{"id":"https://openalex.org/W7164013460","doi":"https://doi.org/10.48550/arxiv.2606.08810","title":"Continuous Language Diffusion as a Decoder-Interface Problem","display_name":"Continuous Language Diffusion as a Decoder-Interface Problem","publication_year":2026,"publication_date":"2026-06-07","ids":{"openalex":"https://openalex.org/W7164013460","doi":"https://doi.org/10.48550/arxiv.2606.08810"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.08810","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.08810","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.2606.08810","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138242307","display_name":"Zhicheng Du","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Du, Zhicheng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138230015","display_name":"Lan Ma","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ma, Lan","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":0.19519999623298645,"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":0.19519999623298645,"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":0.15850000083446503,"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/T12090","display_name":"Language and cultural evolution","score":0.1574999988079071,"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/language-model","display_name":"Language model","score":0.6723999977111816},{"id":"https://openalex.org/keywords/perplexity","display_name":"Perplexity","score":0.6191999912261963},{"id":"https://openalex.org/keywords/security-token","display_name":"Security token","score":0.5250999927520752},{"id":"https://openalex.org/keywords/sentence","display_name":"Sentence","score":0.4634999930858612},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.3871000111103058},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.38670000433921814},{"id":"https://openalex.org/keywords/interface","display_name":"Interface (matter)","score":0.3824000060558319},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.36730000376701355},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.3504999876022339}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7059999704360962},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.6723999977111816},{"id":"https://openalex.org/C100279451","wikidata":"https://www.wikidata.org/wiki/Q372193","display_name":"Perplexity","level":3,"score":0.6191999912261963},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.5250999927520752},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.4634999930858612},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3903000056743622},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.3871000111103058},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.38670000433921814},{"id":"https://openalex.org/C113843644","wikidata":"https://www.wikidata.org/wiki/Q901882","display_name":"Interface (matter)","level":4,"score":0.3824000060558319},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.36730000376701355},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.3504999876022339},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.34869998693466187},{"id":"https://openalex.org/C110384440","wikidata":"https://www.wikidata.org/wiki/Q1143270","display_name":"Upsampling","level":3,"score":0.3424000144004822},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.3237999975681305},{"id":"https://openalex.org/C57691317","wikidata":"https://www.wikidata.org/wiki/Q1289248","display_name":"Scalar (mathematics)","level":2,"score":0.31029999256134033},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.30000001192092896},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2964000105857849},{"id":"https://openalex.org/C70777604","wikidata":"https://www.wikidata.org/wiki/Q257885","display_name":"Word order","level":2,"score":0.29490000009536743},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.2906999886035919},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.2872999906539917},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.2849000096321106},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.28139999508857727},{"id":"https://openalex.org/C136625980","wikidata":"https://www.wikidata.org/wiki/Q663208","display_name":"Rounding","level":2,"score":0.27649998664855957},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.27070000767707825},{"id":"https://openalex.org/C102392041","wikidata":"https://www.wikidata.org/wiki/Q592860","display_name":"Sliding window protocol","level":3,"score":0.26989999413490295},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.25690001249313354},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.250900000333786}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.08810","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.08810","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.2606.08810","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.08810","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":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.594682514667511}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Gaussian-corrupted":[0],"sentence":[1],"embeddings":[2],"have":[3],"no":[4],"direct":[5],"linguistic":[6,80],"interpretation,":[7],"yet":[8],"continuous":[9],"diffusion":[10,240],"language":[11,241],"models":[12,242],"can":[13,48,78,84,92],"generate":[14],"fluent":[15],"text":[16],"from":[17],"them.":[18],"We":[19,52],"study":[20],"this":[21],"puzzle":[22],"through":[23],"Embedded":[24],"Language":[25,219],"Flows":[26],"(ELF)":[27],"and":[28,65,88,109,137,169,214,234,238],"identify":[29],"a":[30,54,95,134,141,170,186,194],"decoder-basin":[31],"mechanism:":[32],"our":[33],"evidence":[34],"suggests":[35],"that":[36,223],"denoising":[37,202],"becomes":[38],"reliable":[39],"when":[40,230],"trajectories":[41],"reach":[42],"regions":[43],"where":[44],"the":[45,215,224,231],"native":[46,166],"decoder":[47,63,97,111,143,167,235],"read":[49],"stable":[50],"tokens.":[51],"introduce":[53],"diagnostic":[55,207],"protocol":[56],"for":[57],"denoisability,":[58],"semantic":[59],"recoverability,":[60],"order":[61],"sensitivity,":[62,112],"compatibility,":[64],"trajectory":[66],"reliability.":[67],"It":[68],"exposes":[69],"failures":[70],"hidden":[71],"by":[72],"scalar":[73],"metrics:":[74],"low":[75,82],"mean-squared":[76],"error":[77,115],"discard":[79],"content,":[81],"perplexity":[83,183],"reflect":[85],"low-entropy":[86],"collapse,":[87],"clean":[89],"latent":[90,114,239],"reconstruction":[91],"coexist":[93],"with":[94],"narrow":[96],"basin.":[98,144],"A":[99],"decoder-margin":[100],"bound":[101],"explains":[102],"why":[103],"token":[104,147],"recovery":[105],"depends":[106],"on":[107,152,211],"margin":[108,195],"local":[110],"not":[113],"alone.":[116],"Auditing":[117],"public":[118],"ELF":[119,154],"checkpoints":[120],"reveals":[121],"an":[122,181,205],"interface":[123,226],"phase":[124],"diagram:":[125],"early":[126],"predictions":[127,139],"are":[128],"weakly":[129],"readable,":[130],"mid-trajectory":[131],"disagreement":[132],"marks":[133],"competition":[135],"region,":[136],"late":[138],"enter":[140],"high-margin":[142],"Once":[145],"inside,":[146],"realization":[148],"is":[149],"surprisingly":[150],"simple":[151],"generated":[153],"states:":[155],"frozen":[156],"T5":[157],"(Text-to-Text":[158],"Transfer":[159],"Transformer)":[160],"token-embedding":[161],"lookup":[162],"recovers":[163],"$93$--$96\\%$":[164],"of":[165],"decisions,":[168],"single":[171],"linear":[172],"readout":[173],"reaches":[174],"$97.9\\%$":[175],"agreement":[176],"at":[177],"32k":[178],"samples,":[179],"leaving":[180],"$\\approx1.1$--$1.2$":[182],"gap":[184],"in":[185,201],"structured":[187],"residual":[188],"tail.":[189],"Under":[190],"conservative":[191],"held-out":[192],"gates,":[193],"rule":[196],"exits":[197],"roughly":[198],"$17$--$28\\%$":[199],"earlier":[200],"steps":[203],"under":[204],"explicit":[206],"monitor.":[208],"Boundary":[209],"checks":[210],"LangFlow,":[212],"BitstreamDiffusion,":[213],"Continuous":[216,237],"Latent":[217],"Diffusion":[218],"Model":[220],"(Cola-DLM)":[221],"show":[222],"same":[225],"questions":[227],"remain":[228],"meaningful":[229],"state":[232],"object":[233],"change.":[236],"should":[243],"therefore":[244],"be":[245],"evaluated":[246],"as":[247],"representation-decoder":[248],"systems.":[249]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-10T00:00:00"}
