{"id":"https://openalex.org/W7133294462","doi":"https://doi.org/10.48550/arxiv.2603.01410","title":"GraphScout: Empowering Large Language Models with Intrinsic Exploration Ability for Agentic Graph Reasoning","display_name":"GraphScout: Empowering Large Language Models with Intrinsic Exploration Ability for Agentic Graph Reasoning","publication_year":2026,"publication_date":"2026-03-02","ids":{"openalex":"https://openalex.org/W7133294462","doi":"https://doi.org/10.48550/arxiv.2603.01410"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.01410","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01410","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.01410","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5065499866","display_name":"Yuchen Ying","orcid":"https://orcid.org/0009-0000-7646-0378"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ying, Yuchen","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127900295","display_name":"Weiqi Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Weiqi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127916052","display_name":"Tongya Zheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zheng, Tongya","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128026091","display_name":"Yu Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090854835","display_name":"Shunyu Liu","orcid":"https://orcid.org/0000-0003-0584-9129"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Shunyu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127940553","display_name":"Kaixuan Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Kaixuan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5127966997","display_name":"Mingli Song","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song, Mingli","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5065499866"],"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.8130999803543091,"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":0.8130999803543091,"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":0.10320000350475311,"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":0.039900001138448715,"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/inference","display_name":"Inference","score":0.6133999824523926},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5511999726295471},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.4320000112056732},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.41749998927116394},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.39500001072883606},{"id":"https://openalex.org/keywords/automated-reasoning","display_name":"Automated reasoning","score":0.38190001249313354},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.3659999966621399}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7217000126838684},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6133999824523926},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5511999726295471},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.484499990940094},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.4320000112056732},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.41749998927116394},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.41679999232292175},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.39500001072883606},{"id":"https://openalex.org/C195344581","wikidata":"https://www.wikidata.org/wiki/Q2555318","display_name":"Automated reasoning","level":2,"score":0.38190001249313354},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3659999966621399},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.36250001192092896},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.34470000863075256},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.33809998631477356},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.3264999985694885},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3093999922275543},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2996000051498413},{"id":"https://openalex.org/C193221554","wikidata":"https://www.wikidata.org/wiki/Q5153664","display_name":"Commonsense reasoning","level":2,"score":0.29670000076293945},{"id":"https://openalex.org/C89288958","wikidata":"https://www.wikidata.org/wiki/Q7301504","display_name":"Reasoning system","level":2,"score":0.2944999933242798},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.26159998774528503},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.257999986410141},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2529999911785126}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.01410","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01410","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.01410","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.01410","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/8","display_name":"Decent work and economic growth","score":0.4474602937698364}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Knowledge":[0],"graphs":[1,41,59,100],"provide":[2],"structured":[3,103],"and":[4,39,55],"reliable":[5],"information":[6],"for":[7],"many":[8],"real-world":[9],"applications,":[10],"motivating":[11],"increasing":[12],"interest":[13],"in":[14],"combining":[15],"large":[16],"language":[17],"models":[18,94],"(LLMs)":[19],"with":[20,57,86,98,140],"graph-based":[21],"retrieval":[22],"to":[23,42,95,101,110],"improve":[24],"factual":[25],"grounding.":[26],"Recent":[27],"Graph-based":[28],"Retrieval-Augmented":[29],"Generation":[30],"(GraphRAG)":[31],"methods":[32,144],"therefore":[33],"introduce":[34],"iterative":[35],"interaction":[36],"between":[37],"LLMs":[38,148],"knowledge":[40,58,99],"enhance":[43],"reasoning":[44,83,117],"capability.":[45],"However,":[46],"existing":[47],"approaches":[48],"typically":[49],"depend":[50],"on":[51,146],"manually":[52],"designed":[53],"guidance":[54],"interact":[56,97],"through":[60],"a":[61,79,134],"limited":[62],"set":[63],"of":[64,154],"predefined":[65],"tools,":[66],"which":[67,106],"substantially":[68],"constrains":[69],"graph":[70,82,89,116],"exploration.":[71],"To":[72],"address":[73],"these":[74],"limitations,":[75],"we":[76],"propose":[77],"GraphScout,":[78],"training-centric":[80],"agentic":[81,115],"framework":[84],"equipped":[85],"more":[87],"flexible":[88],"exploration":[90],"tools.":[91],"GraphScout":[92,141,163],"enables":[93],"autonomously":[96],"synthesize":[102],"training":[104],"data":[105],"are":[107],"then":[108],"used":[109],"post-train":[111],"LLMs,":[112],"thereby":[113],"internalizing":[114],"ability":[118],"without":[119],"laborious":[120],"manual":[121],"annotation":[122],"or":[123],"task":[124],"curation.":[125],"Extensive":[126],"experiments":[127],"across":[128],"five":[129],"knowledge-graph":[130],"domains":[131],"show":[132],"that":[133],"small":[135],"model":[136],"(e.g.,":[137,149],"Qwen3-4B)":[138],"augmented":[139],"outperforms":[142],"baseline":[143],"built":[145],"leading":[147],"Qwen-Max)":[150],"by":[151],"an":[152],"average":[153],"16.7\\%":[155],"while":[156],"requiring":[157],"significantly":[158],"fewer":[159],"inference":[160],"tokens.":[161],"Moreover,":[162],"exhibits":[164],"robust":[165],"cross-domain":[166],"transfer":[167],"performance.":[168],"Our":[169],"code":[170],"will":[171],"be":[172],"made":[173],"publicly":[174],"available~\\footnote{https://github.com/Ying-Yuchen/_GraphScout_}.":[175]},"counts_by_year":[],"updated_date":"2026-03-04T07:09:34.246503","created_date":"2026-03-04T00:00:00"}