{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:ZFQOI4GSJT4FKRMV4R6IILLUUY","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"1ba66e1f57a46f762df602a8d503d23fc3fbe9a9c30d2d8bd694cdefbfb792a0","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-25T00:52:44Z","title_canon_sha256":"bb7c8dfb25c57555ff7ffa280aadeec1ed1e3cc2f70426f0536c1fa8ebf790bb"},"schema_version":"1.0","source":{"id":"2606.26493","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.26493","created_at":"2026-06-26T01:15:32Z"},{"alias_kind":"arxiv_version","alias_value":"2606.26493v1","created_at":"2026-06-26T01:15:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.26493","created_at":"2026-06-26T01:15:32Z"},{"alias_kind":"pith_short_12","alias_value":"ZFQOI4GSJT4F","created_at":"2026-06-26T01:15:32Z"},{"alias_kind":"pith_short_16","alias_value":"ZFQOI4GSJT4FKRMV","created_at":"2026-06-26T01:15:32Z"},{"alias_kind":"pith_short_8","alias_value":"ZFQOI4GS","created_at":"2026-06-26T01:15:32Z"}],"graph_snapshots":[{"event_id":"sha256:55534b2eb063a65e1c8b1df826706af0dba4d15ca1683a7e462bd8a043612113","target":"graph","created_at":"2026-06-26T01:15:32Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2606.26493/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Diffusion language models offer a promising alternative to autoregressive models due to their potential for parallel and iterative generation. However, existing approaches use a single network for both context representation and iterative denoising, forcing one model to serve both roles and limiting its capacity for either role. We propose TwoTower, a block-wise autoregressive diffusion model that decouples these roles into two towers: a frozen AR context tower that causally processes clean tokens, and a trainable diffusion denoiser tower with bidirectional block attention that refines noisy b","authors_text":"Bryan Catanzaro, Fitsum Reda, John Kamalu, Mohammad Shoeybi, Mostofa Patwary, Roger Waleffe","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-25T00:52:44Z","title":"Nemotron-TwoTower: Diffusion Language Modeling with Pretrained Autoregressive Context"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.26493","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:dad29720eae519ee44b4a8e724d2db4e72f205ef054b501875493a373befd916","target":"record","created_at":"2026-06-26T01:15:32Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"1ba66e1f57a46f762df602a8d503d23fc3fbe9a9c30d2d8bd694cdefbfb792a0","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-25T00:52:44Z","title_canon_sha256":"bb7c8dfb25c57555ff7ffa280aadeec1ed1e3cc2f70426f0536c1fa8ebf790bb"},"schema_version":"1.0","source":{"id":"2606.26493","kind":"arxiv","version":1}},"canonical_sha256":"c960e470d24cf8554595e47c842d74a63658c2f4cafcb664575ed67d1f5eeb30","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c960e470d24cf8554595e47c842d74a63658c2f4cafcb664575ed67d1f5eeb30","first_computed_at":"2026-06-26T01:15:32.711133Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-26T01:15:32.711133Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Xmgto7Q4N7n+i+22DDChyh6Jrh7dsDroKYmsLPK7HxhgJV8rRgzIdSgoKVE2fFomn7EKqI2CqHPyi/2x9qRuCQ==","signature_status":"signed_v1","signed_at":"2026-06-26T01:15:32.711484Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.26493","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dad29720eae519ee44b4a8e724d2db4e72f205ef054b501875493a373befd916","sha256:55534b2eb063a65e1c8b1df826706af0dba4d15ca1683a7e462bd8a043612113"],"state_sha256":"48c44702f33e07994b15ee722bdd8d1315af63edecd67fc3dbb69dadb90304ea"}