{"id":"https://openalex.org/W7164581370","doi":"https://doi.org/10.48550/arxiv.2606.13262","title":"From Verdict to Process: Agentic Reinforcement Learning for Multi-Stage Fact Verification","display_name":"From Verdict to Process: Agentic Reinforcement Learning for Multi-Stage Fact Verification","publication_year":2026,"publication_date":"2026-06-11","ids":{"openalex":"https://openalex.org/W7164581370","doi":"https://doi.org/10.48550/arxiv.2606.13262"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.13262","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.13262","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2606.13262","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5124054088","display_name":"Rongxin Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Rongxin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102751081","display_name":"Shenghong He","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, Shenghong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5105989284","display_name":"S J Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Siyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5043761995","display_name":"C Shijia Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Chao","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/T10028","display_name":"Topic Modeling","score":0.542900025844574,"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":0.542900025844574,"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":0.09929999709129333,"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/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.0478999987244606,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.6866000294685364},{"id":"https://openalex.org/keywords/verdict","display_name":"Verdict","score":0.616599977016449},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.5425999760627747},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5174999833106995},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.4307999908924103},{"id":"https://openalex.org/keywords/workflow","display_name":"Workflow","score":0.3959999978542328},{"id":"https://openalex.org/keywords/isolation","display_name":"Isolation (microbiology)","score":0.3424000144004822},{"id":"https://openalex.org/keywords/agency","display_name":"Agency (philosophy)","score":0.3012999892234802}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7311999797821045},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.6866000294685364},{"id":"https://openalex.org/C2776213154","wikidata":"https://www.wikidata.org/wiki/Q13370881","display_name":"Verdict","level":2,"score":0.616599977016449},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.5425999760627747},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5343999862670898},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5174999833106995},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45249998569488525},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.4307999908924103},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.3959999978542328},{"id":"https://openalex.org/C2775941552","wikidata":"https://www.wikidata.org/wiki/Q25212305","display_name":"Isolation (microbiology)","level":2,"score":0.3424000144004822},{"id":"https://openalex.org/C108170787","wikidata":"https://www.wikidata.org/wiki/Q3951828","display_name":"Agency (philosophy)","level":2,"score":0.3012999892234802},{"id":"https://openalex.org/C166052673","wikidata":"https://www.wikidata.org/wiki/Q83021","display_name":"Empirical evidence","level":2,"score":0.2971000075340271},{"id":"https://openalex.org/C97256817","wikidata":"https://www.wikidata.org/wiki/Q1462316","display_name":"Spurious relationship","level":2,"score":0.29679998755455017},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2879999876022339},{"id":"https://openalex.org/C106189395","wikidata":"https://www.wikidata.org/wiki/Q176789","display_name":"Markov decision process","level":3,"score":0.28439998626708984},{"id":"https://openalex.org/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.26330000162124634},{"id":"https://openalex.org/C2781002164","wikidata":"https://www.wikidata.org/wiki/Q6822311","display_name":"Meta learning (computer science)","level":3,"score":0.26249998807907104},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.26010000705718994},{"id":"https://openalex.org/C111498074","wikidata":"https://www.wikidata.org/wiki/Q173326","display_name":"Formal verification","level":2,"score":0.25940001010894775},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.25619998574256897}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.13262","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.13262","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.13262","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.13262","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.47585776448249817}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Recent":[0],"approaches":[1],"combining":[2],"Large":[3],"Language":[4],"Models":[5],"(LLMs)":[6],"with":[7],"retrieval-augmented":[8],"reasoning":[9],"have":[10],"shown":[11],"promise":[12],"for":[13,77,151],"automated":[14],"fact":[15,82,153],"verification.":[16,154],"To":[17,101],"process":[18],"complex":[19],"claims,":[20],"these":[21],"verification":[22,83,124,137],"pipelines":[23],"typically":[24],"execute":[25],"multi-stage":[26,81,152],"workflows":[27],"that":[28,117,129],"coordinate":[29,91],"tightly":[30],"coupled":[31],"modules,":[32],"including":[33],"claim":[34,92],"decomposition,":[35,93],"evidence":[36,94],"gathering,":[37],"and":[38,60,98,105,139],"verdict":[39,99],"prediction.":[40,100],"However,":[41],"existing":[42],"methods":[43],"optimize":[44],"individual":[45],"stages":[46,59],"in":[47,135],"isolation":[48],"or":[49],"rely":[50],"on":[51],"fixed":[52],"heuristics,":[53],"which":[54],"limits":[55],"adaptive":[56],"coordination":[57],"among":[58],"can":[61],"lead":[62],"to":[63,90],"suboptimal":[64],"outcomes.":[65],"In":[66],"this":[67],"work,":[68],"we":[69],"propose":[70],"ProFact,":[71],"an":[72],"agentic":[73],"reinforcement":[74],"learning":[75,120],"framework":[76],"end-to-end":[78],"optimization":[79,150],"of":[80,147],"trajectories.":[84],"ProFact":[85,113,130],"trains":[86],"a":[87],"unified":[88],"policy":[89],"seeking,":[95],"answer":[96],"generation,":[97],"address":[102],"the":[103,123,145],"sparse":[104],"delayed":[106],"supervision":[107],"provided":[108],"by":[109],"final":[110],"veracity":[111],"labels,":[112],"introduces":[114],"process-aware":[115,148],"rewards":[116],"provide":[118],"stage-level":[119],"signals":[121],"throughout":[122],"process.":[125],"Empirical":[126],"evaluation":[127],"shows":[128],"consistently":[131],"outperforms":[132],"strong":[133],"baselines":[134],"both":[136],"performance":[138],"inference":[140],"efficiency.":[141],"These":[142],"results":[143],"highlight":[144],"effectiveness":[146],"trajectory":[149]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-13T00:00:00"}