{"id":"https://openalex.org/W7153287649","doi":"https://doi.org/10.48550/arxiv.2604.07622","title":"DIVERSED: Relaxed Speculative Decoding via Dynamic Ensemble Verification","display_name":"DIVERSED: Relaxed Speculative Decoding via Dynamic Ensemble Verification","publication_year":2026,"publication_date":"2026-04-08","ids":{"openalex":"https://openalex.org/W7153287649","doi":"https://doi.org/10.48550/arxiv.2604.07622"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.07622","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.07622","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.2604.07622","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133328329","display_name":"Ziyi Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Ziyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041090300","display_name":"Siva Rajesh Kasa","orcid":"https://orcid.org/0000-0003-1509-4313"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kasa, Siva Rajesh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133367095","display_name":"Ankith M S","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"S, Ankith M","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093618351","display_name":"Santhosh Kumar Kasa","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kasa, Santhosh Kumar","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133350670","display_name":"Jiaru Zou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zou, Jiaru","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109167303","display_name":"Sumit Negi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Negi, Sumit","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133377113","display_name":"Ruqi Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Ruqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133380238","display_name":"Nan Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Nan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133361647","display_name":"Qifan Song","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song, Qifan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":9,"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":0.22779999673366547,"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":0.22779999673366547,"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":0.14830000698566437,"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":0.1031000018119812,"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/decoding-methods","display_name":"Decoding methods","score":0.8478999733924866},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.6728000044822693},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.597100019454956},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.5307999849319458},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.5270000100135803},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5033000111579895},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.5}],"concepts":[{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.8478999733924866},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7735000252723694},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.6728000044822693},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.597100019454956},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5562999844551086},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.5307999849319458},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.5270000100135803},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5033000111579895},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.5},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.38499999046325684},{"id":"https://openalex.org/C193969084","wikidata":"https://www.wikidata.org/wiki/Q7452500","display_name":"Sequential decoding","level":4,"score":0.35350000858306885},{"id":"https://openalex.org/C2777472644","wikidata":"https://www.wikidata.org/wiki/Q16968992","display_name":"Approximate inference","level":3,"score":0.29670000076293945},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.25940001010894775},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.25609999895095825},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.2540000081062317},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.2517000138759613},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.2515000104904175}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.07622","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.07622","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.2604.07622","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.07622","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Speculative":[0,71],"decoding":[1,126],"is":[2,22,129],"an":[3,88],"effective":[4],"technique":[5],"for":[6,109],"accelerating":[7],"large":[8],"language":[9],"model":[10,97],"inference":[11,120],"by":[12,25],"drafting":[13],"multiple":[14],"tokens":[15],"in":[16],"parallel.":[17],"In":[18],"practice,":[19],"its":[20],"speedup":[21],"often":[23],"bottlenecked":[24],"a":[26,74,100],"rigid":[27],"verification":[28,76],"step":[29],"that":[30,78,91,115],"strictly":[31],"enforces":[32],"the":[33,40,47,54,93],"accepted":[34],"token":[35],"distribution":[36],"to":[37,46,123],"exactly":[38],"match":[39],"target":[41,96],"model.":[42],"This":[43],"constraint":[44],"leads":[45],"rejection":[48],"of":[49],"many":[50],"plausible":[51],"tokens,":[52],"lowering":[53],"acceptance":[55],"rate":[56],"and":[57,95,102,112],"limiting":[58],"overall":[59],"time":[60,80],"speedup.":[61],"To":[62],"overcome":[63],"this":[64],"limitation,":[65],"we":[66],"propose":[67],"Dynamic":[68],"Verification":[69],"Relaxed":[70],"Decoding":[72],"(DIVERSED),":[73],"relaxed":[75],"framework":[77],"improves":[79],"efficiency":[81,121],"while":[82],"preserving":[83],"generation":[84],"quality.":[85],"DIVERSED":[86,116],"learns":[87],"ensemble-based":[89],"verifier":[90],"blends":[92],"draft":[94],"distributions":[98],"with":[99],"task-dependent":[101],"context-dependent":[103],"weight.":[104],"We":[105],"provide":[106],"theoretical":[107],"justification":[108],"our":[110],"approach":[111],"demonstrate":[113],"empirically":[114],"achieves":[117],"substantially":[118],"higher":[119],"compared":[122],"standard":[124],"speculative":[125],"methods.":[127],"Code":[128],"available":[130],"at:":[131],"https://github.com/comeusr/diversed.":[132]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-04-11T00:00:00"}