{"id":"https://openalex.org/W7163207792","doi":"https://doi.org/10.48550/arxiv.2606.02091","title":"DFlare: Scaling Up Draft Capacity for Block Diffusion Speculative Decoding","display_name":"DFlare: Scaling Up Draft Capacity for Block Diffusion Speculative Decoding","publication_year":2026,"publication_date":"2026-06-01","ids":{"openalex":"https://openalex.org/W7163207792","doi":"https://doi.org/10.48550/arxiv.2606.02091"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.02091","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.02091","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.2606.02091","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137704336","display_name":"Jiebin Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Jiebin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127910150","display_name":"Zhenghan Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Zhenghan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137646470","display_name":"Song Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Song","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113131182","display_name":"Eugene J. Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Eugene J.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137666630","display_name":"Zheng Li","orcid":"https://orcid.org/0000-0003-2229-3599"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Zheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137643539","display_name":"Dawei Zhu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Dawei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137647780","display_name":"Jiangshan Duo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Duo, Jiangshan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137680903","display_name":"Weimin Xiong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xiong, Weimin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137678284","display_name":"Yifan Song","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song, Yifan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137682436","display_name":"Guanghua Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Guanghua","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137634213","display_name":"Jianchen Zhu","orcid":"https://orcid.org/0000-0002-5988-3704"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhu, Jianchen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137671487","display_name":"Sujian Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Sujian","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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.6467999815940857,"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"}},"topics":[{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.6467999815940857,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.03970000147819519,"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/T10028","display_name":"Topic Modeling","score":0.03779999911785126,"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/block","display_name":"Block (permutation group theory)","score":0.6746000051498413},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.6363999843597412},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.554099977016449},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.5335999727249146},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.5157999992370605},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4909999966621399},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.41990000009536743},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.3741999864578247}],"concepts":[{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.6746000051498413},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6689000129699707},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.6363999843597412},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5572999715805054},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.554099977016449},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.5335999727249146},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.5157999992370605},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4909999966621399},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.41990000009536743},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.3741999864578247},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.3598000109195709},{"id":"https://openalex.org/C159254197","wikidata":"https://www.wikidata.org/wiki/Q1144915","display_name":"Lexicographical order","level":2,"score":0.358599990606308},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.32199999690055847},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.31940001249313354},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.3176000118255615},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.31439998745918274},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.30480000376701355},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.30320000648498535},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2937000095844269},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.2694000005722046},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2574999928474426},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.25110000371932983}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.02091","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.02091","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.2606.02091","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.02091","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Block":[0],"diffusion":[1],"speculative":[2],"decoding":[3],"accelerates":[4],"LLM":[5],"inference":[6],"by":[7,190],"predicting":[8],"all":[9,50],"tokens":[10],"within":[11],"a":[12,30,55,62,92,107,127],"block":[13,26],"simultaneously":[14,116],"for":[15],"the":[16,39,45,85,136,158],"target":[17,40,64,111,119],"model":[18,34,138],"to":[19,53,101,139,152,155],"verify":[20],"in":[21],"parallel.":[22],"Predicting":[23],"an":[24],"entire":[25],"at":[27,113,201],"once":[28],"requires":[29],"sufficiently":[31],"capable":[32],"draft":[33,51,74,98,124,137],"and":[35,69,121,169,183,194],"effective":[36],"utilization":[37],"of":[38,73,89,106,110,176],"model's":[41],"internal":[42],"knowledge.":[43],"However,":[44],"state-of-the-art":[46],"method":[47],"DFlash":[48,90,189],"constrains":[49],"layers":[52,112],"share":[54],"single":[56],"fused":[57],"representation":[58],"derived":[59],"from":[60,150],"only":[61],"few":[63],"layers,":[65],"limiting":[66],"per-layer":[67,132],"expressiveness":[68,133],"hindering":[70],"further":[71,146],"scaling":[72,135],"capacity.":[75,160],"In":[76],"this":[77],"paper,":[78],"we":[79],"present":[80],"\\modelname,":[81],"which":[82],"flares":[83],"out":[84],"narrow":[86],"conditioning":[87],"bottleneck":[88],"through":[91],"lightweight":[93],"layer-wise":[94],"fusion":[95],"mechanism:":[96],"each":[97],"layer":[99,125],"attends":[100],"its":[102],"own":[103],"learnable":[104],"combination":[105],"broad":[108],"set":[109],"negligible":[114],"overhead,":[115],"injecting":[117],"richer":[118],"knowledge":[120],"providing":[122],"every":[123],"with":[126,142],"distinct":[128],"input.":[129],"This":[130],"enhanced":[131],"enables":[134],"deeper":[140],"architectures":[141],"consistent":[143],"gains.":[144],"We":[145],"scale":[147],"training":[148],"data":[149],"800K":[151],"2.4M":[153],"samples":[154],"fully":[156],"exploit":[157],"enlarged":[159],"On":[161],"six":[162],"benchmarks":[163],"spanning":[164],"mathematical":[165],"reasoning,":[166],"code":[167,198],"generation,":[168],"conversation,":[170],"\\modelname":[171],"attains":[172],"average":[173],"wall-clock":[174],"speedups":[175],"5.52x":[177],"on":[178,181,185],"Qwen3-4B,":[179],"5.46x":[180],"Qwen3-8B,":[182],"3.91x":[184],"GPT-OSS-20B,":[186],"improving":[187],"over":[188],"roughly":[191],"11\\%,":[192],"8\\%,":[193],"5\\%":[195],"respectively.":[196],"Our":[197],"is":[199],"available":[200],"https://github.com/Tencent/AngelSlim.":[202]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-03T00:00:00"}
