{"id":"https://openalex.org/W4417170292","doi":"https://doi.org/10.48550/arxiv.2504.20456","title":"Reviving Any-Subset Autoregressive Models with Principled Parallel Sampling and Speculative Decoding","display_name":"Reviving Any-Subset Autoregressive Models with Principled Parallel Sampling and Speculative Decoding","publication_year":2025,"publication_date":"2025-04-29","ids":{"openalex":"https://openalex.org/W4417170292","doi":"https://doi.org/10.48550/arxiv.2504.20456"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2504.20456","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2504.20456","pdf_url":"https://arxiv.org/pdf/2504.20456","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2504.20456","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Guo, Gabe","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Gabe","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5091179481","display_name":"Stefano Ermon","orcid":"https://orcid.org/0000-0003-0039-2887"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ermon, Stefano","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":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":0.3813000023365021,"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":0.3813000023365021,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.14090000092983246,"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":0.08820000290870667,"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":0.651199996471405},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.621999979019165},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.5971999764442444},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.5498999953269958},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5379999876022339},{"id":"https://openalex.org/keywords/independence","display_name":"Independence (probability theory)","score":0.5281000137329102},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.482699990272522},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4796000123023987},{"id":"https://openalex.org/keywords/trace","display_name":"TRACE (psycholinguistics)","score":0.4756999909877777},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4366999864578247}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7522000074386597},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.651199996471405},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.621999979019165},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.5971999764442444},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.5498999953269958},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5379999876022339},{"id":"https://openalex.org/C35651441","wikidata":"https://www.wikidata.org/wiki/Q625303","display_name":"Independence (probability theory)","level":2,"score":0.5281000137329102},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.482699990272522},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4796000123023987},{"id":"https://openalex.org/C75291252","wikidata":"https://www.wikidata.org/wiki/Q1315756","display_name":"TRACE (psycholinguistics)","level":2,"score":0.4756999909877777},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.44519999623298645},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4366999864578247},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41440001130104065},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4032999873161316},{"id":"https://openalex.org/C34388435","wikidata":"https://www.wikidata.org/wiki/Q2267362","display_name":"Bounded function","level":2,"score":0.39800000190734863},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.3905999958515167},{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.38940000534057617},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.383899986743927},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3804999887943268},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.3781000077724457},{"id":"https://openalex.org/C79772020","wikidata":"https://www.wikidata.org/wiki/Q5159264","display_name":"Conditional independence","level":2,"score":0.37369999289512634},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3529999852180481},{"id":"https://openalex.org/C2780813799","wikidata":"https://www.wikidata.org/wiki/Q3274237","display_name":"Zero (linguistics)","level":2,"score":0.3183000087738037},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3107999861240387},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.30640000104904175},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.3012999892234802},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2944999933242798},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.2883000075817108},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.27090001106262207},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2680000066757202},{"id":"https://openalex.org/C2778562939","wikidata":"https://www.wikidata.org/wiki/Q1298791","display_name":"Synchronization (alternating current)","level":3,"score":0.2615000009536743},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.2572000026702881}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2504.20456","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2504.20456","pdf_url":"https://arxiv.org/pdf/2504.20456","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2504.20456","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2504.20456","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":"pmh:oai:arXiv.org:2504.20456","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2504.20456","pdf_url":"https://arxiv.org/pdf/2504.20456","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5681216131","display_name":null,"funder_award_id":"DE-SC0025528","funder_id":"https://openalex.org/F4320306084","funder_display_name":"U.S. Department of Energy"},{"id":"https://openalex.org/G6730126378","display_name":null,"funder_award_id":"DE-SC0025528","funder_id":"https://openalex.org/F4320337506","funder_display_name":"Advanced Scientific Computing Research"}],"funders":[{"id":"https://openalex.org/F4320306084","display_name":"U.S. Department of Energy","ror":"https://ror.org/01bj3aw27"},{"id":"https://openalex.org/F4320332359","display_name":"Office of Science","ror":"https://ror.org/00mmn6b08"},{"id":"https://openalex.org/F4320337506","display_name":"Advanced Scientific Computing Research","ror":"https://ror.org/0012c7r22"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4417170292.pdf","grobid_xml":"https://content.openalex.org/works/W4417170292.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0],"arbitrary-order":[1],"language":[2,145,204],"models,":[3,23,65],"it":[4],"is":[5],"an":[6],"open":[7],"question":[8],"how":[9],"to":[10,37,98],"sample":[11],"tokens":[12,26,81,117,136],"in":[13,29,82,86],"parallel":[14],"from":[15,118],"the":[16,24,31,38,71,76,119,124,133,180,196],"correct":[17,99,120],"joint":[18,92,121],"distribution.":[19],"With":[20],"discrete":[21],"diffusion":[22],"more":[25],"they":[27,44],"generate":[28,80],"parallel,":[30],"less":[32],"their":[33,100],"predicted":[34],"distributions":[35],"adhere":[36],"originally":[39],"learned":[40],"data":[41],"distribution,":[42,122],"as":[43],"rely":[45],"on":[46,173,186],"a":[47,61,153,200],"conditional":[48],"independence":[49],"assumption":[50],"that":[51,60,141,164,195],"only":[52],"works":[53],"with":[54,123],"infinitesimally":[55],"small":[56],"timesteps.":[57],"We":[58,138],"find":[59],"different":[62],"class":[63],"of":[64,116,126,135,182,203],"any-subset":[66],"autoregressive":[67],"models":[68,172,183],"(AS-ARMs),":[69],"holds":[70],"solution.":[72],"As":[73],"implied":[74],"by":[75,132],"name,":[77],"AS-ARMs":[78,89,159,165,198],"can":[79],"any":[83],"order,":[84],"and":[85,162,177,191],"parallel.":[87],"Moreover,":[88],"support":[90],"parallelized":[91],"probability":[93],"density":[94],"estimation,":[95],"allowing":[96],"them":[97],"own":[101],"parallel-generated":[102],"token":[103],"distributions,":[104],"via":[105],"our":[106],"Any-Subset":[107],"Speculative":[108],"Decoding":[109],"(ASSD)":[110],"algorithm.":[111],"ASSD":[112,142],"provably":[113],"enables":[114],"generation":[115],"number":[125,134],"neural":[127],"network":[128],"calls":[129],"upper":[130],"bounded":[131],"predicted.":[137],"empirically":[139],"verify":[140],"speeds":[143],"up":[144],"generation,":[146,161],"without":[147],"sacrificing":[148],"quality.":[149],"Furthermore,":[150],"we":[151],"provide":[152],"mathematically":[154],"justified":[155],"scheme":[156],"for":[157,160],"training":[158],"show":[163],"achieve":[166],"state-of-the-art":[167],"performance":[168,181],"among":[169],"sub-200M":[170],"parameter":[171],"infilling":[174],"benchmark":[175],"tasks,":[176],"nearly":[178],"match":[179],"50X":[184],"larger":[185],"code":[187],"generation.":[188],"Our":[189],"theoretical":[190],"empirical":[192],"results":[193],"indicate":[194],"once-forgotten":[197],"are":[199],"promising":[201],"direction":[202],"modeling.":[205]},"counts_by_year":[],"updated_date":"2026-07-03T08:13:44.112507","created_date":"2025-10-10T00:00:00"}
