{"id":"https://openalex.org/W7155077712","doi":"https://doi.org/10.48550/arxiv.2604.17433","title":"Self-Consistency from Only Two Samples: CoT-PoT Ensembling for Efficient LLM Reasoning","display_name":"Self-Consistency from Only Two Samples: CoT-PoT Ensembling for Efficient LLM Reasoning","publication_year":2026,"publication_date":"2026-04-19","ids":{"openalex":"https://openalex.org/W7155077712","doi":"https://doi.org/10.48550/arxiv.2604.17433"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.17433","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.17433","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.2604.17433","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134110361","display_name":"Raman Saparkhan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Saparkhan, Raman","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021151663","display_name":"Majd Hawasly","orcid":"https://orcid.org/0000-0003-1823-5580"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hawasly, Majd","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134204853","display_name":"Md Rizwan Parvez","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Parvez, Md Rizwan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5124915289","display_name":"Mohammad Raza","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Raza, Mohammad","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"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":0.5889999866485596,"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":0.5889999866485596,"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":0.1851000040769577,"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":0.0348999984562397,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.37400001287460327},{"id":"https://openalex.org/keywords/case-based-reasoning","display_name":"Case-based reasoning","score":0.3580999970436096},{"id":"https://openalex.org/keywords/factor","display_name":"Factor (programming language)","score":0.34700000286102295},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.3098999857902527},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.2766000032424927}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7796000242233276},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6607999801635742},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4733999967575073},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.37400001287460327},{"id":"https://openalex.org/C20162079","wikidata":"https://www.wikidata.org/wiki/Q1151406","display_name":"Case-based reasoning","level":2,"score":0.3580999970436096},{"id":"https://openalex.org/C2781039887","wikidata":"https://www.wikidata.org/wiki/Q1391724","display_name":"Factor (programming language)","level":2,"score":0.34700000286102295},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3425999879837036},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3098999857902527},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.2766000032424927},{"id":"https://openalex.org/C159032336","wikidata":"https://www.wikidata.org/wiki/Q2488768","display_name":"Non-monotonic logic","level":2,"score":0.2680000066757202}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.17433","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.17433","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.2604.17433","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.17433","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Self-consistency":[0],"(SC)":[1],"is":[2],"a":[3,24,34,56,101],"popular":[4],"technique":[5],"for":[6,59,73,98],"improving":[7],"the":[8,40,93,107],"reasoning":[9,65],"accuracy":[10],"of":[11,43,47,64,95,103,109],"large":[12],"language":[13],"models":[14],"by":[15,100],"aggregating":[16],"multiple":[17],"sampled":[18],"outputs,":[19],"but":[20,89],"it":[21],"comes":[22],"at":[23],"high":[25],"computational":[26],"cost":[27],"due":[28],"to":[29],"extensive":[30],"sampling.":[31],"We":[32,54,79],"introduce":[33],"hybrid":[35],"ensembling":[36,83],"approach":[37],"that":[38,81],"leverages":[39],"complementary":[41],"strengths":[42],"two":[44,62,117],"distinct":[45],"modes":[46],"reasoning:":[48],"Chain-of-Thought":[49],"(CoT)":[50],"and":[51,77],"Program-of-Thought":[52],"(PoT).":[53],"describe":[55],"general":[57],"framework":[58],"combining":[60],"these":[61],"forms":[63],"in":[66],"self-consistency,":[67],"as":[68,70],"well":[69],"particular":[71],"strategies":[72],"both":[74],"full":[75],"sampling":[76],"early-stopping.":[78],"show":[80],"CoT-PoT":[82],"not":[84,121],"only":[85,116],"improves":[86],"overall":[87],"accuracy,":[88],"also":[90],"drastically":[91],"reduces":[92],"number":[94],"samples":[96],"required":[97],"SC":[99,127],"factor":[102],"9.3x.":[104],"In":[105],"particular,":[106],"majority":[108],"tasks":[110],"(78.6%)":[111],"can":[112],"be":[113],"addressed":[114],"with":[115,124],"samples,":[118],"which":[119],"has":[120],"been":[122],"possible":[123],"any":[125],"prior":[126],"methods.":[128]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-04-22T00:00:00"}
