{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:KT6DH7YVCZ4ZQVUP3ZFMZPAE63","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":"adeb911dde0d7deded8b3683d810f679e52ae234ae47a78877372e8f09dddeb9","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-04T16:59:03Z","title_canon_sha256":"611d80926fb0ec210ff3aa9d034a3ae06cbf1ec6b7fda7c37b48b6a4bb07287d"},"schema_version":"1.0","source":{"id":"2502.02483","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.02483","created_at":"2026-07-05T11:33:03Z"},{"alias_kind":"arxiv_version","alias_value":"2502.02483v3","created_at":"2026-07-05T11:33:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.02483","created_at":"2026-07-05T11:33:03Z"},{"alias_kind":"pith_short_12","alias_value":"KT6DH7YVCZ4Z","created_at":"2026-07-05T11:33:03Z"},{"alias_kind":"pith_short_16","alias_value":"KT6DH7YVCZ4ZQVUP","created_at":"2026-07-05T11:33:03Z"},{"alias_kind":"pith_short_8","alias_value":"KT6DH7YV","created_at":"2026-07-05T11:33:03Z"}],"graph_snapshots":[{"event_id":"sha256:fef41cdaeec0b296a1f53615e3db1115e39c4036c52c6f945d80aa8cf8f115c5","target":"graph","created_at":"2026-07-05T11:33:03Z","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/2502.02483/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Diffusion models generate high-quality synthetic data. They operate by defining a continuous-time forward process which gradually adds Gaussian noise to data until fully corrupted. The corresponding reverse process progressively \"denoises\" a Gaussian sample into a sample from the data distribution. However, generating high-quality outputs requires many discretization steps to obtain a faithful approximation of the reverse process. This is expensive and has motivated the development of many acceleration methods. We propose to accomplish sample generation by learning the posterior {\\em distribut","authors_text":"Alexandre Galashov, Arnaud Doucet, Arthur Gretton, Guangyao Zhou, J. Swaroop Guntupalli, Kevin Murphy, Valentin De Bortoli","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-04T16:59:03Z","title":"Distributional Diffusion Models with Scoring Rules"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.02483","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":"sha256:3c41e0fe49f864dbfa9f3a36ba5b2b88ab7f8ab7fc2e30629f4550f738bdf0b9","target":"record","created_at":"2026-07-05T11:33:03Z","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":"adeb911dde0d7deded8b3683d810f679e52ae234ae47a78877372e8f09dddeb9","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-02-04T16:59:03Z","title_canon_sha256":"611d80926fb0ec210ff3aa9d034a3ae06cbf1ec6b7fda7c37b48b6a4bb07287d"},"schema_version":"1.0","source":{"id":"2502.02483","kind":"arxiv","version":3}},"canonical_sha256":"54fc33ff15167998568fde4accbc04f6d28da4521a8cdf11f91ac7d5f85dcde7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"54fc33ff15167998568fde4accbc04f6d28da4521a8cdf11f91ac7d5f85dcde7","first_computed_at":"2026-07-05T11:33:03.630888Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:33:03.630888Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"GMZbT0Jw9M+MqkuRU5iYjK/KNVfH4uRp6dADs77cDXsPSU/WmkE8rHYtsXSoyyfplh6CvSzxxaWdv2dhgT2FBA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:33:03.631394Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.02483","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3c41e0fe49f864dbfa9f3a36ba5b2b88ab7f8ab7fc2e30629f4550f738bdf0b9","sha256:fef41cdaeec0b296a1f53615e3db1115e39c4036c52c6f945d80aa8cf8f115c5"],"state_sha256":"a1f96097676f8622f2293e55261d8c448e68df142512bd6d9f7cc3aaebe38e6a"}