computer Science and Engineering, Beihang University, No. 37 Xue Yuan Road, Hai Dian District, Beijing 100191, China"], "raw_orcid": "https://orcid.org/0009-0005-1328-8431", "affiliations": [{"raw_affiliation_string": "State Key Laboratory of Software Development Environment, School of Computer Science and Engineering, Beihang University, No. 37 Xue Yuan Road, Hai Dian District, Beijing 100191, China", "institution_ids": ["https://openalex.org/I4391767781", "https://openalex.org/I82880672"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5083255173", "display_name": "Jinxiang Xia", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I4391767781", "display_name": "State Key Laboratory of Software Development Environment", "ror": "https://ror.org/017r4dj03", "country_code": null, "type": "facility", "lineage": ["https://openalex.org/I4391767781", "https://openalex.org/I82880672"]}, {"id": "https://openalex.org/I82880672", "display_name": "Beihang University", "ror": "https://ror.org/00wk2mp56", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I82880672"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Jinxiang Xia", "raw_affiliation_strings": ["State Key Laboratory of Software Development Environment, School of Computer Science and Engineering, Beihang University, No. 37 Xue Yuan Road, Hai Dian District, Beijing 100191, China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "State Key Laboratory of Software Development Environment, School of Computer Science and Engineering, Beihang University, No. 37 Xue Yuan Road, Hai Dian District, Beijing 100191, China", "institution_ids": ["https://openalex.org/I4391767781", "https://openalex.org/I82880672"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5041041638", "display_name": "Ruomei Liu", "orcid": "https://orcid.org/0009-0002-6533-6229", "observed_orcids": ["https://orcid.org/0009-0002-6533-6229"]}, "institutions": [{"id": "https://openalex.org/I4391767781", "display_name": "State Key Laboratory of Software Development Environment", "ror": "https://ror.org/017r4dj03", "country_code": null, "type": "facility", "lineage": ["https://openalex.org/I4391767781", "https://openalex.org/I82880672"]}, {"id": "https://openalex.org/I82880672", "display_name": "Beihang University", "ror": "https://ror.org/00wk2mp56", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I82880672"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Ruomei Liu", "raw_affiliation_strings": ["State Key Laboratory of Software Development Environment, School of Computer Science and Engineering, Beihang University, No. 37 Xue Yuan Road, Hai Dian District, Beijing 100191, China"], "raw_orcid": "https://orcid.org/0009-0002-6533-6229", "affiliations": [{"raw_affiliation_string": "State Key Laboratory of Software Development Environment, School of Computer Science and Engineering, Beihang University, No. 37 Xue Yuan Road, Hai Dian District, Beijing 100191, China", "institution_ids": ["https://openalex.org/I4391767781", "https://openalex.org/I82880672"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5044404549", "display_name": "Bowen Deng", "orcid": "https://orcid.org/0009-0008-0170-5674", "observed_orcids": ["https://orcid.org/0009-0008-0170-5674"]}, "institutions": [{"id": "https://openalex.org/I69356397", "display_name": "Xi\u2019an Jiaotong-Liverpool University", "ror": "https://ror.org/03zmrmn05", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I69356397"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Bowen Deng", "raw_affiliation_strings": ["School of Advanced Technology, Xi\u2019an Jiaotong-Liverpool University, Suzhou 215213, China", "School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou 215213, China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "School of Advanced Technology, Xi\u2019an Jiaotong-Liverpool University, Suzhou 215213, China", "institution_ids": ["https://openalex.org/I69356397"]}, {"raw_affiliation_string": "School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou 215213, China", "institution_ids": ["https://openalex.org/I69356397"]}]}], "abstract_inverted_index": {"Hierarchical": [0], "text": [1], "classification": [2], "(HTC)": [3], "is": [4, 132], "a": [5, 13, 128, 155], "challenging": [6], "task": [7], "that": [8, 46, 152], "requires": [9], "classifiers": [10], "to": [11, 29, 50, 62, 119, 134], "solve": [12], "series": [14], "of": [15, 69, 79, 102], "multi-label": [16], "subtasks": [17], "considering": [18], "hierarchical": [19], "dependencies": [20], "among": [21], "labels.": [22], "Recent": [23], "studies": [24, 82], "have": [25], "introduced": [26], "prompt": [27, 52, 57, 71, 120], "tuning": [28, 72], "create": [30], "closer": [31], "connections": [32], "between": [33], "the": [34, 39, 47, 51, 56, 60, 63, 67, 77, 99, 103, 112, 117, 125, 139, 142], "language": [35, 85], "model": [36], "(LM)": [37], "and": [38, 110, 137], "complex": [40], "label": [41, 104], "hierarchy.": [42], "However,": [43], "we": [44, 88, 97], "find": [45], "model\u2019s": [48], "attention": [49], "gradually": [53], "decreases": [54], "as": [55, 116], "moves": [58], "from": [59], "input": [61], "output": [64], "layer,": [65], "revealing": [66], "limitations": [68], "previous": [70], "methods": [73], "for": [74], "HTC.": [75], "Given": [76], "success": [78], "prefix": [80, 118, 140], "tuning-based": [81], "in": [83, 124], "natural": [84], "understanding": [86], "tasks,": [87], "introduce": [89], "Structural": [90], "entroPy": [91], "guIded": [92], "pRefIx": [93], "Tuning": [94], "(SPIRIT).": [95], "Specifically,": [96], "extract": [98], "essential": [100], "structure": [101], "hierarchy": [105], "via": [106], "structural": [107, 114], "entropy": [108], "minimization": [109], "decode": [111], "abstractive": [113], "information": [115], "all": [121], "intermediate": [122], "layers": [123], "LM.": [126], "Additionally,": [127], "depth-wise": [129], "reparameterization": [130], "strategy": [131], "developed": [133], "enhance": [135], "optimization": [136], "propagate": [138], "throughout": [141], "LM": [143], "layers.": [144], "Extensive": [145], "evaluation": [146], "on": [147], "four": [148], "popular": [149], "datasets": [150], "demonstrates": [151], "SPIRIT": [153], "achieves": [154], "state-of-the-art": [156], "performance.": [157]}, "primary_topic": {"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.663100004196167, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.663100004196167, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T11307", "display_name": "Domain Adaptation and Few-Shot Learning", "score": 0.18029999732971191, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T11550", "display_name": "Text and Document Classification Technologies", "score": 0.12389999628067017, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "doi:10.3390/e27020128", "is_oa": true, "landing_page_url": "https://doi.org/10.3390/e27020128", "pdf_url": "https://www.mdpi.com/1099-4300/27/2/128/pdf?version=1737858462", "source": {"id": "https://openalex.org/S195231649", "display_name": "Entropy", "issn_l": "1099-4300", "issn": ["1099-4300"], "is_oa": true, "is_in_doaj": true, "is_core": true, "listed_in": ["cwts-core", "doaj", "norway-1"], "host_organization": "https://openalex.org/P4310310987", "host_organization_name": "Multidisciplinary Digital Publishing Institute", "host_organization_lineage": ["https://openalex.org/P4310310987"], "host_organization_lineage_names": ["Multidisciplinary Digital Publishing Institute"], "type": "journal"}, "license": "cc-by", "license_id": "https://openalex.org/licenses/cc-by", "version": "publishedVersion", "is_accepted": true, "is_published": true, "raw_source_name": "Entropy", "raw_type": "journal-article"}, "cited_by_count": 2, "is_retracted": false}, {"id": "https://openalex.org/W4414955865", "doi": "https://doi.org/10.1109/jstars.2025.3619423", "title": "DeepAndes: A Self-Supervised Vision Foundation Model for Multispectral Remote Sensing Imagery of the Andes", "publication_year": 2025, "publication_date": "2025-01-01", "type": "article", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5100929773", "display_name": "Junlin Guo", "orcid": "https://orcid.org/0000-0001-9300-0314", "observed_orcids": ["https://orcid.org/0000-0001-9300-0314"]}, "institutions": [{"id": "https://openalex.org/I200719446", "display_name": "Vanderbilt University", "ror": "https://ror.org/02vm5rt34", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I200719446"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Junlin Guo", "raw_affiliation_strings": ["Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA"], "raw_orcid": "https://orcid.org/0000-0001-9300-0314", "affiliations": [{"raw_affiliation_string": "Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA", "institution_ids": ["https://openalex.org/I200719446"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5081424959", "display_name": "James R. Zimmer-Dauphinee", "orcid": "https://orcid.org/0000-0003-1489-7747", "observed_orcids": ["https://orcid.org/0000-0003-1489-7747"]}, "institutions": [{"id": "https://openalex.org/I200719446", "display_name": "Vanderbilt University", "ror": "https://ror.org/02vm5rt34", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I200719446"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "James R. Zimmer-Dauphinee", "raw_affiliation_strings": ["Department of Anthropology, Vanderbilt University, Nashville, TN, USA"], "raw_orcid": "https://orcid.org/0000-0003-1489-7747", "affiliations": [{"raw_affiliation_string": "Department of Anthropology, Vanderbilt University, Nashville, TN, USA", "institution_ids": ["https://openalex.org/I200719446"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5114482675", "display_name": "Jordan M. Nieusma", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I200719446", "display_name": "Vanderbilt University", "ror": "https://ror.org/02vm5rt34", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I200719446"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Jordan M. Nieusma", "raw_affiliation_strings": ["Data Science Institute, Vanderbilt University, Nashville, TN, USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Data Science Institute, Vanderbilt University, Nashville, TN, USA", "institution_ids": ["https://openalex.org/I200719446"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5079530249", "display_name": "Siqi Lu", "orcid": "https://orcid.org/0009-0003-1415-5097", "observed_orcids": ["https://orcid.org/0009-0003-1415-5097"]}, "institutions": [{"id": "https://openalex.org/I200719446", "display_name": "Vanderbilt University", "ror": "https://ror.org/02vm5rt34", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I200719446"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Siqi Lu", "raw_affiliation_strings": ["Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA"], "raw_orcid": "https://orcid.org/0009-0003-1415-5097", "affiliations": [{"raw_affiliation_string": "Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA", "institution_ids": ["https://openalex.org/I200719446"]}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Quan Liu", "orcid": "https://orcid.org/0000-0002-8609-3556", "observed_orcids": ["https://orcid.org/0000-0002-8609-3556"]}, "institutions": [{"id": "https://openalex.org/I200719446", "display_name": "Vanderbilt University", "ror": "https://ror.org/02vm5rt34", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I200719446"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Quan Liu", "raw_affiliation_strings": ["Department of Computer Science, Vanderbilt University, Nashville, TN, USA"], "raw_orcid": "https://orcid.org/0000-0002-8609-3556", "affiliations": [{"raw_affiliation_string": "Department of Computer Science, Vanderbilt University, Nashville, TN, USA", "institution_ids": ["https://openalex.org/I200719446"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5037133367", "display_name": "Ruining Deng", "orcid": "https://orcid.org/0000-0001-6300-8518", "observed_orcids": ["https://orcid.org/0000-0001-6300-8518"]}, "institutions": [{"id": "https://openalex.org/I200719446", "display_name": "Vanderbilt University", "ror": "https://ror.org/02vm5rt34", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I200719446"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Ruining Deng", "raw_affiliation_strings": ["Department of Computer Science, Vanderbilt University, Nashville, TN, USA"], "raw_orcid": "https://orcid.org/0000-0001-6300-8518", "affiliations": [{"raw_affiliation_string": "Department of Computer Science, Vanderbilt University, Nashville, TN, USA", "institution_ids": ["https://openalex.org/I200719446"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5103187104", "display_name": "Can Cui", "orcid": "https://orcid.org/0000-0002-9517-7823", "observed_orcids": ["https://orcid.org/0000-0002-9517-7823", "https://orcid.org/0000-0002-6226-5747"]}, "institutions": [{"id": "https://openalex.org/I200719446", "display_name": "Vanderbilt University", "ror": "https://ror.org/02vm5rt34", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I200719446"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Can Cui", "raw_affiliation_strings": ["Department of Computer Science, Vanderbilt University, Nashville, TN, USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Computer Science, Vanderbilt University, Nashville, TN, USA", "institution_ids": ["https://openalex.org/I200719446"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5114232242", "display_name": "Jialin Yue", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I200719446", "display_name": "Vanderbilt University", "ror": "https://ror.org/02vm5rt34", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I200719446"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Jialin Yue", "raw_affiliation_strings": ["Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA", "institution_ids": ["https://openalex.org/I200719446"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5102816360", "display_name": "Yuan Lin", "orcid": "https://orcid.org/0000-0003-4150-7759", "observed_orcids": ["https://orcid.org/0000-0003-4150-7759"]}, "institutions": [{"id": "https://openalex.org/I200719446", "display_name": "Vanderbilt University", "ror": "https://ror.org/02vm5rt34", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I200719446"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Yizhe Lin", "raw_affiliation_strings": ["Department of Mathematics, Vanderbilt University, Nashville, TN, USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Mathematics, Vanderbilt University, Nashville, TN, USA", "institution_ids": ["https://openalex.org/I200719446"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5064015426", "display_name": "Tianyuan Yao", "orcid": "https://orcid.org/0000-0002-1848-079X", "observed_orcids": ["https://orcid.org/0000-0002-1848-079X", "https://orcid.org/0000-0002-8301-7144"]}, "institutions": [{"id": "https://openalex.org/I200719446", "display_name": "Vanderbilt University", "ror": "https://ror.org/02vm5rt34", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I200719446"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Tianyuan Yao", "raw_affiliation_strings": ["Department of Computer Science, Vanderbilt University, Nashville, TN, USA"], "raw_orcid": "https://orcid.org/0000-0002-1848-079X", "affiliations": [{"raw_affiliation_string": "Department of Computer Science, Vanderbilt University, Nashville, TN, USA", "institution_ids": ["https://openalex.org/I200719446"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5111152727", "display_name": "Juming Xiong", "orcid": "https://orcid.org/0009-0009-4345-4671", "observed_orcids": ["https://orcid.org/0009-0009-4345-4671"]}, "institutions": [{"id": "https://openalex.org/I200719446", "display_name": "Vanderbilt University", "ror": "https://ror.org/02vm5rt34", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I200719446"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Juming Xiong", "raw_affiliation_strings": ["Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA"], "raw_orcid": "https://orcid.org/0009-0009-4345-4671", "affiliations": [{"raw_affiliation_string": "Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA", "institution_ids": ["https://openalex.org/I200719446"]}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Junchao Zhu", "orcid": "https://orcid.org/0000-0001-6610-9808", "observed_orcids": ["https://orcid.org/0000-0001-6610-9808"]}, "institutions": [{"id": "https://openalex.org/I200719446", "display_name": "Vanderbilt University", "ror": "https://ror.org/02vm5rt34", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I200719446"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Junchao Zhu", "raw_affiliation_strings": ["Department of Computer Science, Vanderbilt University, Nashville, TN, USA"], "raw_orcid": "https://orcid.org/0000-0001-6610-9808", "affiliations": [{"raw_affiliation_string": "Department of Computer Science, Vanderbilt University, Nashville, TN, USA", "institution_ids": ["https://openalex.org/I200719446"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5102765244", "display_name": "Chongyu Qu", "orcid": "https://orcid.org/0009-0008-8893-2748", "observed_orcids": ["https://orcid.org/0009-0008-8893-2748"]}, "institutions": [{"id": "https://openalex.org/I200719446", "display_name": "Vanderbilt University", "ror": "https://ror.org/02vm5rt34", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I200719446"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Chongyu Qu", "raw_affiliation_strings": ["Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA"], "raw_orcid": "https://orcid.org/0009-0008-8893-2748", "affiliations": [{"raw_affiliation_string": "Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA", "institution_ids": ["https://openalex.org/I200719446"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5101259625", "display_name": "Yuechen Yang", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I200719446", "display_name": "Vanderbilt University", "ror": "https://ror.org/02vm5rt34", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I200719446"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Yuechen Yang", "raw_affiliation_strings": ["Department of Computer Science, Vanderbilt University, Nashville, TN, USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Computer Science, Vanderbilt University, Nashville, TN, USA", "institution_ids": ["https://openalex.org/I200719446"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5113473287", "display_name": "Mitchell Wilkes", "orcid": "https://orcid.org/0000-0002-9693-3539", "observed_orcids": ["https://orcid.org/0000-0002-9693-3539"]}, "institutions": [{"id": "https://openalex.org/I200719446", "display_name": "Vanderbilt University", "ror": "https://ror.org/02vm5rt34", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I200719446"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Mitchell Wilkes", "raw_affiliation_strings": ["Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA"], "raw_orcid": "https://orcid.org/0000-0002-9693-3539", "affiliations": [{"raw_affiliation_string": "Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA", "institution_ids": ["https://openalex.org/I200719446"]}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Xiao Wang", "orcid": "https://orcid.org/0000-0001-6545-1943", "observed_orcids": ["https://orcid.org/0000-0001-6545-1943"]}, "institutions": [{"id": "https://openalex.org/I1289243028", "display_name": "Oak Ridge National Laboratory", "ror": "https://ror.org/01qz5mb56", "country_code": "US", "type": "facility", "lineage": ["https://openalex.org/I1289243028", "https://openalex.org/I1330989302", "https://openalex.org/I39565521", "https://openalex.org/I4210159294"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Xiao Wang", "raw_affiliation_strings": ["Oak Ridge National Laboratory, Oak Ridge, TN, USA"], "raw_orcid": "https://orcid.org/0000-0001-6545-1943", "affiliations": [{"raw_affiliation_string": "Oak Ridge National Laboratory, Oak Ridge, TN, USA", "institution_ids": ["https://openalex.org/I1289243028"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5045335101", "display_name": "Parker VanValkenburgh", "orcid": "https://orcid.org/0000-0002-4289-6170", "observed_orcids": ["https://orcid.org/0000-0002-4289-6170"]}, "institutions": [{"id": "https://openalex.org/I27804330", "display_name": "Brown University", "ror": "https://ror.org/05gq02987", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I27804330"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Parker VanValkenburgh", "raw_affiliation_strings": ["Department of Anthropology, Brown University, Providence, RI, USA"], "raw_orcid": "https://orcid.org/0000-0002-4289-6170", "affiliations": [{"raw_affiliation_string": "Department of Anthropology, Brown University, Providence, RI, USA", "institution_ids": ["https://openalex.org/I27804330"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5020533177", "display_name": "Steven A. Wernke", "orcid": "https://orcid.org/0000-0001-8180-9896", "observed_orcids": ["https://orcid.org/0000-0001-8180-9896"]}, "institutions": [{"id": "https://openalex.org/I200719446", "display_name": "Vanderbilt University", "ror": "https://ror.org/02vm5rt34", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I200719446"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Steven A. Wernke", "raw_affiliation_strings": ["Department of Anthropology, Vanderbilt University, Nashville, TN, USA"], "raw_orcid": "https://orcid.org/0000-0001-8180-9896", "affiliations": [{"raw_affiliation_string": "Department of Anthropology, Vanderbilt University, Nashville, TN, USA", "institution_ids": ["https://openalex.org/I200719446"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5067191302", "display_name": "Yuankai Huo", "orcid": "https://orcid.org/0000-0002-2096-8065", "observed_orcids": ["https://orcid.org/0000-0002-2096-8065"]}, "institutions": [{"id": "https://openalex.org/I200719446", "display_name": "Vanderbilt University", "ror": "https://ror.org/02vm5rt34", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I200719446"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Yuankai Huo", "raw_affiliation_strings": ["Department of Computer Science, Vanderbilt University, Nashville, TN, USA", "Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA"], "raw_orcid": "https://orcid.org/0000-0002-2096-8065", "affiliations": [{"raw_affiliation_string": "Department of Computer Science, Vanderbilt University, Nashville, TN, USA", "institution_ids": ["https://openalex.org/I200719446"]}, {"raw_affiliation_string": "Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA", "institution_ids": ["https://openalex.org/I200719446"]}]}], "abstract_inverted_index": {"By": [0], "mapping": [1], "sites": [2], "at": [3, 63], "large": [4], "scales": [5], "using": [6], "remotely": [7], "sensed": [8], "data,": [9], "archaeologists": [10], "can": [11, 37], "generate": [12], "unique": [13], "insights": [14], "into": [15], "long-term": [16], "demographic": [17], "trends,": [18], "inter-regional": [19], "social": [20], "networks,": [21], "and": [22, 34, 46, 167, 184], "human": [23], "adaptations": [24], "in": [25, 58, 76, 104, 187, 210], "the": [26, 100, 144, 151, 204], "past.": [27], "Remote": [28], "sensing": [29, 80], "surveys": [30], "complement": [31], "field-based": [32], "approaches,": [33], "their": [35], "reach": [36], "be": [38, 216], "especially": [39], "great": [40], "when": [41], "combined": [42], "with": [43, 82], "deep": [44, 53], "learning": [45, 54, 77, 136, 189], "computer": [47], "vision": [48, 69, 115], "techniques.": [49], "However,": [50], "conventional": [51], "supervised": [52], "methods": [55], "face": [56], "challenges": [57], "annotating": [59], "fine-grained": [60], "archaeological": [61, 211], "features": [62], "scale.": [64], "In": [65, 107], "addition,": [66], "while": [67], "recent": [68], "foundation": [70, 116, 146], "models": [71, 193], "have": [72], "shown": [73], "remarkable": [74], "success": [75], "large-scale": [78, 207], "remote": [79, 212], "data": [81, 102], "minimal": [83], "annotations,": [84], "most": [85], "off-the-shelf": [86], "solutions": [87], "are": [88], "designed": [89, 148], "for": [90, 127, 139, 150], "RGB": [91], "images": [92], "rather": [93], "than": [94], "multi-spectral": [95, 122, 141], "satellite": [96, 123], "imagery,": [97, 142], "such": [98], "as": [99], "8-band": [101, 140], "used": [103], "our": [105], "study.": [106], "this": [108], "paper,": [109], "we": [110], "introduce": [111], "DeepAndes,": [112], "a": [113, 132], "transformer-based": [114], "model": [117, 147], "trained": [118, 194], "on": [119, 199], "three": [120], "million": [121], "images,": [124], "specifically": [125], "tailored": [126], "Andean": [128], "archaeology.": [129], "DeepAndes": [130, 176], "incorporates": [131], "customized": [133], "DINOv2": [134], "self-supervised": [135, 208], "algorithm": [137], "optimized": [138], "marking": [143], "first": [145], "explicitly": [149], "Andes": [152], "region.": [153], "We": [154], "evaluate": [155], "its": [156], "image": [157, 162, 164], "understanding": [158], "performance": [159], "through": [160], "imbalanced": [161], "classification,": [163], "instance": [165], "retrieval,": [166], "pixel-level": [168], "semantic": [169], "segmentation": [170], "tasks.": [171], "Our": [172], "experiments": [173], "show": [174], "that": [175], "achieves": [177], "superior": [178], "F1": [179], "scores,": [180], "mean": [181], "average": [182], "precision,": [183], "Dice": [185], "scores": [186], "few-shot": [188], "scenarios,": [190], "significantly": [191], "outperforming": [192], "from": [195], "scratch": [196], "or": [197], "pre-trained": [198], "smaller": [200], "datasets.": [201], "This": [202], "underscores": [203], "effectiveness": [205], "of": [206], "pre-training": [209], "sensing.": [213], "Codes": [214], "will": [215], "available": [217], "onhttps://github.com/geopacha/DeepAndes.": [218]}, "primary_topic": {"id": "https://openalex.org/T10689", "display_name": "Remote-Sensing Image Classification", "score": 0.7035999894142151, "subfield": {"id": "https://openalex.org/subfields/2214", "display_name": "Media Technology"}, "field": {"id": "https://openalex.org/fields/22", "display_name": "Engineering"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T10689", "display_name": "Remote-Sensing Image Classification", "score": 0.7035999894142151, "subfield": {"id": "https://openalex.org/subfields/2214", "display_name": "Media Technology"}, "field": {"id": "https://openalex.org/fields/22", "display_name": "Engineering"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10036", "display_name": "Advanced Neural Network Applications", "score": 0.11509999632835388, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T12364", "display_name": "Archaeological Research and Protection", "score": 0.1005999967455864, "subfield": {"id": "https://openalex.org/subfields/1912", "display_name": "Space and Planetary Science"}, "field": {"id": "https://openalex.org/fields/19", "display_name": "Earth and Planetary Sciences"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "doi:10.1109/jstars.2025.3619423", "is_oa": true, "landing_page_url": "https://doi.org/10.1109/jstars.2025.3619423", "pdf_url": null, "source": {"id": "https://openalex.org/S117727964", "display_name": "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing", "issn_l": "1939-1404", "issn": ["1939-1404", "2151-1535"], "is_oa": true, "is_in_doaj": true, "is_core": true, "listed_in": ["cwts-core", "doaj", "jufo-1", "norway-1"], "host_organization": "https://openalex.org/P4310319808", "host_organization_name": "Institute of Electrical and Electronics Engineers", "host_organization_lineage": ["https://openalex.org/P4310319808"], "host_organization_lineage_names": ["Institute of Electrical and Electronics Engineers"], "type": "journal"}, "license": "cc-by", "license_id": "https://openalex.org/licenses/cc-by", "version": "publishedVersion", "is_accepted": true, "is_published": true, "raw_source_name": "IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing", "raw_type": "journal-article"}, "cited_by_count": 4, "is_retracted": false}, {"id": "https://openalex.org/W4411336859", "doi": "https://doi.org/10.1109/tpami.2025.3580234", "title": "Toward a Holistic Evaluation of Robustness in CLIP Models", "publication_year": 2025, "publication_date": "2025-06-16", "type": "article", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5049740771", "display_name": "Weijie Tu", "orcid": "https://orcid.org/0009-0002-7364-298X", "observed_orcids": ["https://orcid.org/0009-0002-7364-298X"]}, "institutions": [{"id": "https://openalex.org/I118347636", "display_name": "Australian National University", "ror": "https://ror.org/019wvm592", "country_code": "AU", "type": "education", "lineage": ["https://openalex.org/I118347636"]}], "countries": ["AU"], "is_corresponding": false, "raw_author_name": "Weijie Tu", "raw_affiliation_strings": ["School of Computing, Australian National University, Canberra, ACT, Australia", "School of Computing, The Australian National University, Canberra, ACT, Australia"], "raw_orcid": "https://orcid.org/0009-0002-7364-298X", "affiliations": [{"raw_affiliation_string": "School of Computing, Australian National University, Canberra, ACT, Australia", "institution_ids": ["https://openalex.org/I118347636"]}, {"raw_affiliation_string": "School of Computing, The Australian National University, Canberra, ACT, Australia", "institution_ids": ["https://openalex.org/I118347636"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5065550142", "display_name": "Weijian Deng", "orcid": "https://orcid.org/0000-0003-1565-742X", "observed_orcids": ["https://orcid.org/0000-0003-1565-742X"]}, "institutions": [{"id": "https://openalex.org/I118347636", "display_name": "Australian National University", "ror": "https://ror.org/019wvm592", "country_code": "AU", "type": "education", "lineage": ["https://openalex.org/I118347636"]}], "countries": ["AU"], "is_corresponding": false, "raw_author_name": "Weijian Deng", "raw_affiliation_strings": ["School of Computing, Australian National University, Canberra, ACT, Australia", "School of Computing, The Australian National University, Canberra, ACT, Australia"], "raw_orcid": "https://orcid.org/0000-0003-1565-742X", "affiliations": [{"raw_affiliation_string": "School of Computing, Australian National University, Canberra, ACT, Australia", "institution_ids": ["https://openalex.org/I118347636"]}, {"raw_affiliation_string": "School of Computing, The Australian National University, Canberra, ACT, Australia", "institution_ids": ["https://openalex.org/I118347636"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5111839117", "display_name": "Tom Gedeon", "orcid": "https://orcid.org/0000-0001-8356-4909", "observed_orcids": ["https://orcid.org/0000-0001-8356-4909"]}, "institutions": [{"id": "https://openalex.org/I118347636", "display_name": "Australian National University", "ror": "https://ror.org/019wvm592", "country_code": "AU", "type": "education", "lineage": ["https://openalex.org/I118347636"]}], "countries": ["AU"], "is_corresponding": false, "raw_author_name": "Tom Gedeon", "raw_affiliation_strings": ["Australian National University, University of &#x00D3;buda, Budapest, Hungary", "Australian National University, Australia"], "raw_orcid": "https://orcid.org/0000-0001-8356-4909", "affiliations": [{"raw_affiliation_string": "Australian National University, University of &#x00D3;buda, Budapest, Hungary", "institution_ids": ["https://openalex.org/I118347636"]}, {"raw_affiliation_string": "Australian National University, Australia", "institution_ids": ["https://openalex.org/I118347636"]}]}], "abstract_inverted_index": {"Contrastive": [0], "Language-Image": [1], "Pre-training": [2], "(CLIP)": [3], "models": [4, 74, 112, 185, 207], "have": [5], "shown": [6], "significant": [7, 176], "potential,": [8], "particularly": [9], "in": [10, 48, 89, 172, 178, 218], "zero-shot": [11], "classification": [12, 23, 64, 127, 219], "across": [13], "diverse": [14], "distribution": [15], "shifts.": [16], "Building": [17], "on": [18, 122, 139, 204], "existing": [19], "evaluations": [20], "of": [21, 34, 136, 168, 241], "overall": [22], "robustness,": [24], "this": [25, 124, 197], "work": [26], "aims": [27], "to": [28, 46, 86, 187, 200, 231], "provide": [29], "a": [30, 175, 189], "more": [31], "comprehensive": [32], "assessment": [33], "CLIP": [35, 73, 90, 116, 140, 173, 184, 212, 225, 242], "by": [36], "introducing": [37], "several": [38, 158], "new": [39], "perspectives.": [40], "First,": [41], "we": [42, 53, 67, 82, 99, 132], "investigate": [43], "their": [44, 179], "robustness": [45, 180, 238], "variations": [47], "specific": [49], "visual": [50, 119, 170], "factors.": [51], "Second,": [52], "assess": [54], "two": [55], "critical": [56], "safety": [57], "objectives-confidence": [58], "uncertainty": [59], "and": [60, 78, 105, 152, 239], "out-of-distribution": [61], "detection-beyond": [62], "mere": [63], "accuracy.": [65], "Third,": [66], "evaluate": [68], "the": [69, 76, 101, 118, 134, 166, 169, 211, 237], "finesse": [70], "with": [71], "which": [72], "bridge": [75], "image": [77, 96], "text": [79], "modalities.": [80], "Fourth,": [81], "extend": [83], "our": [84], "examination": [85], "3D": [87, 182], "awareness": [88], "models,": [91], "moving": [92], "beyond": [93], "traditional": [94], "2D": [95], "understanding.": [97], "Finally,": [98], "explore": [100], "interaction": [102, 125], "between": [103], "vision": [104, 213], "language": [106], "encoders": [107], "within": [108], "modern": [109], "large": [110], "multimodal": [111], "(LMMs)": [113], "that": [114], "utilize": [115], "as": [117], "backbone,": [120], "focusing": [121], "how": [123], "impacts": [126], "robustness.": [128], "In": [129], "each": [130], "aspect,": [131], "consider": [133], "impact": [135], "six": [137], "factors": [138], "models:": [141], "model": [142], "architecture,": [143], "training": [144, 146], "distribution,": [145], "set": [147], "size,": [148], "fine-tuning,": [149], "contrastive": [150], "loss,": [151], "test-time": [153], "prompts.": [154], "Our": [155, 227], "study": [156], "uncovers": [157], "previously": [159], "unknown": [160], "insights": [161], "into": [162], "CLIP.": [163], "For": [164], "instance,": [165], "architecture": [167], "encoder": [171], "plays": [174], "role": [177], "against": [181], "corruption.": [183], "tend": [186], "exhibit": [188, 216], "bias": [190, 198], "towards": [191], "shape": [192], "when": [193], "making": [194], "predictions.": [195], "Moreover,": [196], "tends": [199], "diminish": [201], "after": [202], "fine-tuning": [203], "ImageNet.": [205], "Vision-language": [206], "like": [208], "LLaVA,": [209], "leveraging": [210], "encoder,": [214], "could": [215], "benefits": [217], "performance": [220], "for": [221, 235], "challenging": [222], "categories": [223], "over": [224], "alone.": [226], "findings": [228], "are": [229], "poised": [230], "offer": [232], "valuable": [233], "guidance": [234], "enhancing": [236], "reliability": [240], "models.": [243]}, "primary_topic": {"id": "https://openalex.org/T11714", "display_name": "Multimodal Machine Learning Applications", "score": 0.9916999936103821, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T11714", "display_name": "Multimodal Machine Learning Applications", "score": 0.9916999936103821, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T11307", "display_name": "Domain Adaptation and Few-Shot Learning", "score": 0.004399999976158142, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10036", "display_name": "Advanced Neural Network Applications", "score": 0.002300000051036477, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "doi:10.1109/tpami.2025.3580234", "is_oa": false, "landing_page_url": "https://doi.org/10.1109/tpami.2025.3580234", "pdf_url": null, "source": {"id": "https://openalex.org/S199944782", "display_name": "IEEE Transactions on Pattern Analysis and Machine Intelligence", "issn_l": "0162-8828", "issn": ["0162-8828", "1939-3539", "2160-9292"], "is_oa": false, "is_in_doaj": false, "is_core": true, "listed_in": ["cwts-core", "doyens", "jufo-3", "medline", "norway-2"], "host_organization": "https://openalex.org/P4310320439", "host_organization_name": "IEEE Computer Society", "host_organization_lineage": ["https://openalex.org/P4310320439", "https://openalex.org/P4310319808"], "host_organization_lineage_names": ["IEEE Computer Society", "Institute of Electrical and Electronics Engineers"], "type": "journal"}, "license": null, "license_id": null, "version": "publishedVersion", "is_accepted": true, "is_published": true, "raw_source_name": "IEEE Transactions on Pattern Analysis and Machine Intelligence", "raw_type": "journal-article"}, "cited_by_count": 10, "is_retracted": false}, {"id": "https://openalex.org/W4412883168", "doi": "https://doi.org/10.1007/s13369-025-10472-8", "title": "Enhancing Large Language Models with In-Context Learning for Reservoir Management Tasks", "publication_year": 2025, "publication_date": "2025-08-04", "type": "article", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5019514304", "display_name": "Menhal Abdulbaqi Al-Ismael", "orcid": "https://orcid.org/0000-0002-8128-672X", "observed_orcids": ["https://orcid.org/0000-0002-8128-672X"]}, "institutions": [{"id": "https://openalex.org/I134085113", "display_name": "King Fahd University of Petroleum and Minerals", "ror": "https://ror.org/03yez3163", "country_code": "SA", "type": "education", "lineage": ["https://openalex.org/I134085113"]}, {"id": "https://openalex.org/I4210143841", "display_name": "Saudi Aramco (Saudi Arabia)", "ror": "https://ror.org/03ypap427", "country_code": "SA", "type": "company", "lineage": ["https://openalex.org/I4210143841"]}], "countries": ["SA"], "is_corresponding": true, "raw_author_name": "Menhal A. Al-Ismael", "raw_affiliation_strings": ["Department of Petroleum Engineering, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia", "Saudi Aramco, Dhahran, Saudi Arabia"], "raw_orcid": "https://orcid.org/0000-0002-8128-672X", "affiliations": [{"raw_affiliation_string": "Department of Petroleum Engineering, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia", "institution_ids": ["https://openalex.org/I134085113"]}, {"raw_affiliation_string": "Saudi Aramco, Dhahran, Saudi Arabia", "institution_ids": ["https://openalex.org/I4210143841"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5079680487", "display_name": "Abeeb A. Awotunde", "orcid": "https://orcid.org/0000-0002-2281-1483", "observed_orcids": ["https://orcid.org/0000-0002-2281-1483"]}, "institutions": [{"id": "https://openalex.org/I134085113", "display_name": "King Fahd University of Petroleum and Minerals", "ror": "https://ror.org/03yez3163", "country_code": "SA", "type": "education", "lineage": ["https://openalex.org/I134085113"]}], "countries": ["SA"], "is_corresponding": true, "raw_author_name": "Abeeb A. Awotunde", "raw_affiliation_strings": ["CIPR, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia", "Department of Petroleum Engineering, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "CIPR, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia", "institution_ids": ["https://openalex.org/I134085113"]}, {"raw_affiliation_string": "Department of Petroleum Engineering, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia", "institution_ids": ["https://openalex.org/I134085113"]}]}], "abstract_inverted_index": null, "primary_topic": {"id": "https://openalex.org/T11801", "display_name": "Reservoir Engineering and Simulation Methods", "score": 0.989300012588501, "subfield": {"id": "https://openalex.org/subfields/2212", "display_name": "Ocean Engineering"}, "field": {"id": "https://openalex.org/fields/22", "display_name": "Engineering"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T11801", "display_name": "Reservoir Engineering and Simulation Methods", "score": 0.989300012588501, "subfield": {"id": "https://openalex.org/subfields/2212", "display_name": "Ocean Engineering"}, "field": {"id": "https://openalex.org/fields/22", "display_name": "Engineering"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10491", "display_name": "Enhanced Oil Recovery Techniques", "score": 0.0015999999595806003, "subfield": {"id": "https://openalex.org/subfields/2212", "display_name": "Ocean Engineering"}, "field": {"id": "https://openalex.org/fields/22", "display_name": "Engineering"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T13904", "display_name": "Artificial Intelligence Applications", "score": 0.001500000013038516, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "doi:10.1007/s13369-025-10472-8", "is_oa": false, "landing_page_url": "https://doi.org/10.1007/s13369-025-10472-8", "pdf_url": null, "source": {"id": "https://openalex.org/S4210185304", "display_name": "Arabian Journal for Science and Engineering", "issn_l": "1319-8025", "issn": ["1319-8025", "2191-4281", "2193-567X"], "is_oa": false, "is_in_doaj": false, "is_core": true, "listed_in": ["cwts-core", "jufo-1", "norway-1"], "host_organization": "https://openalex.org/P4310319900", "host_organization_name": "Springer Science+Business Media", "host_organization_lineage": ["https://openalex.org/P4310319900", "https://openalex.org/P4310319965"], "host_organization_lineage_names": ["Springer Science+Business Media", "Springer Nature"], "type": "journal"}, "license": null, "license_id": null, "version": "publishedVersion", "is_accepted": true, "is_published": true, "raw_source_name": "Arabian Journal for Science and Engineering", "raw_type": "journal-article"}, "cited_by_count": 1, "is_retracted": false}, {"id": "https://openalex.org/W4414804968", "doi": "https://doi.org/10.48550/arxiv.2510.00229", "title": "AgentFlux: Decoupled Fine-Tuning & Inference for On-Device Agentic Systems", "publication_year": 2025, "publication_date": "2025-09-30", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5065009857", "display_name": "Rohan Kadekodi", "orcid": "https://orcid.org/0000-0002-1213-0342", "observed_orcids": ["https://orcid.org/0000-0002-1213-0342"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Kadekodi, Rohan", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5112751536", "display_name": "Zhan Jin", "orcid": "https://orcid.org/0000-0002-7923-0049", "observed_orcids": ["https://orcid.org/0000-0002-7923-0049"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Jin, Zhan", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5057762250", "display_name": "Keisuke Kamahori", "orcid": "https://orcid.org/0000-0001-9384-7064", "observed_orcids": ["https://orcid.org/0000-0001-9384-7064"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Kamahori, Keisuke", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5102591922", "display_name": "Yile Gu", "orcid": "https://orcid.org/0009-0009-8292-7232", "observed_orcids": ["https://orcid.org/0009-0009-8292-7232"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Gu, Yile", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5119839379", "display_name": "Sean Khatiri", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Khatiri, Sean", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5119839380", "display_name": "Noah H. Bayindirli", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Bayindirli, Noah H.", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": null, "display_name": "Gorbunov, Sergey", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Gorbunov, Sergey", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "last", "author": {"id": "https://openalex.org/A5050964144", "display_name": "Baris Kasikci", "orcid": "https://orcid.org/0000-0001-6122-8998", "observed_orcids": ["https://orcid.org/0000-0001-6122-8998"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Kasikci, Baris", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}], "abstract_inverted_index": {"The": [0], "deployment": [1], "of": [2, 103, 130, 168, 194, 209], "Large": [3], "Language": [4], "Models": [5], "(LLMs)": [6], "as": [7], "agentic": [8], "orchestrators": [9], "has": [10], "revolutionized": [11], "task": [12, 63], "automation,": [13], "but": [14], "the": [15, 104, 115, 128, 138, 151, 166, 177, 182, 190, 195], "need": [16], "for": [17, 51, 90, 101, 171], "privacy-preserving,": [18], "cost-effective": [19], "solutions": [20], "demands": [21], "on-device": [22], "inference": [23, 111], "capabilities.": [24], "However,": [25], "local": [26, 131, 203], "LLMs": [27], "consistently": [28], "underperform": [29], "compared": [30], "to": [31, 85, 122, 155, 164], "frontier": [32], "models": [33, 132, 208, 216], "in": [34, 212, 221], "tool": [35, 41, 45, 68, 91, 143, 157, 172, 191], "calling": [36, 192], "scenarios,": [37], "struggling": [38], "with": [39, 127], "both": [40], "selection": [42, 69, 92, 144], "from": [43], "large": [44], "sets": [46], "and": [47, 70, 93, 145, 148, 200, 206, 215], "accurate": [48], "argument": [49, 71, 95, 146], "generation": [50, 96, 140], "complex": [52], "parameter": [53], "structures.": [54], "We": [55, 73], "introduce": [56], "a": [57, 61, 77], "methodology": [58], "that": [59, 81, 113, 181, 217], "disaggregates": [60], "tool-calling": [62], "into": [64, 142], "two": [65], "distinct": [66], "subtasks:": [67], "generation.": [72], "propose": [74], "\"decoupled": [75], "fine-tuning\",": [76], "novel": [78], "post-training": [79], "approach": [80], "employs": [82], "LoRA": [83, 88, 116, 153], "fine-tuning": [84, 121, 188], "create": [86], "dedicated": [87], "adapters": [89, 117, 154], "tool-specific": [94], "using": [97, 119, 186], "separate": [98], "loss": [99], "masking": [100], "each": [102], "subtasks.": [105], "Furthermore,": [106], "we": [107], "present": [108], "AgentFlux,": [109], "an": [110], "framework": [112], "leverages": [114], "created": [118], "decoupled": [120, 187], "perform": [123], "efficient": [124], "agent": [125], "orchestration": [126, 163], "help": [129], "on": [133, 176], "end-user": [134], "devices.": [135], "AgentFlux": [136, 160], "decomposes": [137], "tool-call": [139], "step": [141], "generation,": [147], "dynamically": [149], "loads": [150], "corresponding": [152], "generate": [156], "calls.": [158], "Additionally,": [159], "implements": [161], "hierarchical": [162], "restrict": [165], "number": [167], "tools": [169], "required": [170], "selection.": [173], "Our": [174], "experiments": [175], "MCP-Bench": [178], "benchmark": [179], "demonstrate": [180], "Qwen-2.5-7B": [183], "model": [184, 197], "trained": [185], "improves": [189], "accuracy": [193], "base": [196], "by": [198], "46%,": [199], "outperforms": [201], "other": [202], "reasoning,": [204], "non-reasoning": [205], "fine-tuned": [207], "similar": [210], "size": [211], "all": [213], "cases,": [214], "are": [218], "2x": [219], "larger,": [220], "most": [222], "cases.": [223]}, "primary_topic": {"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.36039999127388, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.36039999127388, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10181", "display_name": "Natural Language Processing Techniques", "score": 0.2190999984741211, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T11307", "display_name": "Domain Adaptation and Few-Shot Learning", "score": 0.11349999904632568, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2510.00229", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2510.00229", "pdf_url": "https://arxiv.org/pdf/2510.00229", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": null, "license_id": null, "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4408443884", "doi": "https://doi.org/10.3390/a18030168", "title": "Chinese Story Generation Based on Style Control of Transformer Model and Content Evaluation Method", "publication_year": 2025, "publication_date": "2025-03-14", "type": "article", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5090005360", "display_name": "Jhe-Wei Lin", "orcid": "https://orcid.org/0000-0002-7922-6269", "observed_orcids": ["https://orcid.org/0000-0002-7922-6269"]}, "institutions": [{"id": "https://openalex.org/I4880106", "display_name": "Feng Chia University", "ror": "https://ror.org/05vhczg54", "country_code": "TW", "type": "education", "lineage": ["https://openalex.org/I4880106"]}], "countries": ["TW"], "is_corresponding": false, "raw_author_name": "Jhe-Wei Lin", "raw_affiliation_strings": ["Department of Information Engineering and Computer Science, Feng Chia University, Taichung City 407102, Taiwan"], "raw_orcid": "https://orcid.org/0000-0002-7922-6269", "affiliations": [{"raw_affiliation_string": "Department of Information Engineering and Computer Science, Feng Chia University, Taichung City 407102, Taiwan", "institution_ids": ["https://openalex.org/I4880106"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5114174021", "display_name": "Tang-Wei Su", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I4880106", "display_name": "Feng Chia University", "ror": "https://ror.org/05vhczg54", "country_code": "TW", "type": "education", "lineage": ["https://openalex.org/I4880106"]}], "countries": ["TW"], "is_corresponding": false, "raw_author_name": "Tang-Wei Su", "raw_affiliation_strings": ["Department of Information Engineering and Computer Science, Feng Chia University, Taichung City 407102, Taiwan"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Information Engineering and Computer Science, Feng Chia University, Taichung City 407102, Taiwan", "institution_ids": ["https://openalex.org/I4880106"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5071158971", "display_name": "Che-Cheng Chang", "orcid": "https://orcid.org/0000-0002-7526-9990", "observed_orcids": ["https://orcid.org/0000-0002-7526-9990"]}, "institutions": [{"id": "https://openalex.org/I4880106", "display_name": "Feng Chia University", "ror": "https://ror.org/05vhczg54", "country_code": "TW", "type": "education", "lineage": ["https://openalex.org/I4880106"]}], "countries": ["TW"], "is_corresponding": true, "raw_author_name": "Che-Cheng Chang", "raw_affiliation_strings": ["Department of Information Engineering and Computer Science, Feng Chia University, Taichung City 407102, Taiwan"], "raw_orcid": "https://orcid.org/0000-0002-7526-9990", "affiliations": [{"raw_affiliation_string": "Department of Information Engineering and Computer Science, Feng Chia University, Taichung City 407102, Taiwan", "institution_ids": ["https://openalex.org/I4880106"]}]}], "abstract_inverted_index": {"Natural": [0], "language": [1, 18], "processing": [2], "(NLP)": [3], "has": [4, 8], "numerous": [5], "applications": [6], "and": [7, 24, 89, 113], "been": [9, 28], "extensively": [10], "developed": [11], "in": [12], "deep": [13], "learning.": [14], "In": [15], "recent": [16], "years,": [17], "models": [19], "such": [20], "as": [21, 136], "Transformer,": [22], "BERT,": [23], "GPT": [25], "have": [26, 38], "frequently": [27], "the": [29, 42, 56, 82, 95, 107, 114, 122, 127, 132, 140, 143], "foundation": [30], "for": [31, 66], "related": [32, 90], "research.": [33], "However,": [34], "relatively": [35], "few": [36], "studies": [37], "focused": [39], "on": [40, 94, 101], "evaluating": [41], "quality": [43], "of": [44, 109, 116, 131, 142], "generated": [45, 67, 84, 123, 144], "sentences.": [46], "While": [47], "traditional": [48], "evaluation": [49, 76], "methods": [50, 92], "like": [51], "BLEU": [52], "can": [53], "be": [54], "applied,": [55], "challenge": [57], "is": [58, 61, 134], "that": [59], "there": [60], "no": [62], "ground": [63], "truth": [64], "reference": [65], "sentences,": [68], "making": [69], "it": [70, 137], "difficult": [71], "to": [72], "establish": [73], "a": [74], "reliable": [75], "criterion.": [77], "Therefore,": [78], "this": [79], "study": [80], "examines": [81], "content": [83], "by": [85, 105], "Bidirectional": [86], "Encoder": [87], "Representations": [88], "recurrent": [91], "based": [93], "Transformer": [96], "model.": [97], "Specifically,": [98], "we": [99], "focus": [100], "analyzing": [102], "sentence": [103], "fluency": [104], "assessing": [106], "degree": [108], "part-of-speech": [110], "(PoS)": [111], "matching": [112], "coherence": [115], "PoS": [117, 129], "context": [118], "ordering.": [119], "Determining": [120], "whether": [121], "sentences": [124], "align": [125], "with": [126], "expected": [128], "structure": [130], "model": [133], "crucial,": [135], "significantly": [138], "impacts": [139], "readability": [141], "text.": [145]}, "primary_topic": {"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.9531000256538391, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.9531000256538391, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10181", "display_name": "Natural Language Processing Techniques", "score": 0.0357000008225441, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10201", "display_name": "Speech Recognition and Synthesis", "score": 0.002899999963119626, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "doi:10.3390/a18030168", "is_oa": true, "landing_page_url": "https://doi.org/10.3390/a18030168", "pdf_url": null, "source": {"id": "https://openalex.org/S190629608", "display_name": "Algorithms", "issn_l": "1999-4893", "issn": ["1999-4893"], "is_oa": true, "is_in_doaj": true, "is_core": true, "listed_in": ["cwts-core", "doaj", "jufo-1", "norway-1"], "host_organization": "https://openalex.org/P4310310987", "host_organization_name": "Multidisciplinary Digital Publishing Institute", "host_organization_lineage": ["https://openalex.org/P4310310987"], "host_organization_lineage_names": ["Multidisciplinary Digital Publishing Institute"], "type": "journal"}, "license": "cc-by", "license_id": "https://openalex.org/licenses/cc-by", "version": "publishedVersion", "is_accepted": true, "is_published": true, "raw_source_name": "Algorithms", "raw_type": "journal-article"}, "cited_by_count": 2, "is_retracted": false}, {"id": "https://openalex.org/W7117761599", "doi": "https://doi.org/10.48550/arxiv.2512.22628", "title": "M2G-Eval: Enhancing and Evaluating Multi-granularity Multilingual Code Generation", "publication_year": 2025, "publication_date": "2025-12-27", "type": "preprint", "language": null, "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5121664679", "display_name": "Fanglin Xu", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Xu, Fanglin", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5121606180", "display_name": "Wei Zhang", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Zhang, Wei", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5121666933", "display_name": "Jian Yang", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Yang, Jian", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5121650985", "display_name": "Guo Chen", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Chen, Guo", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5014870180", "display_name": "Aishan Liu", "orcid": "https://orcid.org/0000-0002-4224-1318", "observed_orcids": ["https://orcid.org/0000-0002-4224-1318"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Liu, Aishan", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5022818809", "display_name": "Zhoujun Li", "orcid": "https://orcid.org/0000-0002-9603-9713", "observed_orcids": ["https://orcid.org/0000-0002-9603-9713"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Li, Zhoujun", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5129944627", "display_name": "Xianglong Liu", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Liu, Xianglong", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "last", "author": {"id": "https://openalex.org/A5121665631", "display_name": "Bryan Dai", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Dai, Bryan", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}], "abstract_inverted_index": {"The": [0], "rapid": [1], "advancement": [2], "of": [3, 160], "code": [4, 18, 44, 58, 161], "large": [5, 61], "language": [6, 62], "models": [7, 26, 63, 91, 105, 151], "(LLMs)": [8, 64], "has": [9], "sparked": [10], "significant": [11], "research": [12], "interest": [13], "in": [14, 60, 168], "systematically": [15], "evaluating": [16, 57], "their": [17], "generation": [19, 59, 162], "capabilities,": [20], "yet": [21], "existing": [22], "benchmarks": [23], "predominantly": [24], "assess": [25], "at": [27], "a": [28, 52], "single": [29], "structural": [30], "granularity": [31], "and": [32, 46, 71, 82, 98, 127, 137, 144, 164], "focus": [33], "on": [34], "limited": [35], "programming": [36, 75, 154], "languages,": [37, 76], "obscuring": [38], "fine-grained": [39, 158], "capability": [40], "variations": [41], "across": [42, 65], "different": [43], "scopes": [45], "multilingual": [47, 54], "scenarios.": [48], "We": [49, 88], "introduce": [50], "M2G-Eval,": [51], "multi-granularity,": [53], "framework": [55], "for": [56], "four": [66], "levels:": [67], "Class,": [68], "Function,": [69], "Block,": [70], "Line.": [72], "Spanning": [73], "18": [74], "M2G-Eval": [77, 156], "includes": [78], "17K+": [79], "training": [80, 93], "tasks": [81, 125], "1,286": [83], "human-annotated,": [84], "contamination-controlled": [85], "test": [86], "instances.": [87], "develop": [89], "M2G-Eval-Coder": [90, 112], "by": [92], "Qwen3-8B": [94], "with": [95, 123], "supervised": [96], "fine-tuning": [97], "Group": [99], "Relative": [100], "Policy": [101], "Optimization.": [102], "Evaluating": [103], "30": [104], "(28": [106], "state-of-the-art": [107], "LLMs": [108], "plus": [109], "our": [110], "two": [111], "variants)": [113], "reveals": [114], "three": [115], "main": [116], "findings:": [117], "(1)": [118], "an": [119], "apparent": [120], "difficulty": [121], "hierarchy,": [122], "Line-level": [124], "easiest": [126], "Class-level": [128], "most": [129], "challenging;": [130], "(2)": [131], "widening": [132], "performance": [133], "gaps": [134], "between": [135], "full-": [136], "partial-granularity": [138], "languages": [139], "as": [140], "task": [141], "complexity": [142], "increases;": [143], "(3)": [145], "strong": [146], "cross-language": [147], "correlations,": [148], "suggesting": [149], "that": [150], "learn": [152], "transferable": [153], "concepts.": [155], "enables": [157], "diagnosis": [159], "capabilities": [163], "highlights": [165], "persistent": [166], "challenges": [167], "synthesizing": [169], "complex,": [170], "long-form": [171], "code.": [172]}, "primary_topic": {"id": "https://openalex.org/T10260", "display_name": "Software Engineering Research", "score": 0.9961000084877014, "subfield": {"id": "https://openalex.org/subfields/1710", "display_name": "Information Systems"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T10260", "display_name": "Software Engineering Research", "score": 0.9961000084877014, "subfield": {"id": "https://openalex.org/subfields/1710", "display_name": "Information Systems"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.0013000000035390258, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10533", "display_name": "Teaching and Learning Programming", "score": 0.000699999975040555, "subfield": {"id": "https://openalex.org/subfields/1706", "display_name": "Computer Science Applications"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2512.22628", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2512.22628", "pdf_url": "https://arxiv.org/pdf/2512.22628", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": null, "license_id": null, "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4408028155", "doi": "https://doi.org/10.1145/3721127", "title": "Accountability in Code Review: The Role of Intrinsic Drivers and the Impact of LLMs", "publication_year": 2025, "publication_date": "2025-02-28", "type": "article", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5016367018", "display_name": "Adam Alami", "orcid": "https://orcid.org/0000-0003-4483-0105", "observed_orcids": ["https://orcid.org/0000-0003-4483-0105"]}, "institutions": [{"id": "https://openalex.org/I177969490", "display_name": "University of Southern Denmark", "ror": "https://ror.org/03yrrjy16", "country_code": "DK", "type": "education", "lineage": ["https://openalex.org/I177969490"]}], "countries": ["DK"], "is_corresponding": false, "raw_author_name": "Adam Alami", "raw_affiliation_strings": ["The University of Southern Denmark, S\u00f8nderborg, Denmark"], "raw_orcid": "https://orcid.org/0000-0003-4483-0105", "affiliations": [{"raw_affiliation_string": "The University of Southern Denmark, S\u00f8nderborg, Denmark", "institution_ids": ["https://openalex.org/I177969490"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5011178887", "display_name": "Victor Vadmand Jensen", "orcid": "https://orcid.org/0000-0003-4270-7891", "observed_orcids": ["https://orcid.org/0000-0003-4270-7891"]}, "institutions": [{"id": "https://openalex.org/I204337017", "display_name": "Aarhus University", "ror": "https://ror.org/01aj84f44", "country_code": "DK", "type": "education", "lineage": ["https://openalex.org/I204337017"]}], "countries": ["DK"], "is_corresponding": false, "raw_author_name": "Victor Jensen", "raw_affiliation_strings": ["Aarhus University, Aarhus, Denmark"], "raw_orcid": "https://orcid.org/0000-0003-4270-7891", "affiliations": [{"raw_affiliation_string": "Aarhus University, Aarhus, Denmark", "institution_ids": ["https://openalex.org/I204337017"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5033114444", "display_name": "Neil Ernst", "orcid": "https://orcid.org/0000-0001-5992-2366", "observed_orcids": ["https://orcid.org/0000-0001-5992-2366"]}, "institutions": [{"id": "https://openalex.org/I212119943", "display_name": "University of Victoria", "ror": "https://ror.org/04s5mat29", "country_code": "CA", "type": "education", "lineage": ["https://openalex.org/I212119943"]}], "countries": ["CA"], "is_corresponding": false, "raw_author_name": "Neil Ernst", "raw_affiliation_strings": ["Department of Computer Science, University of Victoria, Victoria, BC, Canada"], "raw_orcid": "https://orcid.org/0000-0001-5992-2366", "affiliations": [{"raw_affiliation_string": "Department of Computer Science, University of Victoria, Victoria, BC, Canada", "institution_ids": ["https://openalex.org/I212119943"]}]}], "abstract_inverted_index": {"Accountability": [0], "is": [1, 78], "an": [2, 240], "innate": [3], "part": [4], "of": [5, 39, 62, 69, 122, 128, 171, 217, 227, 248], "social": [6, 22, 60, 254], "systems.": [7], "It": [8], "maintains": [9], "stability": [10], "and": [11, 31, 41, 157, 187, 256], "ensures": [12], "positive": [13], "pressure": [14], "on": [15, 91, 134], "individuals\u2019": [16], "decision-making.": [17], "As": [18], "actors": [19], "in": [20, 46, 144, 183, 231], "a": [21, 101, 145, 193, 199], "system,": [23], "software": [24, 47, 66, 123], "developers": [25], "are": [26, 49, 166, 209], "accountable": [27, 238], "to": [28, 117, 203, 239], "their": [29, 34, 126], "team": [30], "organization": [32], "for": [33, 71, 130, 173], "decisions.": [35], "However,": [36], "the": [37, 59, 89, 119, 215, 225, 246], "drivers": [38, 121, 170], "accountability": [40, 70, 92, 129, 172, 205, 222, 228, 258], "how": [42, 58], "it": [43], "changes": [44], "behavior": [45], "development": [48], "less": [50], "understood.": [51], "In": [52, 110, 137, 192], "this": [53, 221], "study,": [54], "we": [55, 86, 115, 140, 197], "look": [56], "at": [57], "aspects": [61], "code": [63, 72, 96, 131, 174, 184, 207], "review": [64, 160], "affect": [65], "engineers\u2019": [67], "sense": [68, 127], "quality.": [73], "Since": [74], "Software": [75], "Engineering": [76], "(SE)": [77], "increasingly": [79], "involving": [80], "Large": [81], "Language": [82], "Models": [83], "(LLM)": [84], "assistance,": [85], "also": [87, 212], "evaluate": [88], "impact": [90], "when": [93, 206], "introducing": [94], "LLM-assisted": [95, 159, 218], "reviews.": [97], "We": [98, 162, 211], "carried": [99], "out": [100], "two-phased": [102], "sequential": [103], "qualitative": [104], "study": [105], "(": [106], "\\(\\textbf{interviews}\\rightarrow\\textbf{focus": [107], "groups}\\)": [108], ").": [109], "Phase": [111, 138], "I": [112], "(16": [113], "interviews),": [114], "sought": [116], "investigate": [118], "intrinsic": [120, 169], "engineers": [124], "influencing": [125], "quality,": [132], "relying": [133], "self-reported": [135], "claims.": [136], "II,": [139], "tested": [141], "these": [142], "traits": [143], "more": [146], "natural": [147], "setting": [148], "by": [149], "simulating": [150], "traditional": [151, 194], "peer-led": [152, 195, 232], "reviews": [153, 208, 219], "with": [154], "focus": [155], "groups": [156], "then": [158], "sessions.": [161], "found": [163, 213], "that": [164, 214, 245], "there": [165], "four": [167], "key": [168], "quality:": [175], "personal": [176], "standards": [177], ",": [178, 181, 186], "professional": [179], "integrity": [180, 255], "pride": [182], "quality": [185], "maintaining": [188], "one\u2019s": [189], "reputation": [190], ".": [191], "review,": [196], "observed": [198], "transition": [200], "from": [201], "individual": [202], "collective": [204, 257], "initiated.": [210], "introduction": [216, 247], "disrupts": [220], "process,": [223], "challenging": [224], "reciprocity": [226], "taking": [229], "place": [230], "evaluations,": [233], "i.e.,": [234], "one": [235], "cannot": [236], "be": [237], "LLM.": [241], "Our": [242], "findings": [243], "imply": [244], "AI": [249], "into": [250], "SE": [251], "must": [252], "preserve": [253], "mechanisms.": [259]}, "primary_topic": {"id": "https://openalex.org/T10430", "display_name": "Software Engineering Techniques and Practices", "score": 0.9442999958992004, "subfield": {"id": "https://openalex.org/subfields/1710", "display_name": "Information Systems"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T10430", "display_name": "Software Engineering Techniques and Practices", "score": 0.9442999958992004, "subfield": {"id": "https://openalex.org/subfields/1710", "display_name": "Information Systems"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10260", "display_name": "Software Engineering Research", "score": 0.05460000038146973, "subfield": {"id": "https://openalex.org/subfields/1710", "display_name": "Information Systems"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T11675", "display_name": "Open Source Software Innovations", "score": 0.00019999999494757503, "subfield": {"id": "https://openalex.org/subfields/1706", "display_name": "Computer Science Applications"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "doi:10.1145/3721127", "is_oa": false, "landing_page_url": "https://doi.org/10.1145/3721127", "pdf_url": null, "source": {"id": "https://openalex.org/S142627899", "display_name": "ACM Transactions on Software Engineering and Methodology", "issn_l": "1049-331X", "issn": ["1049-331X", "1557-7392"], "is_oa": false, "is_in_doaj": false, "is_core": true, "listed_in": ["cwts-core", "jufo-3", "norway-2"], "host_organization": "https://openalex.org/P4310319798", "host_organization_name": "Association for Computing Machinery", "host_organization_lineage": ["https://openalex.org/P4310319798"], "host_organization_lineage_names": ["Association for Computing Machinery"], "type": "journal"}, "license": null, "license_id": null, "version": "publishedVersion", "is_accepted": true, "is_published": true, "raw_source_name": "ACM Transactions on Software Engineering and Methodology", "raw_type": "journal-article"}, "cited_by_count": 10, "is_retracted": false}, {"id": "https://openalex.org/W4415311089", "doi": "https://doi.org/10.48550/arxiv.2506.13909", "title": "Few-Shot Learning for Industrial Time Series: A Comparative Analysis Using the Example of Screw-Fastening Process Monitoring", "publication_year": 2025, "publication_date": "2025-01-01", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5078203211", "display_name": "Xinyuan Tu", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": true, "raw_author_name": "Tu, Xinyuan", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}], "abstract_inverted_index": {"Few-shot": [0], "learning": [1, 150, 190], "(FSL)": [2], "has": [3], "shown": [4], "promise": [5], "in": [6, 118, 124, 218], "vision": [7], "but": [8, 195], "remains": [9], "largely": [10], "unexplored": [11], "for": [12], "\\emph{industrial}": [13], "time-series": [14], "data,": [15], "where": [16], "annotating": [17], "every": [18], "new": [19], "defect": [20, 46], "is": [21], "prohibitively": [22], "expensive.": [23], "We": [24, 199], "present": [25], "a": [26, 35, 52, 114], "systematic": [27], "FSL": [28, 75, 217], "study": [29], "on": [30], "screw-fastening": [31], "process": [32], "monitoring,": [33], "using": [34], "2\\,300-sample": [36], "multivariate": [37], "torque": [38], "dataset": [39], "that": [40, 56, 172], "covers": [41], "16": [42], "uni-": [43], "and": [44, 83, 97, 122, 143, 154, 204, 211], "multi-factorial": [45], "types.": [47], "Beyond": [48], "benchmarking,": [49], "we": [50], "introduce": [51], "\\textbf{label-aware": [53], "episodic": [54], "sampler}": [55], "collapses": [57], "multi-label": [58, 126], "sequences": [59], "into": [60], "multiple": [61], "single-label": [62], "tasks,": [63], "keeping": [64], "the": [65, 79, 84, 98, 107, 119, 125, 170, 214], "output": [66], "dimensionality": [67], "fixed": [68], "while": [69, 135], "preserving": [70], "combinatorial": [71], "label": [72], "information.": [73], "Two": [74], "paradigms": [76], "are": [77, 176, 181], "investigated:": [78], "metric-based": [80], "\\emph{Prototypical": [81], "Network}": [82], "gradient-based": [85], "\\emph{Model-Agnostic": [86], "Meta-Learning}": [87], "(MAML),": [88], "each": [89], "paired": [90], "with": [91, 187], "three": [92], "backbones:": [93], "1D": [94], "CNN,": [95], "InceptionTime": [96, 108], "341": [99], "M-parameter": [100], "transformer": [101], "\\emph{Moment}.": [102], "On": [103], "10-shot,": [104], "3-way": [105], "evaluation,": [106], "+": [109], "Prototypical": [110], "Network": [111], "combination": [112], "achieves": [113], "\\textbf{0.944": [115], "weighted": [116], "F1}": [117], "multi-class": [120], "regime": [121], "\\textbf{0.935}": [123], "regime,": [127], "outperforming": [128], "finetuned": [129], "Moment": [130], "by": [131], "up": [132], "to": [133, 207, 212], "5.3\\%": [134], "requiring": [136], "two": [137], "orders": [138], "of": [139, 216], "magnitude": [140], "fewer": [141], "parameters": [142], "training": [144], "time.": [145], "Across": [146], "all": [147], "backbones,": [148], "metric": [149, 189], "consistently": [151], "surpasses": [152], "MAML,": [153], "our": [155], "label-aware": [156], "sampling": [157], "yields": [158], "an": [159], "additional": [160], "1.7\\%": [161], "F1": [162], "over": [163], "traditional": [164], "class-based": [165], "sampling.": [166], "These": [167], "findings": [168], "challenge": [169], "assumption": [171], "large": [173], "foundation": [174], "models": [175], "always": [177], "superior:": [178], "when": [179], "data": [180, 202], "scarce,": [182], "lightweight": [183], "CNN": [184], "architectures": [185], "augmented": [186], "simple": [188], "not": [191], "only": [192], "converge": [193], "faster": [194], "also": [196], "generalize": [197], "better.": [198], "release": [200], "code,": [201], "splits": [203], "pre-trained": [205], "weights": [206], "foster": [208], "reproducible": [209], "research": [210], "catalyze": [213], "adoption": [215], "high-value": [219], "manufacturing": [220], "inspection.": [221]}, "primary_topic": {"id": "https://openalex.org/T11307", "display_name": "Domain Adaptation and Few-Shot Learning", "score": 0.49149999022483826, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T11307", "display_name": "Domain Adaptation and Few-Shot Learning", "score": 0.49149999022483826, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10220", "display_name": "Machine Fault Diagnosis Techniques", "score": 0.1404000073671341, "subfield": {"id": "https://openalex.org/subfields/2207", "display_name": "Control and Systems Engineering"}, "field": {"id": "https://openalex.org/fields/22", "display_name": "Engineering"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T12205", "display_name": "Time Series Analysis and Forecasting", "score": 0.09719999879598618, "subfield": {"id": "https://openalex.org/subfields/1711", "display_name": "Signal Processing"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2506.13909", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2506.13909", "pdf_url": "https://arxiv.org/pdf/2506.13909", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": null, "license_id": null, "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4415280674", "doi": "https://doi.org/10.48550/arxiv.2510.08608", "title": "MMA-ASIA: A Multilingual and Multimodal Alignment Framework for Culturally-Grounded Evaluation", "publication_year": 2025, "publication_date": "2025-10-07", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5113059708", "display_name": "Weihua Zheng", "orcid": "https://orcid.org/0009-0008-5251-5467", "observed_orcids": ["https://orcid.org/0009-0008-5251-5467"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Zheng, Weihua", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5003055263", "display_name": "Zhengyuan Liu", "orcid": "https://orcid.org/0009-0003-8897-4140", "observed_orcids": ["https://orcid.org/0009-0003-8897-4140"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Liu, Zhengyuan", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5046521217", "display_name": "Tanmoy Chakraborty", "orcid": "https://orcid.org/0000-0002-0210-0369", "observed_orcids": ["https://orcid.org/0000-0002-0210-0369"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Chakraborty, Tanmoy", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5100585032", "display_name": "Weiwen Xu", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Xu, Weiwen", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5101856962", "display_name": "Xiaoxue Gao", "orcid": "https://orcid.org/0000-0003-1920-5228", "observed_orcids": ["https://orcid.org/0000-0003-1920-5228"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Gao, Xiaoxue", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": null, "display_name": "Tan, Bryan Chen Zhengyu", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Tan, Bryan Chen Zhengyu", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5076654908", "display_name": "Bowei Zou", "orcid": "https://orcid.org/0000-0002-9624-0759", "observed_orcids": ["https://orcid.org/0000-0002-9624-0759"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Zou, Bowei", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5100353261", "display_name": "Chang Liu", "orcid": "https://orcid.org/0000-0002-5511-6858", "observed_orcids": ["https://orcid.org/0000-0002-5511-6858"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Liu, Chang", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5101956873", "display_name": "Yujia Hu", "orcid": "https://orcid.org/0009-0002-7306-3144", "observed_orcids": ["https://orcid.org/0009-0002-7306-3144"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Hu, Yujia", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5044651577", "display_name": "Xing Xie", "orcid": "https://orcid.org/0000-0002-8608-8482", "observed_orcids": ["https://orcid.org/0000-0002-8608-8482", "https://orcid.org/0000-0001-5139-4634", "https://orcid.org/0000-0001-7615-9673"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Xie, Xing", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5040961637", "display_name": "Xiaoyuan Yi", "orcid": "https://orcid.org/0000-0003-2710-1613", "observed_orcids": ["https://orcid.org/0000-0003-2710-1613"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Yi, Xiaoyuan", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": null, "display_name": "Yao, Jing", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Yao, Jing", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5003237783", "display_name": "Chaojun Wang", "orcid": "https://orcid.org/0000-0002-1632-1728", "observed_orcids": ["https://orcid.org/0000-0002-1632-1728", "https://orcid.org/0000-0003-3282-375X"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Wang, Chaojun", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5100408811", "display_name": "Long Li", "orcid": "https://orcid.org/0009-0008-5898-8628", "observed_orcids": ["https://orcid.org/0009-0008-5898-8628"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Li, Long", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5103227545", "display_name": "Rui Liu", "orcid": "https://orcid.org/0009-0005-3136-3853", "observed_orcids": ["https://orcid.org/0009-0005-3136-3853", "https://orcid.org/0000-0001-5553-9095"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Liu, Rui", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": null, "display_name": "Liu, Huiyao", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Liu, Huiyao", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5058775745", "display_name": "Koji Inoue", "orcid": "https://orcid.org/0000-0002-2929-2559", "observed_orcids": ["https://orcid.org/0000-0002-2929-2559"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Inoue, Koji", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5114400286", "display_name": "Ryuichi Sumida", "orcid": "https://orcid.org/0009-0005-9757-0273", "observed_orcids": ["https://orcid.org/0009-0005-9757-0273"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Sumida, Ryuichi", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5038044080", "display_name": "Tatsuya Kawahara", "orcid": "https://orcid.org/0000-0002-2686-2296", "observed_orcids": ["https://orcid.org/0000-0002-2686-2296"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Kawahara, Tatsuya", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5101607448", "display_name": "Fan Xu", "orcid": "https://orcid.org/0000-0002-1358-0193", "observed_orcids": ["https://orcid.org/0000-0002-1358-0193", "https://orcid.org/0009-0005-8021-9524"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Xu, Fan", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5070396778", "display_name": "Linzan Ye", "orcid": "https://orcid.org/0000-0002-7297-4432", "observed_orcids": ["https://orcid.org/0000-0002-7297-4432"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Ye, Lingyu", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": null, "display_name": "Tian, Wei", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Tian, Wei", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5100609433", "display_name": "Dong-Jun Kim", "orcid": "https://orcid.org/0000-0003-4510-2396", "observed_orcids": ["https://orcid.org/0000-0003-4510-2396"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Kim, Dongjun", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": null, "display_name": "Jung, Jimin", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Jung, Jimin", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5014883878", "display_name": "Jaehyung Seo", "orcid": "https://orcid.org/0000-0002-4761-9818", "observed_orcids": ["https://orcid.org/0000-0002-4761-9818"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Seo, Jaehyung", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5120035820", "display_name": "Nadya Yuki Wangsajaya", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Wangsajaya, Nadya Yuki", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5120035821", "display_name": "Pham Minh Duc", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Duc, Pham Minh", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5120273631", "display_name": "Ojasva Saxena", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Saxena, Ojasva", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5093836244", "display_name": "Palash Nandi", "orcid": "https://orcid.org/0000-0002-0775-0723", "observed_orcids": ["https://orcid.org/0000-0002-0775-0723"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Nandi, Palash", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": null, "display_name": "Tao, Xiyan", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Tao, Xiyan", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5120035823", "display_name": "Wiwik Karlina", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Karlina, Wiwik", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": null, "display_name": "Luong, Tuan", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Luong, Tuan", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5120035825", "display_name": "Keertana Arun Vasan", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Vasan, Keertana Arun", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5089793938", "display_name": "Roy Ka-Wei Lee", "orcid": "https://orcid.org/0000-0002-1986-7750", "observed_orcids": ["https://orcid.org/0000-0002-1986-7750"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Lee, Roy Ka-Wei", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "last", "author": {"id": "https://openalex.org/A5014190404", "display_name": "Nancy F. Chen", "orcid": "https://orcid.org/0000-0003-0872-5877", "observed_orcids": ["https://orcid.org/0000-0003-0872-5877"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Chen, Nancy F.", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}], "abstract_inverted_index": {"Large": [0], "language": [1], "models": [2, 174], "(LLMs)": [3], "are": [4], "now": [5], "used": [6], "worldwide,": [7], "yet": [8], "their": [9], "multimodal": [10, 187], "understanding": [11], "and": [12, 43, 52, 93, 129, 163, 178], "reasoning": [13, 63], "often": [14], "degrade": [15], "outside": [16], "Western,": [17], "high-resource": [18], "settings.": [19], "We": [20], "propose": [21, 107], "MMA-ASIA,": [22], "a": [23, 32, 40, 108, 137], "comprehensive": [24], "framework": [25], "to": [26], "evaluate": [27], "LLMs'": [28], "cultural": [29, 66, 126, 151], "awareness": [30], "with": [31], "focus": [33], "on": [34, 39, 103], "Asian": [35, 50], "contexts.": [36], "MMA-ASIA": [37], "centers": [38], "human-curated,": [41], "multilingual,": [42], "multimodally": [44], "aligned": [45, 80], "multiple-choice": [46], "benchmark": [47], "covering": [48], "8": [49], "countries": [51], "10": [53], "languages,": [54], "comprising": [55], "27,000": [56], "questions;": [57], "over": [58], "79": [59], "percent": [60], "require": [61], "multi-step": [62], "grounded": [64], "in": [65], "context,": [67], "moving": [68], "beyond": [69], "simple": [70], "memorization.": [71], "To": [72, 133], "our": [73], "knowledge,": [74], "this": [75, 104], "is": [76], "the": [77, 82, 149], "first": [78], "dataset": [79], "at": [81], "input": [83], "level": [84], "across": [85, 117, 176], "three": [86], "modalities:": [87], "text,": [88], "image": [89], "(visual": [90], "question": [91], "answering),": [92], "speech.": [94], "This": [95], "enables": [96], "direct": [97], "tests": [98], "of": [99], "cross-modal": [100, 123], "transfer.": [101], "Building": [102], "benchmark,": [105], "we": [106, 171], "five-dimensional": [109], "evaluation": [110], "protocol": [111], "that": [112], "measures:": [113], "(i)": [114], "cultural-awareness": [115], "disparities": [116], "countries,": [118], "(ii)": [119], "cross-lingual": [120], "consistency,": [121, 124], "(iii)": [122], "(iv)": [125], "knowledge": [127, 152], "generalization,": [128], "(v)": [130], "grounding": [131], "validity.": [132], "ensure": [134], "rigorous": [135], "assessment,": [136], "Cultural": [138], "Awareness": [139], "Grounding": [140], "Validation": [141], "Module": [142], "detects": [143], "\"shortcut": [144], "learning\"": [145], "by": [146], "checking": [147], "whether": [148], "requisite": [150], "supports": [153], "correct": [154], "answers.": [155], "Finally,": [156], "through": [157], "comparative": [158], "model": [159], "analysis,": [160], "attention": [161], "tracing,": [162], "an": [164], "innovative": [165], "Vision-ablated": [166], "Prefix": [167], "Replay": [168], "(VPR)": [169], "method,": [170], "probe": [172], "why": [173], "diverge": [175], "languages": [177], "modalities,": [179], "offering": [180], "actionable": [181], "insights": [182], "for": [183], "building": [184], "culturally": [185], "reliable": [186], "LLMs.": [188]}, "primary_topic": {"id": "https://openalex.org/T11714", "display_name": "Multimodal Machine Learning Applications", "score": 0.9955999851226807, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T11714", "display_name": "Multimodal Machine Learning Applications", "score": 0.9955999851226807, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.0031999999191612005, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T11307", "display_name": "Domain Adaptation and Few-Shot Learning", "score": 0.00019999999494757503, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2510.08608", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2510.08608", "pdf_url": "https://arxiv.org/pdf/2510.08608", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": null, "license_id": null, "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4415052348", "doi": "https://doi.org/10.62311/nesx/rp-3-sep-25", "title": "Real-Time Causal Self-Auditing for Foundation Models: A Framework for Regulatory-Grade AI Explainability", "publication_year": 2025, "publication_date": "2025-09-30", "type": "article", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5019613463", "display_name": "Murali Krishna Pasupuleti", "orcid": "https://orcid.org/0009-0007-1023-7035", "observed_orcids": ["https://orcid.org/0009-0007-1023-7035"]}, "institutions": [], "countries": [], "is_corresponding": true, "raw_author_name": "Murali Krishna Pasupuleti", "raw_affiliation_strings": [], "raw_orcid": "https://orcid.org/0009-0007-1023-7035", "affiliations": []}], "abstract_inverted_index": {"Abstract:": [0], "This": [1, 91], "paper": [2, 92], "presents": [3], "a": [4, 94], "Real-Time": [5, 96], "Causal": [6, 97, 156], "Self-Auditing": [7, 98], "(RCSA)": [8, 99], "framework": [9, 34, 100], "designed": [10], "to": [11, 67, 134, 137, 152], "address": [12], "the": [13, 32], "growing": [14], "regulatory": [15, 64], "and": [16, 40, 63, 78, 89, 107, 118, 139], "explainability": [17, 69], "challenges": [18, 86], "associated": [19], "with": [20, 28, 150], "foundation": [21, 51, 113], "models.": [22, 114], "By": [23], "combining": [24], "causal": [25, 61, 103, 129], "inference": [26], "methods": [27], "real-time": [29], "auditing": [30], "pipelines,": [31], "RCSA": [33, 127], "delivers": [35], "regulatory-grade": [36], "transparency,": [37], "enabling": [38], "traceable": [39], "interpretable": [41], "AI": [42], "systems.": [43], "Through": [44, 115], "synthetic": [45, 116], "but": [46], "realistic": [47], "experiments": [48], "across": [49], "multiple": [50], "model": [52, 121], "scales,": [53], "we": [54, 124], "demonstrate": [55, 125], "significant": [56, 85], "improvements": [57], "in": [58, 76], "audit": [59], "latency,": [60], "coverage,": [62], "traceability": [65], "compared": [66, 136], "baseline": [68], "techniques.": [70], "Foundation": [71, 158], "models": [72, 149], "are": [73], "increasingly": [74], "deployed": [75], "sensitive": [77], "regulated": [79], "domains,": [80], "yet": [81], "their": [82], "opacity": [83], "poses": [84], "for": [87, 148], "accountability": [88], "oversight.": [90], "introduces": [93], "novel": [95], "that": [101, 126], "integrates": [102], "probing,": [104], "counterfactual": [105], "interventions,": [106], "adaptive": [108], "monitoring": [109], "layers": [110], "into": [111], "large": [112, 119], "benchmarks": [117], "language": [120], "case": [122], "studies,": [123], "improves": [128], "attribution": [130], "fidelity": [131], "by": [132], "up": [133, 151], "34%": [135], "SHAP": [138], "Integrated": [140], "Gradients,": [141], "while": [142], "adding": [143], "only": [144], "8\u201312%": [145], "computational": [146], "overhead": [147], "13B": [153], "parameters.": [154], "Keywords:": [155], "AI,": [157], "Models,": [159], "Regulatory": [160], "Compliance,": [161], "Model": [162], "Auditing,": [163], "Explainability": [164]}, "primary_topic": {"id": "https://openalex.org/T12026", "display_name": "Explainable Artificial Intelligence (XAI)", "score": 1.0, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T12026", "display_name": "Explainable Artificial Intelligence (XAI)", "score": 1.0, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10883", "display_name": "Ethics and Social Impacts of AI", "score": 0.0, "subfield": {"id": "https://openalex.org/subfields/3311", "display_name": "Safety Research"}, "field": {"id": "https://openalex.org/fields/33", "display_name": "Social Sciences"}, "domain": {"id": "https://openalex.org/domains/2", "display_name": "Social Sciences"}}, {"id": "https://openalex.org/T10743", "display_name": "Software Testing and Debugging Techniques", "score": 0.0, "subfield": {"id": "https://openalex.org/subfields/1712", "display_name": "Software"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "doi:10.62311/nesx/rp-3-sep-25", "is_oa": true, "landing_page_url": "https://doi.org/10.62311/nesx/rp-3-sep-25", "pdf_url": "https://www.nationaleducationservices.org/file-download/1504192/Download Full-Text Research Paper PDF Pp 30-43.pdf", "source": {"id": "https://openalex.org/S5407047243", "display_name": "International Journal of Academic and Industrial Research Innovations(IJAIRI)", "issn_l": "3049-2343", "issn": ["3049-2343"], "is_oa": false, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": null, "host_organization_name": null, "host_organization_lineage": [], "host_organization_lineage_names": [], "type": "journal"}, "license": null, "license_id": null, "version": "publishedVersion", "is_accepted": true, "is_published": true, "raw_source_name": "International Journal of Academic and Industrial Research Innovations(IJAIRI)", "raw_type": "journal-article"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4415130110", "doi": "https://doi.org/10.48550/arxiv.2506.02018", "title": "Enhancing Paraphrase Type Generation: The Impact of DPO and RLHF Evaluated with Human-Ranked Data", "publication_year": 2025, "publication_date": "2025-05-28", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5119977587", "display_name": "Christopher Lee L\u00fcbbers", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I74656192", "display_name": "University of G\u00f6ttingen", "ror": "https://ror.org/01y9bpm73", "country_code": "DE", "type": "education", "lineage": ["https://openalex.org/I74656192"]}], "countries": ["DE"], "is_corresponding": true, "raw_author_name": "L\u00fcbbers, Christopher Lee", "raw_affiliation_strings": ["University of G\u00f6ttingen , Germany"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "University of G\u00f6ttingen , Germany", "institution_ids": ["https://openalex.org/I74656192"]}]}], "abstract_inverted_index": {"Paraphrasing": [0], "re-expresses": [1], "meaning": [2], "to": [3, 35, 71], "enhance": [4], "applications": [5, 151], "like": [6], "text": [7], "simplification,": [8], "machine": [9], "translation,": [10], "and": [11, 20, 40, 51, 65, 93, 129, 140, 156, 166, 184], "question-answering.": [12, 159], "Specific": [13], "paraphrase": [14, 174], "types": [15], "facilitate": [16], "accurate": [17, 147], "semantic": [18, 49], "analysis": [19], "robust": [21, 158], "language": [22, 182], "models.": [23], "However,": [24], "existing": [25], "paraphrase-type": [26, 63, 82, 114, 177], "generation": [27, 83, 183], "methods": [28], "often": [29], "misalign": [30], "with": [31, 76], "human": [32, 77, 95], "preferences": [33], "due": [34], "reliance": [36], "on": [37], "automated": [38, 164], "metrics": [39, 165], "limited": [41], "human-annotated": [42, 105], "training": [43, 80, 142], "data,": [44], "obscuring": [45], "crucial": [46], "aspects": [47], "of": [48, 120], "fidelity": [50], "linguistic": [52], "transformations.": [53], "This": [54], "study": [55], "addresses": [56], "this": [57], "gap": [58], "by": [59, 85, 98], "leveraging": [60], "a": [61, 90, 113, 168, 186], "human-ranked": [62], "dataset": [64, 106], "integrating": [66], "Direct": [67], "Preference": [68], "Optimization": [69], "(DPO)": [70], "align": [72], "model": [73, 116, 162], "outputs": [74], "directly": [75], "judgments.": [78], "DPO-based": [79], "increases": [81], "accuracy": [84], "3": [86], "percentage": [87, 100], "points": [88], "over": [89], "supervised": [91], "baseline": [92], "raises": [94], "preference": [96, 138], "ratings": [97], "7": [99], "points.": [101], "A": [102], "newly": [103], "created": [104], "supports": [107], "more": [108, 144, 157, 169], "rigorous": [109], "future": [110, 190], "evaluations.": [111], "Additionally,": [112], "detection": [115], "achieves": [117], "F1": [118], "scores": [119], "0.91": [121], "for": [122, 125, 131, 172, 189], "addition/deletion,": [123], "0.78": [124], "same": [126], "polarity": [127], "substitution,": [128], "0.70": [130], "punctuation": [132], "changes.": [133], "These": [134], "findings": [135], "demonstrate": [136], "that": [137], "data": [139], "DPO": [141], "produce": [143], "reliable,": [145], "semantically": [146], "paraphrases,": [148], "enabling": [149], "downstream": [150], "such": [152], "as": [153], "improved": [154], "summarization": [155], "The": [160], "PTD": [161], "surpasses": [163], "provides": [167], "reliable": [170], "framework": [171], "evaluating": [173], "quality,": [175], "advancing": [176], "research": [178], "toward": [179], "richer,": [180], "user-aligned": [181], "establishing": [185], "stronger": [187], "foundation": [188], "evaluations": [191], "grounded": [192], "in": [193], "human-centric": [194], "criteria.": [195]}, "primary_topic": {"id": "https://openalex.org/T10181", "display_name": "Natural Language Processing Techniques", "score": 0.6875, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T10181", "display_name": "Natural Language Processing Techniques", "score": 0.6875, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.28760001063346863, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T13629", "display_name": "Text Readability and Simplification", "score": 0.00570000009611249, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2506.02018", "is_oa": true, "landing_page_url": "https://arxiv.org/abs/2506.02018", "pdf_url": "https://arxiv.org/pdf/2506.02018", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": "cc-by-sa", "license_id": "https://openalex.org/licenses/cc-by-sa", "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4413251392", "doi": "https://doi.org/10.1007/s44187-025-00552-x", "title": "Leveraging LLMs for predictive insights in food policy and behavioral interventions", "publication_year": 2025, "publication_date": "2025-08-08", "type": "article", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5012106405", "display_name": "Micha Kaiser", "orcid": "https://orcid.org/0000-0002-4357-6072", "observed_orcids": ["https://orcid.org/0000-0002-4357-6072"]}, "institutions": [{"id": "https://openalex.org/I241749", "display_name": "University of Cambridge", "ror": "https://ror.org/013meh722", "country_code": "GB", "type": "education", "lineage": ["https://openalex.org/I241749"]}], "countries": ["GB"], "is_corresponding": true, "raw_author_name": "Micha Kaiser", "raw_affiliation_strings": ["El-Erian Institute of Behavioural Economics and Policy, Cambridge Judge Business School, University of Cambridge, Trumpington Street, Cambridge, CB2 1AG, Cambridgeshire, UK", "YNOT Institute, Queens\u2019 College, Cambridge, University of Cambridge, Silver Street, Cambridge, CB3 9ET, Cambridgeshire, UK"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "El-Erian Institute of Behavioural Economics and Policy, Cambridge Judge Business School, University of Cambridge, Trumpington Street, Cambridge, CB2 1AG, Cambridgeshire, UK", "institution_ids": ["https://openalex.org/I241749"]}, {"raw_affiliation_string": "YNOT Institute, Queens\u2019 College, Cambridge, University of Cambridge, Silver Street, Cambridge, CB3 9ET, Cambridgeshire, UK", "institution_ids": ["https://openalex.org/I241749"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5079907340", "display_name": "Paul Marvin Lohmann", "orcid": "https://orcid.org/0000-0003-4724-9957", "observed_orcids": ["https://orcid.org/0000-0003-4724-9957"]}, "institutions": [{"id": "https://openalex.org/I241749", "display_name": "University of Cambridge", "ror": "https://ror.org/013meh722", "country_code": "GB", "type": "education", "lineage": ["https://openalex.org/I241749"]}], "countries": ["GB"], "is_corresponding": false, "raw_author_name": "Paul M. Lohmann", "raw_affiliation_strings": ["El-Erian Institute of Behavioural Economics and Policy, Cambridge Judge Business School, University of Cambridge, Trumpington Street, Cambridge, CB2 1AG, Cambridgeshire, UK"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "El-Erian Institute of Behavioural Economics and Policy, Cambridge Judge Business School, University of Cambridge, Trumpington Street, Cambridge, CB2 1AG, Cambridgeshire, UK", "institution_ids": ["https://openalex.org/I241749"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5114658640", "display_name": "Peter Ochieng", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I241749", "display_name": "University of Cambridge", "ror": "https://ror.org/013meh722", "country_code": "GB", "type": "education", "lineage": ["https://openalex.org/I241749"]}], "countries": ["GB"], "is_corresponding": false, "raw_author_name": "Peter Ochieng", "raw_affiliation_strings": ["YNOT Institute, Queens\u2019 College, Cambridge, University of Cambridge, Silver Street, Cambridge, CB3 9ET, Cambridgeshire, UK", "YNOT Institute, Queens' College, Cambridge, University of Cambridge, Silver Street, Cambridge, CB3 9ET, Cambridgeshire, UK"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "YNOT Institute, Queens\u2019 College, Cambridge, University of Cambridge, Silver Street, Cambridge, CB3 9ET, Cambridgeshire, UK", "institution_ids": ["https://openalex.org/I241749"]}, {"raw_affiliation_string": "YNOT Institute, Queens' College, Cambridge, University of Cambridge, Silver Street, Cambridge, CB3 9ET, Cambridgeshire, UK", "institution_ids": ["https://openalex.org/I241749"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5114658641", "display_name": "Billy Shi", "orcid": "https://orcid.org/0000-0002-5421-7147", "observed_orcids": ["https://orcid.org/0000-0002-5421-7147"]}, "institutions": [{"id": "https://openalex.org/I241749", "display_name": "University of Cambridge", "ror": "https://ror.org/013meh722", "country_code": "GB", "type": "education", "lineage": ["https://openalex.org/I241749"]}], "countries": ["GB"], "is_corresponding": false, "raw_author_name": "Billy Shi", "raw_affiliation_strings": ["Department of Engineering, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ, Cambridgeshire, UK", "YNOT Institute, Queens\u2019 College, Cambridge, University of Cambridge, Silver Street, Cambridge, CB3 9ET, Cambridgeshire, UK", "YNOT Institute, Queens' College, Cambridge, University of Cambridge, Silver Street, Cambridge, CB3 9ET, Cambridgeshire, UK"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Engineering, University of Cambridge, Trumpington Street, Cambridge, CB2 1PZ, Cambridgeshire, UK", "institution_ids": ["https://openalex.org/I241749"]}, {"raw_affiliation_string": "YNOT Institute, Queens\u2019 College, Cambridge, University of Cambridge, Silver Street, Cambridge, CB3 9ET, Cambridgeshire, UK", "institution_ids": ["https://openalex.org/I241749"]}, {"raw_affiliation_string": "YNOT Institute, Queens' College, Cambridge, University of Cambridge, Silver Street, Cambridge, CB3 9ET, Cambridgeshire, UK", "institution_ids": ["https://openalex.org/I241749"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5052372722", "display_name": "Cass Robert Sunstein", "orcid": "https://orcid.org/0000-0003-4194-3008", "observed_orcids": ["https://orcid.org/0000-0003-4194-3008"]}, "institutions": [{"id": "https://openalex.org/I136199984", "display_name": "Harvard University", "ror": "https://ror.org/03vek6s52", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I136199984"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Cass R. Sunstein", "raw_affiliation_strings": ["Harvard Law School, Harvard University, 1585 Massachusetts Ave., Cambridge, 02138, Massachusetts, USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Harvard Law School, Harvard University, 1585 Massachusetts Ave., Cambridge, 02138, Massachusetts, USA", "institution_ids": ["https://openalex.org/I136199984"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5010399782", "display_name": "Lucia A. Reisch", "orcid": "https://orcid.org/0000-0002-5731-4209", "observed_orcids": ["https://orcid.org/0000-0002-5731-4209"]}, "institutions": [{"id": "https://openalex.org/I241749", "display_name": "University of Cambridge", "ror": "https://ror.org/013meh722", "country_code": "GB", "type": "education", "lineage": ["https://openalex.org/I241749"]}], "countries": ["GB"], "is_corresponding": false, "raw_author_name": "Lucia A. Reisch", "raw_affiliation_strings": ["El-Erian Institute of Behavioural Economics and Policy, Cambridge Judge Business School, University of Cambridge, Trumpington Street, Cambridge, CB2 1AG, Cambridgeshire, UK", "YNOT Institute, Queens\u2019 College, Cambridge, University of Cambridge, Silver Street, Cambridge, CB3 9ET, Cambridgeshire, UK"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "El-Erian Institute of Behavioural Economics and Policy, Cambridge Judge Business School, University of Cambridge, Trumpington Street, Cambridge, CB2 1AG, Cambridgeshire, UK", "institution_ids": ["https://openalex.org/I241749"]}, {"raw_affiliation_string": "YNOT Institute, Queens\u2019 College, Cambridge, University of Cambridge, Silver Street, Cambridge, CB3 9ET, Cambridgeshire, UK", "institution_ids": ["https://openalex.org/I241749"]}]}], "abstract_inverted_index": {"Abstract": [0], "Food": [1], "consumption": [2, 34], "and": [3, 33, 40, 54, 120, 143], "production": [4, 32], "significantly": [5], "contribute": [6], "to": [7, 60, 103, 145], "global": [8], "greenhouse": [9], "gas": [10], "emissions,": [11], "making": [12], "them": [13], "key": [14], "targets": [15], "for": [16, 163], "climate": [17], "change": [18], "mitigation.": [19], "Over": [20], "the": [21, 113, 127], "past": [22], "two": [23], "decades,": [24], "food": [25, 38, 166], "policy": [26, 167], "initiatives": [27], "have": [28], "focused": [29], "on": [30, 47, 157], "reshaping": [31], "patterns": [35], "by": [36], "reducing": [37], "waste": [39], "curbing": [41], "ruminant": [42], "meat": [43], "consumption.": [44], "While": [45], "evidence": [46], "effective": [48], "interventions": [49], "is": [50], "improving,": [51], "assessing": [52], "appropriate": [53], "context-specific": [55], "policies": [56], "remains": [57], "difficult": [58], "due": [59, 102], "external": [61], "validity": [62], "concerns.": [63], "This": [64], "paper": [65], "demonstrates": [66], "that": [67, 153], "a": [68, 121, 160], "fine-tuned": [69, 156], "large": [70], "language": [71], "model": [72], "(LLM)": [73], "can": [74], "accurately": [75], "predict": [76], "outcome": [77], "directions": [78], "in": [79, 140], "approximately": [80], "80%": [81], "of": [82], "empirical": [83], "studies": [84], "evaluating": [85], "dietary": [86], "interventions.": [87], "Predictive": [88], "accuracy": [89, 142], "improves": [90], "with": [91], "richer": [92], "input": [93], "detail,": [94], "peaking": [95], "at": [96], "around": [97], "75": [98], "prompts": [99], "before": [100], "declining": [101], "overfitting": [104], "or": [105], "saturation.": [106], "To": [107], "contextualize": [108], "these": [109], "results,": [110], "we": [111], "benchmark": [112], "LLM": [114], "against": [115], "both": [116], "classical": [117], "random-effects": [118], "meta-regression": [119], "prompt-based": [122], "variant": [123], "executed": [124], "entirely": [125], "within": [126], "model.": [128], "Although": [129], "traditional": [130], "approaches": [131], "yield": [132], "reasonable": [133], "magnitude": [134], "estimates,": [135], "they": [136], "lag": [137], "behind": [138], "LLMs": [139], "directional": [141], "adaptability": [144], "diverse": [146], "intervention": [147], "formats.": [148], "Together,": [149], "our": [150], "findings": [151], "suggest": [152], "LLMs-especially": [154], "when": [155], "curated": [158], "evidence-offer": [159], "scalable": [161], "pathway": [162], "data-driven,": [164], "context-sensitive": [165], "modeling.": [168]}, "primary_topic": {"id": "https://openalex.org/T11259", "display_name": "Agriculture Sustainability and Environmental Impact", "score": 0.9911999702453613, "subfield": {"id": "https://openalex.org/subfields/2303", "display_name": "Ecology"}, "field": {"id": "https://openalex.org/fields/23", "display_name": "Environmental Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T11259", "display_name": "Agriculture Sustainability and Environmental Impact", "score": 0.9911999702453613, "subfield": {"id": "https://openalex.org/subfields/2303", "display_name": "Ecology"}, "field": {"id": "https://openalex.org/fields/23", "display_name": "Environmental Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T13910", "display_name": "Computational and Text Analysis Methods", "score": 0.001500000013038516, "subfield": {"id": "https://openalex.org/subfields/3300", "display_name": "General Social Sciences"}, "field": {"id": "https://openalex.org/fields/33", "display_name": "Social Sciences"}, "domain": {"id": "https://openalex.org/domains/2", "display_name": "Social Sciences"}}, {"id": "https://openalex.org/T10206", "display_name": "Meta-analysis and systematic reviews", "score": 0.0013000000035390258, "subfield": {"id": "https://openalex.org/subfields/1804", "display_name": "Statistics, Probability and Uncertainty"}, "field": {"id": "https://openalex.org/fields/18", "display_name": "Decision Sciences"}, "domain": {"id": "https://openalex.org/domains/2", "display_name": "Social Sciences"}}], "primary_location": {"id": "doi:10.1007/s44187-025-00552-x", "is_oa": true, "landing_page_url": "https://doi.org/10.1007/s44187-025-00552-x", "pdf_url": "https://link.springer.com/content/pdf/10.1007/s44187-025-00552-x.pdf", "source": {"id": "https://openalex.org/S4210181059", "display_name": "Discover Food", "issn_l": "2731-4286", "issn": ["2731-4286"], "is_oa": true, "is_in_doaj": true, "is_core": true, "listed_in": ["cwts-core", "doaj", "jufo-1", "norway-1"], "host_organization": "https://openalex.org/P4310319965", "host_organization_name": "Springer Nature", "host_organization_lineage": ["https://openalex.org/P4310319965"], "host_organization_lineage_names": ["Springer Nature"], "type": "journal"}, "license": "cc-by", "license_id": "https://openalex.org/licenses/cc-by", "version": "publishedVersion", "is_accepted": true, "is_published": true, "raw_source_name": "Discover Food", "raw_type": "journal-article"}, "cited_by_count": 3, "is_retracted": false}, {"id": "https://openalex.org/W4413922724", "doi": "https://doi.org/10.1016/j.eswa.2025.129493", "title": "Automating construction contract question answering using large language model and fine-tuning", "publication_year": 2025, "publication_date": "2025-09-02", "type": "article", "language": "en", "authorships": [{"author_position": "first", "author": {"id": null, "display_name": "Mingyu Zhang", "orcid": "https://orcid.org/0009-0009-3883-061X", "observed_orcids": ["https://orcid.org/0009-0009-3883-061X"]}, "institutions": [{"id": "https://openalex.org/I162868743", "display_name": "Tianjin University", "ror": "https://ror.org/012tb2g32", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I162868743"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Mingyu Zhang", "raw_affiliation_strings": ["College of Management and Economics, Tianjin University, China"], "raw_orcid": "https://orcid.org/0009-0009-3883-061X", "affiliations": [{"raw_affiliation_string": "College of Management and Economics, Tianjin University, China", "institution_ids": ["https://openalex.org/I162868743"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5045582317", "display_name": "Chenglong Xu", "orcid": "https://orcid.org/0000-0003-4459-1845", "observed_orcids": ["https://orcid.org/0000-0003-4459-1845"]}, "institutions": [{"id": "https://openalex.org/I200769079", "display_name": "Hong Kong University of Science and Technology", "ror": "https://ror.org/00q4vv597", "country_code": "HK", "type": "education", "lineage": ["https://openalex.org/I200769079"]}], "countries": ["HK"], "is_corresponding": false, "raw_author_name": "Chenglong Xu", "raw_affiliation_strings": ["Department of Civil and Environmental Engineering, Hong Kong University of Science and Technology, Hong Kong"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Civil and Environmental Engineering, Hong Kong University of Science and Technology, Hong Kong", "institution_ids": ["https://openalex.org/I200769079"]}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Yihong Gan", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I162868743", "display_name": "Tianjin University", "ror": "https://ror.org/012tb2g32", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I162868743"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Yihong Gan", "raw_affiliation_strings": ["College of Management and Economics, Tianjin University, China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "College of Management and Economics, Tianjin University, China", "institution_ids": ["https://openalex.org/I162868743"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5046701060", "display_name": "Yu Wang", "orcid": "https://orcid.org/0000-0002-8768-1093", "observed_orcids": ["https://orcid.org/0000-0002-8768-1093"]}, "institutions": [{"id": "https://openalex.org/I165488957", "display_name": "Lingnan University", "ror": "https://ror.org/0563pg902", "country_code": "HK", "type": "education", "lineage": ["https://openalex.org/I165488957"]}], "countries": ["HK"], "is_corresponding": true, "raw_author_name": "Yu Wang", "raw_affiliation_strings": ["School of Data Science, Lingnan University, Hong Kong"], "raw_orcid": "https://orcid.org/0000-0002-8768-1093", "affiliations": [{"raw_affiliation_string": "School of Data Science, Lingnan University, Hong Kong", "institution_ids": ["https://openalex.org/I165488957"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5101923814", "display_name": "Yi Fu", "orcid": "https://orcid.org/0000-0002-7079-7057", "observed_orcids": ["https://orcid.org/0000-0002-7079-7057", "https://orcid.org/0009-0008-3627-5416"]}, "institutions": [{"id": "https://openalex.org/I162868743", "display_name": "Tianjin University", "ror": "https://ror.org/012tb2g32", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I162868743"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Yi Fu", "raw_affiliation_strings": ["College of Management and Economics, Tianjin University, China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "College of Management and Economics, Tianjin University, China", "institution_ids": ["https://openalex.org/I162868743"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5024521255", "display_name": "Yongqiang Chen", "orcid": "https://orcid.org/0000-0003-1387-3583", "observed_orcids": ["https://orcid.org/0000-0003-1387-3583"]}, "institutions": [{"id": "https://openalex.org/I162868743", "display_name": "Tianjin University", "ror": "https://ror.org/012tb2g32", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I162868743"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Yongqiang Chen", "raw_affiliation_strings": ["College of Management and Economics, Tianjin University, China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "College of Management and Economics, Tianjin University, China", "institution_ids": ["https://openalex.org/I162868743"]}]}], "abstract_inverted_index": null, "primary_topic": {"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.9550999999046326, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.9550999999046326, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10181", "display_name": "Natural Language Processing Techniques", "score": 0.014999999664723873, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T13643", "display_name": "Artificial Intelligence in Law", "score": 0.011900000274181366, "subfield": {"id": "https://openalex.org/subfields/3320", "display_name": "Political Science and International Relations"}, "field": {"id": "https://openalex.org/fields/33", "display_name": "Social Sciences"}, "domain": {"id": "https://openalex.org/domains/2", "display_name": "Social Sciences"}}], "primary_location": {"id": "doi:10.1016/j.eswa.2025.129493", "is_oa": false, "landing_page_url": "https://doi.org/10.1016/j.eswa.2025.129493", "pdf_url": null, "source": {"id": "https://openalex.org/S13144211", "display_name": "Expert Systems with Applications", "issn_l": "0957-4174", "issn": ["0957-4174", "1873-6793"], "is_oa": false, "is_in_doaj": false, "is_core": true, "listed_in": ["cwts-core", "doyens", "jufo-2", "ki-jl-2", "norway-2"], "host_organization": "https://openalex.org/P4310320990", "host_organization_name": "Elsevier BV", "host_organization_lineage": ["https://openalex.org/P4310320990"], "host_organization_lineage_names": ["Elsevier BV"], "type": "journal"}, "license": null, "license_id": null, "version": "publishedVersion", "is_accepted": true, "is_published": true, "raw_source_name": "Expert Systems with Applications", "raw_type": "journal-article"}, "cited_by_count": 9, "is_retracted": false}, {"id": "https://openalex.org/W4410092779", "doi": "https://doi.org/10.1002/oto2.70125", "title": "Domain\u2010Specific Customization for Language Models in Otolaryngology: The ENT GPT Assistant", "publication_year": 2025, "publication_date": "2025-04-01", "type": "article", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5093284767", "display_name": "Brenton T. Bicknell", "orcid": "https://orcid.org/0009-0000-2101-2543", "observed_orcids": ["https://orcid.org/0009-0000-2101-2543"]}, "institutions": [{"id": "https://openalex.org/I32389192", "display_name": "University of Alabama at Birmingham", "ror": "https://ror.org/008s83205", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I32389192"]}, {"id": "https://openalex.org/I4390039250", "display_name": "UAB Medicine", "ror": "https://ror.org/036554539", "country_code": null, "type": "healthcare", "lineage": ["https://openalex.org/I4390039250"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Brenton T. Bicknell", "raw_affiliation_strings": ["UAB Heersink School of Medicine University of Alabama at Birmingham Birmingham Alabama USA"], "raw_orcid": "https://orcid.org/0009-0000-2101-2543", "affiliations": [{"raw_affiliation_string": "UAB Heersink School of Medicine University of Alabama at Birmingham Birmingham Alabama USA", "institution_ids": ["https://openalex.org/I32389192", "https://openalex.org/I4390039250"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5036824004", "display_name": "Nicholas J. Rivers", "orcid": "https://orcid.org/0000-0002-6097-2894", "observed_orcids": ["https://orcid.org/0000-0002-6097-2894"]}, "institutions": [{"id": "https://openalex.org/I32389192", "display_name": "University of Alabama at Birmingham", "ror": "https://ror.org/008s83205", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I32389192"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Nicholas J. Rivers", "raw_affiliation_strings": ["Department of Otolaryngology\u2013Head and Neck Surgery University of Alabama at Birmingham Birmingham Alabama USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Otolaryngology\u2013Head and Neck Surgery University of Alabama at Birmingham Birmingham Alabama USA", "institution_ids": ["https://openalex.org/I32389192"]}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Adam Skelton", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I32389192", "display_name": "University of Alabama at Birmingham", "ror": "https://ror.org/008s83205", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I32389192"]}, {"id": "https://openalex.org/I4390039250", "display_name": "UAB Medicine", "ror": "https://ror.org/036554539", "country_code": null, "type": "healthcare", "lineage": ["https://openalex.org/I4390039250"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Adam Skelton", "raw_affiliation_strings": ["UAB Heersink School of Medicine University of Alabama at Birmingham Birmingham Alabama USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "UAB Heersink School of Medicine University of Alabama at Birmingham Birmingham Alabama USA", "institution_ids": ["https://openalex.org/I32389192", "https://openalex.org/I4390039250"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5036249081", "display_name": "Delaney Sheehan", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I32389192", "display_name": "University of Alabama at Birmingham", "ror": "https://ror.org/008s83205", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I32389192"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Delaney Sheehan", "raw_affiliation_strings": ["Department of Otolaryngology\u2013Head and Neck Surgery University of Alabama at Birmingham Birmingham Alabama USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Otolaryngology\u2013Head and Neck Surgery University of Alabama at Birmingham Birmingham Alabama USA", "institution_ids": ["https://openalex.org/I32389192"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5117437814", "display_name": "Charis C Hodges", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I32389192", "display_name": "University of Alabama at Birmingham", "ror": "https://ror.org/008s83205", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I32389192"]}, {"id": "https://openalex.org/I4390039250", "display_name": "UAB Medicine", "ror": "https://ror.org/036554539", "country_code": null, "type": "healthcare", "lineage": ["https://openalex.org/I4390039250"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Charis Hodges", "raw_affiliation_strings": ["UAB Heersink School of Medicine University of Alabama at Birmingham Birmingham Alabama USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "UAB Heersink School of Medicine University of Alabama at Birmingham Birmingham Alabama USA", "institution_ids": ["https://openalex.org/I32389192", "https://openalex.org/I4390039250"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5117268232", "display_name": "Stevan C Fairburn", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I32389192", "display_name": "University of Alabama at Birmingham", "ror": "https://ror.org/008s83205", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I32389192"]}, {"id": "https://openalex.org/I4390039250", "display_name": "UAB Medicine", "ror": "https://ror.org/036554539", "country_code": null, "type": "healthcare", "lineage": ["https://openalex.org/I4390039250"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Stevan C. Fairburn", "raw_affiliation_strings": ["UAB Heersink School of Medicine University of Alabama at Birmingham Birmingham Alabama USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "UAB Heersink School of Medicine University of Alabama at Birmingham Birmingham Alabama USA", "institution_ids": ["https://openalex.org/I32389192", "https://openalex.org/I4390039250"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5105407668", "display_name": "Benjamin Greene", "orcid": "https://orcid.org/0000-0003-3973-9753", "observed_orcids": ["https://orcid.org/0000-0003-3973-9753"]}, "institutions": [{"id": "https://openalex.org/I32389192", "display_name": "University of Alabama at Birmingham", "ror": "https://ror.org/008s83205", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I32389192"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Benjamin J. Greene", "raw_affiliation_strings": ["Department of Otolaryngology\u2013Head and Neck Surgery University of Alabama at Birmingham Birmingham Alabama USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Otolaryngology\u2013Head and Neck Surgery University of Alabama at Birmingham Birmingham Alabama USA", "institution_ids": ["https://openalex.org/I32389192"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5004139362", "display_name": "Bharat Akhanda Panuganti", "orcid": "https://orcid.org/0000-0003-1149-9532", "observed_orcids": ["https://orcid.org/0000-0003-1149-9532"]}, "institutions": [{"id": "https://openalex.org/I204465549", "display_name": "Washington University in St. Louis", "ror": "https://ror.org/01yc7t268", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I204465549"]}], "countries": ["US"], "is_corresponding": false, "raw_author_name": "Bharat Panuganti", "raw_affiliation_strings": ["Department of Otolaryngology\u2013Head and Neck Surgery Washington University in St. Louis St. Louis Missouri USA"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Otolaryngology\u2013Head and Neck Surgery Washington University in St. Louis St. Louis Missouri USA", "institution_ids": ["https://openalex.org/I204465549"]}]}], "abstract_inverted_index": {"Objective:": [0], "To": [1], "develop": [2], "and": [3, 60, 80, 92, 104, 116, 119, 138, 147, 150, 160, 179, 212], "evaluate": [4], "the": [5, 17, 20, 185, 193, 196, 221], "effectiveness": [6], "of": [7, 19, 65, 86, 127, 199, 202, 223], "domain-specific": [8, 200], "customization": [9], "in": [10, 117, 145, 157, 184, 225], "large": [11], "language": [12, 203], "models": [13, 204], "(LLMs)": [14], "by": [15], "assessing": [16], "performance": [18, 85], "ENT": [21], "GPT": [22], "Assistant": [23], "(E-GPT-A),": [24], "a": [25, 63], "model": [26, 142], "specifically": [27], "tailored": [28], "for": [29, 71, 205], "otolaryngology.": [30, 83, 206, 226], "Study": [31], "Design:": [32], "Comparative": [33], "analysis": [34], "using": [35, 77, 192], "multiple-choice": [36], "questions": [37], "(MCQs)": [38], "from": [39, 57], "established": [40], "otolaryngology": [41, 66, 180], "resources.": [42], "Setting:": [43], "Tertiary": [44], "care": [45], "academic": [46], "hospital.": [47], "Methods:": [48], "Two": [49], "hundred": [50], "forty": [51], "clinical-vignette": [52], "style": [53], "MCQs": [54], "were": [55], "sourced": [56], "BoardVitals": [58], "Otolaryngology": [59], "OTOQuest,": [61], "covering": [62], "range": [64], "subspecialties": [67], "(n": [68], "=": [69], "40": [70], "each).": [72], "The": [73, 84, 141, 172], "E-GPT-A": [74, 87, 122, 194], "was": [75, 88, 108, 182], "developed": [76], "targeted": [78], "instructions": [79], "customized": [81], "to": [82, 218], "compared": [89], "against": [90], "top-performing": [91], "widely": [93], "used": [94], "artificial": [95], "intelligence": [96], "(AI)": [97], "LLMs,": [98], "including": [99], "GPT-3.5,": [100], "GPT-4,": [101], "Claude": [102, 105, 132, 135], "2.0,": [103], "2.1.": [106], "Accuracy": [107, 165], "assessed": [109], "across": [110], "subspecialties,": [111], "varying": [112], "question": [113, 169, 186], "difficulty": [114, 170], "tiers,": [115], "diagnostics": [118], "management.": [120], "Results:": [121], "achieved": [123], "an": [124], "overall": [125], "accuracy": [126, 156], "74.6%,": [128], "outperforming": [129], "GPT-3.5": [130], "(60.4%),": [131], "2.0": [133], "(61.7%),": [134], "2.1": [136], "(60.8%),": [137], "GPT-4": [139], "(68.3%).": [140], "performed": [143], "best": [144], "allergy": [146], "rhinology": [148], "(85.0%)": [149], "laryngology": [151], "(82.5%),": [152], "whereas": [153], "showing": [154], "lower": [155], "pediatrics": [158], "(62.5%)": [159], "facial": [161], "plastics/reconstructive": [162], "surgery": [163], "(67.5%).": [164], "also": [166], "declined": [167], "as": [168], "increased.": [171], "average": [173], "correct": [174], "response": [175], "percentage": [176], "among": [177], "otolaryngologists": [178], "trainees": [181], "71.1%": [183], "set.": [187], "Conclusion:": [188], "This": [189], "pilot": [190], "study": [191], "demonstrates": [195], "potential": [197], "benefits": [198], "customizations": [201], "However,": [207], "further": [208], "development,": [209], "continuous": [210], "updates,": [211], "continued": [213], "real-world": [214], "validation": [215], "are": [216], "needed": [217], "fully": [219], "assess": [220], "capabilities": [222], "LLMs": [224]}, "primary_topic": {"id": "https://openalex.org/T11636", "display_name": "Artificial Intelligence in Healthcare and Education", "score": 0.9990000128746033, "subfield": {"id": "https://openalex.org/subfields/2718", "display_name": "Health Informatics"}, "field": {"id": "https://openalex.org/fields/27", "display_name": "Medicine"}, "domain": {"id": "https://openalex.org/domains/4", "display_name": "Health Sciences"}}, "topics": [{"id": "https://openalex.org/T11636", "display_name": "Artificial Intelligence in Healthcare and Education", "score": 0.9990000128746033, "subfield": {"id": "https://openalex.org/subfields/2718", "display_name": "Health Informatics"}, "field": {"id": "https://openalex.org/fields/27", "display_name": "Medicine"}, "domain": {"id": "https://openalex.org/domains/4", "display_name": "Health Sciences"}}, {"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 9.999999747378752e-05, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T11396", "display_name": "Artificial Intelligence in Healthcare", "score": 9.999999747378752e-05, "subfield": {"id": "https://openalex.org/subfields/3605", "display_name": "Health Information Management"}, "field": {"id": "https://openalex.org/fields/36", "display_name": "Health Professions"}, "domain": {"id": "https://openalex.org/domains/4", "display_name": "Health Sciences"}}], "primary_location": {"id": "doi:10.1002/oto2.70125", "is_oa": true, "landing_page_url": "https://doi.org/10.1002/oto2.70125", "pdf_url": "https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/oto2.70125", "source": {"id": "https://openalex.org/S4210205394", "display_name": "OTO Open", "issn_l": "2473-974X", "issn": ["2473-974X"], "is_oa": true, "is_in_doaj": true, "is_core": true, "listed_in": ["cwts-core", "doaj", "jufo-1", "ki-jl-1"], "host_organization": "https://openalex.org/P4310320017", "host_organization_name": "SAGE Publishing", "host_organization_lineage": ["https://openalex.org/P4310320017"], "host_organization_lineage_names": ["SAGE Publishing"], "type": "journal"}, "license": "cc-by-nc", "license_id": "https://openalex.org/licenses/cc-by-nc", "version": "publishedVersion", "is_accepted": true, "is_published": true, "raw_source_name": "OTO Open", "raw_type": "journal-article"}, "cited_by_count": 4, "is_retracted": false}, {"id": "https://openalex.org/W4415333017", "doi": "https://doi.org/10.48550/arxiv.2506.14903", "title": "DETONATE: A Benchmark for Text-to-Image Alignment and Kernelized Direct Preference Optimization", "publication_year": 2025, "publication_date": "2025-06-17", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5113174281", "display_name": "Renjith Prasad", "orcid": "https://orcid.org/0009-0005-0362-5844", "observed_orcids": ["https://orcid.org/0009-0005-0362-5844"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Prasad, Renjith", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": null, "display_name": "Borah, Abhilekh", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Borah, Abhilekh", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5009080427", "display_name": "Hasnat Abdullah", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Abdullah, Hasnat Md", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5079093269", "display_name": "Chathurangi Shyalika", "orcid": "https://orcid.org/0000-0002-5320-5566", "observed_orcids": ["https://orcid.org/0000-0002-5320-5566"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Shyalika, Chathurangi", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": null, "display_name": "Singh, Gurpreet", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Singh, Gurpreet", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": null, "display_name": "Garimella, Ritvik", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Garimella, Ritvik", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5078181730", "display_name": "Rajarshi Roy", "orcid": "https://orcid.org/0000-0002-0202-1169", "observed_orcids": ["https://orcid.org/0000-0002-0202-1169"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Roy, Rajarshi", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5090455695", "display_name": "Harshul Surana", "orcid": "https://orcid.org/0009-0005-9379-1087", "observed_orcids": ["https://orcid.org/0009-0005-9379-1087"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Surana, Harshul", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5056733281", "display_name": "Nasrin Imanpour", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Imanpour, Nasrin", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5017319608", "display_name": "Suranjana Trivedy", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Trivedy, Suranjana", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": null, "display_name": "Sheth, Amit", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Sheth, Amit", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "last", "author": {"id": "https://openalex.org/A5101679308", "display_name": "Amitava Das", "orcid": "https://orcid.org/0000-0003-3418-463X", "observed_orcids": ["https://orcid.org/0000-0003-3418-463X"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Das, Amitava", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}], "abstract_inverted_index": {"Alignment": [0, 174], "is": [1, 31], "crucial": [2], "for": [3, 42, 63, 78, 107], "text-to-image": [4], "(T2I)": [5], "models": [6, 29, 159], "to": [7, 35], "ensure": [8], "that": [9, 195], "generated": [10, 155], "images": [11, 154], "faithfully": [12], "capture": [13], "user": [14], "intent": [15], "while": [16], "maintaining": [17], "safety": [18], "and": [19, 75, 82, 87, 90, 104, 110, 131, 140, 144, 168, 206], "fairness.": [20], "Direct": [21], "Preference": [22], "Optimization": [23], "(DPO),": [24], "prominent": [25], "in": [26], "large": [27], "language": [28], "(LLMs),": [30], "extending": [32], "its": [33, 120], "influence": [34], "T2I": [36, 43, 158], "systems.": [37], "This": [38], "paper": [39], "introduces": [40], "DPO-Kernels": [41, 196], "models,": [44], "a": [45, 178], "novel": [46, 179], "extension": [47], "enhancing": [48], "alignment": [49], "across": [50], "three": [51, 135], "dimensions:": [52], "(i)": [53], "Hybrid": [54], "Loss,": [55], "integrating": [56], "embedding-based": [57], "objectives": [58], "with": [59, 153], "traditional": [60], "probability-based": [61], "loss": [62], "improved": [64], "optimization;": [65], "(ii)": [66], "Kernelized": [67], "Representations,": [68], "employing": [69], "Radial": [70], "Basis": [71], "Function": [72], "(RBF),": [73], "Polynomial,": [74], "Wavelet": [76], "kernels": [77], "richer": [79], "feature": [80], "transformations": [81], "better": [83], "separation": [84], "between": [85], "safe": [86], "unsafe": [88], "inputs;": [89], "(iii)": [91], "Divergence": [92], "Selection,": [93], "expanding": [94], "beyond": [95], "DPO's": [96], "default": [97], "Kullback-Leibler": [98], "(KL)": [99], "regularizer": [100], "by": [101, 156], "incorporating": [102], "Wasserstein": [103], "R'enyi": [105], "divergences": [106], "enhanced": [108], "stability": [109], "robustness.": [111], "We": [112], "introduce": [113], "DETONATE,": [114], "the": [115, 173], "first": [116], "large-scale": [117], "benchmark": [118], "of": [119, 137, 185], "kind,": [121], "comprising": [122], "approximately": [123], "100K": [124], "curated": [125], "image": [126, 187], "pairs": [127], "categorized": [128], "as": [129], "chosen": [130], "rejected.": [132], "DETONATE": [133, 205], "encapsulates": [134], "axes": [136], "social": [138], "bias": [139], "discrimination:": [141], "Race,": [142], "Gender,": [143], "Disability.": [145], "Prompts": [146], "are": [147, 209], "sourced": [148], "from": [149], "hate": [150], "speech": [151], "datasets,": [152], "leading": [157], "including": [160], "Stable": [161, 165], "Diffusion": [162, 166], "3.5": [163], "Large,": [164], "XL,": [167], "Midjourney.": [169], "Additionally,": [170], "we": [171, 193], "propose": [172], "Quality": [175], "Index": [176], "(AQI),": [177], "geometric": [180], "measure": [181], "quantifying": [182], "latent-space": [183], "separability": [184], "safe/unsafe": [186], "activations,": [188], "revealing": [189], "hidden": [190], "vulnerabilities.": [191], "Empirically,": [192], "demonstrate": [194], "maintain": [197], "strong": [198], "generalization": [199], "bounds": [200], "via": [201], "Heavy-Tailed": [202], "Self-Regularization": [203], "(HT-SR).": [204], "complete": [207], "code": [208], "publicly": [210], "released.": [211]}, "primary_topic": {"id": "https://openalex.org/T10775", "display_name": "Generative Adversarial Networks and Image Synthesis", "score": 0.7440999746322632, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T10775", "display_name": "Generative Adversarial Networks and Image Synthesis", "score": 0.7440999746322632, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10883", "display_name": "Ethics and Social Impacts of AI", "score": 0.12120000272989273, "subfield": {"id": "https://openalex.org/subfields/3311", "display_name": "Safety Research"}, "field": {"id": "https://openalex.org/fields/33", "display_name": "Social Sciences"}, "domain": {"id": "https://openalex.org/domains/2", "display_name": "Social Sciences"}}, {"id": "https://openalex.org/T11689", "display_name": "Adversarial Robustness in Machine Learning", "score": 0.050700001418590546, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2506.14903", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2506.14903", "pdf_url": "https://arxiv.org/pdf/2506.14903", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": null, "license_id": null, "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4417116183", "doi": "https://doi.org/10.48550/arxiv.2510.08800", "title": "Benchmarking Chinese Commonsense Reasoning with a Multi-hop Reasoning Perspective", "publication_year": 2025, "publication_date": "2025-10-09", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5113392993", "display_name": "Wangjie You", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "You, Wangjie", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5100721484", "display_name": "Xusheng Wang", "orcid": "https://orcid.org/0000-0003-3145-3830", "observed_orcids": ["https://orcid.org/0000-0003-3145-3830"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Wang, Xusheng", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5101443252", "display_name": "Xing Wang", "orcid": "https://orcid.org/0000-0002-0737-9653", "observed_orcids": ["https://orcid.org/0000-0002-0737-9653"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Wang, Xing", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5023250445", "display_name": "Wenxiang Jiao", "orcid": "https://orcid.org/0000-0003-4951-9420", "observed_orcids": ["https://orcid.org/0000-0003-4951-9420"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Jiao, Wenxiang", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5103124726", "display_name": "Chao Feng", "orcid": "https://orcid.org/0000-0003-3636-7210", "observed_orcids": ["https://orcid.org/0000-0003-3636-7210"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Feng, Chao", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5100657513", "display_name": "Juntao Li", "orcid": "https://orcid.org/0000-0002-3288-4395", "observed_orcids": ["https://orcid.org/0000-0002-3288-4395"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Li, Juntao", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "last", "author": {"id": "https://openalex.org/A5100402952", "display_name": "Min Zhang", "orcid": "https://orcid.org/0000-0002-8230-300X", "observed_orcids": ["https://orcid.org/0000-0002-8230-300X"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Zhang, Min", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}], "abstract_inverted_index": {"While": [0], "Large": [1], "Language": [2], "Models": [3], "(LLMs)": [4], "have": [5], "demonstrated": [6], "advanced": [7], "reasoning": [8], "capabilities,": [9], "their": [10], "comprehensive": [11], "evaluation": [12], "in": [13, 105], "general": [14], "Chinese-language": [15], "contexts": [16], "remains": [17], "understudied.": [18], "To": [19, 73], "bridge": [20], "this": [21], "gap,": [22], "we": [23, 48, 80, 98], "propose": [24], "Chinese": [25], "Commonsense": [26], "Multi-hop": [27], "Reasoning": [28], "(CCMOR),": [29], "a": [30, 51, 82], "novel": [31], "benchmark": [32], "designed": [33], "to": [34, 38, 64, 108], "evaluate": [35, 99], "LLMs'": [36, 106], "ability": [37, 107], "integrate": [39], "Chinese-specific": [40], "factual": [41, 70], "knowledge": [42, 111, 122], "with": [43], "multi-step": [44], "logical": [45], "reasoning.": [46, 115], "Specifically,": [47], "first": [49], "construct": [50], "domain-balanced": [52], "seed": [53], "set": [54], "from": [55], "existing": [56], "QA": [57], "datasets,": [58], "then": [59], "develop": [60], "an": [61], "LLM-powered": [62], "pipeline": [63], "generate": [65], "multi-hop": [66], "questions": [67], "anchored": [68], "on": [69], "unit": [71], "chains.": [72], "ensure": [74], "the": [75, 93], "quality": [76], "of": [77], "resulting": [78], "dataset,": [79], "implement": [81], "human-in-the-loop": [83], "verification": [84], "system,": [85], "where": [86], "domain": [87], "experts": [88], "systematically": [89], "validate": [90], "and": [91, 112], "refine": [92], "generated": [94], "questions.": [95], "Using": [96], "CCMOR,": [97], "state-of-the-art": [100], "LLMs,": [101], "demonstrating": [102], "persistent": [103], "limitations": [104], "process": [109], "long-tail": [110], "execute": [113], "knowledge-intensive": [114], "Notably,": [116], "retrieval-augmented": [117], "generation": [118], "substantially": [119], "mitigates": [120], "these": [121], "gaps,": [123], "yielding": [124], "significant": [125], "performance": [126], "gains.": [127]}, "primary_topic": {"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.9409000277519226, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.9409000277519226, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T11714", "display_name": "Multimodal Machine Learning Applications", "score": 0.03849999979138374, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10181", "display_name": "Natural Language Processing Techniques", "score": 0.00860000029206276, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2510.08800", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2510.08800", "pdf_url": "https://arxiv.org/pdf/2510.08800", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": null, "license_id": null, "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4415084239", "doi": "https://doi.org/10.48550/arxiv.2503.01131", "title": "Beyond QA Pairs: Assessing Parameter-Efficient Fine-Tuning for Fact Embedding in LLMs", "publication_year": 2025, "publication_date": "2025-03-03", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5139725988", "display_name": "Shivam Ratnakar", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Ratnakar, Shivam", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5119957682", "display_name": "Abhiroop Talasila", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Talasila, Abhiroop", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5119957683", "display_name": "Raghav Chamadiya", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Chamadiya, Raghav", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": null, "display_name": "Agarwal, Nikhil", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Agarwal, Nikhil", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "last", "author": {"id": "https://openalex.org/A5119957684", "display_name": "Vinayak K Doifode", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Doifode, Vinayak K", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}], "abstract_inverted_index": {"This": [0], "paper": [1], "presents": [2], "an": [3], "extensive": [4], "examination": [5], "of": [6, 80, 159, 172], "Parameter-Efficient": [7], "Fine-Tuning": [8], "(PEFT)": [9], "for": [10, 112], "embedding": [11, 113], "domain": [12], "specific": [13, 175], "facts": [14, 114], "into": [15, 31, 115], "Large": [16], "Language": [17], "Models": [18], "(LLMs),": [19], "focusing": [20], "on": [21, 47, 66, 72], "improving": [22], "the": [23, 78, 108, 135, 140, 147, 157, 170], "fine-tuning": [24, 83], "process": [25], "by": [26, 130], "categorizing": [27], "question-answer": [28], "(QA)": [29], "pairs": [30], "Factual": [32], "and": [33, 50, 58, 88, 163], "Conceptual": [34], "classes": [35], "using": [36, 52], "a": [37, 131], "BERT-based": [38], "classifier.": [39], "Two": [40], "distinct": [41], "Llama-2": [42, 142], "models": [43, 54, 64], "are": [44, 128], "fine-tuned": [45, 141], "based": [46], "these": [48], "classifications": [49], "evaluated": [51], "larger": [53], "like": [55], "GPT-3.5": [56], "Turbo": [57], "Gemini.": [59], "Our": [60, 126, 154], "results": [61], "indicate": [62], "that": [63, 103], "trained": [65, 71], "conceptual": [67], "datasets": [68], "outperform": [69], "those": [70], "factual": [73], "datasets.": [74], "Additionally,": [75], "we": [76], "compare": [77], "efficiency": [79], "two": [81], "synthetic": [82, 164], "dataset": [84, 133, 165], "generation": [85, 166], "techniques,": [86], "D-RAG": [87], "D-Naive,": [89], "with": [90], "D-Naive": [91], "demonstrating": [92], "superior": [93], "performance.": [94], "Although": [95], "PEFT": [96], "has": [97, 119], "shown": [98], "effectiveness,": [99], "our": [100], "research": [101], "indicates": [102], "it": [104, 118], "may": [105], "not": [106], "be": [107], "most": [109], "optimal": [110], "method": [111], "LLMs.": [116], "However,": [117], "demonstrated": [120], "exceptional": [121], "performance": [122, 171], "in": [123, 134, 150, 168, 174], "instruction-based": [124], "tasks.": [125], "findings": [127], "reinforced": [129], "1000-sample": [132], "data": [136], "center": [137], "domain,": [138], "where": [139], "7B": [143], "model": [144, 149], "significantly": [145], "outperforms": [146], "baseline": [148], "generating": [151], "product": [152], "recommendations.": [153], "study": [155], "highlights": [156], "importance": [158], "QA": [160], "pair": [161], "categorization": [162], "techniques": [167], "enhancing": [169], "LLMs": [173], "domains.": [176]}, "primary_topic": {"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.9452999830245972, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.9452999830245972, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T11307", "display_name": "Domain Adaptation and Few-Shot Learning", "score": 0.04969999939203262, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T11714", "display_name": "Multimodal Machine Learning Applications", "score": 0.0007999999797903001, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2503.01131", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2503.01131", "pdf_url": "https://arxiv.org/pdf/2503.01131", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": null, "license_id": null, "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4415965504", "doi": "https://doi.org/10.48550/arxiv.2510.17602", "title": "LexChain: Modeling Legal Reasoning Chains for Chinese Tort Case Analysis", "publication_year": 2025, "publication_date": "2025-10-20", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": null, "display_name": "Xie, Huiyuan", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I99065089", "display_name": "Tsinghua University", "ror": "https://ror.org/03cve4549", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I99065089"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Xie, Huiyuan", "raw_affiliation_strings": ["Tsinghua University"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Tsinghua University", "institution_ids": ["https://openalex.org/I99065089"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5100436392", "display_name": "Chenyang Li", "orcid": "https://orcid.org/0000-0002-7888-0840", "observed_orcids": ["https://orcid.org/0000-0002-7888-0840", "https://orcid.org/0000-0002-5554-1889", "https://orcid.org/0009-0004-0810-651X", "https://orcid.org/0009-0006-8864-1049"]}, "institutions": [{"id": "https://openalex.org/I139759216", "display_name": "Beijing University of Posts and Telecommunications", "ror": "https://ror.org/04w9fbh59", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I139759216"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Li, Chenyang", "raw_affiliation_strings": ["Beijing University of Posts and Telecommunications"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Beijing University of Posts and Telecommunications", "institution_ids": ["https://openalex.org/I139759216"]}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Zhu, Huining", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I78974044", "display_name": "East China University of Political Science and Law", "ror": "https://ror.org/01d1erx18", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I78974044"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Zhu, Huining", "raw_affiliation_strings": ["East China University of Political Science and Law"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "East China University of Political Science and Law", "institution_ids": ["https://openalex.org/I78974044"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5120282559", "display_name": "Chubin Zhang", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I139759216", "display_name": "Beijing University of Posts and Telecommunications", "ror": "https://ror.org/04w9fbh59", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I139759216"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Zhang, Chubin", "raw_affiliation_strings": ["Beijing University of Posts and Telecommunications"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Beijing University of Posts and Telecommunications", "institution_ids": ["https://openalex.org/I139759216"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5113848847", "display_name": "Yuxiao Ye", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I99065089", "display_name": "Tsinghua University", "ror": "https://ror.org/03cve4549", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I99065089"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Ye, Yuxiao", "raw_affiliation_strings": ["Tsinghua University"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Tsinghua University", "institution_ids": ["https://openalex.org/I99065089"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5070196864", "display_name": "Zhenghao Liu", "orcid": "https://orcid.org/0000-0001-8067-1477", "observed_orcids": ["https://orcid.org/0000-0001-8067-1477"]}, "institutions": [{"id": "https://openalex.org/I12912129", "display_name": "Northeastern University", "ror": "https://ror.org/04t5xt781", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I12912129"]}, {"id": "https://openalex.org/I99065089", "display_name": "Tsinghua University", "ror": "https://ror.org/03cve4549", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I99065089"]}], "countries": ["CN", "US"], "is_corresponding": false, "raw_author_name": "Liu, Zhenghao", "raw_affiliation_strings": ["Northeastern University", "Tsinghua University"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Northeastern University", "institution_ids": ["https://openalex.org/I12912129"]}, {"raw_affiliation_string": "Tsinghua University", "institution_ids": ["https://openalex.org/I99065089"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5100320711", "display_name": "Zhiyuan Liu", "orcid": "https://orcid.org/0000-0001-9231-8195", "observed_orcids": ["https://orcid.org/0000-0001-9231-8195", "https://orcid.org/0009-0005-3802-4635"]}, "institutions": [{"id": "https://openalex.org/I12912129", "display_name": "Northeastern University", "ror": "https://ror.org/04t5xt781", "country_code": "US", "type": "education", "lineage": ["https://openalex.org/I12912129"]}, {"id": "https://openalex.org/I99065089", "display_name": "Tsinghua University", "ror": "https://ror.org/03cve4549", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I99065089"]}], "countries": ["CN", "US"], "is_corresponding": false, "raw_author_name": "Liu, Zhiyuan", "raw_affiliation_strings": ["Northeastern University", "Tsinghua University"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Northeastern University", "institution_ids": ["https://openalex.org/I12912129"]}, {"raw_affiliation_string": "Tsinghua University", "institution_ids": ["https://openalex.org/I99065089"]}]}], "abstract_inverted_index": {"Legal": [0], "reasoning": [1, 16, 21, 64, 78, 108, 122, 139, 173, 187, 203, 208], "is": [2], "a": [3, 57], "fundamental": [4], "component": [5], "of": [6, 34, 68, 92, 105, 159, 199, 210], "legal": [7, 15, 35, 63, 77, 107, 138, 161, 186, 193, 202], "analysis": [8, 67, 82, 194], "and": [9, 109, 188], "decision-making.": [10], "Existing": [11], "computational": [12], "approaches": [13, 168], "to": [14, 60, 114, 191, 205], "predominantly": [17], "rely": [18], "on": [19, 43], "generic": [20], "frameworks": [22], "such": [23], "as": [24], "syllogism,": [25], "which": [26], "do": [27], "not": [28], "comprehensively": [29], "examine": [30], "the": [31, 66, 76, 84, 98, 103, 117, 197, 207], "nuanced": [32], "process": [33, 79], "reasoning.": [36, 162], "Moreover,": [37], "current": [38, 149], "research": [39], "has": [40], "largely": [41], "focused": [42], "criminal": [44], "cases,": [45], "with": [46, 88], "insufficient": [47], "modeling": [48, 201], "for": [49, 124, 136], "civil": [50, 71, 142], "cases.": [51, 72], "In": [52], "this": [53, 128], "work,": [54], "we": [55, 101, 130, 164], "present": [56], "novel": [58], "framework": [59], "explicitly": [61, 170, 200], "model": [62], "in": [65, 80, 141, 154, 184], "Chinese": [69], "tort-related": [70, 185], "We": [73], "first": [74], "operationalize": [75], "tort": [81, 106, 125, 143, 160], "into": [83], "three-module": [85], "LexChain": [86, 99], "framework,": [87, 100], "each": [89], "module": [90], "consisting": [91], "multiple": [93], "finer-grained": [94], "sub-steps.": [95], "Informed": [96], "by": [97], "introduce": [102, 165], "task": [104], "construct": [110], "an": [111], "evaluation": [112], "benchmark": [113], "systematically": [115], "assess": [116], "critical": [118], "steps": [119], "within": [120], "analytical": [121], "chains": [123, 204], "analysis.": [126], "Leveraging": [127], "benchmark,": [129], "evaluate": [131], "existing": [132], "large": [133], "language": [134, 211], "models": [135, 150], "their": [137], "ability": [140], "contexts.": [144], "Our": [145], "results": [146], "indicate": [147], "that": [148, 169], "still": [151], "fall": [152], "short": [153], "accurately": [155], "handling": [156], "crucial": [157], "elements": [158], "Furthermore,": [163], "several": [166], "baseline": [167], "incorporate": [171], "LexChain-style": [172], "through": [174], "prompting": [175], "or": [176], "post-training.": [177], "The": [178], "proposed": [179], "baselines": [180], "achieve": [181], "significant": [182], "improvements": [183], "generalize": [189], "well": [190], "related": [192], "tasks,": [195], "demonstrating": [196], "value": [198], "enhance": [206], "capabilities": [209], "models.": [212]}, "primary_topic": {"id": "https://openalex.org/T13643", "display_name": "Artificial Intelligence in Law", "score": 1.0, "subfield": {"id": "https://openalex.org/subfields/3320", "display_name": "Political Science and International Relations"}, "field": {"id": "https://openalex.org/fields/33", "display_name": "Social Sciences"}, "domain": {"id": "https://openalex.org/domains/2", "display_name": "Social Sciences"}}, "topics": [{"id": "https://openalex.org/T13643", "display_name": "Artificial Intelligence in Law", "score": 1.0, "subfield": {"id": "https://openalex.org/subfields/3320", "display_name": "Political Science and International Relations"}, "field": {"id": "https://openalex.org/fields/33", "display_name": "Social Sciences"}, "domain": {"id": "https://openalex.org/domains/2", "display_name": "Social Sciences"}}, {"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.0, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10906", "display_name": "AI-based Problem Solving and Planning", "score": 0.0, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2510.17602", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2510.17602", "pdf_url": "https://arxiv.org/pdf/2510.17602", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": "cc-by", "license_id": "https://openalex.org/licenses/cc-by", "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4414779914", "doi": "https://doi.org/10.14569/ijacsa.2025.0160978", "title": "Benchmarking Large Language Models for Hate Speech Detection in Arabic Dialects: Focus on the Saudi Dialects", "publication_year": 2025, "publication_date": "2025-01-01", "type": "article", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5018766538", "display_name": "Omaima Fallatah", "orcid": "https://orcid.org/0000-0002-5466-9119", "observed_orcids": ["https://orcid.org/0000-0002-5466-9119"]}, "institutions": [{"id": "https://openalex.org/I199693650", "display_name": "Umm al-Qura University", "ror": "https://ror.org/01xjqrm90", "country_code": "SA", "type": "education", "lineage": ["https://openalex.org/I199693650"]}], "countries": ["SA"], "is_corresponding": true, "raw_author_name": "Omaima Fallatah", "raw_affiliation_strings": ["Department of Data Science-College of Computing , Umm Al-Qura University , Makkah , Saudi Arabia"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Data Science-College of Computing , Umm Al-Qura University , Makkah , Saudi Arabia", "institution_ids": ["https://openalex.org/I199693650"]}]}], "abstract_inverted_index": {"This": [0], "study": [1], "investigates": [2], "the": [3, 23, 58, 103, 116], "effectiveness": [4], "of": [5, 26, 120], "large": [6], "language": [7], "models": [8, 50], "(LLMs)": [9], "in": [10, 74, 111], "detecting": [11, 75], "Arabic": [12, 121], "hate": [13], "speech,": [14], "with": [15, 55], "a": [16], "particular": [17], "focus": [18], "on": [19], "prompt-based": [20], "learning": [21], "and": [22, 36, 41, 81, 94, 114], "sociolinguistic": [24], "challenges": [25], "Saudi": [27], "dialects.": [28, 122], "We": [29, 86], "evaluate": [30], "four": [31], "LLMs,": [32], "GPT-4o,": [33], "LLaMA3,": [34], "Gemma2,": [35], "ALLaM,": [37], "using": [38], "zero-shot,": [39], "one-shot,": [40], "three-shot": [42], "prompting": [43, 64], "strategies.": [44], "The": [45], "results": [46], "show": [47], "that": [48], "all": [49, 63], "benefit": [51], "from": [52], "in-context": [53], "examples,": [54], "GPT-4o": [56], "achieving": [57], "highest": [59], "overall": [60], "performance": [61], "across": [62], "settings.": [65], "A": [66], "detailed": [67], "error": [68], "analysis": [69], "reveals": [70], "persistent": [71], "challenges,": [72], "particularly": [73], "implicit": [76], "hate,": [77], "handling": [78], "dialectal": [79], "variation,": [80], "interpreting": [82], "culturally": [83], "embedded": [84], "expressions.": [85], "also": [87], "highlight": [88], "limitations": [89], "related": [90], "to": [91], "topic": [92], "bias": [93], "annotation": [95], "ambiguity,": [96], "which": [97], "further": [98], "complicate": [99], "model": [100], "evaluation.": [101], "Overall,": [102], "findings": [104], "offer": [105], "key": [106], "insights": [107], "for": [108], "evaluating": [109], "LLMs": [110], "low-resource": [112], "settings": [113], "addressing": [115], "unique": [117], "linguistic": [118], "complexities": [119]}, "primary_topic": {"id": "https://openalex.org/T12262", "display_name": "Hate Speech and Cyberbullying Detection", "score": 0.9994999766349792, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T12262", "display_name": "Hate Speech and Cyberbullying Detection", "score": 0.9994999766349792, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10664", "display_name": "Sentiment Analysis and Opinion Mining", "score": 0.00019999999494757503, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 9.999999747378752e-05, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "doi:10.14569/ijacsa.2025.0160978", "is_oa": true, "landing_page_url": "https://doi.org/10.14569/ijacsa.2025.0160978", "pdf_url": "https://thesai.org/Downloads/Volume16No9/Paper_78-Benchmarking_Large_Language_Models_for_Hate_Speech_Detection.pdf", "source": {"id": "https://openalex.org/S23629721", "display_name": "International Journal of Advanced Computer Science and Applications", "issn_l": "2156-5570", "issn": ["2156-5570", "2158-107X"], "is_oa": true, "is_in_doaj": false, "is_core": true, "listed_in": ["cwts-core", "norway-1"], "host_organization": "https://openalex.org/P4310311819", "host_organization_name": "Science and Information Organization", "host_organization_lineage": ["https://openalex.org/P4310311819"], "host_organization_lineage_names": ["Science and Information Organization"], "type": "journal"}, "license": "cc-by", "license_id": "https://openalex.org/licenses/cc-by", "version": "publishedVersion", "is_accepted": true, "is_published": true, "raw_source_name": "International Journal of Advanced Computer Science and Applications", "raw_type": "journal-article"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W7120081806", "doi": "https://doi.org/10.33851/jmis.2025.12.4.141", "title": "Deep Learning-Based Context-Aware NER for Neologism Detection Across Generations", "publication_year": 2025, "publication_date": "2025-12-31", "type": "article", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5122545634", "display_name": "Junhaeng Lee", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I4210117332", "display_name": "Sungkyul University", "ror": "https://ror.org/02dzzka09", "country_code": "KR", "type": "education", "lineage": ["https://openalex.org/I4210117332"]}], "countries": ["KR"], "is_corresponding": false, "raw_author_name": "Junhaeng Lee", "raw_affiliation_strings": ["Department of Computer Engineering, Sungkyul University, Anyang, Korea, hkhk9495@sungkyul.ac.kr, ohy414@sungkyul.ac.kr, ykang@sungkyul.ac.kr"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Computer Engineering, Sungkyul University, Anyang, Korea, hkhk9495@sungkyul.ac.kr, ohy414@sungkyul.ac.kr, ykang@sungkyul.ac.kr", "institution_ids": ["https://openalex.org/I4210117332"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5026430614", "display_name": "Hyunyoung Oh", "orcid": "https://orcid.org/0000-0002-5873-4547", "observed_orcids": ["https://orcid.org/0000-0002-5873-4547"]}, "institutions": [{"id": "https://openalex.org/I4210117332", "display_name": "Sungkyul University", "ror": "https://ror.org/02dzzka09", "country_code": "KR", "type": "education", "lineage": ["https://openalex.org/I4210117332"]}], "countries": ["KR"], "is_corresponding": false, "raw_author_name": "Hyunyoung Oh", "raw_affiliation_strings": ["Department of Computer Engineering, Sungkyul University, Anyang, Korea, hkhk9495@sungkyul.ac.kr, ohy414@sungkyul.ac.kr, ykang@sungkyul.ac.kr"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Computer Engineering, Sungkyul University, Anyang, Korea, hkhk9495@sungkyul.ac.kr, ohy414@sungkyul.ac.kr, ykang@sungkyul.ac.kr", "institution_ids": ["https://openalex.org/I4210117332"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5059178827", "display_name": "Young-myoung Kang", "orcid": "https://orcid.org/0000-0001-8226-6440", "observed_orcids": ["https://orcid.org/0000-0001-8226-6440"]}, "institutions": [{"id": "https://openalex.org/I4210117332", "display_name": "Sungkyul University", "ror": "https://ror.org/02dzzka09", "country_code": "KR", "type": "education", "lineage": ["https://openalex.org/I4210117332"]}], "countries": ["KR"], "is_corresponding": false, "raw_author_name": "Young-myoung Kang", "raw_affiliation_strings": ["Department of Computer Engineering, Sungkyul University, Anyang, Korea, hkhk9495@sungkyul.ac.kr, ohy414@sungkyul.ac.kr, ykang@sungkyul.ac.kr"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Department of Computer Engineering, Sungkyul University, Anyang, Korea, hkhk9495@sungkyul.ac.kr, ohy414@sungkyul.ac.kr, ykang@sungkyul.ac.kr", "institution_ids": ["https://openalex.org/I4210117332"]}]}], "abstract_inverted_index": {"The": [0, 65], "rapid": [1], "proliferation": [2], "of": [3, 36, 103], "neologisms": [4, 59], "in": [5, 113, 159], "online": [6], "and": [7, 20, 26, 60, 78, 109, 133, 151], "social": [8], "communication": [9, 162], "has": [10], "intensified": [11], "linguistic": [12, 149], "disparities": [13], "across": [14, 123], "generations,": [15], "posing": [16], "challenges": [17], "for": [18, 75, 147], "timely": [19], "accurate": [21], "neologism": [22, 97], "detection.": [23], "Conventional": [24], "dictionary-": [25], "rule-based": [27], "methods": [28], "struggle": [29], "to": [30, 56, 111, 130, 155], "capture": [31], "the": [32, 92, 118, 140], "dynamic,": [33], "context-dependent": [34], "nature": [35], "evolving": [37, 156], "expressions,": [38], "limiting": [39], "their": [40], "practical": [41], "applicability.": [42], "This": [43], "study": [44], "proposes": [45], "a": [46, 69, 144], "deep": [47], "learning-based,": [48], "context-aware": [49], "Named": [50], "Entity": [51], "Recognition": [52], "(NER)": [53], "model": [54, 66, 73, 94, 119], "designed": [55], "effectively": [57], "identify": [58], "mitigate": [61], "generational": [62, 161], "language": [63, 72, 135], "gaps.": [64, 163], "leverages": [67], "KoELECTRA,": [68], "pre-trained": [70], "Korean": [71], "optimized": [74], "contextual": [76], "understanding,": [77], "is": [79], "evaluated": [80], "on": [81], "large-scale": [82], "datasets": [83], "with": [84, 100], "varying": [85], "entity": [86, 125], "distributions.": [87], "Experimental": [88], "results": [89], "demonstrate": [90], "that": [91], "proposed": [93], "achieves": [95], "robust": [96], "detection": [98], "performance,": [99], "an": [101], "F1-score": [102], "0.89": [104], "under": [105], "real-world": [106], "imbalanced": [107], "conditions": [108], "up": [110], "0.92": [112], "controlled,": [114], "uniform": [115], "settings.": [116], "Furthermore,": [117], "exhibits": [120], "balanced": [121], "recognition": [122], "diverse": [124], "types,": [126], "confirming": [127], "its": [128], "applicability": [129], "both": [131], "formal": [132], "informal": [134], "environments.": [136], "These": [137], "findings": [138], "highlight": [139], "model\u2019s": [141], "potential": [142], "as": [143], "scalable": [145], "solution": [146], "enhancing": [148], "accessibility": [150], "supporting": [152], "real-time": [153], "adaptation": [154], "language,": [157], "particularly": [158], "bridging": [160]}, "primary_topic": {"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.9771000146865845, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.9771000146865845, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10181", "display_name": "Natural Language Processing Techniques", "score": 0.014499999582767487, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10664", "display_name": "Sentiment Analysis and Opinion Mining", "score": 0.0007999999797903001, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "doi:10.33851/jmis.2025.12.4.141", "is_oa": true, "landing_page_url": "https://doi.org/10.33851/jmis.2025.12.4.141", "pdf_url": "http://www.jmis.org/download/download_pdf?pid=jmis-12-4-141", "source": {"id": "https://openalex.org/S4210218417", "display_name": "Journal of Multimedia Information System", "issn_l": "2383-7632", "issn": ["2383-7632"], "is_oa": true, "is_in_doaj": false, "is_core": true, "listed_in": ["cwts-core"], "host_organization": null, "host_organization_name": null, "host_organization_lineage": [], "host_organization_lineage_names": [], "type": "journal"}, "license": "cc-by-nc", "license_id": "https://openalex.org/licenses/cc-by-nc", "version": "publishedVersion", "is_accepted": true, "is_published": true, "raw_source_name": "Journal of Multimedia Information System", "raw_type": "journal-article"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4413340016", "doi": "https://doi.org/10.3390/app15169012", "title": "A Spatiotemporal\u2013Semantic Coupling Intelligent Q&A Method for Land Use Approval Based on Knowledge Graphs and Intelligent Agents", "publication_year": 2025, "publication_date": "2025-08-15", "type": "article", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5102817080", "display_name": "Huimin Liu", "orcid": "https://orcid.org/0000-0002-7419-5838", "observed_orcids": ["https://orcid.org/0000-0002-7419-5838"]}, "institutions": [{"id": "https://openalex.org/I139660479", "display_name": "Central South University", "ror": "https://ror.org/00f1zfq44", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I139660479"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Huimin Liu", "raw_affiliation_strings": ["Hunan Geospatial Information Engineering and Technology Research Center, Changsha 410018, China", "School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"], "raw_orcid": "https://orcid.org/0000-0002-7419-5838", "affiliations": [{"raw_affiliation_string": "Hunan Geospatial Information Engineering and Technology Research Center, Changsha 410018, China", "institution_ids": []}, {"raw_affiliation_string": "School of Geosciences and Info-Physics, Central South University, Changsha 410083, China", "institution_ids": ["https://openalex.org/I139660479"]}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Shutong Yin", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I139660479", "display_name": "Central South University", "ror": "https://ror.org/00f1zfq44", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I139660479"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Shutong Yin", "raw_affiliation_strings": ["School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "School of Geosciences and Info-Physics, Central South University, Changsha 410083, China", "institution_ids": ["https://openalex.org/I139660479"]}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Xin Hu", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I139660479", "display_name": "Central South University", "ror": "https://ror.org/00f1zfq44", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I139660479"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Xin Hu", "raw_affiliation_strings": ["School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "School of Geosciences and Info-Physics, Central South University, Changsha 410083, China", "institution_ids": ["https://openalex.org/I139660479"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5101738456", "display_name": "Min Deng", "orcid": "https://orcid.org/0000-0003-3305-9757", "observed_orcids": ["https://orcid.org/0000-0003-3305-9757"]}, "institutions": [{"id": "https://openalex.org/I139660479", "display_name": "Central South University", "ror": "https://ror.org/00f1zfq44", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I139660479"]}, {"id": "https://openalex.org/I53592917", "display_name": "Jiangxi Normal University", "ror": "https://ror.org/05nkgk822", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I53592917"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Min Deng", "raw_affiliation_strings": ["Hunan Geospatial Information Engineering and Technology Research Center, Changsha 410018, China", "School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China", "School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Hunan Geospatial Information Engineering and Technology Research Center, Changsha 410018, China", "institution_ids": []}, {"raw_affiliation_string": "School of Geography and Environment, Jiangxi Normal University, Nanchang 330022, China", "institution_ids": ["https://openalex.org/I53592917"]}, {"raw_affiliation_string": "School of Geosciences and Info-Physics, Central South University, Changsha 410083, China", "institution_ids": ["https://openalex.org/I139660479"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5027239954", "display_name": "Xuexi Yang", "orcid": "https://orcid.org/0000-0001-5531-5841", "observed_orcids": ["https://orcid.org/0000-0001-5531-5841"]}, "institutions": [{"id": "https://openalex.org/I139660479", "display_name": "Central South University", "ror": "https://ror.org/00f1zfq44", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I139660479"]}], "countries": ["CN"], "is_corresponding": true, "raw_author_name": "Xuexi Yang", "raw_affiliation_strings": ["Hunan Geospatial Information Engineering and Technology Research Center, Changsha 410018, China", "School of Geosciences and Info-Physics, Central South University, Changsha 410083, China"], "raw_orcid": "https://orcid.org/0000-0001-5531-5841", "affiliations": [{"raw_affiliation_string": "Hunan Geospatial Information Engineering and Technology Research Center, Changsha 410018, China", "institution_ids": []}, {"raw_affiliation_string": "School of Geosciences and Info-Physics, Central South University, Changsha 410083, China", "institution_ids": ["https://openalex.org/I139660479"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5006963488", "display_name": "Gang Xu", "orcid": "https://orcid.org/0000-0002-5124-3024", "observed_orcids": ["https://orcid.org/0000-0002-5124-3024"]}, "institutions": [{"id": "https://openalex.org/I4405278738", "display_name": "Zhejiang College of Security Technology", "ror": "https://ror.org/00fkk2t28", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I4405278738"]}], "countries": ["CN"], "is_corresponding": true, "raw_author_name": "Gang Xu", "raw_affiliation_strings": ["School of New Energy Equipment, Zhejiang College of Security Technology, Wenzhou 325016, China", "Wenzhou Future City Research Institute, Wenzhou 325016, China"], "raw_orcid": "https://orcid.org/0000-0002-5124-3024", "affiliations": [{"raw_affiliation_string": "School of New Energy Equipment, Zhejiang College of Security Technology, Wenzhou 325016, China", "institution_ids": ["https://openalex.org/I4405278738"]}, {"raw_affiliation_string": "Wenzhou Future City Research Institute, Wenzhou 325016, China", "institution_ids": []}]}], "abstract_inverted_index": {"The": [0, 192], "rapid": [1], "retrieval": [2, 39], "and": [3, 17, 59, 73, 108, 117, 143, 153, 178, 189, 204, 218, 233, 238, 255, 262], "precise": [4, 190], "acquisition": [5], "of": [6, 19, 31, 119, 167], "land": [7, 20, 32, 81, 100, 120, 125, 183], "use": [8, 21, 33, 82, 101, 121, 126, 166], "approval": [9, 34, 57, 83, 102, 127, 144, 184], "information": [10], "are": [11], "crucial": [12], "for": [13, 26, 50, 99], "enhancing": [14], "the": [15, 28, 80, 115, 165, 211, 226, 246, 250], "efficiency": [16, 58, 116], "quality": [18, 118], "approval,": [22], "as": [23, 25, 55, 138], "well": [24], "promoting": [27], "intelligent": [29, 96, 109, 154], "transformation": [30], "processes.": [35], "As": [36], "an": [37], "advanced": [38], "method,": [40], "question-answering": [41], "(Q&A)": [42], "technology": [43], "has": [44], "become": [45], "a": [46, 93, 124, 157], "core": [47], "technical": [48], "support": [49], "addressing": [51], "current": [52], "issues": [53], "such": [54, 137], "low": [56], "difficulty": [60], "in": [61, 75, 79, 220, 252], "obtaining": [62], "information.": [63], "However,": [64], "existing": [65], "Q&A": [66, 97, 160, 200, 205, 223, 229], "technologies": [67], "suffer": [68], "from": [69, 258], "significant": [70], "hallucination": [71], "problems": [72], "limitations": [74], "considering": [76], "spatiotemporal": [77, 168], "factors": [78, 180], "domain.": [84], "To": [85], "effectively": [86], "address": [87], "these": [88], "issues,": [89], "this": [90, 171], "study": [91], "proposes": [92], "spatiotemporal\u2013semantic": [94, 158, 247], "coupling": [95, 159], "method": [98, 213], "based": [103, 201, 206], "on": [104, 202, 207], "knowledge": [105, 128, 136, 222], "graphs": [106], "(KGs)": [107], "agent": [110, 155], "technology,": [111, 156], "aiming": [112], "to": [113, 198, 242, 260, 264], "enhance": [114], "approval.": [122], "Firstly,": [123], "graph": [129], "(LUAKG)": [130], "is": [131, 162], "constructed,": [132], "systematically": [133], "integrating": [134], "domain": [135], "policy": [139], "clauses,": [140], "legal": [141], "regulations,": [142], "procedures.": [145], "Then,": [146], "by": [147, 216, 236], "combining": [148], "large": [149], "language": [150], "models": [151], "(LLMs)": [152], "framework": [161, 172], "designed.": [163], "Through": [164], "analysis": [169], "tools,": [170], "can": [173], "comprehensively": [174], "consider": [175], "spatial,": [176], "temporal,": [177], "semantic": [179], "when": [181], "handling": [182], "tasks,": [185], "enabling": [186], "dynamic": [187], "decision-making": [188], "reasoning.": [191], "research": [193], "results": [194], "show": [195], "that,": [196], "compared": [197, 241], "traditional": [199], "LLMs": [203], "retrieval-enhanced": [208], "generation": [209], "(RAG),": [210], "proposed": [212], "improves": [214], "accuracy": [215, 234, 256], "16%": [217], "9%": [219], "general": [221], "tasks.": [224], "In": [225], "project": [227], "review": [228], "task,": [230], "F1": [231, 253], "scores": [232], "increase": [235], "2%": [237], "9%,": [239], "respectively,": [240], "RAG-QA.": [243], "Particularly,": [244], "under": [245], "multidimensional": [248], "analysis,": [249], "improvement": [251], "score": [254], "ranges": [257], "2": [259], "6%": [261], "7": [263], "10%,": [265], "respectively.": [266]}, "primary_topic": {"id": "https://openalex.org/T10215", "display_name": "Semantic Web and Ontologies", "score": 0.4846000075340271, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T10215", "display_name": "Semantic Web and Ontologies", "score": 0.4846000075340271, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T10028", "display_name": "Topic Modeling", "score": 0.31349998712539673, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T11273", "display_name": "Advanced Graph Neural Networks", "score": 0.020800000056624413, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "doi:10.3390/app15169012", "is_oa": true, "landing_page_url": "https://doi.org/10.3390/app15169012", "pdf_url": "https://www.mdpi.com/2076-3417/15/16/9012/pdf?version=1755588384", "source": {"id": "https://openalex.org/S4210205812", "display_name": "Applied Sciences", "issn_l": "2076-3417", "issn": ["2076-3417"], "is_oa": true, "is_in_doaj": true, "is_core": true, "listed_in": ["cwts-core", "doaj", "norway-1"], "host_organization": "https://openalex.org/P4310310987", "host_organization_name": "Multidisciplinary Digital Publishing Institute", "host_organization_lineage": ["https://openalex.org/P4310310987"], "host_organization_lineage_names": ["Multidisciplinary Digital Publishing Institute"], "type": "journal"}, "license": "cc-by", "license_id": "https://openalex.org/licenses/cc-by", "version": "publishedVersion", "is_accepted": true, "is_published": true, "raw_source_name": "Applied Sciences", "raw_type": "journal-article"}, "cited_by_count": 3, "is_retracted": false}, {"id": "https://openalex.org/W4416542159", "doi": "https://doi.org/10.48550/arxiv.2504.11895", "title": "Search is All You Need for Few-shot Anomaly Detection", "publication_year": 2025, "publication_date": "2025-04-16", "type": "preprint", "language": "en", "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5101462604", "display_name": "Qishan Wang", "orcid": "https://orcid.org/0000-0003-3463-9040", "observed_orcids": ["https://orcid.org/0000-0003-3463-9040"]}, "institutions": [{"id": "https://openalex.org/I24943067", "display_name": "Fudan University", "ror": "https://ror.org/013q1eq08", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I24943067"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Wang, Qishan", "raw_affiliation_strings": ["Academy for Engineering and Technology Fudan University Shanghai , China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Academy for Engineering and Technology Fudan University Shanghai , China", "institution_ids": ["https://openalex.org/I24943067"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5034897476", "display_name": "J.T. Guo", "orcid": "https://orcid.org/0009-0001-0549-0614", "observed_orcids": ["https://orcid.org/0009-0001-0549-0614"]}, "institutions": [{"id": "https://openalex.org/I99065089", "display_name": "Tsinghua University", "ror": "https://ror.org/03cve4549", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I99065089"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Guo, Jia", "raw_affiliation_strings": ["School of Biomedical Engineering Tsinghua University Beijing , China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "School of Biomedical Engineering Tsinghua University Beijing , China", "institution_ids": ["https://openalex.org/I99065089"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5048271189", "display_name": "Shuyong Gao", "orcid": "https://orcid.org/0000-0002-8992-0756", "observed_orcids": ["https://orcid.org/0000-0002-8992-0756"]}, "institutions": [{"id": "https://openalex.org/I24943067", "display_name": "Fudan University", "ror": "https://ror.org/013q1eq08", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I24943067"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Gao, Shuyong", "raw_affiliation_strings": ["School of Computer Science Fudan university Shanghai , China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "School of Computer Science Fudan university Shanghai , China", "institution_ids": ["https://openalex.org/I24943067"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5101664305", "display_name": "Haofen Wang", "orcid": "https://orcid.org/0009-0002-0145-7873", "observed_orcids": ["https://orcid.org/0009-0002-0145-7873", "https://orcid.org/0000-0003-3018-3824"]}, "institutions": [{"id": "https://openalex.org/I116953780", "display_name": "Tongji University", "ror": "https://ror.org/03rc6as71", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I116953780"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Wang, Haofen", "raw_affiliation_strings": ["College of Design & Innovation Tongji University Shanghai , China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "College of Design & Innovation Tongji University Shanghai , China", "institution_ids": ["https://openalex.org/I116953780"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5105932899", "display_name": "Li Xiong", "orcid": "https://orcid.org/0009-0000-9299-1013", "observed_orcids": ["https://orcid.org/0009-0000-9299-1013"]}, "institutions": [{"id": "https://openalex.org/I4210117818", "display_name": "Hexi University", "ror": "https://ror.org/02axfzt86", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I4210117818"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Xiong, Li", "raw_affiliation_strings": ["School of Physics and Mechanical & Electrical Engineering Hexi University Gansu , China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "School of Physics and Mechanical & Electrical Engineering Hexi University Gansu , China", "institution_ids": ["https://openalex.org/I4210117818"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5101982146", "display_name": "Junjie Hu", "orcid": "https://orcid.org/0000-0002-5750-0511", "observed_orcids": ["https://orcid.org/0000-0002-5750-0511", "https://orcid.org/0009-0009-5098-9025"]}, "institutions": [{"id": "https://openalex.org/I24943067", "display_name": "Fudan University", "ror": "https://ror.org/013q1eq08", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I24943067"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Hu, Junjie", "raw_affiliation_strings": ["School of Computer Science Fudan university Shanghai , China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "School of Computer Science Fudan university Shanghai , China", "institution_ids": ["https://openalex.org/I24943067"]}]}, {"author_position": "middle", "author": {"id": null, "display_name": "Guo, Hanqi", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I24943067", "display_name": "Fudan University", "ror": "https://ror.org/013q1eq08", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I24943067"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Guo, Hanqi", "raw_affiliation_strings": ["School of Computer Science Fudan university Shanghai , China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "School of Computer Science Fudan university Shanghai , China", "institution_ids": ["https://openalex.org/I24943067"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5100669255", "display_name": "Wenqiang Zhang", "orcid": "https://orcid.org/0000-0002-3339-8751", "observed_orcids": ["https://orcid.org/0000-0002-3339-8751"]}, "institutions": [{"id": "https://openalex.org/I24943067", "display_name": "Fudan University", "ror": "https://ror.org/013q1eq08", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I24943067"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Zhang, Wenqiang", "raw_affiliation_strings": ["Engineering Research Center of AI&Robotics , Ministry of Education Academy for Engineering&Technology Fudan university Shanghai , China"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Engineering Research Center of AI&Robotics , Ministry of Education Academy for Engineering&Technology Fudan university Shanghai , China", "institution_ids": ["https://openalex.org/I24943067"]}]}], "abstract_inverted_index": {"Few-shot": [0], "anomaly": [1, 43], "detection": [2], "(FSAD)": [3], "has": [4], "emerged": [5], "as": [6, 169], "a": [7, 24, 62, 142], "crucial": [8], "yet": [9, 86], "challenging": [10], "task": [11], "in": [12, 71], "industrial": [13], "inspection,": [14], "where": [15, 211], "normal": [16, 26, 167], "distribution": [17], "modeling": [18], "must": [19], "be": [20], "accomplished": [21], "with": [22, 136], "only": [23, 165], "few": [25], "images.": [27], "While": [28], "existing": [29], "approaches": [30, 187], "typically": [31], "employ": [32], "multi-modal": [33], "foundation": [34, 92], "models": [35, 93], "combining": [36], "language": [37], "and": [38, 52, 74, 97, 112, 132, 140, 157, 181, 193, 198], "vision": [39, 91], "modalities": [40], "for": [41, 208], "prompt-guided": [42], "detection,": [44], "these": [45], "methods": [46], "often": [47], "demand": [48], "sophisticated": [49], "prompt": [50], "engineering": [51], "extensive": [53], "manual": [54], "tuning.": [55], "In": [56], "this": [57], "paper,": [58], "we": [59], "demonstrate": [60, 160], "that": [61, 94, 126], "straightforward": [63], "nearest-neighbor": [64], "search": [65], "framework": [66], "can": [67], "surpass": [68], "state-of-the-art": [69, 186], "performance": [70], "both": [72, 128], "single-class": [73], "multi-class": [75, 150], "FSAD": [76], "scenarios.": [77], "Our": [78], "proposed": [79], "method,": [80], "VisionAD,": [81], "consists": [82], "of": [83, 119, 178, 202], "four": [84], "simple": [85], "essential": [87], "components:": [88], "(1)": [89], "scalable": [90], "extract": [95], "universal": [96], "discriminative": [98], "features;": [99], "(2)": [100], "dual": [101], "augmentation": [102, 106, 114], "strategies": [103], "-": [104], "support": [105], "to": [107, 115, 217], "enhance": [108], "feature": [109, 124], "matching": [110], "adaptability": [111], "query": [113], "address": [116], "the": [117], "oversights": [118], "single-view": [120], "prediction;": [121], "(3)": [122], "multi-layer": [123], "integration": [125], "captures": [127], "low-frequency": [129], "global": [130], "context": [131], "high-frequency": [133], "local": [134], "details": [135], "minimal": [137], "computational": [138], "overhead;": [139], "(4)": [141], "class-aware": [143], "visual": [144], "memory": [145], "bank": [146], "enabling": [147], "efficient": [148], "one-for-all": [149], "detection.": [151], "Extensive": [152], "evaluations": [153], "across": [154], "MVTec-AD,": [155], "VisA,": [156], "Real-IAD": [158], "benchmarks": [159], "VisionAD's": [161], "exceptional": [162], "performance.": [163], "Using": [164], "1": [166], "images": [168], "support,": [170], "our": [171], "method": [172], "achieves": [173], "remarkable": [174], "image-level": [175], "AUROC": [176], "scores": [177], "97.4%,": [179], "94.8%,": [180], "70.8%": [182], "respectively,": [183], "outperforming": [184], "current": [185], "by": [188], "significant": [189], "margins": [190], "(+1.6%,": [191], "+3.2%,": [192], "+1.4%).": [194], "The": [195], "training-free": [196], "nature": [197], "superior": [199], "few-shot": [200], "capabilities": [201], "VisionAD": [203], "make": [204], "it": [205], "particularly": [206], "appealing": [207], "real-world": [209], "applications": [210], "samples": [212], "are": [213], "scarce": [214], "or": [215], "expensive": [216], "obtain.": [218], "Code": [219], "is": [220], "available": [221], "at": [222], "https://github.com/Qiqigeww/VisionAD.": [223]}, "primary_topic": {"id": "https://openalex.org/T11512", "display_name": "Anomaly Detection Techniques and Applications", "score": 0.7567999958992004, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T11512", "display_name": "Anomaly Detection Techniques and Applications", "score": 0.7567999958992004, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T12111", "display_name": "Industrial Vision Systems and Defect Detection", "score": 0.2101999968290329, "subfield": {"id": "https://openalex.org/subfields/2209", "display_name": "Industrial and Manufacturing Engineering"}, "field": {"id": "https://openalex.org/fields/22", "display_name": "Engineering"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T11307", "display_name": "Domain Adaptation and Few-Shot Learning", "score": 0.004900000058114529, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2504.11895", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2504.11895", "pdf_url": "https://arxiv.org/pdf/2504.11895", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": null, "license_id": null, "version": "submittedVersion", "is_accepted": false, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4415372914", "doi": "https://doi.org/10.21203/rs.3.rs-7844536/v1", "title": "OrganSegBench: A Comprehensive Multi-Organ Benchmark for Segmentation Foundation Models with a Practical Synergy Pathway to Clinical Application", "publication_year": 2025, "publication_date": "2025-10-21", "type": "preprint", "language": null, "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5101981252", "display_name": "Chengyan Wang", "orcid": "https://orcid.org/0000-0002-8890-4973", "observed_orcids": ["https://orcid.org/0000-0002-8890-4973"]}, "institutions": [{"id": "https://openalex.org/I24943067", "display_name": "Fudan University", "ror": "https://ror.org/013q1eq08", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I24943067"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Chengyan Wang", "raw_affiliation_strings": ["Human Phenome Institution, Fudan University"], "raw_orcid": "https://orcid.org/0000-0002-8890-4973", "affiliations": [{"raw_affiliation_string": "Human Phenome Institution, Fudan University", "institution_ids": ["https://openalex.org/I24943067"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5101456622", "display_name": "Qing Li", "orcid": "https://orcid.org/0000-0002-4288-0347", "observed_orcids": ["https://orcid.org/0000-0002-4288-0347"]}, "institutions": [{"id": "https://openalex.org/I24943067", "display_name": "Fudan University", "ror": "https://ror.org/013q1eq08", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I24943067"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Qing Li", "raw_affiliation_strings": ["Human Phenome Institution, Fudan University"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Human Phenome Institution, Fudan University", "institution_ids": ["https://openalex.org/I24943067"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5101959001", "display_name": "Yizhe Zhang", "orcid": "https://orcid.org/0000-0003-2624-3329", "observed_orcids": ["https://orcid.org/0000-0003-2624-3329", "https://orcid.org/0009-0005-5112-1720"]}, "institutions": [{"id": "https://openalex.org/I36399199", "display_name": "Nanjing University of Science and Technology", "ror": "https://ror.org/00xp9wg62", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I36399199"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Yizhe Zhang", "raw_affiliation_strings": ["GE Healthcare", "School of Computer Science and Engineering, Nanjing University of Science and Technology"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "GE Healthcare", "institution_ids": []}, {"raw_affiliation_string": "School of Computer Science and Engineering, Nanjing University of Science and Technology", "institution_ids": ["https://openalex.org/I36399199"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5100749152", "display_name": "Xin Guo", "orcid": "https://orcid.org/0009-0003-3414-8564", "observed_orcids": ["https://orcid.org/0009-0003-3414-8564"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Xin Guo", "raw_affiliation_strings": ["Shanghai Academy of Artificial Intelligence for Science"], "raw_orcid": "https://orcid.org/0009-0003-3414-8564", "affiliations": [{"raw_affiliation_string": "Shanghai Academy of Artificial Intelligence for Science", "institution_ids": []}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5081395707", "display_name": "Haosen Zhang", "orcid": null, "observed_orcids": []}, "institutions": [{"id": "https://openalex.org/I24943067", "display_name": "Fudan University", "ror": "https://ror.org/013q1eq08", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I24943067"]}, {"id": "https://openalex.org/I4210127380", "display_name": "Pudong Medical Center", "ror": "https://ror.org/02nptez24", "country_code": "CN", "type": "healthcare", "lineage": ["https://openalex.org/I4210127380"]}, {"id": "https://openalex.org/I47508984", "display_name": "Imperial College London", "ror": "https://ror.org/041kmwe10", "country_code": "GB", "type": "education", "lineage": ["https://openalex.org/I47508984"]}], "countries": ["CN", "GB"], "is_corresponding": false, "raw_author_name": "Haosen Zhang", "raw_affiliation_strings": ["Bioengineering Department and Imperial-X, Imperial College London", "Human Phenome Institute and Shanghai Pudong Hospital, Fudan University"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Bioengineering Department and Imperial-X, Imperial College London", "institution_ids": ["https://openalex.org/I47508984"]}, {"raw_affiliation_string": "Human Phenome Institute and Shanghai Pudong Hospital, Fudan University", "institution_ids": ["https://openalex.org/I24943067", "https://openalex.org/I4210127380"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5100380402", "display_name": "Yan Li", "orcid": "https://orcid.org/0000-0002-9481-8086", "observed_orcids": ["https://orcid.org/0000-0002-9481-8086"]}, "institutions": [{"id": "https://openalex.org/I183067930", "display_name": "Shanghai Jiao Tong University", "ror": "https://ror.org/0220qvk04", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I183067930"]}, {"id": "https://openalex.org/I2801556517", "display_name": "Ruijin Hospital", "ror": "https://ror.org/01hv94n30", "country_code": "CN", "type": "healthcare", "lineage": ["https://openalex.org/I2801556517"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Yan Li", "raw_affiliation_strings": ["Ruijin Hospital, Shanghai Jiaotong University School of Medicine"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Ruijin Hospital, Shanghai Jiaotong University School of Medicine", "institution_ids": ["https://openalex.org/I183067930", "https://openalex.org/I2801556517"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5101148687", "display_name": "Mo Yang", "orcid": "https://orcid.org/0009-0004-8565-7235", "observed_orcids": ["https://orcid.org/0009-0004-8565-7235"]}, "institutions": [{"id": "https://openalex.org/I24943067", "display_name": "Fudan University", "ror": "https://ror.org/013q1eq08", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I24943067"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Mo Yang", "raw_affiliation_strings": ["Fudan University"], "raw_orcid": "https://orcid.org/0009-0004-8565-7235", "affiliations": [{"raw_affiliation_string": "Fudan University", "institution_ids": ["https://openalex.org/I24943067"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5047547960", "display_name": "Yajing Zhang", "orcid": "https://orcid.org/0009-0002-8645-091X", "observed_orcids": ["https://orcid.org/0009-0002-8645-091X"]}, "institutions": [{"id": "https://openalex.org/I36399199", "display_name": "Nanjing University of Science and Technology", "ror": "https://ror.org/00xp9wg62", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I36399199"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Yajing Zhang", "raw_affiliation_strings": ["GE Healthcare", "School of Computer Science and Engineering, Nanjing University of Science and Technology"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "GE Healthcare", "institution_ids": []}, {"raw_affiliation_string": "School of Computer Science and Engineering, Nanjing University of Science and Technology", "institution_ids": ["https://openalex.org/I36399199"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5059155659", "display_name": "Mengting Sun", "orcid": "https://orcid.org/0000-0001-8022-0510", "observed_orcids": ["https://orcid.org/0000-0001-8022-0510"]}, "institutions": [{"id": "https://openalex.org/I24943067", "display_name": "Fudan University", "ror": "https://ror.org/013q1eq08", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I24943067"]}, {"id": "https://openalex.org/I4210127380", "display_name": "Pudong Medical Center", "ror": "https://ror.org/02nptez24", "country_code": "CN", "type": "healthcare", "lineage": ["https://openalex.org/I4210127380"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Mengting Sun", "raw_affiliation_strings": ["Human Phenome Institute and Shanghai Pudong Hospital, Fudan University"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Human Phenome Institute and Shanghai Pudong Hospital, Fudan University", "institution_ids": ["https://openalex.org/I24943067", "https://openalex.org/I4210127380"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5111121544", "display_name": "Longyu Sun", "orcid": "https://orcid.org/0009-0007-3687-7997", "observed_orcids": ["https://orcid.org/0009-0007-3687-7997", "https://orcid.org/0009-0002-6272-6854"]}, "institutions": [{"id": "https://openalex.org/I20231570", "display_name": "Peking University", "ror": "https://ror.org/02v51f717", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I20231570"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Longyu Sun", "raw_affiliation_strings": ["School of Mechanics and Engineering Science, Peking University"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "School of Mechanics and Engineering Science, Peking University", "institution_ids": ["https://openalex.org/I20231570"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5101644047", "display_name": "Haoyang Zhang", "orcid": "https://orcid.org/0000-0001-6848-0563", "observed_orcids": ["https://orcid.org/0000-0001-6848-0563"]}, "institutions": [{"id": "https://openalex.org/I24943067", "display_name": "Fudan University", "ror": "https://ror.org/013q1eq08", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I24943067"]}, {"id": "https://openalex.org/I4210127380", "display_name": "Pudong Medical Center", "ror": "https://ror.org/02nptez24", "country_code": "CN", "type": "healthcare", "lineage": ["https://openalex.org/I4210127380"]}, {"id": "https://openalex.org/I47508984", "display_name": "Imperial College London", "ror": "https://ror.org/041kmwe10", "country_code": "GB", "type": "education", "lineage": ["https://openalex.org/I47508984"]}], "countries": ["CN", "GB"], "is_corresponding": false, "raw_author_name": "Haoyang Zhang", "raw_affiliation_strings": ["Bioengineering Department and Imperial-X, Imperial College London", "Human Phenome Institute and Shanghai Pudong Hospital, Fudan University"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Bioengineering Department and Imperial-X, Imperial College London", "institution_ids": ["https://openalex.org/I47508984"]}, {"raw_affiliation_string": "Human Phenome Institute and Shanghai Pudong Hospital, Fudan University", "institution_ids": ["https://openalex.org/I24943067", "https://openalex.org/I4210127380"]}]}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5053957663", "display_name": "Junhong Liu", "orcid": "https://orcid.org/0000-0003-4021-4698", "observed_orcids": ["https://orcid.org/0000-0003-4021-4698"]}, "institutions": [{"id": "https://openalex.org/I24943067", "display_name": "Fudan University", "ror": "https://ror.org/013q1eq08", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I24943067"]}, {"id": "https://openalex.org/I4210127380", "display_name": "Pudong Medical Center", "ror": "https://ror.org/02nptez24", "country_code": "CN", "type": "healthcare", "lineage": ["https://openalex.org/I4210127380"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Junhong Liu", "raw_affiliation_strings": ["Human Phenome Institute and Shanghai Pudong Hospital, Fudan University"], "raw_orcid": null, "affiliations": [{"raw_affiliation_string": "Human Phenome Institute and Shanghai Pudong Hospital, Fudan University", "institution_ids": ["https://openalex.org/I24943067", "https://openalex.org/I4210127380"]}]}, {"author_position": "last", "author": {"id": "https://openalex.org/A5100680262", "display_name": "Shuo Wang", "orcid": "https://orcid.org/0000-0002-2947-8783", "observed_orcids": ["https://orcid.org/0000-0002-2947-8783", "https://orcid.org/0000-0002-4881-9344"]}, "institutions": [{"id": "https://openalex.org/I24943067", "display_name": "Fudan University", "ror": "https://ror.org/013q1eq08", "country_code": "CN", "type": "education", "lineage": ["https://openalex.org/I24943067"]}], "countries": ["CN"], "is_corresponding": false, "raw_author_name": "Shuo Wang", "raw_affiliation_strings": ["Fudan University"], "raw_orcid": "https://orcid.org/0000-0002-2947-8783", "affiliations": [{"raw_affiliation_string": "Fudan University", "institution_ids": ["https://openalex.org/I24943067"]}]}], "abstract_inverted_index": null, "primary_topic": {"id": "https://openalex.org/T10052", "display_name": "Medical Image Segmentation Techniques", "score": 0.9667999744415283, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T10052", "display_name": "Medical Image Segmentation Techniques", "score": 0.9667999744415283, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T11636", "display_name": "Artificial Intelligence in Healthcare and Education", "score": 0.01720000058412552, "subfield": {"id": "https://openalex.org/subfields/2718", "display_name": "Health Informatics"}, "field": {"id": "https://openalex.org/fields/27", "display_name": "Medicine"}, "domain": {"id": "https://openalex.org/domains/4", "display_name": "Health Sciences"}}, {"id": "https://openalex.org/T10036", "display_name": "Advanced Neural Network Applications", "score": 0.008100000210106373, "subfield": {"id": "https://openalex.org/subfields/1707", "display_name": "Computer Vision and Pattern Recognition"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "doi:10.21203/rs.3.rs-7844536/v1", "is_oa": true, "landing_page_url": "https://doi.org/10.21203/rs.3.rs-7844536/v1", "pdf_url": "https://www.researchsquare.com/article/rs-7844536/latest.pdf", "source": {"id": "https://openalex.org/S4306525896", "display_name": "Research Square", "issn_l": null, "issn": null, "is_oa": false, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": null, "host_organization_name": null, "host_organization_lineage": [], "host_organization_lineage_names": [], "type": "repository"}, "license": "cc-by", "license_id": "https://openalex.org/licenses/cc-by", "version": "acceptedVersion", "is_accepted": true, "is_published": false, "raw_source_name": "Research Square", "raw_type": "posted-content"}, "cited_by_count": 0, "is_retracted": false}, {"id": "https://openalex.org/W4415952316", "doi": "https://doi.org/10.48550/arxiv.2510.16363", "title": "End-to-End Argument Mining through Autoregressive Argumentative Structure Prediction", "publication_year": 2025, "publication_date": "2025-10-18", "type": "preprint", "language": null, "authorships": [{"author_position": "first", "author": {"id": "https://openalex.org/A5109806715", "display_name": "Nilmadhab Das", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Das, Nilmadhab", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": null, "display_name": "Vaibhav, Vishal", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Vaibhav, Vishal", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5070805283", "display_name": "Yash Vardhan Choudhary", "orcid": null, "observed_orcids": []}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Choudhary, Yash Sunil", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "middle", "author": {"id": "https://openalex.org/A5109364773", "display_name": "V. Vijaya Saradhi", "orcid": "https://orcid.org/0000-0002-7856-5322", "observed_orcids": ["https://orcid.org/0000-0002-7856-5322"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Saradhi, V. Vijaya", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}, {"author_position": "last", "author": {"id": "https://openalex.org/A5040626471", "display_name": "Ashish Anand", "orcid": "https://orcid.org/0000-0002-0024-3358", "observed_orcids": ["https://orcid.org/0000-0002-0024-3358"]}, "institutions": [], "countries": [], "is_corresponding": false, "raw_author_name": "Anand, Ashish", "raw_affiliation_strings": [], "raw_orcid": null, "affiliations": []}], "abstract_inverted_index": {"Argument": [0, 14], "Mining": [1], "(AM)": [2], "helps": [3], "in": [4, 29, 82, 142, 153, 176, 183], "automating": [5], "the": [6, 35, 54, 66, 77, 100, 117, 127, 138, 148], "extraction": [7], "of": [8, 38, 53, 80, 124, 129, 150], "complex": [9], "argumentative": [10, 31, 67, 118, 139, 151], "structures": [11, 119, 140], "such": [12], "as": [13, 120], "Components": [15], "(ACs)": [16], "like": [17, 25], "Premise,": [18], "Claim": [19], "etc.": [20, 28], "and": [21, 48, 179], "Argumentative": [22, 88], "Relations": [23], "(ARs)": [24], "Support,": [26], "Attack": [27], "an": [30, 83, 143, 154], "text.": [32], "Due": [33], "to": [34, 71, 146], "inherent": [36], "complexity": [37], "reasoning": [39, 152], "involved": [40], "with": [41, 126], "this": [42, 58, 73], "task,": [43], "modelling": [44], "dependencies": [45], "between": [46], "ACs": [47], "ARs": [49], "is": [50, 97], "challenging.": [51], "Most": [52], "recent": [55], "approaches": [56], "formulate": [57], "task": [59], "through": [60], "a": [61, 130], "generative": [62], "paradigm": [63], "by": [64], "flattening": [65], "structures.": [68], "In": [69], "contrast": [70], "that,": [72], "study": [74], "jointly": [75], "formulates": [76], "key": [78], "tasks": [79, 175], "AM": [81, 163, 174], "end-to-end": [84], "fashion": [85], "using": [86], "Autoregressive": [87], "Structure": [89], "Prediction": [90], "(AASP)": [91], "framework.": [92], "The": [93], "proposed": [94], "AASP": [95, 114, 167], "framework": [96, 104, 115], "based": [98], "on": [99, 160], "autoregressive": [101, 144], "structure": [102], "prediction": [103], "that": [105, 166], "has": [106], "given": [107], "good": [108], "performance": [109], "for": [110], "several": [111], "NLP": [112], "tasks.": [113], "models": [116], "constrained": [121], "pre-defined": [122], "sets": [123], "actions": [125, 136], "help": [128], "conditional": [131], "pre-trained": [132], "language": [133], "model.": [134], "These": [135], "build": [137], "step-by-step": [141], "manner": [145], "capture": [147], "flow": [149], "efficient": [155], "way.": [156], "Extensive": [157], "experiments": [158], "conducted": [159], "three": [161], "standard": [162], "benchmarks": [164, 178], "demonstrate": [165], "achieves": [168], "state-of-theart": [169], "(SoTA)": [170], "results": [171, 182], "across": [172], "all": [173], "two": [177], "delivers": [180], "strong": [181], "one": [184], "benchmark.": [185]}, "primary_topic": {"id": "https://openalex.org/T10664", "display_name": "Sentiment Analysis and Opinion Mining", "score": 0.35420000553131104, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, "topics": [{"id": "https://openalex.org/T10664", "display_name": "Sentiment Analysis and Opinion Mining", "score": 0.35420000553131104, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}, {"id": "https://openalex.org/T12119", "display_name": "Linguistics and Discourse Analysis", "score": 0.20029999315738678, "subfield": {"id": "https://openalex.org/subfields/1211", "display_name": "Philosophy"}, "field": {"id": "https://openalex.org/fields/12", "display_name": "Arts and Humanities"}, "domain": {"id": "https://openalex.org/domains/2", "display_name": "Social Sciences"}}, {"id": "https://openalex.org/T10456", "display_name": "Multi-Agent Systems and Negotiation", "score": 0.13899999856948853, "subfield": {"id": "https://openalex.org/subfields/1702", "display_name": "Artificial Intelligence"}, "field": {"id": "https://openalex.org/fields/17", "display_name": "Computer Science"}, "domain": {"id": "https://openalex.org/domains/3", "display_name": "Physical Sciences"}}], "primary_location": {"id": "pmh:oai:arXiv.org:2510.16363", "is_oa": true, "landing_page_url": "http://arxiv.org/abs/2510.16363", "pdf_url": "https://arxiv.org/pdf/2510.16363", "source": {"id": "https://openalex.org/S4306400194", "display_name": "arXiv (Cornell University)", "issn_l": "2331-8422", "issn": ["2331-8422"], "is_oa": true, "is_in_doaj": false, "is_core": false, "listed_in": [], "host_organization": "https://openalex.org/I205783295", "host_organization_name": "Cornell University", "host_organization_lineage": ["https://openalex.org/I205783295"], "host_organization_lineage_names": [], "type": "repository"}, "license": null, "license_id": null, "version": "acceptedVersion", "is_accepted": true, "is_published": false, "raw_source_name": null, "raw_type": "text"}, "cited_by_count": 0, "is_retracted": false}], "group_by": []}