{"id":"https://openalex.org/W7133238215","doi":"https://doi.org/10.48550/arxiv.2602.23792","title":"Divide and Conquer: Accelerating Diffusion-Based Large Language Models via Adaptive Parallel Decoding","display_name":"Divide and Conquer: Accelerating Diffusion-Based Large Language Models via Adaptive Parallel Decoding","publication_year":2026,"publication_date":"2026-02-27","ids":{"openalex":"https://openalex.org/W7133238215","doi":"https://doi.org/10.48550/arxiv.2602.23792"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.23792","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5060833244","display_name":"Xiangzhong Luo","orcid":"https://orcid.org/0000-0002-0758-2248"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luo, Xiangzhong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127847133","display_name":"Yilin An","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"An, Yilin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127857872","display_name":"Zhicheng Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Zhicheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5038598144","display_name":"Weichen Liu","orcid":"https://orcid.org/0000-0001-9348-4662"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Weichen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5127862882","display_name":"Xu Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Xu","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":0.46950000524520874,"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":0.46950000524520874,"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":0.07999999821186066,"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"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.06340000033378601,"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/decoding-methods","display_name":"Decoding methods","score":0.8061000108718872},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.6256999969482422},{"id":"https://openalex.org/keywords/decodes","display_name":"Decodes","score":0.6223999857902527},{"id":"https://openalex.org/keywords/divide-and-conquer-algorithms","display_name":"Divide and conquer algorithms","score":0.5785999894142151},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.5026999711990356},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4887999892234802},{"id":"https://openalex.org/keywords/parallelism","display_name":"Parallelism (grammar)","score":0.48339998722076416},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.44920000433921814}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8065999746322632},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.8061000108718872},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.6256999969482422},{"id":"https://openalex.org/C2778858076","wikidata":"https://www.wikidata.org/wiki/Q5249539","display_name":"Decodes","level":3,"score":0.6223999857902527},{"id":"https://openalex.org/C71559656","wikidata":"https://www.wikidata.org/wiki/Q671298","display_name":"Divide and conquer algorithms","level":2,"score":0.5785999894142151},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.5026999711990356},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4887999892234802},{"id":"https://openalex.org/C2781172179","wikidata":"https://www.wikidata.org/wiki/Q853109","display_name":"Parallelism (grammar)","level":2,"score":0.48339998722076416},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.44920000433921814},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4244000017642975},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4169999957084656},{"id":"https://openalex.org/C193969084","wikidata":"https://www.wikidata.org/wiki/Q7452500","display_name":"Sequential decoding","level":4,"score":0.39399999380111694},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.3905999958515167},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.38839998841285706},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.38760000467300415},{"id":"https://openalex.org/C554936623","wikidata":"https://www.wikidata.org/wiki/Q199657","display_name":"Reading (process)","level":2,"score":0.358599990606308},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3447999954223633},{"id":"https://openalex.org/C204397858","wikidata":"https://www.wikidata.org/wiki/Q4437907","display_name":"List decoding","level":5,"score":0.31700000166893005},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.3098999857902527},{"id":"https://openalex.org/C2776187449","wikidata":"https://www.wikidata.org/wiki/Q1513879","display_name":"Natural language generation","level":3,"score":0.29350000619888306},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2667999863624573},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.2639999985694885},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.2587999999523163},{"id":"https://openalex.org/C106515295","wikidata":"https://www.wikidata.org/wiki/Q26806595","display_name":"Parallel processing","level":2,"score":0.2551000118255615}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.23792","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"Article"},{"id":"doi:10.48550/arxiv.2602.23792","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.23792","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:doi:10.48550/arxiv.2602.23792","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"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":{"Diffusion-based":[0],"large":[1,20],"language":[2,21],"models":[3,22],"(dLLMs)":[4],"have":[5],"shown":[6],"promising":[7],"performance":[8,84],"across":[9,150],"various":[10],"reasoning":[11],"tasks,":[12],"establishing":[13],"themselves":[14],"as":[15,59,128],"an":[16,93,184],"alternative":[17],"to":[18,67,106,135,190],"autoregressive":[19,25],"(LLMs).":[23],"Unlike":[24],"LLMs":[26],"that":[27,197],"generate":[28],"one":[29],"token":[30],"per":[31],"step":[32],"based":[33],"on":[34],"all":[35],"previous":[36],"tokens,":[37,130],"dLLMs":[38,52],"theoretically":[39],"enable":[40],"parallel":[41,95,148],"generation":[42,56,69,207],"of":[43,85,111,139],"multiple":[44,62],"tokens":[45,64,127,182],"at":[46],"each":[47],"decoding":[48,61,96,149,188],"step.":[49],"However,":[50],"recent":[51],"still":[53],"favor":[54],"one-token-per-step":[55],"in":[57,155],"practice,":[58],"directly":[60],"masked":[63,122,126,181],"often":[65],"leads":[66],"degraded":[68],"quality":[70],"and":[71,82,124,167],"stability.":[72],"This":[73],"reveals":[74],"a":[75,102,137],"substantial":[76],"gap":[77],"between":[78,164],"the":[79,108,114,120,143,156,165,173,178,192],"theoretical":[80],"parallelism":[81,110],"practical":[83],"dLLMs.":[86,112],"To":[87],"bridge":[88],"this":[89],"gap,":[90],"we":[91],"introduce":[92],"adaptive":[94],"approach,":[97],"namely":[98],"DiCo,":[99],"which":[100,131],"features":[101],"three-phase":[103],"divide-and-conquer":[104,160],"paradigm":[105],"unleash":[107],"inherent":[109],"During":[113,142,172],"Divide":[115,157,166],"phase,":[116,145,175],"DiCo":[117,146,176,198],"first":[118],"explores":[119],"input":[121],"sequence":[123],"identifies":[125],"seed":[129],"are":[132],"then":[133],"expanded":[134],"construct":[136],"set":[138],"local":[140,152],"clusters.":[141],"Conquer":[144,168],"performs":[147],"different":[151],"clusters":[153],"constructed":[154],"phase.":[158],"The":[159],"process":[161],"repeatedly":[162],"alternates":[163],"phases":[169],"until":[170],"convergence.":[171],"Finalize":[174],"decodes":[177],"remaining":[179],"few":[180],"using":[183],"effective":[185],"fine-grained":[186],"compound":[187],"scheme":[189],"finalize":[191],"generation.":[193],"Extensive":[194],"experiments":[195],"demonstrate":[196],"can":[199],"achieve":[200],"significant":[201],"inference":[202],"speedups":[203],"while":[204],"maintaining":[205],"competitive":[206],"quality.":[208]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-03T00:00:00"}