{"id":"https://openalex.org/W7223045142","doi":"https://doi.org/10.48550/arxiv.2610.26383","title":"BOSS: Token-Budgeted Online Sampling and Selection for LLM Reasoning","display_name":"BOSS: Token-Budgeted Online Sampling and Selection for LLM Reasoning","publication_year":2026,"publication_date":"2026-10-19","ids":{"openalex":"https://openalex.org/W7223045142","doi":"https://doi.org/10.48550/arxiv.2610.26383"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2610.26383","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.26383","pdf_url":"https://arxiv.org/pdf/2610.26383","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,"listed_in":[],"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"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2610.26383","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102930702","display_name":"Yupeng Chang","orcid":"https://orcid.org/0000-0001-7178-6088","observed_orcids":["https://orcid.org/0000-0001-7178-6088"]},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chang, Yupeng","raw_affiliation_strings":["School of Artificial Intelligence , Jilin University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence , Jilin University","institution_ids":["https://openalex.org/I194450716"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5159096035","display_name":"Yahan Li","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210136246","display_name":"China Telecom (China)","ror":"https://ror.org/03jgnzt20","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210136246"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li, Yahan","raw_affiliation_strings":["Xingchen AGI Lab , China Telecom Artificial Intelligence Technology (Beijing) Co. , Ltd. , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xingchen AGI Lab , China Telecom Artificial Intelligence Technology (Beijing) Co. , Ltd. , China","institution_ids":["https://openalex.org/I4210136246"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101298761","display_name":"Chenlu Guo","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Chenlu","raw_affiliation_strings":["Bilibili Inc. , China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bilibili Inc. , China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009188443","display_name":"Diana Zaiu Inkpen","orcid":"https://orcid.org/0000-0002-0202-2444","observed_orcids":["https://orcid.org/0000-0002-0202-2444"]},"institutions":[{"id":"https://openalex.org/I153718931","display_name":"University of Ottawa","ror":"https://ror.org/03c4mmv16","country_code":"CA","type":"education","lineage":["https://openalex.org/I153718931"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Inkpen, Diana","raw_affiliation_strings":["School of Electrical Engineering and Computer Science , University of Ottawa , Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Electrical Engineering and Computer Science , University of Ottawa , Canada","institution_ids":["https://openalex.org/I153718931"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5159117811","display_name":"Yuan Wu","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wu, Yuan","raw_affiliation_strings":["School of Artificial Intelligence , Jilin University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence , Jilin University","institution_ids":["https://openalex.org/I194450716"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5159101325","display_name":"Yi Chang","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I194450716","display_name":"Jilin University","ror":"https://ror.org/00js3aw79","country_code":"CN","type":"education","lineage":["https://openalex.org/I194450716"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chang, Yi","raw_affiliation_strings":["School of Artificial Intelligence , Jilin University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence , Jilin University","institution_ids":["https://openalex.org/I194450716"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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":0.6753000020980835,"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":0.6753000020980835,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.24979999661445618,"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":0.011699999682605267,"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/llm-reasoning","display_name":"LLM reasoning","score":0.746999979019165},{"id":"https://openalex.org/keywords/answer-selection","display_name":"answer selection","score":0.765999972820282},{"id":"https://openalex.org/keywords/test-time-scaling","display_name":"test-time scaling","score":0.6439999938011169},{"id":"https://openalex.org/keywords/adjudication","display_name":"adjudication","score":0.4480000138282776},{"id":"https://openalex.org/keywords/llama-3-1","display_name":"Llama 3.1","score":0.6990000009536743},{"id":"https://openalex.org/keywords/reasoning-benchmarks","display_name":"reasoning benchmarks","score":0.5960000157356262}],"concepts":[{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.8884000182151794},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7271000146865845},{"id":"https://openalex.org/C2777020290","wikidata":"https://www.wikidata.org/wiki/Q4947493","display_name":"Boss","level":2,"score":0.6998999714851379},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.6766999959945679},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6647999882698059},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5073000192642212},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.47839999198913574},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.43540000915527344},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4262000024318695},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3779999911785126},{"id":"https://openalex.org/C520049643","wikidata":"https://www.wikidata.org/wiki/Q189760","display_name":"Voting","level":3,"score":0.3702000081539154},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35409998893737793},{"id":"https://openalex.org/C2779458634","wikidata":"https://www.wikidata.org/wiki/Q24963715","display_name":"Debiasing","level":2,"score":0.35269999504089355},{"id":"https://openalex.org/C204434341","wikidata":"https://www.wikidata.org/wiki/Q357789","display_name":"Adjudication","level":2,"score":0.33469998836517334},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.30869999527931213},{"id":"https://openalex.org/C40423286","wikidata":"https://www.wikidata.org/wiki/Q284172","display_name":"Selection bias","level":2,"score":0.28839999437332153},{"id":"https://openalex.org/C20162079","wikidata":"https://www.wikidata.org/wiki/Q1151406","display_name":"Case-based reasoning","level":2,"score":0.2639000117778778},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.2599000036716461},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.2533999979496002}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2610.26383","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.26383","pdf_url":"https://arxiv.org/pdf/2610.26383","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,"listed_in":[],"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.2610.26383","doi":"https://doi.org/10.48550/arxiv.2610.26383","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2610.26383","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,"listed_in":[],"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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2610.26383","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.26383","pdf_url":"https://arxiv.org/pdf/2610.26383","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,"listed_in":[],"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"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7223045142.pdf","grobid_xml":"https://content.openalex.org/works/W7223045142.grobid-xml"},"referenced_works_count":8,"referenced_works":["https://openalex.org/W3134642945","https://openalex.org/W3210904070","https://openalex.org/W4221161695","https://openalex.org/W4391124001","https://openalex.org/W4406492615","https://openalex.org/W6910523324","https://openalex.org/W6966787974","https://openalex.org/W7161562758"],"related_works":["https://openalex.org/W7155400466","https://openalex.org/W7166877517","https://openalex.org/W4416143515","https://openalex.org/W7162567160","https://openalex.org/W7172498380","https://openalex.org/W7119511852","https://openalex.org/W4415150942","https://openalex.org/W7211939736","https://openalex.org/W7166873855","https://openalex.org/W7168050054"],"abstract_inverted_index":{"Test-time":[0],"scaling":[1],"can":[2],"improve":[3],"LLM":[4],"reasoning,":[5],"but":[6],"existing":[7],"approaches":[8],"typically":[9],"adapt":[10],"generation":[11,19,62],"or":[12,39],"apply":[13],"answer":[14],"selection":[15],"after":[16],"a":[17,26,49,169],"predetermined":[18],"schedule.":[20],"We":[21],"study":[22],"within-instance":[23],"allocation:":[24],"under":[25,73,97,175],"hard":[27,100],"per-instance":[28,177],"prompt-plus-completion":[29],"token":[30,76],"cap,":[31],"when":[32],"should":[33],"inference":[34,160],"stop,":[35],"generate":[36],"another":[37],"candidate,":[38],"spend":[40],"residual":[41],"tokens":[42],"adjudicating":[43],"candidates":[44],"already":[45],"observed?":[46],"BOSS":[47,86,143],"is":[48,69],"training-free":[50],"policy":[51],"for":[52,113,116,120,173],"this":[53],"three-way":[54],"decision,":[55],"with":[56,131,140,158],"worst-case":[57],"budget":[58],"checks":[59],"before":[60],"every":[61],"and":[63,82,104,118],"judge":[64],"call.":[65],"Its":[66],"key":[67],"contribution":[68],"online":[70],"generation-adjudication":[71,166],"allocation":[72,167],"one":[74],"shared":[75],"ledger.":[77],"Across":[78],"two":[79],"Llama-3.1":[80],"models":[81],"four":[83],"reasoning":[84,174],"benchmarks,":[85],"attains":[87],"the":[88,98,141,153],"highest":[89],"accuracy":[90,110],"in":[91],"22":[92],"of":[93,155],"24":[94],"model-task-budget":[95],"cells":[96],"common":[99],"cap.":[101],"At":[102],"Llama-3.1-8B":[103],"B=4k,":[105],"it":[106],"reaches":[107],"60.5":[108],"macro-average":[109],"versus":[111],"58.6":[112],"Adaptive-Consistency,":[114],"59.1":[115],"TALE-EP,":[117],"58.9":[119],"fixed-schedule":[121],"League.":[122],"On":[123],"identical":[124],"Stage-I":[125],"candidate":[126],"pools,":[127],"randomized":[128],"A/B":[129],"judging":[130],"repeated":[132],"voting":[133],"improves":[134],"plurality":[135],"from":[136],"58.4":[137],"to":[138],"60.3,":[139],"full":[142],"selector":[144],"reaching":[145],"60.5.":[146],"Approximately":[147],"matched-cost":[148],"controls":[149],"further":[150],"characterize":[151],"how":[152],"benefit":[154],"adjudication":[156],"changes":[157],"available":[159],"budget.":[161],"Together,":[162],"these":[163],"results":[164],"establish":[165],"as":[168],"distinct":[170],"design":[171],"axis":[172],"strict":[176],"caps.":[178]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2026-10-21T00:00:00"}
