{"id":"https://openalex.org/W7165638297","doi":"https://doi.org/10.48550/arxiv.2606.23567","title":"Scheduling Thoughts: Learning the Order of Thought in Diffusion Language Models","display_name":"Scheduling Thoughts: Learning the Order of Thought in Diffusion Language Models","publication_year":2026,"publication_date":"2026-06-22","ids":{"openalex":"https://openalex.org/W7165638297","doi":"https://doi.org/10.48550/arxiv.2606.23567"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.23567","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.23567","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.2606.23567","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139154513","display_name":"Jiawei Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Jiawei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035887801","display_name":"M Y Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Minghui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012625793","display_name":"Aakriti Agrawal","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Agrawal, Aakriti","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139176585","display_name":"Yifan Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Yifan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5082387577","display_name":"F T Huang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Furong","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/T10028","display_name":"Topic Modeling","score":0.2800000011920929,"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":0.2800000011920929,"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":0.07680000364780426,"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":0.06469999998807907,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/scheduling","display_name":"Scheduling (production processes)","score":0.6211000084877014},{"id":"https://openalex.org/keywords/upper-and-lower-bounds","display_name":"Upper and lower bounds","score":0.550599992275238},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.5418999791145325},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.5077000260353088},{"id":"https://openalex.org/keywords/job-shop-scheduling","display_name":"Job shop scheduling","score":0.4697999954223633},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.4526999890804291},{"id":"https://openalex.org/keywords/divergence","display_name":"Divergence (linguistics)","score":0.36250001192092896}],"concepts":[{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.6211000084877014},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6158000230789185},{"id":"https://openalex.org/C77553402","wikidata":"https://www.wikidata.org/wiki/Q13222579","display_name":"Upper and lower bounds","level":2,"score":0.550599992275238},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.5418999791145325},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.5077000260353088},{"id":"https://openalex.org/C55416958","wikidata":"https://www.wikidata.org/wiki/Q6206757","display_name":"Job shop scheduling","level":3,"score":0.4697999954223633},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.4526999890804291},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4514000117778778},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.37299999594688416},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.36559998989105225},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.36250001192092896},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.3594000041484833},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3495999872684479},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.3483999967575073},{"id":"https://openalex.org/C182306322","wikidata":"https://www.wikidata.org/wiki/Q1779371","display_name":"Order (exchange)","level":2,"score":0.33719998598098755},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.3359000086784363},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.30640000104904175},{"id":"https://openalex.org/C37404715","wikidata":"https://www.wikidata.org/wiki/Q380679","display_name":"Dynamic programming","level":2,"score":0.29809999465942383},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.28349998593330383},{"id":"https://openalex.org/C176248197","wikidata":"https://www.wikidata.org/wiki/Q458526","display_name":"Probably approximately correct learning","level":4,"score":0.2531999945640564}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.23567","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.23567","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.2606.23567","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.23567","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","score":0.46567773818969727,"display_name":"Peace, Justice and strong institutions"}],"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],"decode":[4],"by":[5,40],"iteratively":[6],"unmasking":[7,11],"tokens,":[8],"where":[9],"the":[10,35,41,49],"order":[12,70,94],"defines":[13],"an":[14],"\"order":[15],"of":[16,48],"thought\"":[17],"that":[18],"strongly":[19],"influences":[20],"generation":[21,163],"quality":[22],"yet":[23],"is":[24],"typically":[25],"chosen":[26],"heuristically.":[27],"We":[28,82],"derive":[29],"a":[30,62,73,79,92],"tractable":[31],"upper":[32],"bound":[33,60],"on":[34,143,150],"sequential":[36],"decoding":[37],"mismatch,":[38],"measured":[39],"Kullback-Leibler":[42],"divergence":[43],"and":[44,101,107,126,149,165],"expressed":[45],"in":[46],"terms":[47],"model's":[50],"pathwise":[51],"log-likelihood,":[52],"with":[53,78,112,129,138],"tightness":[54],"under":[55],"sufficient":[56],"model":[57],"expressivity.":[58],"This":[59],"induces":[61],"dense":[63],"self-aware":[64],"reward":[65],"over":[66],"ordered":[67],"trajectories,":[68],"casting":[69],"selection":[71],"as":[72,86],"principled":[74],"policy":[75,95],"optimization":[76],"problem":[77],"frozen":[80],"denoiser.":[81],"instantiate":[83],"this":[84],"idea":[85],"Self-Aware":[87],"Scheduling":[88],"(SAS),":[89],"which":[90],"learns":[91],"lightweight":[93],"using":[96],"Group":[97],"Relative":[98],"Policy":[99],"Optimization":[100],"applies":[102],"seamlessly":[103],"to":[104,124,147,154],"both":[105],"any-order":[106],"semi-autoregressive":[108],"decoding.":[109],"On":[110,135],"Sudoku":[111],"1B":[113],"MDM,":[114],"SAS":[115,140],"improves":[116,141],"puzzle":[117],"accuracy":[118],"from":[119,145,152],"82.0%":[120],"(best":[121],"heuristic":[122,160],"schedule)":[123],"91.8%,":[125],"reaches":[127],"97.5%":[128],"second-stage":[130],"fine-tuning":[131],"along":[132],"learned":[133],"trajectories.":[134],"mathematical":[136],"reasoning":[137],"LLaDA-8B,":[139],"pass@1":[142],"GSM8K":[144],"64%":[146],"76%":[148],"MBPP":[151],"39.5%":[153],"41%,":[155],"consistently":[156],"matching":[157],"or":[158],"exceeding":[159],"schedules":[161],"across":[162],"lengths":[164],"block":[166],"sizes.":[167],"Project":[168],"page:":[169],"https://jimmyxu123.github.io/SAS":[170]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-24T00:00:00"}