{"id":"https://openalex.org/W7154597597","doi":"https://doi.org/10.48550/arxiv.2604.13618","title":"C2: Scalable Rubric-Augmented Reward Modeling from Binary Preferences","display_name":"C2: Scalable Rubric-Augmented Reward Modeling from Binary Preferences","publication_year":2026,"publication_date":"2026-04-15","ids":{"openalex":"https://openalex.org/W7154597597","doi":"https://doi.org/10.48550/arxiv.2604.13618"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.13618","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.13618","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":"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"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.13618","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133805686","display_name":"Akira Kawabata","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Kawabata, Akira","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5038103607","display_name":"Saku Sugawara","orcid":"https://orcid.org/0000-0002-0061-0680"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sugawara, Saku","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5133805686"],"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.48579999804496765,"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":0.48579999804496765,"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":0.18770000338554382,"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":0.042399998754262924,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/rubric","display_name":"Rubric","score":0.9794999957084656},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.548799991607666},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.5404000282287598},{"id":"https://openalex.org/keywords/binary-number","display_name":"Binary number","score":0.48829999566078186},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4747999906539917},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.42340001463890076}],"concepts":[{"id":"https://openalex.org/C111640148","wikidata":"https://www.wikidata.org/wiki/Q847349","display_name":"Rubric","level":2,"score":0.9794999957084656},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6890000104904175},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.548799991607666},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.5404000282287598},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.48829999566078186},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4747999906539917},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4269999861717224},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.42340001463890076},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4228000044822693},{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.3400000035762787},{"id":"https://openalex.org/C153701036","wikidata":"https://www.wikidata.org/wiki/Q659974","display_name":"Trustworthiness","level":2,"score":0.33730000257492065},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.31529998779296875},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.3091000020503998},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.2775000035762787},{"id":"https://openalex.org/C49453240","wikidata":"https://www.wikidata.org/wiki/Q1592163","display_name":"Construct validity","level":3,"score":0.26440000534057617},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.26080000400543213},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2547000050544739},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.25130000710487366}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.13618","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.13618","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.13618","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.13618","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":"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"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/17","display_name":"Partnerships for the goals","score":0.49332866072654724}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Rubric-augmented":[0],"verification":[1,207],"guides":[2],"reward":[3,43,60,68,74,103,150,184,209],"models":[4,44,151,210],"with":[5,78,158,190],"explicit":[6],"evaluation":[7],"criteria,":[8],"yielding":[9],"more":[10,211],"reliable":[11],"judgments":[12,70],"than":[13,46],"single-model":[14],"verification.":[15],"However,":[16],"most":[17],"existing":[18],"methods":[19],"require":[20],"costly":[21],"rubric":[22,30,80,94,100,120,132,178],"annotations,":[23,179],"limiting":[24],"scalability.":[25],"Moreover,":[26],"we":[27,55,89,116],"find":[28],"that":[29,65,201],"generation":[31],"is":[32],"vulnerable":[33],"to":[34,122,130,162,186],"a":[35,63,79,118,127,193,214],"failure":[36],"of":[37,52,160],"cooperation;":[38],"low-quality":[39],"rubrics":[40,140,191],"actively":[41],"mislead":[42],"rather":[45],"help.":[47],"Inspired":[48],"by":[49,71,96],"the":[50,73,102,109,154],"principle":[51],"cooperative":[53,119],"communication,":[54],"propose":[56,123],"Cooperative":[57],"yet":[58],"Critical":[59],"modeling":[61],"(C2),":[62],"framework":[64],"significantly":[66],"improves":[67],"model":[69,75,104,185],"having":[72],"critically":[76],"collaborate":[77],"generator":[81,121],"trained":[82,152],"solely":[83],"from":[84,108,192],"binary":[85,156],"preferences.":[86],"In":[87],"C2,":[88],"synthesize":[90],"helpful":[91,124,143],"and":[92,126,167],"misleading":[93],"pairs":[95],"measuring":[97],"how":[98],"each":[99],"shifts":[101],"toward":[105],"or":[106],"away":[107],"correct":[110],"preference.":[111],"Using":[112],"these":[113],"contrastive":[114],"pairs,":[115],"train":[117],"rubrics,":[125],"critical":[128],"verifier":[129],"assess":[131],"validity":[133],"before":[134],"making":[135],"its":[136],"judgment,":[137],"following":[138],"only":[139],"it":[141],"deems":[142],"at":[144],"inference":[145],"time.":[146],"C2":[147,180],"outperforms":[148],"reasoning":[149],"on":[153,165,173],"same":[155],"preferences,":[157],"gains":[159],"up":[161],"6.5":[163],"points":[164,169],"RM-Bench":[166],"6.0":[168],"length-controlled":[170],"win":[171],"rate":[172],"AlpacaEval":[174],"2.0.":[175],"Without":[176],"external":[177],"enables":[181],"an":[182],"8B":[183],"match":[187],"performance":[188],"achieved":[189],"4$\\times$":[194],"larger":[195],"model.":[196],"Overall,":[197],"our":[198],"work":[199],"demonstrates":[200],"eliciting":[202],"deliberate":[203],"cooperation":[204],"in":[205,213],"rubric-augmented":[206],"makes":[208],"trustworthy":[212],"scalable":[215],"way.":[216]},"counts_by_year":[],"updated_date":"2026-04-17T06:04:52.305304","created_date":"2026-04-17T00:00:00"}