{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:GX4VJYB6NTWWRSZHB3D2IJX3BR","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":"f616183ef9a7c8dc88ab325f2931e286ce47d207d60ddb9a4a01072d2a5b562d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-10-23T14:04:22Z","title_canon_sha256":"0cc86a6e95746ea64005aacb09cc092c1083a7c7f4a4f4096242afa4aef901f4"},"schema_version":"1.0","source":{"id":"2410.17891","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.17891","created_at":"2026-05-20T19:53:53Z"},{"alias_kind":"arxiv_version","alias_value":"2410.17891v3","created_at":"2026-05-20T19:53:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.17891","created_at":"2026-05-20T19:53:53Z"},{"alias_kind":"pith_short_12","alias_value":"GX4VJYB6NTWW","created_at":"2026-05-20T19:53:53Z"},{"alias_kind":"pith_short_16","alias_value":"GX4VJYB6NTWWRSZH","created_at":"2026-05-20T19:53:53Z"},{"alias_kind":"pith_short_8","alias_value":"GX4VJYB6","created_at":"2026-05-20T19:53:53Z"}],"graph_snapshots":[{"event_id":"sha256:666df5a50f26837dbd01dc25e9e2f50072c5a1d6695568c992e60cb3e0fa6de5","target":"graph","created_at":"2026-05-20T19:53:53Z","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/2410.17891/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Diffusion Language Models (DLMs) have emerged as a promising new paradigm for text generative modeling, potentially addressing limitations of autoregressive (AR) models. However, current DLMs have been studied at a smaller scale compared to their AR counterparts and lack fair comparison on language modeling benchmarks. Additionally, training diffusion models from scratch at scale remains challenging. Given the prevalence of open-source AR language models, we propose adapting these models to build text diffusion models. We demonstrate connections between AR and diffusion modeling objectives and","authors_text":"Chenxin An, Hao Peng, Jiacheng Ye, Jiawei Han, Lingpeng Kong, Lin Zheng, Mukai Li, Peilin Zhao, Shansan Gong, Shivam Agarwal, Wei Bi, Yizhe Zhang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-10-23T14:04:22Z","title":"Scaling Diffusion Language Models via Adaptation from Autoregressive Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.17891","kind":"arxiv","version":3},"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:f8efe88b54f641de3e3467ff9e2d7594af0f25efced9898e1c4763fcbb214ba5","target":"record","created_at":"2026-05-20T19:53:53Z","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":"f616183ef9a7c8dc88ab325f2931e286ce47d207d60ddb9a4a01072d2a5b562d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-10-23T14:04:22Z","title_canon_sha256":"0cc86a6e95746ea64005aacb09cc092c1083a7c7f4a4f4096242afa4aef901f4"},"schema_version":"1.0","source":{"id":"2410.17891","kind":"arxiv","version":3}},"canonical_sha256":"35f954e03e6ced68cb270ec7a426fb0c59f0903864928926c5b107f0badcd252","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"35f954e03e6ced68cb270ec7a426fb0c59f0903864928926c5b107f0badcd252","first_computed_at":"2026-05-20T19:53:53.668724Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T19:53:53.668724Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"tP0diijiBpvlWRRgOBFUB9Qi8H7JkXKFnVC+L7Q13mJJ0gQp++9eYpFk1BbcdKS+MzUVa5GalrsCFceEOSzuCA==","signature_status":"signed_v1","signed_at":"2026-05-20T19:53:53.670822Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.17891","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f8efe88b54f641de3e3467ff9e2d7594af0f25efced9898e1c4763fcbb214ba5","sha256:666df5a50f26837dbd01dc25e9e2f50072c5a1d6695568c992e60cb3e0fa6de5"],"state_sha256":"fb7dc4fc375d91483f7153ad9cc3825a016beb010e7131946dd8a540abe207a3"}