{"id":"https://openalex.org/W7154974177","doi":"https://doi.org/10.48550/arxiv.2604.15672","title":"Faster LLM Inference via Sequential Monte Carlo","display_name":"Faster LLM Inference via Sequential Monte Carlo","publication_year":2026,"publication_date":"2026-04-17","ids":{"openalex":"https://openalex.org/W7154974177","doi":"https://doi.org/10.48550/arxiv.2604.15672"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.15672","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.15672","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,"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.2604.15672","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5120726023","display_name":"Yahya Emara","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Emara, Yahya","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134010152","display_name":"Mauricio Barba da Costa","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"da Costa, Mauricio Barba","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134061933","display_name":"Chi-Chih Chang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chang, Chi-Chih","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124794216","display_name":"Cameron Freer","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Freer, Cameron","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134007643","display_name":"Tim Vieira","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vieira, Tim","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061951606","display_name":"Ryan Cotterell","orcid":"https://orcid.org/0000-0003-4080-1833"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cotterell, Ryan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5134048776","display_name":"Mohamed S. Abdelfattah","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Abdelfattah, Mohamed S.","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/T10181","display_name":"Natural Language Processing Techniques","score":0.25769999623298645,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.25769999623298645,"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":0.12210000306367874,"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/T10028","display_name":"Topic Modeling","score":0.10520000010728836,"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/inference","display_name":"Inference","score":0.6309999823570251},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.5479000210762024},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.4781999886035919},{"id":"https://openalex.org/keywords/resampling","display_name":"Resampling","score":0.4616999924182892},{"id":"https://openalex.org/keywords/approximate-inference","display_name":"Approximate inference","score":0.41990000009536743},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.35409998893737793},{"id":"https://openalex.org/keywords/importance-sampling","display_name":"Importance sampling","score":0.3467000126838684},{"id":"https://openalex.org/keywords/sequential-estimation","display_name":"Sequential estimation","score":0.3440000116825104}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.685699999332428},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6309999823570251},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5685999989509583},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.5479000210762024},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.4781999886035919},{"id":"https://openalex.org/C150921843","wikidata":"https://www.wikidata.org/wiki/Q1170431","display_name":"Resampling","level":2,"score":0.4616999924182892},{"id":"https://openalex.org/C2777472644","wikidata":"https://www.wikidata.org/wiki/Q16968992","display_name":"Approximate inference","level":3,"score":0.41990000009536743},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.35409998893737793},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.3467000126838684},{"id":"https://openalex.org/C86426650","wikidata":"https://www.wikidata.org/wiki/Q7452504","display_name":"Sequential estimation","level":2,"score":0.3440000116825104},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.33730000257492065},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.3230000138282776},{"id":"https://openalex.org/C45357846","wikidata":"https://www.wikidata.org/wiki/Q2001982","display_name":"Notation","level":2,"score":0.31769999861717224},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.314300000667572},{"id":"https://openalex.org/C52421305","wikidata":"https://www.wikidata.org/wiki/Q1151499","display_name":"Particle filter","level":3,"score":0.31279999017715454},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.28299999237060547},{"id":"https://openalex.org/C179518139","wikidata":"https://www.wikidata.org/wiki/Q5140297","display_name":"Coding (social sciences)","level":2,"score":0.27489998936653137},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2728999853134155},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2721000015735626},{"id":"https://openalex.org/C80478641","wikidata":"https://www.wikidata.org/wiki/Q195771","display_name":"Sequential analysis","level":2,"score":0.2651999890804291},{"id":"https://openalex.org/C134261354","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical inference","level":2,"score":0.25920000672340393},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.25220000743865967}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.15672","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.15672","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,"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.2604.15672","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.15672","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,"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],"decoding":[1,63,145],"(SD)":[2],"accelerates":[3],"language":[4],"model":[5,14,22],"inference":[6,83,102],"by":[7],"drafting":[8],"tokens":[9,47],"from":[10],"a":[11,73,80,131,147],"cheap":[12],"proposal":[13],"and":[15,40,112,146,165],"verifying":[16],"them":[17,115],"against":[18],"an":[19],"expensive":[20],"target":[21,41,159],"via":[23],"rejection":[24,27,68],"sampling.":[25],"Because":[26,100],"truncates":[28],"the":[29,33,106,158],"draft":[30,39,46,76,110],"block":[31],"at":[32],"first":[34],"error,":[35],"throughput":[36],"degrades":[37],"when":[38],"diverge.":[42],"Rather":[43],"than":[44],"rejecting":[45],"outright,":[48],"we":[49,57],"propose":[50],"to":[51,109,113,127],"reweight":[52],"them.":[53],"To":[54],"this":[55],"end,":[56],"introduce":[58],"sequential":[59],"Monte":[60],"Carlo":[61],"speculative":[62,144],"(SMC-SD),":[64],"which":[65],"replaces":[66],"token-level":[67],"with":[69,135],"importance-weighted":[70],"resampling":[71],"over":[72,143,150],"population":[74],"of":[75,157],"particles.":[77],"SMC-SD":[78,123,139],"is":[79,103],"principled":[81],"approximate":[82],"scheme":[84],"that":[85],"trades":[86],"exactness":[87],"for":[88,120],"additional":[89],"speed,":[90],"while":[91,153],"preserving":[92],"theoretical":[93],"bounds":[94],"on":[95,162],"its":[96],"per-step":[97],"approximation":[98],"error.":[99],"LLM":[101],"memory":[104],"bandwidth-bound,":[105],"arithmetic":[107],"needed":[108],"particles":[111],"score":[114],"in":[116],"parallel":[117],"comes":[118],"nearly":[119],"free":[121],"--":[122],"uses":[124],"idle":[125],"compute":[126],"turn":[128],"verification":[129],"into":[130],"vectorized,":[132],"fixed-size":[133],"operation":[134],"no":[136],"rollback.":[137],"Empirically,":[138],"achieves":[140],"2.36x":[141],"speed-up":[142,149],"5.2x":[148],"autoregressive":[151],"decoding,":[152],"remaining":[154],"within":[155],"3%":[156],"model's":[160],"accuracy":[161],"reasoning,":[163],"instruction-following,":[164],"coding":[166],"benchmarks.":[167]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-21T00:00:00"}