{"id":"https://openalex.org/W7162804081","doi":"https://doi.org/10.48550/arxiv.2605.29607","title":"Cluster-Level Attention-Guided Parallel Decoding for Masked Diffusion Language Models","display_name":"Cluster-Level Attention-Guided Parallel Decoding for Masked Diffusion Language Models","publication_year":2026,"publication_date":"2026-05-28","ids":{"openalex":"https://openalex.org/W7162804081","doi":"https://doi.org/10.48550/arxiv.2605.29607"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.29607","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.29607","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":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.2605.29607","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137386891","display_name":"Heqiang Qi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qi, Heqiang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137345391","display_name":"Wei Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Wei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081238418","display_name":"Mingyuan Bai","orcid":"https://orcid.org/0000-0002-2454-4219"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bai, Mingyuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137379491","display_name":"Xiangming Meng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Meng, Xiangming","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":0.12870000302791595,"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":0.12870000302791595,"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":0.11349999904632568,"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":0.11249999701976776,"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":0.8425999879837036},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5849000215530396},{"id":"https://openalex.org/keywords/commit","display_name":"Commit","score":0.535099983215332},{"id":"https://openalex.org/keywords/granularity","display_name":"Granularity","score":0.5202000141143799},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.45890000462532043},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.4442000091075897},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.3788999915122986},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.37779998779296875}],"concepts":[{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.8425999879837036},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7210000157356262},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5849000215530396},{"id":"https://openalex.org/C153180980","wikidata":"https://www.wikidata.org/wiki/Q19776675","display_name":"Commit","level":2,"score":0.535099983215332},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.5202000141143799},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4993000030517578},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.45890000462532043},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.4442000091075897},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.3788999915122986},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.37779998779296875},{"id":"https://openalex.org/C75291252","wikidata":"https://www.wikidata.org/wiki/Q1315756","display_name":"TRACE (psycholinguistics)","level":2,"score":0.37439998984336853},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.34790000319480896},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.328000009059906},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.323199987411499},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.3176000118255615},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.31060001254081726},{"id":"https://openalex.org/C2778067643","wikidata":"https://www.wikidata.org/wiki/Q166507","display_name":"Interval (graph theory)","level":2,"score":0.3003000020980835},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.29420000314712524},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.28780001401901245},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.2777999937534332},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.2651999890804291},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.2637999951839447}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.29607","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.29607","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":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.2605.29607","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.29607","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":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":{"Masked":[0],"diffusion":[1],"language":[2],"models":[3],"(MDLMs)":[4],"enable":[5],"parallel":[6,50,95],"decoding":[7,130],"by":[8],"predicting":[9],"all":[10],"masked":[11],"positions":[12,24],"at":[13,27],"each":[14],"denoising":[15],"step,":[16],"yet":[17],"existing":[18],"training-free":[19,104],"samplers":[20],"usually":[21],"decide":[22],"which":[23],"to":[25,83],"commit":[26],"token-level":[28],"granularity.":[29],"We":[30,59,73],"revisit":[31],"this":[32],"granularity":[33],"and":[34,112,119],"observe":[35],"that":[36,46,123],"reliable":[37],"predictions":[38],"often":[39],"emerge":[40],"as":[41,69],"contiguous":[42],"high-confidence":[43,63],"spans,":[44],"suggesting":[45],"the":[47,79],"unit":[48],"of":[49,90],"commitment":[51],"can":[52],"be":[53],"larger":[54],"than":[55],"a":[56,103],"single":[57],"token.":[58],"first":[60],"group":[61],"adjacent":[62],"candidates":[64],"into":[65],"confidence-induced":[66],"clusters":[67],"(CICs)":[68],"span-level":[70],"update":[71],"units.":[72],"then":[74],"use":[75],"self-attention":[76],"maps":[77],"from":[78],"same":[80],"forward":[81],"pass":[82],"estimate":[84],"inter-cluster":[85],"dependencies,":[86],"enabling":[87],"conflict-aware":[88],"selection":[89],"mutually":[91],"compatible":[92],"CICs":[93],"for":[94,107],"commitment.":[96],"This":[97],"yields":[98],"CLAD":[99,124],"(Cluster-Level":[100],"Attention-Guided":[101],"Decoding),":[102],"cluster-level":[105],"decoder":[106],"MDLMs.":[108],"Experiments":[109],"on":[110],"LLaDA":[111],"Dream":[113],"model":[114],"families":[115],"across":[116],"four":[117],"reasoning":[118],"code-generation":[120],"benchmarks":[121],"show":[122],"achieves":[125],"1.77x--8.47x":[126],"speedups":[127],"over":[128],"Vanilla":[129],"while":[131],"maintaining":[132],"broadly":[133],"comparable":[134],"task":[135],"accuracy":[136],"in":[137],"most":[138],"settings.":[139]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-30T00:00:00"}