{"id":"https://openalex.org/W7164202783","doi":"https://doi.org/10.48550/arxiv.2606.10829","title":"Attention-Discounted Adaptive Sampler for Masked Diffusion Language Models","display_name":"Attention-Discounted Adaptive Sampler for Masked Diffusion Language Models","publication_year":2026,"publication_date":"2026-06-09","ids":{"openalex":"https://openalex.org/W7164202783","doi":"https://doi.org/10.48550/arxiv.2606.10829"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.10829","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.10829","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.2606.10829","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138392352","display_name":"Yusuf Sahin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sahin, Yusuf","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138325087","display_name":"Ahmed Rockey Saikia","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Saikia, Ahmed Rockey","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138296818","display_name":"Volkan Cevher","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cevher, Volkan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138349237","display_name":"Paolo Favaro","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Favaro, Paolo","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":0.19329999387264252,"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":0.19329999387264252,"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/T12090","display_name":"Language and cultural evolution","score":0.11569999903440475,"subfield":{"id":"https://openalex.org/subfields/3316","display_name":"Cultural Studies"},"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/T10201","display_name":"Speech Recognition and Synthesis","score":0.09390000253915787,"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/language-model","display_name":"Language model","score":0.520799994468689},{"id":"https://openalex.org/keywords/commit","display_name":"Commit","score":0.5130000114440918},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5041000247001648},{"id":"https://openalex.org/keywords/modular-design","display_name":"Modular design","score":0.48899999260902405},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.4302000105381012},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.28760001063346863},{"id":"https://openalex.org/keywords/modularity","display_name":"Modularity (biology)","score":0.2827000021934509}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7763000130653381},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.520799994468689},{"id":"https://openalex.org/C153180980","wikidata":"https://www.wikidata.org/wiki/Q19776675","display_name":"Commit","level":2,"score":0.5130000114440918},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5041000247001648},{"id":"https://openalex.org/C101468663","wikidata":"https://www.wikidata.org/wiki/Q1620158","display_name":"Modular design","level":2,"score":0.48899999260902405},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.4302000105381012},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.39070001244544983},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37630000710487366},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.30809998512268066},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3057999908924103},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.28760001063346863},{"id":"https://openalex.org/C2779478453","wikidata":"https://www.wikidata.org/wiki/Q6889748","display_name":"Modularity (biology)","level":2,"score":0.2827000021934509},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2818000018596649},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.2793000042438507},{"id":"https://openalex.org/C42058472","wikidata":"https://www.wikidata.org/wiki/Q810214","display_name":"Base (topology)","level":2,"score":0.27880001068115234},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.27880001068115234},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2727000117301941},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.2687999904155731},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.262800008058548},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2590000033378601}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.10829","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.10829","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.2606.10829","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.10829","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":[{"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions","score":0.43574953079223633}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Masked":[0],"diffusion":[1,76,190],"language":[2,191],"models":[3],"can":[4],"reduce":[5],"inference":[6],"steps":[7],"by":[8,56,154],"revealing":[9],"multiple":[10],"tokens":[11,49],"per":[12],"denoising":[13],"iteration,":[14],"but":[15],"this":[16],"parallelism":[17],"is":[18,175],"fragile:":[19],"positions":[20,103],"that":[21,59,111,171],"are":[22,34],"individually":[23],"confident":[24],"may":[25],"be":[26],"unsafe":[27],"to":[28,50,100,181],"commit":[29],"together":[30],"when":[31,96],"their":[32],"predictions":[33,105],"coupled.":[35],"Existing":[36],"training-free":[37,70],"samplers":[38],"such":[39],"as":[40,125],"Top-\\(k\\),":[41,143],"Fast-dLLM,":[42,144],"and":[43,86,122,132,138,145,156,178],"EB-Sampler":[44,146],"mainly":[45],"control":[46],"how":[47],"many":[48],"reveal,":[51],"while":[52],"often":[53],"ranking":[54],"candidates":[55],"token-wise":[57],"scores":[58],"ignore":[60],"interactions":[61],"within":[62],"the":[63,80],"selected":[64,102],"set.":[65],"We":[66],"propose":[67],"ADAS,":[68],"a":[69,94,126,176],"reranking":[71,174],"rule":[72,84],"for":[73,188],"parallel":[74,186],"masked":[75,189],"decoding.":[77],"ADAS":[78,118,141],"leaves":[79],"base":[81],"sampler's":[82],"stopping":[83],"unchanged":[85],"modifies":[87],"only":[88],"subset":[89],"construction:":[90],"it":[91,97,124],"greedily":[92],"discounts":[93],"candidate":[95],"attends":[98],"strongly":[99],"already":[101],"whose":[104],"remain":[106],"uncertain.":[107],"Unlike":[108],"graph-constrained":[109],"methods":[110],"turn":[112],"attention":[113,120],"into":[114,142],"hard":[115],"compatibility":[116],"constraints,":[117],"keeps":[119],"continuous":[121],"uses":[123],"soft":[127,172],"marginal":[128],"penalty.":[129],"Across":[130],"LLaDA-8B-Base":[131],"Dream-7B-Base":[133],"on":[134,160],"GSM8K,":[135],"MATH500,":[136],"HumanEval,":[137],"MBPP,":[139],"plugging":[140],"improves":[147],"low-NFE":[148],"performance":[149],"at":[150],"matched":[151],"denoiser":[152],"evaluations":[153],"\\(9.11\\)":[155],"\\(10.46\\)":[157],"percentage":[158],"points":[159],"average,":[161],"respectively,":[162],"with":[163],"\\(3.1\\%\\)":[164],"per-forward":[165],"runtime":[166],"overhead.":[167],"These":[168],"results":[169],"show":[170],"attention-discounted":[173],"simple":[177],"modular":[179],"way":[180],"improve":[182],"quality":[183],"in":[184],"highly":[185],"decoding":[187],"models.":[192]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-11T00:00:00"}