{"id":"https://openalex.org/W7152719565","doi":"https://doi.org/10.48550/arxiv.2604.06389","title":"SELFDOUBT: Uncertainty Quantification for Reasoning LLMs via the Hedge-to-Verify Ratio","display_name":"SELFDOUBT: Uncertainty Quantification for Reasoning LLMs via the Hedge-to-Verify Ratio","publication_year":2026,"publication_date":"2026-04-07","ids":{"openalex":"https://openalex.org/W7152719565","doi":"https://doi.org/10.48550/arxiv.2604.06389"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.06389","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.06389","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2604.06389","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101336497","display_name":"Satwik Pandey","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pandey, Satwik","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133301905","display_name":"Suresh Raghu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Raghu, Suresh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5051142062","display_name":"Shashwat Pandey","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pandey, Shashwat","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/T11596","display_name":"Constraint Satisfaction and Optimization","score":0.14030000567436218,"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"}},"topics":[{"id":"https://openalex.org/T11596","display_name":"Constraint Satisfaction and Optimization","score":0.14030000567436218,"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"}},{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.12729999423027039,"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":0.08110000193119049,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.7440000176429749},{"id":"https://openalex.org/keywords/uncertainty-quantification","display_name":"Uncertainty quantification","score":0.5565000176429749},{"id":"https://openalex.org/keywords/trace","display_name":"TRACE (psycholinguistics)","score":0.5238999724388123},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.47780001163482666},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.4641000032424927},{"id":"https://openalex.org/keywords/model-based-reasoning","display_name":"Model-based reasoning","score":0.41269999742507935},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.4052000045776367},{"id":"https://openalex.org/keywords/opportunistic-reasoning","display_name":"Opportunistic reasoning","score":0.39879998564720154},{"id":"https://openalex.org/keywords/qualitative-reasoning","display_name":"Qualitative reasoning","score":0.3686999976634979}],"concepts":[{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.7440000176429749},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6632999777793884},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.5565000176429749},{"id":"https://openalex.org/C75291252","wikidata":"https://www.wikidata.org/wiki/Q1315756","display_name":"TRACE (psycholinguistics)","level":2,"score":0.5238999724388123},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.51910001039505},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.47780001163482666},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.4641000032424927},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4251999855041504},{"id":"https://openalex.org/C37335422","wikidata":"https://www.wikidata.org/wiki/Q6888134","display_name":"Model-based reasoning","level":3,"score":0.41269999742507935},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.4052000045776367},{"id":"https://openalex.org/C86827895","wikidata":"https://www.wikidata.org/wiki/Q7098582","display_name":"Opportunistic reasoning","level":4,"score":0.39879998564720154},{"id":"https://openalex.org/C83725634","wikidata":"https://www.wikidata.org/wiki/Q7268699","display_name":"Qualitative reasoning","level":2,"score":0.3686999976634979},{"id":"https://openalex.org/C115086926","wikidata":"https://www.wikidata.org/wiki/Q17004651","display_name":"Causal reasoning","level":3,"score":0.35899999737739563},{"id":"https://openalex.org/C89288958","wikidata":"https://www.wikidata.org/wiki/Q7301504","display_name":"Reasoning system","level":2,"score":0.3522000014781952},{"id":"https://openalex.org/C94361409","wikidata":"https://www.wikidata.org/wiki/Q7882500","display_name":"Uncertainty reduction theory","level":2,"score":0.35040000081062317},{"id":"https://openalex.org/C20162079","wikidata":"https://www.wikidata.org/wiki/Q1151406","display_name":"Case-based reasoning","level":2,"score":0.3483999967575073},{"id":"https://openalex.org/C175291020","wikidata":"https://www.wikidata.org/wiki/Q1156822","display_name":"Offset (computer science)","level":2,"score":0.3425999879837036},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3416999876499176},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.33500000834465027},{"id":"https://openalex.org/C195344581","wikidata":"https://www.wikidata.org/wiki/Q2555318","display_name":"Automated reasoning","level":2,"score":0.33489999175071716},{"id":"https://openalex.org/C159032336","wikidata":"https://www.wikidata.org/wiki/Q2488768","display_name":"Non-monotonic logic","level":2,"score":0.29919999837875366},{"id":"https://openalex.org/C137209882","wikidata":"https://www.wikidata.org/wiki/Q1403517","display_name":"Measurement uncertainty","level":2,"score":0.2973000109195709},{"id":"https://openalex.org/C3746660","wikidata":"https://www.wikidata.org/wiki/Q1068763","display_name":"Rule of inference","level":2,"score":0.2944999933242798},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.2822999954223633},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.26489999890327454},{"id":"https://openalex.org/C7493553","wikidata":"https://www.wikidata.org/wiki/Q1520777","display_name":"Certainty","level":2,"score":0.2599000036716461},{"id":"https://openalex.org/C95167961","wikidata":"https://www.wikidata.org/wiki/Q4483495","display_name":"Fiducial inference","level":5,"score":0.2563000023365021},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.25619998574256897},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.25130000710487366}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.06389","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.06389","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.06389","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.06389","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":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Uncertainty":[0],"estimation":[1,219],"for":[2,37,127,217],"reasoning":[3,39,77,90,122,145,222],"language":[4],"models":[5,141],"remains":[6],"difficult":[7],"to":[8],"deploy":[9],"in":[10],"practice:":[11],"sampling-based":[12,184],"methods":[13,107],"are":[14,28,100,158],"computationally":[15],"expensive,":[16],"while":[17],"common":[18],"single-pass":[19,63],"proxies":[20],"such":[21],"as":[22,212],"verbalized":[23],"confidence":[24,168],"or":[25,113],"trace":[26,78,91],"length":[27],"often":[29],"inconsistent":[30],"across":[31,139],"models.":[32,223],"This":[33],"problem":[34],"is":[35],"compounded":[36],"proprietary":[38,134,221],"APIs":[40],"that":[41,66,108],"expose":[42],"neither":[43],"logits":[44],"nor":[45],"intermediate":[46],"token":[47],"probabilities,":[48],"leaving":[49],"practitioners":[50],"with":[51],"no":[52,155],"reliable":[53],"uncertainty":[54,64,93,218],"signal":[55],"at":[56,170,187,201],"inference":[57,190],"time.":[58],"We":[59,136],"propose":[60],"SELFDOUBT,":[61],"a":[62,89,119,213],"framework":[65],"resolves":[67],"this":[68],"impasse":[69],"by":[70,102],"extracting":[71],"behavioral":[72],"signals":[73],"directly":[74],"from":[75],"the":[76,83,162,175,178],"itself.":[79],"Our":[80],"key":[81],"signal,":[82],"Hedge-to-Verify":[84],"Ratio":[85],"(HVR),":[86],"detects":[87],"whether":[88,98],"contains":[92],"markers":[94,157],"and,":[95],"if":[96],"so,":[97],"they":[99],"offset":[101],"explicit":[103],"selfchecking":[104],"behavior.":[105],"Unlike":[106],"require":[109],"multiple":[110],"sampled":[111],"traces":[112,153],"model":[114],"internals,":[115],"SELFDOUBT":[116,138,180,211],"operates":[117],"on":[118],"single":[120],"observed":[121],"trajectory,":[123],"making":[124],"it":[125],"suitable":[126],"latency-":[128],"and":[129,142,149],"cost-constrained":[130],"deployment":[131,193],"over":[132,220],"any":[133,205],"API.":[135],"evaluate":[137],"seven":[140],"three":[143],"multi-step":[144],"benchmarks":[146],"(BBH,":[147],"GPQA-Diamond,":[148],"MMLU-Pro).":[150],"Most":[151],"notably,":[152],"containing":[154],"hedging":[156],"correct":[159],"96%":[160],"of":[161],"time,":[163],"revealing":[164],"an":[165],"emergent":[166],"high-precision":[167],"gate":[169],"zero":[171],"additional":[172],"cost.":[173,191],"For":[174],"remaining":[176],"cases,":[177],"full":[179],"score":[181],"significantly":[182],"outperforms":[183],"semantic":[185],"entropy":[186],"10x":[188],"lower":[189],"A":[192],"cascade":[194],"combining":[195],"both":[196],"stages":[197],"attains":[198],"90%":[199],"accuracy":[200],"71%":[202],"coverage":[203],"without":[204],"task-specific":[206],"labels.":[207],"These":[208],"results":[209],"establish":[210],"scalable,":[214],"production-ready":[215],"foundation":[216]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-10T00:00:00"}
