{"id":"https://openalex.org/W7166021934","doi":"https://doi.org/10.48550/arxiv.2606.26493","title":"Nemotron-Labs-TwoTower: Diffusion Language Modeling with Pretrained Autoregressive Context","display_name":"Nemotron-Labs-TwoTower: Diffusion Language Modeling with Pretrained Autoregressive Context","publication_year":2026,"publication_date":"2026-06-25","ids":{"openalex":"https://openalex.org/W7166021934","doi":"https://doi.org/10.48550/arxiv.2606.26493"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.26493","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.26493","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.2606.26493","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139445745","display_name":"Fitsum Reda","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Reda, Fitsum","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047315375","display_name":"John Kamalu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kamalu, John","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039817377","display_name":"Roger Waleffe","orcid":"https://orcid.org/0000-0002-3795-4997"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Waleffe, Roger","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139416899","display_name":"Mostofa Patwary","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Patwary, Mostofa","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139396017","display_name":"Mohammad Shoeybi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shoeybi, Mohammad","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139404230","display_name":"Bryan Catanzaro","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Catanzaro, Bryan","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":0.2475000023841858,"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":0.2475000023841858,"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/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.15970000624656677,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"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":0.08649999648332596,"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/autoregressive-model","display_name":"Autoregressive model","score":0.867900013923645},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.660099983215332},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.5238000154495239},{"id":"https://openalex.org/keywords/star-model","display_name":"STAR model","score":0.5012999773025513},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.49630001187324524},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.47040000557899475},{"id":"https://openalex.org/keywords/diffusion","display_name":"Diffusion","score":0.3962000012397766},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.38839998841285706},{"id":"https://openalex.org/keywords/limiting","display_name":"Limiting","score":0.3725999891757965}],"concepts":[{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.867900013923645},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6984000205993652},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.660099983215332},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.5238000154495239},{"id":"https://openalex.org/C194657046","wikidata":"https://www.wikidata.org/wiki/Q7394685","display_name":"STAR model","level":4,"score":0.5012999773025513},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.49630001187324524},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.47040000557899475},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.460999995470047},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4408999979496002},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.3962000012397766},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.38839998841285706},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.3725999891757965},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.351500004529953},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.31049999594688416},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.30709999799728394},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.302700012922287},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.30250000953674316},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.29179999232292175},{"id":"https://openalex.org/C2777831296","wikidata":"https://www.wikidata.org/wiki/Q12518","display_name":"Tower","level":2,"score":0.28439998626708984},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.28029999136924744},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.272599995136261},{"id":"https://openalex.org/C42536954","wikidata":"https://www.wikidata.org/wiki/Q7049462","display_name":"Nonlinear autoregressive exogenous model","level":3,"score":0.2662999927997589},{"id":"https://openalex.org/C30795276","wikidata":"https://www.wikidata.org/wiki/Q7389877","display_name":"SETAR","level":5,"score":0.2590999901294708},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.25690001249313354},{"id":"https://openalex.org/C197115733","wikidata":"https://www.wikidata.org/wiki/Q1003136","display_name":"Forcing (mathematics)","level":2,"score":0.2549999952316284},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.26493","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.26493","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.2606.26493","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.26493","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":[],"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":[2,9],"offer":[3],"a":[4,23,50,62,73],"promising":[5],"alternative":[6],"to":[7,11,36,88],"autoregressive":[8,52,112],"due":[10],"their":[12],"potential":[13],"for":[14,26,44],"parallel":[15],"and":[16,30,40,72,101,126],"iterative":[17,31],"generation.":[18],"However,":[19],"existing":[20],"approaches":[21],"use":[22],"single":[24],"network":[25],"both":[27,38],"context":[28,65],"representation":[29],"denoising,":[32],"forcing":[33],"one":[34],"model":[35,54,127],"serve":[37],"roles":[39,58],"limiting":[41],"its":[42],"capacity":[43],"either":[45],"role.":[46],"We":[47,122],"propose":[48],"TwoTower,":[49],"block-wise":[51],"diffusion":[53,75],"that":[55,67,82],"decouples":[56],"these":[57],"into":[59],"two":[60],"towers:":[61],"frozen":[63],"AR":[64],"tower":[66,77],"causally":[68],"processes":[69],"clean":[70],"tokens,":[71,106],"trainable":[74],"denoiser":[76],"with":[78],"bidirectional":[79],"block":[80],"attention":[81],"refines":[83],"noisy":[84],"blocks":[85],"via":[86],"cross-attention":[87],"the":[89,111,124],"context.":[90],"Built":[91],"on":[92,103],"Nemotron-3-Nano-30B-A3B,":[93],"an":[94],"open-weight":[95],"30B":[96],"hybrid":[97],"Mamba-Transformer":[98],"MoE":[99],"model,":[100],"trained":[102],"approximately":[104],"2.1T":[105],"Nemotron-Labs-TwoTower":[107],"retains":[108],"98.7%":[109],"of":[110],"baseline's":[113],"quality":[114],"while":[115],"offering":[116],"2.42X":[117],"higher":[118],"wall-clock":[119],"generation":[120],"throughput.":[121],"release":[123],"code":[125],"weights":[128],"at":[129],"https://huggingface.co/collections/nvidia/nemotron-labs-twotower.":[130]},"counts_by_year":[],"updated_date":"2026-07-02T06:12:58.138171","created_date":"2026-06-27T00:00:00"}
