{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:ZMTC7JVNBNTHUN33IQBXZY55QA","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":"21643f02819c8f7660d8b91db09a89c17da1b6e8860cb2a938998d36f92d7dd5","cross_cats_sorted":["cs.LG","math.ST","stat.AP","stat.ME","stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-08-05T16:51:29Z","title_canon_sha256":"b7dfa49c45cc0b7d0c487d85a32c60c6603d47ea67a537edbf38d2b7b318d417"},"schema_version":"1.0","source":{"id":"2508.03636","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.03636","created_at":"2026-07-14T01:19:54Z"},{"alias_kind":"arxiv_version","alias_value":"2508.03636v3","created_at":"2026-07-14T01:19:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.03636","created_at":"2026-07-14T01:19:54Z"},{"alias_kind":"pith_short_12","alias_value":"ZMTC7JVNBNTH","created_at":"2026-07-14T01:19:54Z"},{"alias_kind":"pith_short_16","alias_value":"ZMTC7JVNBNTHUN33","created_at":"2026-07-14T01:19:54Z"},{"alias_kind":"pith_short_8","alias_value":"ZMTC7JVN","created_at":"2026-07-14T01:19:54Z"}],"graph_snapshots":[{"event_id":"sha256:7ca21d2c32ce04a606ed79495094eebac09319ef7bc918937ca85442a98e4415","target":"graph","created_at":"2026-07-14T01:19:54Z","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/2508.03636/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We propose a Likelihood Matching approach for training diffusion models by first establishing an equivalence between the likelihood of the target data distribution and a likelihood along the sample path of the reverse diffusion. To efficiently compute the reverse sample likelihood, a quasi-likelihood is considered to approximate each reverse transition density by a Gaussian distribution with matched conditional mean and covariance, respectively. The score and Hessian functions for the diffusion generation are estimated by maximizing the quasi-likelihood, ensuring a consistent matching of both ","authors_text":"Lei Qian, Song Xi Chen, Wu Su, Yanqi Huang","cross_cats":["cs.LG","math.ST","stat.AP","stat.ME","stat.TH"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-08-05T16:51:29Z","title":"Likelihood Matching for Diffusion Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.03636","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:e43012daf1553d5b2248eb4ea7260bf7d6f715e8e6ad9840a6d23d0ddf1f034a","target":"record","created_at":"2026-07-14T01:19:54Z","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":"21643f02819c8f7660d8b91db09a89c17da1b6e8860cb2a938998d36f92d7dd5","cross_cats_sorted":["cs.LG","math.ST","stat.AP","stat.ME","stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-08-05T16:51:29Z","title_canon_sha256":"b7dfa49c45cc0b7d0c487d85a32c60c6603d47ea67a537edbf38d2b7b318d417"},"schema_version":"1.0","source":{"id":"2508.03636","kind":"arxiv","version":3}},"canonical_sha256":"cb262fa6ad0b667a377b44037ce3bd803981bc49514b20fd7aad1291ed1c7f93","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cb262fa6ad0b667a377b44037ce3bd803981bc49514b20fd7aad1291ed1c7f93","first_computed_at":"2026-07-14T01:19:54.328162Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-14T01:19:54.328162Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5qHCqNHJhnh1n57fSbDo+N0tehpMglg+MFrivIcmt4merJEk///C5BDVMItDX3VRphp11mlb3fLS4/1QfWLnAg==","signature_status":"signed_v1","signed_at":"2026-07-14T01:19:54.329220Z","signed_message":"canonical_sha256_bytes"},"source_id":"2508.03636","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e43012daf1553d5b2248eb4ea7260bf7d6f715e8e6ad9840a6d23d0ddf1f034a","sha256:7ca21d2c32ce04a606ed79495094eebac09319ef7bc918937ca85442a98e4415"],"state_sha256":"f964793bf3b56aa50f1fb823d24be12d27576ca9ef2c80810425db4f40b6752e"}