{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:XXCVR527NSOHCCAI6NMLDRMX5J","short_pith_number":"pith:XXCVR527","canonical_record":{"source":{"id":"2602.23089","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2026-02-26T15:10:45Z","cross_cats_sorted":[],"title_canon_sha256":"966ed38018ccb6281d77d39f16ff6eb23fedb6e2581a7984eaa793e2f5a522be","abstract_canon_sha256":"655912b396d051732a9fea416051c5e7d2d3edfdcbd9ff33b4c6d064434c08f8"},"schema_version":"1.0"},"canonical_sha256":"bdc558f75f6c9c710808f358b1c597ea77321b6266f3af344da3d9cfa55924cb","source":{"kind":"arxiv","id":"2602.23089","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2602.23089","created_at":"2026-05-18T02:45:05Z"},{"alias_kind":"arxiv_version","alias_value":"2602.23089v2","created_at":"2026-05-18T02:45:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.23089","created_at":"2026-05-18T02:45:05Z"},{"alias_kind":"pith_short_12","alias_value":"XXCVR527NSOH","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"XXCVR527NSOHCCAI","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"XXCVR527","created_at":"2026-05-18T12:33:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:XXCVR527NSOHCCAI6NMLDRMX5J","target":"record","payload":{"canonical_record":{"source":{"id":"2602.23089","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2026-02-26T15:10:45Z","cross_cats_sorted":[],"title_canon_sha256":"966ed38018ccb6281d77d39f16ff6eb23fedb6e2581a7984eaa793e2f5a522be","abstract_canon_sha256":"655912b396d051732a9fea416051c5e7d2d3edfdcbd9ff33b4c6d064434c08f8"},"schema_version":"1.0"},"canonical_sha256":"bdc558f75f6c9c710808f358b1c597ea77321b6266f3af344da3d9cfa55924cb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:45:05.099629Z","signature_b64":"0CD9ocjhAqx6iQ0e73bLkrYVl1Zn5qmmpdQS328o92RLpKPilL0Cv3s2Im7UyXZ35u2GpsmFL1mW/zenDNclBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bdc558f75f6c9c710808f358b1c597ea77321b6266f3af344da3d9cfa55924cb","last_reissued_at":"2026-05-18T02:45:05.099030Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:45:05.099030Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2602.23089","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":"2026-05-18T02:45:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cHx5uSziCicbVTyAwdKEaN5/BdvHbRMpSk/J1rSIaGpgJWTpDsoamPby8Q80HwKmwtnG3Onip91SRplwMFYSAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T12:29:44.822759Z"},"content_sha256":"f1b52b644ff2b6ed54bbddc5de6851c43792c0f50c433fce58be373f1cd76180","schema_version":"1.0","event_id":"sha256:f1b52b644ff2b6ed54bbddc5de6851c43792c0f50c433fce58be373f1cd76180"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:XXCVR527NSOHCCAI6NMLDRMX5J","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Physics-informed neural particle flow for the Bayesian update step","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"Coupling the log-homotopy path to the continuity equation produces a master PDE that a neural network solves unsupervised to transport prior densities to posteriors.","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Domonkos Csuzdi, Oliv\\'er T\\\"or\\H{o}, Tam\\'as B\\'ecsi","submitted_at":"2026-02-26T15:10:45Z","abstract_excerpt":"The Bayesian update step poses significant computational challenges in high-dimensional nonlinear estimation. While log-homotopy particle flow filters offer an alternative to stochastic sampling, existing formulations usually yield stiff differential equations. Conversely, existing deep learning approximations typically treat the update as a black-box task or rely on asymptotic relaxation, neglecting the exact geometric structure of the finite-horizon probability transport. In this work, we propose a physics-informed neural particle flow, which is an amortized inference framework. To construct"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"By embedding this PDE as a physical constraint into the loss function, we train a neural network to approximate the transport velocity field. This approach enables purely unsupervised training, eliminating the need for ground-truth posterior samples.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The log-homotopy trajectory of the prior to posterior density function can be coupled with the continuity equation to yield a well-posed master PDE whose solution is accurately approximated by a neural network without introducing new instabilities or bias.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A neural network approximates the velocity field of log-homotopy particle flow by enforcing a derived master PDE from the continuity equation, enabling unsupervised amortized Bayesian updates with reduced stiffness.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Coupling the log-homotopy path to the continuity equation produces a master PDE that a neural network solves unsupervised to transport prior densities to posteriors.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"4260f894de20646b7ed81716b3fee7a0029f95ec060e27925edf27ad49f2233d"},"source":{"id":"2602.23089","kind":"arxiv","version":2},"verdict":{"id":"cc9afcba-9132-49ff-8e83-6b41ac304aaa","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T18:38:07.088699Z","strongest_claim":"By embedding this PDE as a physical constraint into the loss function, we train a neural network to approximate the transport velocity field. This approach enables purely unsupervised training, eliminating the need for ground-truth posterior samples.","one_line_summary":"A neural network approximates the velocity field of log-homotopy particle flow by enforcing a derived master PDE from the continuity equation, enabling unsupervised amortized Bayesian updates with reduced stiffness.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The log-homotopy trajectory of the prior to posterior density function can be coupled with the continuity equation to yield a well-posed master PDE whose solution is accurately approximated by a neural network without introducing new instabilities or bias.","pith_extraction_headline":"Coupling the log-homotopy path to the continuity equation produces a master PDE that a neural network solves unsupervised to transport prior densities to posteriors."},"references":{"count":72,"sample":[{"doi":"","year":2012,"title":"M.-H. Chen, Q.-M. Shao, J. G. Ibrahim, Monte Carlo methods in Bayesian computation, Springer Science & Business Media, 2012","work_id":"1658c9bd-131c-4b2e-aa5a-c1afaf297228","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2022,"title":"G. L. Jones, Q. Qin, Markov chain Monte Carlo in practice, Annual Review of Statistics and Its Application 9 (1) (2022) 557–578","work_id":"bb75bd6d-7a1d-48e7-942a-968bcfcc61ee","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2011,"title":"S. Asmussen, P. W. Glynn, A new proof of convergence of MCMC via the ergodic theorem, Statistics & Proba- bility Letters 81 (10) (2011) 1482–1485","work_id":"306765d6-575a-49c1-872d-40abe85bd05d","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1007/978-981-13-2971-5","year":2020,"title":"A. Barbu, S.-C. Zhu, Monte Carlo Methods, Springer Singapore, Singapore, 2020.doi:10.1007/ 978-981-13-2971-5. URLhttp://link.springer.com/10.1007/978-981-13-2971-5","work_id":"94df91cb-83ef-4bea-93d6-08d7b65cbeb6","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2004,"title":"C. P. Robert, G. Casella, Monte Carlo Statistical Methods, 2nd Edition, Springer, 2004","work_id":"3e9403b0-35ff-4ab7-8cf3-ca74dec4094f","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":72,"snapshot_sha256":"4f505a0a0855d09ae6a4d9d6f2ba7009f69ec87bb5ef10ac42a5ba852afadd26","internal_anchors":1},"formal_canon":{"evidence_count":2,"snapshot_sha256":"9af4963e6c4c4a8cdf9859597a694abe6ba437df6ce4cefe3495a0f2f9cbd04f"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"cc9afcba-9132-49ff-8e83-6b41ac304aaa"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:45:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IbevdEMMo7ftTadVMZrKr/9YgpUXSxzEGS5Ymz2q8FWJS9F6ZsybqjUzG22tDb8H0OhlMTgOfy3EcvX7hfG0Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T12:29:44.823416Z"},"content_sha256":"d9c0508afecc4a9536f6fcf02be9a8ba4d1fb3d6266ca4ec6a776c768ca270ae","schema_version":"1.0","event_id":"sha256:d9c0508afecc4a9536f6fcf02be9a8ba4d1fb3d6266ca4ec6a776c768ca270ae"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XXCVR527NSOHCCAI6NMLDRMX5J/bundle.json","state_url":"https://pith.science/pith/XXCVR527NSOHCCAI6NMLDRMX5J/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XXCVR527NSOHCCAI6NMLDRMX5J/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":"2026-05-21T12:29:44Z","links":{"resolver":"https://pith.science/pith/XXCVR527NSOHCCAI6NMLDRMX5J","bundle":"https://pith.science/pith/XXCVR527NSOHCCAI6NMLDRMX5J/bundle.json","state":"https://pith.science/pith/XXCVR527NSOHCCAI6NMLDRMX5J/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XXCVR527NSOHCCAI6NMLDRMX5J/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:XXCVR527NSOHCCAI6NMLDRMX5J","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":"655912b396d051732a9fea416051c5e7d2d3edfdcbd9ff33b4c6d064434c08f8","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2026-02-26T15:10:45Z","title_canon_sha256":"966ed38018ccb6281d77d39f16ff6eb23fedb6e2581a7984eaa793e2f5a522be"},"schema_version":"1.0","source":{"id":"2602.23089","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2602.23089","created_at":"2026-05-18T02:45:05Z"},{"alias_kind":"arxiv_version","alias_value":"2602.23089v2","created_at":"2026-05-18T02:45:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.23089","created_at":"2026-05-18T02:45:05Z"},{"alias_kind":"pith_short_12","alias_value":"XXCVR527NSOH","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"XXCVR527NSOHCCAI","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"XXCVR527","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:d9c0508afecc4a9536f6fcf02be9a8ba4d1fb3d6266ca4ec6a776c768ca270ae","target":"graph","created_at":"2026-05-18T02:45:05Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"By embedding this PDE as a physical constraint into the loss function, we train a neural network to approximate the transport velocity field. This approach enables purely unsupervised training, eliminating the need for ground-truth posterior samples."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The log-homotopy trajectory of the prior to posterior density function can be coupled with the continuity equation to yield a well-posed master PDE whose solution is accurately approximated by a neural network without introducing new instabilities or bias."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"A neural network approximates the velocity field of log-homotopy particle flow by enforcing a derived master PDE from the continuity equation, enabling unsupervised amortized Bayesian updates with reduced stiffness."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Coupling the log-homotopy path to the continuity equation produces a master PDE that a neural network solves unsupervised to transport prior densities to posteriors."}],"snapshot_sha256":"4260f894de20646b7ed81716b3fee7a0029f95ec060e27925edf27ad49f2233d"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"9af4963e6c4c4a8cdf9859597a694abe6ba437df6ce4cefe3495a0f2f9cbd04f"},"paper":{"abstract_excerpt":"The Bayesian update step poses significant computational challenges in high-dimensional nonlinear estimation. While log-homotopy particle flow filters offer an alternative to stochastic sampling, existing formulations usually yield stiff differential equations. Conversely, existing deep learning approximations typically treat the update as a black-box task or rely on asymptotic relaxation, neglecting the exact geometric structure of the finite-horizon probability transport. In this work, we propose a physics-informed neural particle flow, which is an amortized inference framework. To construct","authors_text":"Domonkos Csuzdi, Oliv\\'er T\\\"or\\H{o}, Tam\\'as B\\'ecsi","cross_cats":[],"headline":"Coupling the log-homotopy path to the continuity equation produces a master PDE that a neural network solves unsupervised to transport prior densities to posteriors.","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2026-02-26T15:10:45Z","title":"Physics-informed neural particle flow for the Bayesian update step"},"references":{"count":72,"internal_anchors":1,"resolved_work":72,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"M.-H. Chen, Q.-M. Shao, J. G. Ibrahim, Monte Carlo methods in Bayesian computation, Springer Science & Business Media, 2012","work_id":"1658c9bd-131c-4b2e-aa5a-c1afaf297228","year":2012},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"G. L. Jones, Q. Qin, Markov chain Monte Carlo in practice, Annual Review of Statistics and Its Application 9 (1) (2022) 557–578","work_id":"bb75bd6d-7a1d-48e7-942a-968bcfcc61ee","year":2022},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"S. Asmussen, P. W. Glynn, A new proof of convergence of MCMC via the ergodic theorem, Statistics & Proba- bility Letters 81 (10) (2011) 1482–1485","work_id":"306765d6-575a-49c1-872d-40abe85bd05d","year":2011},{"cited_arxiv_id":"","doi":"10.1007/978-981-13-2971-5","is_internal_anchor":false,"ref_index":4,"title":"A. Barbu, S.-C. Zhu, Monte Carlo Methods, Springer Singapore, Singapore, 2020.doi:10.1007/ 978-981-13-2971-5. URLhttp://link.springer.com/10.1007/978-981-13-2971-5","work_id":"94df91cb-83ef-4bea-93d6-08d7b65cbeb6","year":2020},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"C. P. Robert, G. Casella, Monte Carlo Statistical Methods, 2nd Edition, Springer, 2004","work_id":"3e9403b0-35ff-4ab7-8cf3-ca74dec4094f","year":2004}],"snapshot_sha256":"4f505a0a0855d09ae6a4d9d6f2ba7009f69ec87bb5ef10ac42a5ba852afadd26"},"source":{"id":"2602.23089","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-15T18:38:07.088699Z","id":"cc9afcba-9132-49ff-8e83-6b41ac304aaa","model_set":{"reader":"grok-4.3"},"one_line_summary":"A neural network approximates the velocity field of log-homotopy particle flow by enforcing a derived master PDE from the continuity equation, enabling unsupervised amortized Bayesian updates with reduced stiffness.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Coupling the log-homotopy path to the continuity equation produces a master PDE that a neural network solves unsupervised to transport prior densities to posteriors.","strongest_claim":"By embedding this PDE as a physical constraint into the loss function, we train a neural network to approximate the transport velocity field. This approach enables purely unsupervised training, eliminating the need for ground-truth posterior samples.","weakest_assumption":"The log-homotopy trajectory of the prior to posterior density function can be coupled with the continuity equation to yield a well-posed master PDE whose solution is accurately approximated by a neural network without introducing new instabilities or bias."}},"verdict_id":"cc9afcba-9132-49ff-8e83-6b41ac304aaa"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:f1b52b644ff2b6ed54bbddc5de6851c43792c0f50c433fce58be373f1cd76180","target":"record","created_at":"2026-05-18T02:45:05Z","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":"655912b396d051732a9fea416051c5e7d2d3edfdcbd9ff33b4c6d064434c08f8","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2026-02-26T15:10:45Z","title_canon_sha256":"966ed38018ccb6281d77d39f16ff6eb23fedb6e2581a7984eaa793e2f5a522be"},"schema_version":"1.0","source":{"id":"2602.23089","kind":"arxiv","version":2}},"canonical_sha256":"bdc558f75f6c9c710808f358b1c597ea77321b6266f3af344da3d9cfa55924cb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bdc558f75f6c9c710808f358b1c597ea77321b6266f3af344da3d9cfa55924cb","first_computed_at":"2026-05-18T02:45:05.099030Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:45:05.099030Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0CD9ocjhAqx6iQ0e73bLkrYVl1Zn5qmmpdQS328o92RLpKPilL0Cv3s2Im7UyXZ35u2GpsmFL1mW/zenDNclBg==","signature_status":"signed_v1","signed_at":"2026-05-18T02:45:05.099629Z","signed_message":"canonical_sha256_bytes"},"source_id":"2602.23089","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f1b52b644ff2b6ed54bbddc5de6851c43792c0f50c433fce58be373f1cd76180","sha256:d9c0508afecc4a9536f6fcf02be9a8ba4d1fb3d6266ca4ec6a776c768ca270ae"],"state_sha256":"c414cfa91a24155208c473c60dd4884479dd5bc53647e5a43933c165c9098a03"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xmB2LkkAAsJtIh1PDYn1iUuoVAkb7Qoa8RyoffzqGpDdCnskrCsctLO3cI6/fvB0Kt51ixKqVDHEagdYGzjmBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-21T12:29:44.826135Z","bundle_sha256":"020c24c8c83754b144ae5bf611ab4ad13c83902d5c8c1e65ad1e737831deb2eb"}}