{"id":"https://openalex.org/W7154567729","doi":"https://doi.org/10.48550/arxiv.2604.13634","title":"Calibrated Speculative Decoding: Frequency-Guided Candidate Selection for Efficient Inference","display_name":"Calibrated Speculative Decoding: Frequency-Guided Candidate Selection for Efficient Inference","publication_year":2026,"publication_date":"2026-04-15","ids":{"openalex":"https://openalex.org/W7154567729","doi":"https://doi.org/10.48550/arxiv.2604.13634"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.13634","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.13634","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.2604.13634","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133794378","display_name":"Xuwen Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Xuwen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017670541","display_name":"Fangxin Liu","orcid":"https://orcid.org/0000-0002-8769-293X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Fangxin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115596972","display_name":"Chao Wang","orcid":"https://orcid.org/0009-0007-3911-4645"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Chao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129674411","display_name":"Xiao Zheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zheng, Xiao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133806040","display_name":"Hao Zheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zheng, Hao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133783718","display_name":"Min He","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, Min","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133782376","display_name":"Li Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Li","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133801581","display_name":"Haibing Guan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guan, Haibing","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/T10028","display_name":"Topic Modeling","score":0.41620001196861267,"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":0.41620001196861267,"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":0.2915000021457672,"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/T13910","display_name":"Computational and Text Analysis Methods","score":0.03999999910593033,"subfield":{"id":"https://openalex.org/subfields/3300","display_name":"General Social Sciences"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.7171000242233276},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.6061999797821045},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5486999750137329},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5432000160217285},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.5275999903678894},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.5097000002861023},{"id":"https://openalex.org/keywords/divergence","display_name":"Divergence (linguistics)","score":0.45190000534057617},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.4156000018119812}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7754999995231628},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.7171000242233276},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.6061999797821045},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5486999750137329},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5432000160217285},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.5275999903678894},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.5097000002861023},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.45190000534057617},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44749999046325684},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.4156000018119812},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.3741999864578247},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.36010000109672546},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.34929999709129333},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33719998598098755},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.3075999915599823},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.30660000443458557},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.3059000074863434},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.2874999940395355},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.26460000872612},{"id":"https://openalex.org/C134752490","wikidata":"https://www.wikidata.org/wiki/Q374182","display_name":"Logical consequence","level":2,"score":0.2529999911785126},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.25220000743865967}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.13634","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.13634","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.2604.13634","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.13634","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":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Speculative":[0,37],"decoding":[1],"accelerates":[2],"autoregressive":[3],"generation":[4],"by":[5,48,52],"letting":[6],"draft":[7,22],"tokens":[8,46],"bypass":[9],"full":[10],"verification,":[11],"but":[12,27],"conventional":[13],"frameworks":[14],"suffer":[15],"from":[16],"frequent":[17],"false":[18],"rejections,":[19],"particularly":[20],"when":[21],"models":[23,104],"produce":[24],"semantically":[25],"correct":[26],"lexically":[28],"divergent":[29],"outputs.":[30],"In":[31],"this":[32],"paper,":[33],"we":[34],"present":[35],"Calibrated":[36],"Decoding":[38],"(CSD),":[39],"a":[40,112,138],"training-free":[41],"framework":[42],"that":[43,106],"recovers":[44],"valid":[45],"discarded":[47],"standard":[49],"verification.":[50],"Guided":[51],"the":[53],"principle":[54],"of":[55,94,116],"\"Frequency-Guided":[56],"Candidate":[57],"Selection":[58],"and":[59,82],"Probability-Guarded":[60],"Acceptance,\"":[61],"CSD":[62,107,118,136],"incorporates":[63],"two":[64],"lightweight":[65,141],"modules:":[66],"Online":[67],"Correction":[68],"Memory,":[69],"which":[70,86],"aggregates":[71],"historical":[72],"rejections":[73],"to":[74],"propose":[75],"recurring":[76],"divergence":[77],"patterns":[78],"as":[79,137],"rescue":[80],"candidates,":[81],"Semantic":[83],"Consistency":[84],"Gating,":[85],"verifies":[87],"candidate":[88],"admissibility":[89],"using":[90],"probability":[91],"ratios":[92],"instead":[93],"exact":[95],"token":[96],"matching.":[97],"Our":[98],"evaluation":[99],"across":[100,122],"diverse":[101],"large":[102],"language":[103],"demonstrates":[105],"outperforms":[108],"existing":[109],"methods,":[110],"achieving":[111],"peak":[113],"throughput":[114],"speedup":[115],"2.33x.":[117],"preserves":[119],"model":[120],"accuracy":[121],"all":[123],"tasks":[124],"while":[125],"further":[126],"boosting":[127],"performance":[128],"on":[129],"complex":[130],"reasoning":[131],"datasets.":[132],"These":[133],"results":[134],"establish":[135],"highly":[139],"effective,":[140],"solution":[142],"for":[143],"practical":[144],"LLM":[145],"deployments.":[146]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-17T00:00:00"}