{"id":"https://openalex.org/W4417099852","doi":"https://doi.org/10.18653/v1/2026.acl-long.1256","title":"Stop When Enough: Adaptive Early-Stopping for Chain-of-Thought Reasoning","display_name":"Stop When Enough: Adaptive Early-Stopping for Chain-of-Thought Reasoning","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W4417099852","doi":"https://doi.org/10.18653/v1/2026.acl-long.1256"},"language":"en","primary_location":{"id":"doi:10.18653/v1/2026.acl-long.1256","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.acl-long.1256","pdf_url":"https://aclanthology.org/2026.acl-long.1256.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://aclanthology.org/2026.acl-long.1256.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5111089828","display_name":"Renliang Sun","orcid":null},"institutions":[{"id":"https://openalex.org/I2799798094","display_name":"UCLA Health","ror":"https://ror.org/01d88se56","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I2799798094"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Renliang Sun","raw_affiliation_strings":["UCLA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"UCLA","institution_ids":["https://openalex.org/I2799798094"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Wei Cheng","orcid":null},"institutions":[{"id":"https://openalex.org/I4210107353","display_name":"NEC (United States)","ror":"https://ror.org/01v791m31","country_code":"US","type":"company","lineage":["https://openalex.org/I118347220","https://openalex.org/I4210107353"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei Cheng","raw_affiliation_strings":["NEC Labs America"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NEC Labs America","institution_ids":["https://openalex.org/I4210107353"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100324971","display_name":"Dawei Li","orcid":"https://orcid.org/0000-0003-1548-2666"},"institutions":[{"id":"https://openalex.org/I55732556","display_name":"Arizona State University","ror":"https://ror.org/03efmqc40","country_code":"US","type":"education","lineage":["https://openalex.org/I55732556"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dawei Li","raw_affiliation_strings":["Arizona State University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Arizona State University","institution_ids":["https://openalex.org/I55732556"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Haifeng Chen","orcid":null},"institutions":[{"id":"https://openalex.org/I4210107353","display_name":"NEC (United States)","ror":"https://ror.org/01v791m31","country_code":"US","type":"company","lineage":["https://openalex.org/I118347220","https://openalex.org/I4210107353"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Haifeng Chen","raw_affiliation_strings":["NEC Labs America"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"NEC Labs America","institution_ids":["https://openalex.org/I4210107353"]}]},{"author_position":"last","author":{"id":null,"display_name":"Wei Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I2799798094","display_name":"UCLA Health","ror":"https://ror.org/01d88se56","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I2799798094"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei Wang","raw_affiliation_strings":["UCLA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"UCLA","institution_ids":["https://openalex.org/I2799798094"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.02836555,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"27250","last_page":"27268"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.17649999260902405,"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"}},"topics":[{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.17649999260902405,"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"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.11670000106096268,"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":0.08609999716281891,"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":0.6394000053405762},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5759000182151794},{"id":"https://openalex.org/keywords/discriminator","display_name":"Discriminator","score":0.5212000012397766},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.4180999994277954},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.3476000130176544},{"id":"https://openalex.org/keywords/analytic-reasoning","display_name":"Analytic reasoning","score":0.3100000023841858}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6736999750137329},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6394000053405762},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5759000182151794},{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.5212000012397766},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4636000096797943},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.4180999994277954},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.3476000130176544},{"id":"https://openalex.org/C103057564","wikidata":"https://www.wikidata.org/wiki/Q4751139","display_name":"Analytic reasoning","level":3,"score":0.3100000023841858},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.30880001187324524},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.3057999908924103},{"id":"https://openalex.org/C89288958","wikidata":"https://www.wikidata.org/wiki/Q7301504","display_name":"Reasoning system","level":2,"score":0.29319998621940613},{"id":"https://openalex.org/C195344581","wikidata":"https://www.wikidata.org/wiki/Q2555318","display_name":"Automated reasoning","level":2,"score":0.2651999890804291},{"id":"https://openalex.org/C159032336","wikidata":"https://www.wikidata.org/wiki/Q2488768","display_name":"Non-monotonic logic","level":2,"score":0.25839999318122864},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.2522999942302704}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.18653/v1/2026.acl-long.1256","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.acl-long.1256","pdf_url":"https://aclanthology.org/2026.acl-long.1256.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2510.10103","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2510.10103","pdf_url":"https://arxiv.org/pdf/2510.10103","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2510.10103","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2510.10103","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:10.18653/v1/2026.acl-long.1256","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/2026.acl-long.1256","pdf_url":"https://aclanthology.org/2026.acl-long.1256.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2805110178","display_name":"Building BRIDGEs: Coordinating Standards, Diversity, and Ethics to Advance Biomedical AI","funder_award_id":"5u54hg012517-02","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G2901143931","display_name":"III: Medium: Collaborative Research: Collaborative Machine-Learning-Centric Data Analytics at Scale","funder_award_id":"2106859","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"},{"id":"https://openalex.org/G6968259562","display_name":"CONNECT: Collaborative Network for Nurturing Ecosystems of Common Fund Team Science","funder_award_id":"1u54od036472-01","funder_id":"https://openalex.org/F4320332161","funder_display_name":"National Institutes of Health"},{"id":"https://openalex.org/G8481604737","display_name":"Collaborative Research: III: Medium: VirtualLab: Integrating Deep Graph Learning and Causal Inference for Multi-Agent Dynamical Systems","funder_award_id":"2312501","funder_id":"https://openalex.org/F4320306076","funder_display_name":"National Science Foundation"}],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320332161","display_name":"National Institutes of Health","ror":"https://ror.org/01cwqze88"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4417099852.pdf","grobid_xml":"https://content.openalex.org/works/W4417099852.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Chain-of-Thought":[0],"(CoT)":[1],"reasoning":[2,22,53,67],"has":[3],"driven":[4],"recent":[5],"gains":[6],"of":[7,138],"large":[8],"language":[9],"models":[10,142],"(LLMs)":[11],"on":[12],"reasoning-intensive":[13],"tasks":[14],"by":[15,102],"externalizing":[16],"intermediate":[17],"steps.However,":[18],"excessive":[19],"or":[20,89,106],"redundant":[21,66],"-so-called":[23],"overthinking":[24],"-can":[25],"increase":[26],"inference":[27],"costs":[28],"and":[29,68,94,115,124,135],"lead":[30],"LLMs":[31],"toward":[32],"incorrect":[33],"conclusions.In":[34],"this":[35],"paper,":[36],"we":[37],"present":[38],"REFRAIN":[39,98],"(REFlective-Redundancy":[40],"for":[41],"Adaptive":[42],"INference),":[43],"a":[44,58,69,133],"training-free":[45],"framework":[46],"that":[47],"adaptively":[48],"determines":[49],"when":[50],"to":[51,54,62,78,84,110,143],"stop":[52,60],"mitigate":[55],"overthinking.REFRAIN":[56],"integrates":[57],"two-stage":[59],"discriminator":[61],"identify":[63],"reflective":[64],"yet":[65],"sliding-window":[70],"Upper":[71],"Confidence":[72],"Bound":[73],"(SW-UCB)":[74],"multi-armed":[75],"bandit":[76],"controller":[77],"dynamically":[79],"adjust":[80],"stopping":[81],"thresholds":[82],"according":[83],"problem":[85],"difficulty":[86],"without":[87],"supervision":[88],"fine-tuning.Across":[90],"four":[91],"representative":[92],"benchmarks":[93],"two":[95],"model":[96],"families,":[97],"reduces":[99],"token":[100],"usage":[101],"20-55%":[103],"while":[104],"maintaining":[105],"improving":[107],"accuracy":[108],"compared":[109],"standard":[111],"CoT":[112],"prompting.Extensive":[113],"ablation":[114],"robustness":[116],"analyses":[117],"demonstrate":[118],"its":[119],"stability":[120],"across":[121],"models,":[122],"scorers,":[123],"prompt":[125],"variations.In":[126],"summary,":[127],"our":[128],"findings":[129],"highlight":[130],"when-tostop":[131],"as":[132],"new":[134],"practical":[136],"axis":[137],"test-time":[139],"scaling":[140],"-enabling":[141],"reason":[144],"not":[145],"just":[146,149],"more,":[147],"but":[148],"enough.":[150]},"counts_by_year":[],"updated_date":"2026-08-26T07:47:46.906454","created_date":"2025-10-15T00:00:00"}
