{"id":"https://openalex.org/W7156370138","doi":"https://doi.org/10.48550/arxiv.2604.22266","title":"Large Language Models Decide Early and Explain Later","display_name":"Large Language Models Decide Early and Explain Later","publication_year":2026,"publication_date":"2026-04-24","ids":{"openalex":"https://openalex.org/W7156370138","doi":"https://doi.org/10.48550/arxiv.2604.22266"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.22266","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.22266","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.2604.22266","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101310377","display_name":"Ayan Datta","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Datta, Ayan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134721311","display_name":"Zhixue Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Zhixue","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043357930","display_name":"Bhuvanesh Verma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Verma, Bhuvanesh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134696323","display_name":"Radhika Mamidi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mamidi, Radhika","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062052248","display_name":"Mounika Marreddy","orcid":"https://orcid.org/0000-0003-1184-640X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Marreddy, Mounika","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134727788","display_name":"Alexander Mehler","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mehler, Alexander","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"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":0.26420000195503235,"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":0.26420000195503235,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.09589999914169312,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.07890000194311142,"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/security-token","display_name":"Security token","score":0.7200000286102295},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6413000226020813},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.48820000886917114},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.4422999918460846},{"id":"https://openalex.org/keywords/opportunistic-reasoning","display_name":"Opportunistic reasoning","score":0.39980000257492065},{"id":"https://openalex.org/keywords/psychology-of-reasoning","display_name":"Psychology of reasoning","score":0.39809998869895935},{"id":"https://openalex.org/keywords/fraction","display_name":"Fraction (chemistry)","score":0.3402000069618225},{"id":"https://openalex.org/keywords/non-monotonic-logic","display_name":"Non-monotonic logic","score":0.3382999897003174}],"concepts":[{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.7200000286102295},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6520000100135803},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6413000226020813},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.48820000886917114},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4659000039100647},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.46129998564720154},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.4422999918460846},{"id":"https://openalex.org/C86827895","wikidata":"https://www.wikidata.org/wiki/Q7098582","display_name":"Opportunistic reasoning","level":4,"score":0.39980000257492065},{"id":"https://openalex.org/C183521366","wikidata":"https://www.wikidata.org/wiki/Q7256422","display_name":"Psychology of reasoning","level":4,"score":0.39809998869895935},{"id":"https://openalex.org/C149629883","wikidata":"https://www.wikidata.org/wiki/Q660926","display_name":"Fraction (chemistry)","level":2,"score":0.3402000069618225},{"id":"https://openalex.org/C159032336","wikidata":"https://www.wikidata.org/wiki/Q2488768","display_name":"Non-monotonic logic","level":2,"score":0.3382999897003174},{"id":"https://openalex.org/C89288958","wikidata":"https://www.wikidata.org/wiki/Q7301504","display_name":"Reasoning system","level":2,"score":0.33070001006126404},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.3156999945640564},{"id":"https://openalex.org/C195344581","wikidata":"https://www.wikidata.org/wiki/Q2555318","display_name":"Automated reasoning","level":2,"score":0.30469998717308044},{"id":"https://openalex.org/C97364631","wikidata":"https://www.wikidata.org/wiki/Q484284","display_name":"Deductive reasoning","level":2,"score":0.30250000953674316},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.301800012588501},{"id":"https://openalex.org/C37335422","wikidata":"https://www.wikidata.org/wiki/Q6888134","display_name":"Model-based reasoning","level":3,"score":0.289000004529953},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.26820001006126404},{"id":"https://openalex.org/C2985684807","wikidata":"https://www.wikidata.org/wiki/Q1513879","display_name":"Text generation","level":2,"score":0.25780001282691956},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2508000135421753}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.22266","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.22266","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.2604.22266","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.22266","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":[{"score":0.48033562302589417,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Large":[0],"Language":[1],"Models":[2],"often":[3],"achieve":[4],"strong":[5],"performance":[6],"by":[7,127,156],"generating":[8],"long":[9],"intermediate":[10,35,70],"chain-of-thought":[11,178],"reasoning.":[12],"However,":[13],"it":[14],"remains":[15],"unclear":[16],"when":[17],"a":[18,118,164,174],"model's":[19,69],"final":[20,100],"answer":[21,29,64,101,140],"is":[22,30,180],"actually":[23],"determined":[24],"during":[25],"generation.":[26],"If":[27],"the":[28,54,68,99,104,122,139],"already":[31],"fixed":[32],"at":[33,72],"an":[34,107],"stage,":[36],"subsequent":[37],"reasoning":[38,60,74,112,124,153],"tokens":[39,113,158],"may":[40],"constitute":[41],"post-decision":[42],"explanation,":[43],"increasing":[44],"inference":[45],"cost":[46],"and":[47,79,182],"latency":[48],"without":[49],"improving":[50],"correctness.":[51],"We":[52,143],"study":[53],"evolution":[55],"of":[56,95,109,121,177],"predicted":[57,89],"answers":[58,90],"over":[59],"steps":[61],"using":[62],"forced":[63],"completion,":[65],"which":[66],"elicits":[67],"predictions":[71],"partial":[73],"prefixes.":[75],"Focusing":[76],"on":[77,189],"Qwen3-4B":[78],"averaging":[80],"results":[81,171],"across":[82],"all":[83],"datasets":[84],"considered,":[85],"we":[86,130],"find":[87],"that":[88,135,145,173],"change":[91],"in":[92,167],"only":[93,163],"32%":[94],"queries.":[96],"Moreover,":[97],"once":[98,138],"switch":[102],"occurs,":[103],"model":[105],"generates":[106],"average":[108],"760":[110],"additional":[111],"per":[114,159],"query,":[115],"accounting":[116],"for":[117],"substantial":[119],"fraction":[120],"total":[123],"budget.":[125],"Motivated":[126],"these":[128],"findings,":[129],"investigate":[131],"early":[132],"stopping":[133],"strategies":[134],"halt":[136],"generation":[137,179],"has":[141],"stabilized.":[142],"show":[144],"simple":[146],"heuristics,":[147],"including":[148],"probe-based":[149],"stopping,":[150],"can":[151,183],"reduce":[152],"token":[154],"usage":[155],"500":[157],"query":[160],"while":[161],"incurring":[162],"2%":[165],"drop":[166],"accuracy.":[168],"Together,":[169],"our":[170],"indicate":[172],"large":[175],"portion":[176],"redundant":[181],"be":[184],"reduced":[185],"with":[186],"minimal":[187],"impact":[188],"performance.":[190]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-04-28T00:00:00"}
