{"id":"https://openalex.org/W7163195816","doi":"https://doi.org/10.48550/arxiv.2606.02326","title":"Repair Before Veto: Repair-Augmented Constraint Learning for Contextual Decisions","display_name":"Repair Before Veto: Repair-Augmented Constraint Learning for Contextual Decisions","publication_year":2026,"publication_date":"2026-06-01","ids":{"openalex":"https://openalex.org/W7163195816","doi":"https://doi.org/10.48550/arxiv.2606.02326"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.02326","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.02326","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.2606.02326","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137641379","display_name":"Yifan Wang","orcid":"https://orcid.org/0000-0002-8828-4268"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Wang, Yifan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5137641379"],"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":0.3041999936103821,"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":0.3041999936103821,"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":0.07509999722242355,"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/T11596","display_name":"Constraint Satisfaction and Optimization","score":0.07190000265836716,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.5135999917984009},{"id":"https://openalex.org/keywords/budget-constraint","display_name":"Budget constraint","score":0.39989998936653137},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.38769999146461487},{"id":"https://openalex.org/keywords/ticket","display_name":"Ticket","score":0.37959998846054077},{"id":"https://openalex.org/keywords/incentive-compatibility","display_name":"Incentive compatibility","score":0.3610000014305115},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.29030001163482666}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5347999930381775},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.5135999917984009},{"id":"https://openalex.org/C8505890","wikidata":"https://www.wikidata.org/wiki/Q605095","display_name":"Budget constraint","level":2,"score":0.39989998936653137},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.38769999146461487},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38519999384880066},{"id":"https://openalex.org/C2776540713","wikidata":"https://www.wikidata.org/wiki/Q7800647","display_name":"Ticket","level":2,"score":0.37959998846054077},{"id":"https://openalex.org/C91810955","wikidata":"https://www.wikidata.org/wiki/Q7731670","display_name":"Incentive compatibility","level":3,"score":0.3610000014305115},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.29030001163482666},{"id":"https://openalex.org/C123650614","wikidata":"https://www.wikidata.org/wiki/Q282491","display_name":"Strategic dominance","level":2,"score":0.2809000015258789},{"id":"https://openalex.org/C28901747","wikidata":"https://www.wikidata.org/wiki/Q177571","display_name":"Decision theory","level":2,"score":0.27630001306533813},{"id":"https://openalex.org/C113336015","wikidata":"https://www.wikidata.org/wiki/Q574010","display_name":"Complete information","level":2,"score":0.27309998869895935},{"id":"https://openalex.org/C206952183","wikidata":"https://www.wikidata.org/wiki/Q1193100","display_name":"Preemption","level":2,"score":0.2581999897956848},{"id":"https://openalex.org/C2780378061","wikidata":"https://www.wikidata.org/wiki/Q25351891","display_name":"Service (business)","level":2,"score":0.25519999861717224}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.02326","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.02326","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.2606.02326","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.02326","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":[{"score":0.6867821216583252,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Hard":[0],"constraints":[1],"are":[2],"usually":[3],"treated":[4],"as":[5,46],"terminal":[6],"vetoes:":[7],"once":[8],"a":[9,12,30,40,48,52,89,120,127],"candidate":[10,103],"violates":[11],"requirement,":[13],"the":[14,25,36,99,117,159,170,177,194,201],"learned":[15],"rule":[16],"rejects":[17],"it":[18,111],"and":[19,63,113,152,155,165,173],"any":[20],"repair":[21,96,109,128,174],"is":[22,104],"handled":[23],"outside":[24],"decision":[26,91],"semantics.":[27,101],"This":[28,130],"misses":[29],"common":[31],"deployed":[32],"regime":[33],"in":[34],"which":[35],"system":[37,118],"already":[38],"knows":[39],"finite":[41],"menu":[42],"of":[43],"modifications,":[44],"such":[45],"adding":[47],"ticket":[49],"option,":[50],"changing":[51],"configuration,":[53],"or":[54],"requesting":[55],"an":[56,75,107,139],"available":[57],"service":[58],"upgrade.":[59],"Existing":[60],"constraint-learning,":[61],"soft-relaxation,":[62],"recourse":[64],"methods":[65],"address":[66],"nearby":[67],"problems,":[68],"but":[69],"they":[70],"do":[71],"not":[72],"learn":[73],"whether":[74],"option":[76],"should":[77],"be":[78],"repaired":[79],"before":[80],"being":[81],"vetoed.":[82],"We":[83],"introduce":[84],"Repair-Augmented":[85],"Constraint":[86],"Learning":[87],"(RACL),":[88],"contextual":[90],"framework":[92],"that":[93],"lifts":[94],"known":[95],"operators":[97],"into":[98],"classifier":[100],"A":[102],"accepted":[105],"when":[106,125],"affordable":[108],"makes":[110],"feasible":[112],"preferred":[114],"enough;":[115],"otherwise":[116],"returns":[119],"structured":[121],"rejection":[122],"credit":[123,172],"and,":[124],"applicable,":[126],"plan.":[129],"repair-before-veto":[131],"view":[132],"strictly":[133],"generalizes":[134],"no-repair":[135],"HASSLE-style":[136],"semantics,":[137],"reveals":[138],"irreducible":[140],"false-veto":[141],"gap":[142],"for":[143,158,193],"terminal-veto":[144],"rules,":[145],"separates":[146],"binary-label":[147],"non-identifiability":[148],"from":[149],"decision-rule":[150],"learnability,":[151],"gives":[153],"capacity":[154],"calibration":[156],"bounds":[157],"observed-feasibility":[160],"shared-weight":[161],"setting.":[162],"Across":[163],"controlled":[164],"DB1B-derived":[166],"benchmarks,":[167],"RACL":[168,182],"recovers":[169],"intended":[171],"structure.":[175],"On":[176],"hardest":[178],"raw-data-derived":[179],"tier,":[180],"validation-selected":[181],"reduces":[183],"false":[184],"vetoes":[185],"to":[186],"10/4039":[187],"(FVR":[188],"0.0025),":[189],"versus":[190],"about":[191],"1064/4039":[192],"strongest":[195],"repair-search":[196],"black-box":[197],"baseline,":[198],"while":[199],"making":[200],"FVR/EDR":[202],"trade-off":[203],"explicit.":[204]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-03T00:00:00"}
