{"id":"https://openalex.org/W7162773333","doi":"https://doi.org/10.48550/arxiv.2605.29707","title":"Domino: Decoupling Causal Modeling from Autoregressive Drafting in Speculative Decoding","display_name":"Domino: Decoupling Causal Modeling from Autoregressive Drafting in Speculative Decoding","publication_year":2026,"publication_date":"2026-05-28","ids":{"openalex":"https://openalex.org/W7162773333","doi":"https://doi.org/10.48550/arxiv.2605.29707"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.29707","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.29707","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.2605.29707","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137365438","display_name":"Jianuo Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Jianuo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137370786","display_name":"Yaojie Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Yaojie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102662097","display_name":"Qituan Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Qituan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137351758","display_name":"Hao Lin","orcid":"https://orcid.org/0009-0005-6008-815X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lin, Hao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137367963","display_name":"Hanlin Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Hanlin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137320127","display_name":"Linfeng Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Linfeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"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":0.289900004863739,"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":0.289900004863739,"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":0.1639000028371811,"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":0.08470000326633453,"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/speedup","display_name":"Speedup","score":0.8720999956130981},{"id":"https://openalex.org/keywords/domino","display_name":"Domino","score":0.6155999898910522},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.5386000275611877},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.5073000192642212},{"id":"https://openalex.org/keywords/dependency","display_name":"Dependency (UML)","score":0.4805000126361847},{"id":"https://openalex.org/keywords/causal-model","display_name":"Causal model","score":0.42590001225471497},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.41370001435279846}],"concepts":[{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.8720999956130981},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7404000163078308},{"id":"https://openalex.org/C2776416436","wikidata":"https://www.wikidata.org/wiki/Q3751781","display_name":"Domino","level":3,"score":0.6155999898910522},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.5386000275611877},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.5073000192642212},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.4805000126361847},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.42590001225471497},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.42010000348091125},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.41370001435279846},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.4056999981403351},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.39910000562667847},{"id":"https://openalex.org/C205606062","wikidata":"https://www.wikidata.org/wiki/Q5249645","display_name":"Decoupling (probability)","level":2,"score":0.3547999858856201},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.33000001311302185},{"id":"https://openalex.org/C157764524","wikidata":"https://www.wikidata.org/wiki/Q1383412","display_name":"Throughput","level":3,"score":0.31049999594688416},{"id":"https://openalex.org/C2777317252","wikidata":"https://www.wikidata.org/wiki/Q18393516","display_name":"Rare events","level":2,"score":0.30809998512268066},{"id":"https://openalex.org/C43364308","wikidata":"https://www.wikidata.org/wiki/Q8799","display_name":"Byte","level":2,"score":0.27549999952316284},{"id":"https://openalex.org/C115086926","wikidata":"https://www.wikidata.org/wiki/Q17004651","display_name":"Causal reasoning","level":3,"score":0.2702000141143799},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.26589998602867126}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.29707","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.29707","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.2605.29707","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.29707","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],"decoding":[1,64],"accelerates":[2],"LLM":[3],"inference":[4],"by":[5,24],"drafting":[6,31,49],"multiple":[7],"tokens":[8,40],"and":[9,30,92,124,152],"verifying":[10],"them":[11,101],"in":[12],"parallel":[13,46,80,122],"with":[14,102],"the":[15,25,89,121,130,149],"target":[16],"model.":[17],"However,":[18],"its":[19],"practical":[20],"speedup":[21,147,157],"is":[22],"constrained":[23],"trade-off":[26],"between":[27],"draft":[28,39,74,81,86],"quality":[29],"cost:":[32],"autoregressive":[33,73],"drafters":[34,47],"model":[35],"causal":[36,68,104,109],"dependencies":[37],"among":[38],"but":[41,51],"incur":[42],"sequential":[43],"overhead,":[44],"while":[45],"reduce":[48],"cost":[50],"weaken":[52],"intra-block":[53],"dependency":[54,69],"modeling.":[55],"In":[56],"this":[57],"paper,":[58],"we":[59,111],"propose":[60],"Domino,":[61],"a":[62,79,95,114],"speculative":[63],"framework":[65],"that":[66,118,140],"decouples":[67],"modeling":[70],"from":[71],"expensive":[72],"execution.":[75],"Domino":[76,97,141],"first":[77,119],"uses":[78],"backbone":[82,123],"to":[83,99,144,154],"produce":[84],"preliminary":[85],"distributions":[87],"for":[88],"entire":[90],"block,":[91],"then":[93,125],"applies":[94],"lightweight":[96],"head":[98],"refine":[100],"prefix-dependent":[103],"information.":[105],"To":[106],"stabilize":[107],"teacher-forced":[108],"encoding,":[110],"further":[112],"introduce":[113],"base-anchored":[115],"training":[116],"curriculum":[117],"strengthens":[120],"gradually":[126],"shifts":[127],"optimization":[128],"toward":[129],"causally":[131],"corrected":[132],"final":[133],"distribution.":[134],"Experiments":[135],"on":[136],"Qwen3":[137],"models":[138],"show":[139],"achieves":[142],"up":[143,153],"\\(5.49\\times\\)":[145],"end-to-end":[146],"under":[148,158],"Transformers":[150],"backend":[151],"\\(5.8\\times\\)":[155],"throughput":[156],"SGLang":[159],"serving.":[160]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-30T00:00:00"}