{"id":"https://openalex.org/W7161832888","doi":"https://doi.org/10.48550/arxiv.2605.18817","title":"Multi-Token Residual Prediction","display_name":"Multi-Token Residual Prediction","publication_year":2026,"publication_date":"2026-05-12","ids":{"openalex":"https://openalex.org/W7161832888","doi":"https://doi.org/10.48550/arxiv.2605.18817"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.18817","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.18817","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":null,"license_id":null,"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.2605.18817","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136522638","display_name":"Yufeng Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Yufeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124912894","display_name":"Zishuo Bao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bao, Zishuo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136608551","display_name":"Qian Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Qian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136555159","display_name":"Zeshen Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Zeshen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013414457","display_name":"Haoqi Zhang","orcid":"https://orcid.org/0000-0003-3363-7545"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Haoqi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061586815","display_name":"Bowen Peng","orcid":"https://orcid.org/0009-0005-8259-8716"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Peng, Bowen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136589638","display_name":"Ang Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Ang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136568284","display_name":"Rahul Chalamala","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chalamala, Rahul","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136534903","display_name":"Yucheng Lu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Yucheng","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":0.2833999991416931,"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":0.2833999991416931,"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":0.1868000030517578,"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.08500000089406967,"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/residual","display_name":"Residual","score":0.7299000024795532},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.5888000130653381},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.5785999894142151},{"id":"https://openalex.org/keywords/lossless-compression","display_name":"Lossless compression","score":0.5576000213623047},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.5289000272750854},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.49140000343322754},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.45419999957084656},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.43639999628067017},{"id":"https://openalex.org/keywords/discriminator","display_name":"Discriminator","score":0.4092999994754791}],"concepts":[{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.7299000024795532},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6722999811172485},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.5888000130653381},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.5785999894142151},{"id":"https://openalex.org/C81081738","wikidata":"https://www.wikidata.org/wiki/Q55542","display_name":"Lossless compression","level":3,"score":0.5576000213623047},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5404000282287598},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.5289000272750854},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.49140000343322754},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.45419999957084656},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.43639999628067017},{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.4092999994754791},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.3928999900817871},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.3889000117778778},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.38429999351501465},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.3767000138759613},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3479999899864197},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34360000491142273},{"id":"https://openalex.org/C149629883","wikidata":"https://www.wikidata.org/wiki/Q660926","display_name":"Fraction (chemistry)","level":2,"score":0.3334999978542328},{"id":"https://openalex.org/C72169020","wikidata":"https://www.wikidata.org/wiki/Q194404","display_name":"Monotonic function","level":2,"score":0.31029999256134033},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.3075999915599823},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.29899999499320984},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.2799000144004822},{"id":"https://openalex.org/C2983327147","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Image denoising","level":3,"score":0.2782999873161316},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2705000042915344},{"id":"https://openalex.org/C75291252","wikidata":"https://www.wikidata.org/wiki/Q1315756","display_name":"TRACE (psycholinguistics)","level":2,"score":0.2694999873638153},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.2587999999523163},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.2554999887943268}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.18817","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.18817","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.18817","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.18817","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":null,"license_id":null,"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":{"Diffusion":[0],"Language":[1],"Models":[2],"(DLMs)":[3],"generate":[4],"text":[5],"by":[6,35,196],"iteratively":[7],"denoising":[8,63,77,84,114,141,171],"masked":[9],"token":[10],"sequences,":[11],"offering":[12],"a":[13,36,56,65,72,94,121,146,176],"tradeoff":[14],"between":[15,106],"parallelism":[16],"and":[17,39,193,206],"quality":[18,40],"compared":[19],"to":[20,97,163,189,198],"autoregressive":[21],"models.":[22],"In":[23,137,167],"current":[24],"practice,":[25],"the":[26,76,79,92,99,104,109,124,130,138,156,168,186],"number":[27],"of":[28,75,123,134,161,185],"tokens":[29,45,116],"decoded":[30],"per":[31,48,117],"step":[32],"is":[33],"controlled":[34],"confidence":[37],"threshold,":[38],"degrades":[41],"monotonically":[42],"as":[43,145],"more":[44,115],"are":[46,86,153],"denoised":[47],"step.":[49],"We":[50,126],"introduce":[51],"Multi-token":[52],"Residual":[53],"Prediction":[54],"(MRP),":[55],"lightweight":[57],"module":[58],"that":[59,179],"enables":[60],"dependency-aware":[61],"multi-token":[62],"within":[64],"single":[66],"backbone":[67,93,118],"forward":[68,119],"pass.":[69],"MRP":[70,102,128,143,173],"exploits":[71],"key":[73],"property":[74],"process:":[78],"logit":[80],"distributions":[81],"at":[82,120],"adjacent":[83],"steps":[85,107],"remarkably":[87],"similar.":[88],"Rather":[89],"than":[90],"running":[91],"second":[95],"time":[96],"obtain":[98],"next-step":[100],"logits,":[101],"predicts":[103],"residual":[105],"from":[108],"backbone's":[110],"hidden":[111],"states,":[112],"effectively":[113],"fraction":[122],"cost.":[125],"apply":[127],"across":[129],"two":[131],"operating":[132],"regimes":[133],"DLM":[135],"decoding.":[136],"high-quality-low-throughput":[139],"static":[140],"regime,":[142,172],"serves":[144],"drafter":[147],"for":[148],"speculative":[149],"decoding:":[150],"its":[151],"proposals":[152],"verified":[154],"against":[155],"backbone,":[157],"yielding":[158],"lossless":[159],"acceleration":[160],"up":[162,197],"1.4x":[164],"in":[165],"SGLang.":[166],"low-quality-high-throughput":[169],"dynamic":[170],"instead":[174],"drives":[175],"remasking":[177],"scheme":[178],"revokes":[180],"over-eager":[181],"reveals,":[182],"recovering":[183],"most":[184],"accuracy":[187,195],"lost":[188],"aggressive":[190],"low-threshold":[191],"decoding":[192],"improving":[194],"22.6":[199],"points":[200,208],"on":[201,209],"code":[202],"generation":[203],"task":[204,211],"HumanEval":[205],"17.7":[207],"reasoning":[210],"GSM8K.":[212]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-21T00:00:00"}