{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:V73NZBPPZARZ6E7CSC6BHEXY2J","short_pith_number":"pith:V73NZBPP","canonical_record":{"source":{"id":"2506.01904","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-06-02T17:25:03Z","cross_cats_sorted":["cs.LG","stat.CO"],"title_canon_sha256":"e1c910c66c4b6432c8aa1bc2d2e61cbb1480c191782b98d06caf5fb158833b72","abstract_canon_sha256":"b8ed172e5f4d88f68b0205f1f363f2c8b0953595fb54c232078ad5e8ec5d227f"},"schema_version":"1.0"},"canonical_sha256":"aff6dc85efc8239f13e290bc1392f8d2548f754fa835e036797d6d6096a18bbf","source":{"kind":"arxiv","id":"2506.01904","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.01904","created_at":"2026-07-05T11:14:21Z"},{"alias_kind":"arxiv_version","alias_value":"2506.01904v1","created_at":"2026-07-05T11:14:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.01904","created_at":"2026-07-05T11:14:21Z"},{"alias_kind":"pith_short_12","alias_value":"V73NZBPPZARZ","created_at":"2026-07-05T11:14:21Z"},{"alias_kind":"pith_short_16","alias_value":"V73NZBPPZARZ6E7C","created_at":"2026-07-05T11:14:21Z"},{"alias_kind":"pith_short_8","alias_value":"V73NZBPP","created_at":"2026-07-05T11:14:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:V73NZBPPZARZ6E7CSC6BHEXY2J","target":"record","payload":{"canonical_record":{"source":{"id":"2506.01904","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-06-02T17:25:03Z","cross_cats_sorted":["cs.LG","stat.CO"],"title_canon_sha256":"e1c910c66c4b6432c8aa1bc2d2e61cbb1480c191782b98d06caf5fb158833b72","abstract_canon_sha256":"b8ed172e5f4d88f68b0205f1f363f2c8b0953595fb54c232078ad5e8ec5d227f"},"schema_version":"1.0"},"canonical_sha256":"aff6dc85efc8239f13e290bc1392f8d2548f754fa835e036797d6d6096a18bbf","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:14:21.114438Z","signature_b64":"0G2X002WyFXjVpT4G+y3gU9XH3FP5qanqUxWU0BKAu+COhYz4grHa/OobK8wW+FdT0iv2NXhSeW5hflxwrKtCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aff6dc85efc8239f13e290bc1392f8d2548f754fa835e036797d6d6096a18bbf","last_reissued_at":"2026-07-05T11:14:21.113984Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:14:21.113984Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.01904","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:14:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zglXNVINQMZNJWtPabv53KQUotTWtzAS67diSpS32PrOaF++OY/M6m3XYJfpYQsxARVs87S6+u4Lf+U4TTy8Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T11:25:52.827662Z"},"content_sha256":"a202cc35b5964a5fa1857f21631ab80c715f0031ab9b0bbd6476b34f1ef24bd6","schema_version":"1.0","event_id":"sha256:a202cc35b5964a5fa1857f21631ab80c715f0031ab9b0bbd6476b34f1ef24bd6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:V73NZBPPZARZ6E7CSC6BHEXY2J","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Machine-Learned Sampling of Conditioned Path Measures","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","stat.CO"],"primary_cat":"stat.ML","authors_text":"Qijia Jiang, Reuben Cohn-Gordon","submitted_at":"2025-06-02T17:25:03Z","abstract_excerpt":"We propose algorithms for sampling from posterior path measures $P(C([0, T], \\mathbb{R}^d))$ under a general prior process. This leverages ideas from (1) controlled equilibrium dynamics, which gradually transport between two path measures, and (2) optimization in $\\infty$-dimensional probability space endowed with a Wasserstein metric, which can be used to evolve a density curve under the specified likelihood. The resulting algorithms are theoretically grounded and can be integrated seamlessly with neural networks for learning the target trajectory ensembles, without access to data."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.01904","kind":"arxiv","version":1},"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/2506.01904/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":"2026-07-05T11:14:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Tgie9zQV1llJncbggjcGiSSJV/sei6DDOHMiIHOv76gXrz8V1cvUHfDh2mpUFEWQ+etCU00ZdWZflAywsMaQDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T11:25:52.828215Z"},"content_sha256":"d68127f87b37be7abfa217d65b846c695aa9216e0847a7c6dc8a7283ca5fa0a1","schema_version":"1.0","event_id":"sha256:d68127f87b37be7abfa217d65b846c695aa9216e0847a7c6dc8a7283ca5fa0a1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/V73NZBPPZARZ6E7CSC6BHEXY2J/bundle.json","state_url":"https://pith.science/pith/V73NZBPPZARZ6E7CSC6BHEXY2J/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/V73NZBPPZARZ6E7CSC6BHEXY2J/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":"2026-08-08T11:25:52Z","links":{"resolver":"https://pith.science/pith/V73NZBPPZARZ6E7CSC6BHEXY2J","bundle":"https://pith.science/pith/V73NZBPPZARZ6E7CSC6BHEXY2J/bundle.json","state":"https://pith.science/pith/V73NZBPPZARZ6E7CSC6BHEXY2J/state.json","well_known_bundle":"https://pith.science/.well-known/pith/V73NZBPPZARZ6E7CSC6BHEXY2J/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:V73NZBPPZARZ6E7CSC6BHEXY2J","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":"b8ed172e5f4d88f68b0205f1f363f2c8b0953595fb54c232078ad5e8ec5d227f","cross_cats_sorted":["cs.LG","stat.CO"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-06-02T17:25:03Z","title_canon_sha256":"e1c910c66c4b6432c8aa1bc2d2e61cbb1480c191782b98d06caf5fb158833b72"},"schema_version":"1.0","source":{"id":"2506.01904","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.01904","created_at":"2026-07-05T11:14:21Z"},{"alias_kind":"arxiv_version","alias_value":"2506.01904v1","created_at":"2026-07-05T11:14:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.01904","created_at":"2026-07-05T11:14:21Z"},{"alias_kind":"pith_short_12","alias_value":"V73NZBPPZARZ","created_at":"2026-07-05T11:14:21Z"},{"alias_kind":"pith_short_16","alias_value":"V73NZBPPZARZ6E7C","created_at":"2026-07-05T11:14:21Z"},{"alias_kind":"pith_short_8","alias_value":"V73NZBPP","created_at":"2026-07-05T11:14:21Z"}],"graph_snapshots":[{"event_id":"sha256:d68127f87b37be7abfa217d65b846c695aa9216e0847a7c6dc8a7283ca5fa0a1","target":"graph","created_at":"2026-07-05T11:14:21Z","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/2506.01904/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We propose algorithms for sampling from posterior path measures $P(C([0, T], \\mathbb{R}^d))$ under a general prior process. This leverages ideas from (1) controlled equilibrium dynamics, which gradually transport between two path measures, and (2) optimization in $\\infty$-dimensional probability space endowed with a Wasserstein metric, which can be used to evolve a density curve under the specified likelihood. The resulting algorithms are theoretically grounded and can be integrated seamlessly with neural networks for learning the target trajectory ensembles, without access to data.","authors_text":"Qijia Jiang, Reuben Cohn-Gordon","cross_cats":["cs.LG","stat.CO"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-06-02T17:25:03Z","title":"Machine-Learned Sampling of Conditioned Path Measures"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.01904","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:a202cc35b5964a5fa1857f21631ab80c715f0031ab9b0bbd6476b34f1ef24bd6","target":"record","created_at":"2026-07-05T11:14:21Z","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":"b8ed172e5f4d88f68b0205f1f363f2c8b0953595fb54c232078ad5e8ec5d227f","cross_cats_sorted":["cs.LG","stat.CO"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-06-02T17:25:03Z","title_canon_sha256":"e1c910c66c4b6432c8aa1bc2d2e61cbb1480c191782b98d06caf5fb158833b72"},"schema_version":"1.0","source":{"id":"2506.01904","kind":"arxiv","version":1}},"canonical_sha256":"aff6dc85efc8239f13e290bc1392f8d2548f754fa835e036797d6d6096a18bbf","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"aff6dc85efc8239f13e290bc1392f8d2548f754fa835e036797d6d6096a18bbf","first_computed_at":"2026-07-05T11:14:21.113984Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:14:21.113984Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0G2X002WyFXjVpT4G+y3gU9XH3FP5qanqUxWU0BKAu+COhYz4grHa/OobK8wW+FdT0iv2NXhSeW5hflxwrKtCw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:14:21.114438Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.01904","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a202cc35b5964a5fa1857f21631ab80c715f0031ab9b0bbd6476b34f1ef24bd6","sha256:d68127f87b37be7abfa217d65b846c695aa9216e0847a7c6dc8a7283ca5fa0a1"],"state_sha256":"2df4d6041eeb916ae4809bf73b56987a13e15dab9ab0d7aeef7c00cb19b477c0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aFX6SVV5XXCakT96sh3QzrJcZ6Vk+6wtQNScAEKCTX/2lykD9cxdU+TpZUa3J64oe+9qInGJeF1ixIC18knqAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T11:25:52.831966Z","bundle_sha256":"a4fcb326afe0c3d4c7981fe06c1c318d29488010f941361f0c179b953c3d3254"}}