{"id":"https://openalex.org/W7131672010","doi":"https://doi.org/10.48550/arxiv.2602.22067","title":"Semantic Partial Grounding via LLMs","display_name":"Semantic Partial Grounding via LLMs","publication_year":2026,"publication_date":"2026-02-25","ids":{"openalex":"https://openalex.org/W7131672010","doi":"https://doi.org/10.48550/arxiv.2602.22067"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2602.22067","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.22067","pdf_url":null,"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,"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2602.22067","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5091606728","display_name":"Giuseppe Canonaco","orcid":"https://orcid.org/0000-0001-8647-6269"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Canonaco, Giuseppe","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036948935","display_name":"Alberto Pozanco","orcid":"https://orcid.org/0000-0002-3851-1311"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pozanco, Alberto","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5050512952","display_name":"Daniel Borrajo","orcid":"https://orcid.org/0000-0001-5282-0463"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Borrajo, Daniel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10906","display_name":"AI-based Problem Solving and Planning","score":0.8177000284194946,"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/T10906","display_name":"AI-based Problem Solving and Planning","score":0.8177000284194946,"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/T11574","display_name":"Artificial Intelligence in Games","score":0.02810000069439411,"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":0.023800000548362732,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7689999938011169},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.6931999921798706},{"id":"https://openalex.org/keywords/ground","display_name":"Ground","score":0.6051999926567078},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.588699996471405},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.43059998750686646},{"id":"https://openalex.org/keywords/plan","display_name":"Plan (archaeology)","score":0.3882000148296356}],"concepts":[{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7689999938011169},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.6931999921798706},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6341000199317932},{"id":"https://openalex.org/C168993435","wikidata":"https://www.wikidata.org/wiki/Q6501125","display_name":"Ground","level":2,"score":0.6051999926567078},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.588699996471405},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5408999919891357},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.43059998750686646},{"id":"https://openalex.org/C2776505523","wikidata":"https://www.wikidata.org/wiki/Q4785468","display_name":"Plan (archaeology)","level":2,"score":0.3882000148296356},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3824000060558319},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3702000081539154},{"id":"https://openalex.org/C156325361","wikidata":"https://www.wikidata.org/wiki/Q1152864","display_name":"Grounded theory","level":3,"score":0.35510000586509705},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3407000005245209},{"id":"https://openalex.org/C2777877512","wikidata":"https://www.wikidata.org/wiki/Q1116097","display_name":"Common ground","level":2,"score":0.3285999894142151},{"id":"https://openalex.org/C5655090","wikidata":"https://www.wikidata.org/wiki/Q192588","display_name":"Relational database","level":2,"score":0.3167000114917755},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.302700012922287},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2944999933242798},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.2906999886035919},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.28439998626708984}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2602.22067","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.22067","pdf_url":null,"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,"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2602.22067","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.22067","pdf_url":null,"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,"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":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.7960056066513062}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Grounding":[0],"is":[1],"a":[2,12],"critical":[3],"step":[4],"in":[5,20,31,71,125],"classical":[6],"planning,":[7],"yet":[8],"it":[9],"often":[10],"becomes":[11],"computational":[13],"bottleneck":[14],"due":[15],"to":[16,80,87,97],"the":[17,42,65,82,101,104],"exponential":[18],"growth":[19],"grounded":[21,105],"actions":[22],"and":[23,61,67,84,94],"atoms":[24],"as":[25],"task":[26],"size":[27,102],"increases.":[28],"Recent":[29],"advances":[30],"partial":[32],"grounding":[33,40],"have":[34],"addressed":[35],"this":[36],"challenge":[37],"by":[38,47,115],"incrementally":[39],"only":[41],"most":[43],"promising":[44],"operators,":[45],"guided":[46],"predictive":[48],"models.":[49],"However,":[50],"these":[51],"approaches":[52],"primarily":[53],"rely":[54],"on":[55],"relational":[56],"features":[57],"or":[58,121],"learned":[59],"embeddings":[60],"do":[62],"not":[63],"leverage":[64],"textual":[66],"structural":[68],"cues":[69],"present":[70],"PDDL":[72],"descriptions.":[73],"We":[74],"propose":[75],"SPG-LLM,":[76],"which":[77],"uses":[78],"LLMs":[79],"analyze":[81],"domain":[83],"problem":[85],"files":[86],"heuristically":[88],"identify":[89],"potentially":[90],"irrelevant":[91],"objects,":[92],"actions,":[93],"predicates":[95],"prior":[96],"grounding,":[98],"significantly":[99],"reducing":[100],"of":[103,117],"task.":[106],"Across":[107],"seven":[108],"hard-to-ground":[109],"benchmarks,":[110],"SPG-LLM":[111],"achieves":[112],"faster":[113],"grounding-often":[114],"orders":[116],"magnitude-while":[118],"delivering":[119],"comparable":[120],"better":[122],"plan":[123],"costs":[124],"some":[126],"domains.":[127]},"counts_by_year":[],"updated_date":"2026-08-16T07:02:28.622633","created_date":"2026-02-27T00:00:00"}
