{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:KHW57FJHQYTZJFPRY63LUYNN4H","short_pith_number":"pith:KHW57FJH","canonical_record":{"source":{"id":"2105.12603","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.data-an","submitted_at":"2021-05-26T15:03:07Z","cross_cats_sorted":["cond-mat.dis-nn","cond-mat.stat-mech"],"title_canon_sha256":"f2bbeb9b36bbf768e07682bde9bf25cf522c5ff67dc574a6ea24d596a97f833b","abstract_canon_sha256":"43e6fa5eccf71e41aa86bfdd2eb0e7eb1e2681bb73ad5b155641e3c6678a8c75"},"schema_version":"1.0"},"canonical_sha256":"51eddf952786279495f1c7b6ba61ade1e93a8608550030ab5ab0474aa061608e","source":{"kind":"arxiv","id":"2105.12603","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2105.12603","created_at":"2026-07-05T04:22:15Z"},{"alias_kind":"arxiv_version","alias_value":"2105.12603v3","created_at":"2026-07-05T04:22:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.12603","created_at":"2026-07-05T04:22:15Z"},{"alias_kind":"pith_short_12","alias_value":"KHW57FJHQYTZ","created_at":"2026-07-05T04:22:15Z"},{"alias_kind":"pith_short_16","alias_value":"KHW57FJHQYTZJFPR","created_at":"2026-07-05T04:22:15Z"},{"alias_kind":"pith_short_8","alias_value":"KHW57FJH","created_at":"2026-07-05T04:22:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:KHW57FJHQYTZJFPRY63LUYNN4H","target":"record","payload":{"canonical_record":{"source":{"id":"2105.12603","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.data-an","submitted_at":"2021-05-26T15:03:07Z","cross_cats_sorted":["cond-mat.dis-nn","cond-mat.stat-mech"],"title_canon_sha256":"f2bbeb9b36bbf768e07682bde9bf25cf522c5ff67dc574a6ea24d596a97f833b","abstract_canon_sha256":"43e6fa5eccf71e41aa86bfdd2eb0e7eb1e2681bb73ad5b155641e3c6678a8c75"},"schema_version":"1.0"},"canonical_sha256":"51eddf952786279495f1c7b6ba61ade1e93a8608550030ab5ab0474aa061608e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:22:15.992677Z","signature_b64":"IsuZqt0v59essV+W8yXSmHBsVgTGVN5O9cACZTibA4WvjRa7t/ML90X1jtBdOmj/MY9ZBvC2P0QFHHOLrNrADA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"51eddf952786279495f1c7b6ba61ade1e93a8608550030ab5ab0474aa061608e","last_reissued_at":"2026-07-05T04:22:15.992267Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:22:15.992267Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2105.12603","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":"2026-07-05T04:22:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0DZxBpSzpHiUiSKIfnb+tofy1FnFiCpLvaxjkVD9lFKQ3KdOKsRYxYd7AWy+2xhVQqfBvrB8Km8W6EmY608cDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T00:31:24.047282Z"},"content_sha256":"0488746fe9bbedaafa36c819b78544eb91008172d73ed898187e23a16b83765a","schema_version":"1.0","event_id":"sha256:0488746fe9bbedaafa36c819b78544eb91008172d73ed898187e23a16b83765a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:KHW57FJHQYTZJFPRY63LUYNN4H","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Adaptive Monte Carlo augmented with normalizing flows","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cond-mat.dis-nn","cond-mat.stat-mech"],"primary_cat":"physics.data-an","authors_text":"Eric Vanden-Eijnden, Grant M. Rotskoff, Marylou Gabri\\'e","submitted_at":"2021-05-26T15:03:07Z","abstract_excerpt":"Many problems in the physical sciences, machine learning, and statistical inference necessitate sampling from a high-dimensional, multi-modal probability distribution. Markov Chain Monte Carlo (MCMC) algorithms, the ubiquitous tool for this task, typically rely on random local updates to propagate configurations of a given system in a way that ensures that generated configurations will be distributed according to a target probability distribution asymptotically. In high-dimensional settings with multiple relevant metastable basins, local approaches require either immense computational effort o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.12603","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/2105.12603/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":"2026-07-05T04:22:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qT1z0TGTrhTv+ZRAsj4x/xexoeC6XKNTQmvo41ZebH4EmDbHzdNMFgrXWPEkkIbp/rk2lpyXGjfwKZu37wd2Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T00:31:24.047847Z"},"content_sha256":"f30be7b648637f18721c19b5a286005233d86a2982a4da65566010c1faac95bb","schema_version":"1.0","event_id":"sha256:f30be7b648637f18721c19b5a286005233d86a2982a4da65566010c1faac95bb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KHW57FJHQYTZJFPRY63LUYNN4H/bundle.json","state_url":"https://pith.science/pith/KHW57FJHQYTZJFPRY63LUYNN4H/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KHW57FJHQYTZJFPRY63LUYNN4H/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":"2026-08-04T00:31:24Z","links":{"resolver":"https://pith.science/pith/KHW57FJHQYTZJFPRY63LUYNN4H","bundle":"https://pith.science/pith/KHW57FJHQYTZJFPRY63LUYNN4H/bundle.json","state":"https://pith.science/pith/KHW57FJHQYTZJFPRY63LUYNN4H/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KHW57FJHQYTZJFPRY63LUYNN4H/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:KHW57FJHQYTZJFPRY63LUYNN4H","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":"43e6fa5eccf71e41aa86bfdd2eb0e7eb1e2681bb73ad5b155641e3c6678a8c75","cross_cats_sorted":["cond-mat.dis-nn","cond-mat.stat-mech"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.data-an","submitted_at":"2021-05-26T15:03:07Z","title_canon_sha256":"f2bbeb9b36bbf768e07682bde9bf25cf522c5ff67dc574a6ea24d596a97f833b"},"schema_version":"1.0","source":{"id":"2105.12603","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2105.12603","created_at":"2026-07-05T04:22:15Z"},{"alias_kind":"arxiv_version","alias_value":"2105.12603v3","created_at":"2026-07-05T04:22:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.12603","created_at":"2026-07-05T04:22:15Z"},{"alias_kind":"pith_short_12","alias_value":"KHW57FJHQYTZ","created_at":"2026-07-05T04:22:15Z"},{"alias_kind":"pith_short_16","alias_value":"KHW57FJHQYTZJFPR","created_at":"2026-07-05T04:22:15Z"},{"alias_kind":"pith_short_8","alias_value":"KHW57FJH","created_at":"2026-07-05T04:22:15Z"}],"graph_snapshots":[{"event_id":"sha256:f30be7b648637f18721c19b5a286005233d86a2982a4da65566010c1faac95bb","target":"graph","created_at":"2026-07-05T04:22:15Z","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/2105.12603/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Many problems in the physical sciences, machine learning, and statistical inference necessitate sampling from a high-dimensional, multi-modal probability distribution. Markov Chain Monte Carlo (MCMC) algorithms, the ubiquitous tool for this task, typically rely on random local updates to propagate configurations of a given system in a way that ensures that generated configurations will be distributed according to a target probability distribution asymptotically. In high-dimensional settings with multiple relevant metastable basins, local approaches require either immense computational effort o","authors_text":"Eric Vanden-Eijnden, Grant M. Rotskoff, Marylou Gabri\\'e","cross_cats":["cond-mat.dis-nn","cond-mat.stat-mech"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.data-an","submitted_at":"2021-05-26T15:03:07Z","title":"Adaptive Monte Carlo augmented with normalizing flows"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.12603","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":"sha256:0488746fe9bbedaafa36c819b78544eb91008172d73ed898187e23a16b83765a","target":"record","created_at":"2026-07-05T04:22:15Z","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":"43e6fa5eccf71e41aa86bfdd2eb0e7eb1e2681bb73ad5b155641e3c6678a8c75","cross_cats_sorted":["cond-mat.dis-nn","cond-mat.stat-mech"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.data-an","submitted_at":"2021-05-26T15:03:07Z","title_canon_sha256":"f2bbeb9b36bbf768e07682bde9bf25cf522c5ff67dc574a6ea24d596a97f833b"},"schema_version":"1.0","source":{"id":"2105.12603","kind":"arxiv","version":3}},"canonical_sha256":"51eddf952786279495f1c7b6ba61ade1e93a8608550030ab5ab0474aa061608e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"51eddf952786279495f1c7b6ba61ade1e93a8608550030ab5ab0474aa061608e","first_computed_at":"2026-07-05T04:22:15.992267Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:22:15.992267Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"IsuZqt0v59essV+W8yXSmHBsVgTGVN5O9cACZTibA4WvjRa7t/ML90X1jtBdOmj/MY9ZBvC2P0QFHHOLrNrADA==","signature_status":"signed_v1","signed_at":"2026-07-05T04:22:15.992677Z","signed_message":"canonical_sha256_bytes"},"source_id":"2105.12603","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0488746fe9bbedaafa36c819b78544eb91008172d73ed898187e23a16b83765a","sha256:f30be7b648637f18721c19b5a286005233d86a2982a4da65566010c1faac95bb"],"state_sha256":"90daab093da6d662982dda878095c10df16e8ce82178f03fcd83b2f8e05aae43"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RyKh2oW/kdQEnIYpYU/Cpn977tSRFXqaoxnfCyTL5vdX13gLKuKgcQb9oGkqFEdrZPm2p9N9MwMsAhF5HxG1Dw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T00:31:24.052529Z","bundle_sha256":"8236dd891841d8ac8f23764b5faddb8fc13cc95858e00dbba7c571b3756fcec7"}}