{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:347RP2A25ZWSRVJK4QPMTBLS5P","short_pith_number":"pith:347RP2A2","canonical_record":{"source":{"id":"2504.05304","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-04-07T17:59:42Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"0a6b88e1e4924d555bd311a15f77d1fd4a32bd64f73e96bb9727f5001dea0de7","abstract_canon_sha256":"442170f1bb887d345701d6743059779f0d860cc1048b5c31dfc24852f40709b0"},"schema_version":"1.0"},"canonical_sha256":"df3f17e81aee6d28d52ae41ec98572ebfd659ba30c8372a3af97aef71c134534","source":{"kind":"arxiv","id":"2504.05304","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.05304","created_at":"2026-07-05T12:02:00Z"},{"alias_kind":"arxiv_version","alias_value":"2504.05304v3","created_at":"2026-07-05T12:02:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.05304","created_at":"2026-07-05T12:02:00Z"},{"alias_kind":"pith_short_12","alias_value":"347RP2A25ZWS","created_at":"2026-07-05T12:02:00Z"},{"alias_kind":"pith_short_16","alias_value":"347RP2A25ZWSRVJK","created_at":"2026-07-05T12:02:00Z"},{"alias_kind":"pith_short_8","alias_value":"347RP2A2","created_at":"2026-07-05T12:02:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:347RP2A25ZWSRVJK4QPMTBLS5P","target":"record","payload":{"canonical_record":{"source":{"id":"2504.05304","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-04-07T17:59:42Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"0a6b88e1e4924d555bd311a15f77d1fd4a32bd64f73e96bb9727f5001dea0de7","abstract_canon_sha256":"442170f1bb887d345701d6743059779f0d860cc1048b5c31dfc24852f40709b0"},"schema_version":"1.0"},"canonical_sha256":"df3f17e81aee6d28d52ae41ec98572ebfd659ba30c8372a3af97aef71c134534","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:02:00.355378Z","signature_b64":"fouVd8IY6TwZc63koNiXL1J8dCWo4o9wqxFsM5tsXf+spi04B+MJIgqQjUNKG5nLK1+ZBjccaIV/3fwNhEMjAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"df3f17e81aee6d28d52ae41ec98572ebfd659ba30c8372a3af97aef71c134534","last_reissued_at":"2026-07-05T12:02:00.354868Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:02:00.354868Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2504.05304","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T12:02:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0BFPM+2zg4e0rq6dMT2/8AGtkWqra4sjXRfEfUYpsizEIVbwOXryNLuyTkGC3X0nZb+ijV68GoFKJ+bDVthADg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T06:49:45.109488Z"},"content_sha256":"4502659811dd53d5c8c94ea823ca8ef5337a2c63e214b6d089efe558da8b0fbb","schema_version":"1.0","event_id":"sha256:4502659811dd53d5c8c94ea823ca8ef5337a2c63e214b6d089efe558da8b0fbb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:347RP2A25ZWSRVJK4QPMTBLS5P","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Gaussian Mixture Flow Matching Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Fujun Luan, Gordon Wetzstein, Hansheng Chen, Hao Tan, Kai Zhang, Leonidas Guibas, Sai Bi, Zexiang Xu","submitted_at":"2025-04-07T17:59:42Z","abstract_excerpt":"Diffusion models approximate the denoising distribution as a Gaussian and predict its mean, whereas flow matching models reparameterize the Gaussian mean as flow velocity. However, they underperform in few-step sampling due to discretization error and tend to produce over-saturated colors under classifier-free guidance (CFG). To address these limitations, we propose a novel Gaussian mixture flow matching (GMFlow) model: instead of predicting the mean, GMFlow predicts dynamic Gaussian mixture (GM) parameters to capture a multi-modal flow velocity distribution, which can be learned with a KL div"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.05304","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2504.05304/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T12:02:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SR0D+EhrQvBQuL83Kd6ozzyctVt3I/ZS9UY9w6UeYzM124xjkBcDwqhdtoDYrJopftsHXg3IcderRBl7ibU0Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T06:49:45.110087Z"},"content_sha256":"bb4018d967d3b4f5af41fdf87e6280614e684501d8bcba7d89fe2634246734d9","schema_version":"1.0","event_id":"sha256:bb4018d967d3b4f5af41fdf87e6280614e684501d8bcba7d89fe2634246734d9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/347RP2A25ZWSRVJK4QPMTBLS5P/bundle.json","state_url":"https://pith.science/pith/347RP2A25ZWSRVJK4QPMTBLS5P/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/347RP2A25ZWSRVJK4QPMTBLS5P/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-11T06:49:45Z","links":{"resolver":"https://pith.science/pith/347RP2A25ZWSRVJK4QPMTBLS5P","bundle":"https://pith.science/pith/347RP2A25ZWSRVJK4QPMTBLS5P/bundle.json","state":"https://pith.science/pith/347RP2A25ZWSRVJK4QPMTBLS5P/state.json","well_known_bundle":"https://pith.science/.well-known/pith/347RP2A25ZWSRVJK4QPMTBLS5P/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:347RP2A25ZWSRVJK4QPMTBLS5P","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":"442170f1bb887d345701d6743059779f0d860cc1048b5c31dfc24852f40709b0","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-04-07T17:59:42Z","title_canon_sha256":"0a6b88e1e4924d555bd311a15f77d1fd4a32bd64f73e96bb9727f5001dea0de7"},"schema_version":"1.0","source":{"id":"2504.05304","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.05304","created_at":"2026-07-05T12:02:00Z"},{"alias_kind":"arxiv_version","alias_value":"2504.05304v3","created_at":"2026-07-05T12:02:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.05304","created_at":"2026-07-05T12:02:00Z"},{"alias_kind":"pith_short_12","alias_value":"347RP2A25ZWS","created_at":"2026-07-05T12:02:00Z"},{"alias_kind":"pith_short_16","alias_value":"347RP2A25ZWSRVJK","created_at":"2026-07-05T12:02:00Z"},{"alias_kind":"pith_short_8","alias_value":"347RP2A2","created_at":"2026-07-05T12:02:00Z"}],"graph_snapshots":[{"event_id":"sha256:bb4018d967d3b4f5af41fdf87e6280614e684501d8bcba7d89fe2634246734d9","target":"graph","created_at":"2026-07-05T12:02:00Z","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/2504.05304/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Diffusion models approximate the denoising distribution as a Gaussian and predict its mean, whereas flow matching models reparameterize the Gaussian mean as flow velocity. However, they underperform in few-step sampling due to discretization error and tend to produce over-saturated colors under classifier-free guidance (CFG). To address these limitations, we propose a novel Gaussian mixture flow matching (GMFlow) model: instead of predicting the mean, GMFlow predicts dynamic Gaussian mixture (GM) parameters to capture a multi-modal flow velocity distribution, which can be learned with a KL div","authors_text":"Fujun Luan, Gordon Wetzstein, Hansheng Chen, Hao Tan, Kai Zhang, Leonidas Guibas, Sai Bi, Zexiang Xu","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-04-07T17:59:42Z","title":"Gaussian Mixture Flow Matching Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.05304","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":"sha256:4502659811dd53d5c8c94ea823ca8ef5337a2c63e214b6d089efe558da8b0fbb","target":"record","created_at":"2026-07-05T12:02:00Z","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":"442170f1bb887d345701d6743059779f0d860cc1048b5c31dfc24852f40709b0","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-04-07T17:59:42Z","title_canon_sha256":"0a6b88e1e4924d555bd311a15f77d1fd4a32bd64f73e96bb9727f5001dea0de7"},"schema_version":"1.0","source":{"id":"2504.05304","kind":"arxiv","version":3}},"canonical_sha256":"df3f17e81aee6d28d52ae41ec98572ebfd659ba30c8372a3af97aef71c134534","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"df3f17e81aee6d28d52ae41ec98572ebfd659ba30c8372a3af97aef71c134534","first_computed_at":"2026-07-05T12:02:00.354868Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T12:02:00.354868Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"fouVd8IY6TwZc63koNiXL1J8dCWo4o9wqxFsM5tsXf+spi04B+MJIgqQjUNKG5nLK1+ZBjccaIV/3fwNhEMjAw==","signature_status":"signed_v1","signed_at":"2026-07-05T12:02:00.355378Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.05304","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4502659811dd53d5c8c94ea823ca8ef5337a2c63e214b6d089efe558da8b0fbb","sha256:bb4018d967d3b4f5af41fdf87e6280614e684501d8bcba7d89fe2634246734d9"],"state_sha256":"e0fefd9ceff6ddefbfa4cccdec18c0bea961ebb6c9b6190921b6d89a6a8ad0ac"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AweNTO+Lz5UOH3f8tXbzoT6f0/nqMalx805XKWcjxQ4udTa/V5VbPYTq3lFGvuGOVGFTOVpJ0Ve3I8WeJhObAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T06:49:45.114032Z","bundle_sha256":"029e8d8ad70dc10894acd448f92a521ece4e7c872eb6c345f92d1282186b0323"}}