{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:4MXRURUXZ277M4TDVBMMR6EWTI","short_pith_number":"pith:4MXRURUX","canonical_record":{"source":{"id":"2105.12481","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"hep-lat","submitted_at":"2021-05-26T11:31:16Z","cross_cats_sorted":[],"title_canon_sha256":"7e048ca37d70404d0f881f6715cf9a28da6efd3cda4606ceb62f41c01dbb5f39","abstract_canon_sha256":"2fffa50672672f59d7e9f6c55c41930b3cd8c326a04b4abd79d15bd132f55e99"},"schema_version":"1.0"},"canonical_sha256":"e32f1a4697cebff67263a858c8f8969a1361268c5b3387eee4a70257d2974e38","source":{"kind":"arxiv","id":"2105.12481","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2105.12481","created_at":"2026-07-05T03:34:25Z"},{"alias_kind":"arxiv_version","alias_value":"2105.12481v2","created_at":"2026-07-05T03:34:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.12481","created_at":"2026-07-05T03:34:25Z"},{"alias_kind":"pith_short_12","alias_value":"4MXRURUXZ277","created_at":"2026-07-05T03:34:25Z"},{"alias_kind":"pith_short_16","alias_value":"4MXRURUXZ277M4TD","created_at":"2026-07-05T03:34:25Z"},{"alias_kind":"pith_short_8","alias_value":"4MXRURUX","created_at":"2026-07-05T03:34:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:4MXRURUXZ277M4TDVBMMR6EWTI","target":"record","payload":{"canonical_record":{"source":{"id":"2105.12481","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"hep-lat","submitted_at":"2021-05-26T11:31:16Z","cross_cats_sorted":[],"title_canon_sha256":"7e048ca37d70404d0f881f6715cf9a28da6efd3cda4606ceb62f41c01dbb5f39","abstract_canon_sha256":"2fffa50672672f59d7e9f6c55c41930b3cd8c326a04b4abd79d15bd132f55e99"},"schema_version":"1.0"},"canonical_sha256":"e32f1a4697cebff67263a858c8f8969a1361268c5b3387eee4a70257d2974e38","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:34:25.920394Z","signature_b64":"qtPNDw0/wseGN3ikTUzUkjmY1iFN8wMikL+Yfvu6sdrnDgjkVeqbY9QW/IhvZZUTx3EiYaG8BtAX7GSODluhDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e32f1a4697cebff67263a858c8f8969a1361268c5b3387eee4a70257d2974e38","last_reissued_at":"2026-07-05T03:34:25.919948Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:34:25.919948Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2105.12481","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:34:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FYd5wb8RCjt4J6AAHOfOOzAsp56HY7IPtoimLbXCynzdK2SkwYGvXfcKfcNvD0LNSQKO3EPn09eC8Kz7H6YNCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-02T11:37:39.042737Z"},"content_sha256":"098401a27608b9477ca4aab0c4f572f51f0035574c60095a18474d0655634a28","schema_version":"1.0","event_id":"sha256:098401a27608b9477ca4aab0c4f572f51f0035574c60095a18474d0655634a28"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:4MXRURUXZ277M4TDVBMMR6EWTI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Efficient Modelling of Trivializing Maps for Lattice $\\phi^4$ Theory Using Normalizing Flows: A First Look at Scalability","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"hep-lat","authors_text":"Joe Marsh Rossney, Luigi Del Debbio, Michael Wilson","submitted_at":"2021-05-26T11:31:16Z","abstract_excerpt":"General-purpose Markov Chain Monte Carlo sampling algorithms suffer from a dramatic reduction in efficiency as the system being studied is driven towards a critical point. Recently, a series of seminal studies suggested that normalizing flows - a class of deep generative models - can form the basis of a sampling strategy that does not suffer from this 'critical slowing down'. The central idea is to use machine learning techniques to build (approximate) trivializing maps, i.e. field transformations that map the theory of interest into a 'simpler' theory in which the degrees of freedom decouple,"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.12481","kind":"arxiv","version":2},"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/2105.12481/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":"2026-07-05T03:34:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NpMSm1Lx79UqTQx5iNJ36s+EbUTByC6eMMlM6Vq0ng5Yke6+xt3RjyNP/Nm55yrNsopQGZHpZHH3lLVIFGvABw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-02T11:37:39.043241Z"},"content_sha256":"231de226d8c6045a744e758a5bb6408e15a2bd5bb525b3e765f26baecbe74401","schema_version":"1.0","event_id":"sha256:231de226d8c6045a744e758a5bb6408e15a2bd5bb525b3e765f26baecbe74401"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4MXRURUXZ277M4TDVBMMR6EWTI/bundle.json","state_url":"https://pith.science/pith/4MXRURUXZ277M4TDVBMMR6EWTI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4MXRURUXZ277M4TDVBMMR6EWTI/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":"2026-08-02T11:37:39Z","links":{"resolver":"https://pith.science/pith/4MXRURUXZ277M4TDVBMMR6EWTI","bundle":"https://pith.science/pith/4MXRURUXZ277M4TDVBMMR6EWTI/bundle.json","state":"https://pith.science/pith/4MXRURUXZ277M4TDVBMMR6EWTI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4MXRURUXZ277M4TDVBMMR6EWTI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:4MXRURUXZ277M4TDVBMMR6EWTI","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":"2fffa50672672f59d7e9f6c55c41930b3cd8c326a04b4abd79d15bd132f55e99","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"hep-lat","submitted_at":"2021-05-26T11:31:16Z","title_canon_sha256":"7e048ca37d70404d0f881f6715cf9a28da6efd3cda4606ceb62f41c01dbb5f39"},"schema_version":"1.0","source":{"id":"2105.12481","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2105.12481","created_at":"2026-07-05T03:34:25Z"},{"alias_kind":"arxiv_version","alias_value":"2105.12481v2","created_at":"2026-07-05T03:34:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.12481","created_at":"2026-07-05T03:34:25Z"},{"alias_kind":"pith_short_12","alias_value":"4MXRURUXZ277","created_at":"2026-07-05T03:34:25Z"},{"alias_kind":"pith_short_16","alias_value":"4MXRURUXZ277M4TD","created_at":"2026-07-05T03:34:25Z"},{"alias_kind":"pith_short_8","alias_value":"4MXRURUX","created_at":"2026-07-05T03:34:25Z"}],"graph_snapshots":[{"event_id":"sha256:231de226d8c6045a744e758a5bb6408e15a2bd5bb525b3e765f26baecbe74401","target":"graph","created_at":"2026-07-05T03:34:25Z","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/2105.12481/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"General-purpose Markov Chain Monte Carlo sampling algorithms suffer from a dramatic reduction in efficiency as the system being studied is driven towards a critical point. Recently, a series of seminal studies suggested that normalizing flows - a class of deep generative models - can form the basis of a sampling strategy that does not suffer from this 'critical slowing down'. The central idea is to use machine learning techniques to build (approximate) trivializing maps, i.e. field transformations that map the theory of interest into a 'simpler' theory in which the degrees of freedom decouple,","authors_text":"Joe Marsh Rossney, Luigi Del Debbio, Michael Wilson","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"hep-lat","submitted_at":"2021-05-26T11:31:16Z","title":"Efficient Modelling of Trivializing Maps for Lattice $\\phi^4$ Theory Using Normalizing Flows: A First Look at Scalability"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.12481","kind":"arxiv","version":2},"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":"sha256:098401a27608b9477ca4aab0c4f572f51f0035574c60095a18474d0655634a28","target":"record","created_at":"2026-07-05T03:34:25Z","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":"2fffa50672672f59d7e9f6c55c41930b3cd8c326a04b4abd79d15bd132f55e99","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"hep-lat","submitted_at":"2021-05-26T11:31:16Z","title_canon_sha256":"7e048ca37d70404d0f881f6715cf9a28da6efd3cda4606ceb62f41c01dbb5f39"},"schema_version":"1.0","source":{"id":"2105.12481","kind":"arxiv","version":2}},"canonical_sha256":"e32f1a4697cebff67263a858c8f8969a1361268c5b3387eee4a70257d2974e38","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e32f1a4697cebff67263a858c8f8969a1361268c5b3387eee4a70257d2974e38","first_computed_at":"2026-07-05T03:34:25.919948Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:34:25.919948Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"qtPNDw0/wseGN3ikTUzUkjmY1iFN8wMikL+Yfvu6sdrnDgjkVeqbY9QW/IhvZZUTx3EiYaG8BtAX7GSODluhDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T03:34:25.920394Z","signed_message":"canonical_sha256_bytes"},"source_id":"2105.12481","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:098401a27608b9477ca4aab0c4f572f51f0035574c60095a18474d0655634a28","sha256:231de226d8c6045a744e758a5bb6408e15a2bd5bb525b3e765f26baecbe74401"],"state_sha256":"1124f81f5e28436e8ffe40296586c5aef89f0735f56b6365e6306b19d9918315"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"71p9yLEDChQFthp7X6cu1l0FfCps3V/aVDJ9BLOByDdK2d/cnZgubdqjv8IUMrtlkZ6ZtrKRDkel8jBfOH/xDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-02T11:37:39.046883Z","bundle_sha256":"a9cf8df60c1eb1df5975744e822b853e8ececa514e4952faaf3fe5c9a99a4473"}}