{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:25JQ5POQ46MNAGCJTHCHMVILMV","short_pith_number":"pith:25JQ5POQ","canonical_record":{"source":{"id":"2605.15881","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.DS","submitted_at":"2026-05-15T11:58:15Z","cross_cats_sorted":["cs.AI","physics.comp-ph"],"title_canon_sha256":"df6be4044fab410f1205af66f1b279719a66bc092680df80382854979bdc7290","abstract_canon_sha256":"8a53d9ab972d05c6c95cb82df04211ba1a686b2aa8fee112517645009be35d82"},"schema_version":"1.0"},"canonical_sha256":"d7530ebdd0e798d0184999c476550b657bdaea47ea2dfa312e81a394d1677efd","source":{"kind":"arxiv","id":"2605.15881","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.15881","created_at":"2026-05-20T00:01:23Z"},{"alias_kind":"arxiv_version","alias_value":"2605.15881v1","created_at":"2026-05-20T00:01:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.15881","created_at":"2026-05-20T00:01:23Z"},{"alias_kind":"pith_short_12","alias_value":"25JQ5POQ46MN","created_at":"2026-05-20T00:01:23Z"},{"alias_kind":"pith_short_16","alias_value":"25JQ5POQ46MNAGCJ","created_at":"2026-05-20T00:01:23Z"},{"alias_kind":"pith_short_8","alias_value":"25JQ5POQ","created_at":"2026-05-20T00:01:23Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:25JQ5POQ46MNAGCJTHCHMVILMV","target":"record","payload":{"canonical_record":{"source":{"id":"2605.15881","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.DS","submitted_at":"2026-05-15T11:58:15Z","cross_cats_sorted":["cs.AI","physics.comp-ph"],"title_canon_sha256":"df6be4044fab410f1205af66f1b279719a66bc092680df80382854979bdc7290","abstract_canon_sha256":"8a53d9ab972d05c6c95cb82df04211ba1a686b2aa8fee112517645009be35d82"},"schema_version":"1.0"},"canonical_sha256":"d7530ebdd0e798d0184999c476550b657bdaea47ea2dfa312e81a394d1677efd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:01:23.435250Z","signature_b64":"mu0Ouqy42tvIAaNfN5GM6IukuFqQwmA6tWOlSxqWA+3T7c3scgqZ4SsM/deLiBm/idmByNe6wK7dkK9VhZZLBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d7530ebdd0e798d0184999c476550b657bdaea47ea2dfa312e81a394d1677efd","last_reissued_at":"2026-05-20T00:01:23.434387Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:01:23.434387Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.15881","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":"2026-05-20T00:01:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HBWS1QeGRYPAynunZFQn4gZ0KXyMAypGoYubA740adReCbogwOPqXxI2Tf7ysJPtNkO9BhSANNSN5m2p65ucBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T04:14:38.076823Z"},"content_sha256":"4fc4261281ea9dea49a95409dd67e6f944e9cf6f8230d4fa049764fd3f45efeb","schema_version":"1.0","event_id":"sha256:4fc4261281ea9dea49a95409dd67e6f944e9cf6f8230d4fa049764fd3f45efeb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:25JQ5POQ46MNAGCJTHCHMVILMV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Symplectic Neural Operators for Learning Infinite Dimensional Hamiltonian Systems","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Symplectic neural operators preserve structure to guarantee long-term stability in infinite-dimensional Hamiltonian systems.","cross_cats":["cs.AI","physics.comp-ph"],"primary_cat":"math.DS","authors_text":"Takaharu Yaguchi, Takashi Matsubara, Yeang Makara, Yusuke Tanaka","submitted_at":"2026-05-15T11:58:15Z","abstract_excerpt":"The modeling and simulation of infinite-dimensional Hamiltonian systems are central problems in mathematical physics and engineering, however they pose significant computational and structural challenges for standard data-driven architectures. In this work, we introduce the Symplectic Neural Operator, a neural operator architecture designed to preserve the symplectic structure intrinsic to Hamiltonian PDEs. We provide a theoretical characterization of their symplecticity and establish a rigorous long-term stability result based on the combination of symplectic structure preservation and learni"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"We provide a theoretical characterization of their symplecticity and establish a rigorous long-term stability result based on the combination of symplectic structure preservation and learning accuracy.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The learned operator must approximate the true dynamics with sufficient accuracy in addition to preserving symplecticity for the long-term stability result to hold in practice.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Symplectic Neural Operators preserve symplectic structure for learning infinite-dimensional Hamiltonian PDEs and deliver improved long-term energy stability in theory and experiments.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Symplectic neural operators preserve structure to guarantee long-term stability in infinite-dimensional Hamiltonian systems.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"e31975a2f3a9da43a5042740841ff50de618da050251b38788f0b6ada7534c26"},"source":{"id":"2605.15881","kind":"arxiv","version":1},"verdict":{"id":"c92a5bc2-5f17-4150-b899-9978fe9d13c2","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T19:18:53.854982Z","strongest_claim":"We provide a theoretical characterization of their symplecticity and establish a rigorous long-term stability result based on the combination of symplectic structure preservation and learning accuracy.","one_line_summary":"Symplectic Neural Operators preserve symplectic structure for learning infinite-dimensional Hamiltonian PDEs and deliver improved long-term energy stability in theory and experiments.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The learned operator must approximate the true dynamics with sufficient accuracy in addition to preserving symplecticity for the long-term stability result to hold in practice.","pith_extraction_headline":"Symplectic neural operators preserve structure to guarantee long-term stability in infinite-dimensional Hamiltonian systems."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.15881/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_title_agreement","ran_at":"2026-05-19T19:31:19.073783Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T19:31:10.742770Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T17:33:47.528081Z","status":"skipped","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T17:01:55.795138Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"4aae671ec0a3a1ed2f2b477dcac6b29e0aa3dbcb2e1155f00981407294e14084"},"references":{"count":35,"sample":[{"doi":"","year":1989,"title":"Neural Networks , volume =","work_id":"2cef4973-5571-411c-9b0b-b2e2e3939807","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":1995,"title":"IEEE Transactions on Neural Networks , volume =","work_id":"9e50d618-0a0f-4eb5-81b0-e0387546cf44","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.48550/arxiv.1910.03193","year":1910,"title":"DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators","work_id":"bb4e761c-4a1d-4c69-b8a2-91773da2eb63","ref_index":3,"cited_arxiv_id":"1910.03193","is_internal_anchor":true},{"doi":"","year":2021,"title":"Nature Machine Intelligence , volume =","work_id":"86f59837-71a8-4916-9833-4f5d579e23cf","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.48550/arxiv.2003.03485","year":2003,"title":"Neural Operator: Graph Kernel Network for Partial Differential Equations","work_id":"00a591bc-6cad-477c-be12-26d5623f625d","ref_index":5,"cited_arxiv_id":"2003.03485","is_internal_anchor":true}],"resolved_work":35,"snapshot_sha256":"cf113538a3701cc4b2216e4e5025d4cf988f42bcc2ef4ec12adb2d1da20b3dc8","internal_anchors":3},"formal_canon":{"evidence_count":2,"snapshot_sha256":"3d92580a3683f1d7a335f1287a9a566791080ed12b66ab86314ade74b59735f8"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"c92a5bc2-5f17-4150-b899-9978fe9d13c2"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:01:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"A3fDFoS/PXfxCvH9nqBrelTyICXFn4ZInMZyM1FrGviMS8eIqqet8lz913Ux33rWfpUV1tPmfuGdJ0Fx6v9hAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T04:14:38.077426Z"},"content_sha256":"047f0c4325e3680a5d0f748f423e7d1b0c7d9081e11d7aa2657eda387dee5387","schema_version":"1.0","event_id":"sha256:047f0c4325e3680a5d0f748f423e7d1b0c7d9081e11d7aa2657eda387dee5387"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/25JQ5POQ46MNAGCJTHCHMVILMV/bundle.json","state_url":"https://pith.science/pith/25JQ5POQ46MNAGCJTHCHMVILMV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/25JQ5POQ46MNAGCJTHCHMVILMV/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":"2026-05-25T04:14:38Z","links":{"resolver":"https://pith.science/pith/25JQ5POQ46MNAGCJTHCHMVILMV","bundle":"https://pith.science/pith/25JQ5POQ46MNAGCJTHCHMVILMV/bundle.json","state":"https://pith.science/pith/25JQ5POQ46MNAGCJTHCHMVILMV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/25JQ5POQ46MNAGCJTHCHMVILMV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:25JQ5POQ46MNAGCJTHCHMVILMV","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":"8a53d9ab972d05c6c95cb82df04211ba1a686b2aa8fee112517645009be35d82","cross_cats_sorted":["cs.AI","physics.comp-ph"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.DS","submitted_at":"2026-05-15T11:58:15Z","title_canon_sha256":"df6be4044fab410f1205af66f1b279719a66bc092680df80382854979bdc7290"},"schema_version":"1.0","source":{"id":"2605.15881","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.15881","created_at":"2026-05-20T00:01:23Z"},{"alias_kind":"arxiv_version","alias_value":"2605.15881v1","created_at":"2026-05-20T00:01:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.15881","created_at":"2026-05-20T00:01:23Z"},{"alias_kind":"pith_short_12","alias_value":"25JQ5POQ46MN","created_at":"2026-05-20T00:01:23Z"},{"alias_kind":"pith_short_16","alias_value":"25JQ5POQ46MNAGCJ","created_at":"2026-05-20T00:01:23Z"},{"alias_kind":"pith_short_8","alias_value":"25JQ5POQ","created_at":"2026-05-20T00:01:23Z"}],"graph_snapshots":[{"event_id":"sha256:047f0c4325e3680a5d0f748f423e7d1b0c7d9081e11d7aa2657eda387dee5387","target":"graph","created_at":"2026-05-20T00:01:23Z","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":"We provide a theoretical characterization of their symplecticity and establish a rigorous long-term stability result based on the combination of symplectic structure preservation and learning accuracy."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The learned operator must approximate the true dynamics with sufficient accuracy in addition to preserving symplecticity for the long-term stability result to hold in practice."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"Symplectic Neural Operators preserve symplectic structure for learning infinite-dimensional Hamiltonian PDEs and deliver improved long-term energy stability in theory and experiments."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Symplectic neural operators preserve structure to guarantee long-term stability in infinite-dimensional Hamiltonian systems."}],"snapshot_sha256":"e31975a2f3a9da43a5042740841ff50de618da050251b38788f0b6ada7534c26"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"3d92580a3683f1d7a335f1287a9a566791080ed12b66ab86314ade74b59735f8"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"doi_title_agreement","ran_at":"2026-05-19T19:31:19.073783Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T19:31:10.742770Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T17:33:47.528081Z","status":"skipped","version":"1.0.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T17:01:55.795138Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2605.15881/integrity.json","findings":[],"snapshot_sha256":"4aae671ec0a3a1ed2f2b477dcac6b29e0aa3dbcb2e1155f00981407294e14084","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The modeling and simulation of infinite-dimensional Hamiltonian systems are central problems in mathematical physics and engineering, however they pose significant computational and structural challenges for standard data-driven architectures. In this work, we introduce the Symplectic Neural Operator, a neural operator architecture designed to preserve the symplectic structure intrinsic to Hamiltonian PDEs. We provide a theoretical characterization of their symplecticity and establish a rigorous long-term stability result based on the combination of symplectic structure preservation and learni","authors_text":"Takaharu Yaguchi, Takashi Matsubara, Yeang Makara, Yusuke Tanaka","cross_cats":["cs.AI","physics.comp-ph"],"headline":"Symplectic neural operators preserve structure to guarantee long-term stability in infinite-dimensional Hamiltonian systems.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.DS","submitted_at":"2026-05-15T11:58:15Z","title":"Symplectic Neural Operators for Learning Infinite Dimensional Hamiltonian Systems"},"references":{"count":35,"internal_anchors":3,"resolved_work":35,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Neural Networks , volume =","work_id":"2cef4973-5571-411c-9b0b-b2e2e3939807","year":1989},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"IEEE Transactions on Neural Networks , volume =","work_id":"9e50d618-0a0f-4eb5-81b0-e0387546cf44","year":1995},{"cited_arxiv_id":"1910.03193","doi":"10.48550/arxiv.1910.03193","is_internal_anchor":true,"ref_index":3,"title":"DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators","work_id":"bb4e761c-4a1d-4c69-b8a2-91773da2eb63","year":1910},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Nature Machine Intelligence , volume =","work_id":"86f59837-71a8-4916-9833-4f5d579e23cf","year":2021},{"cited_arxiv_id":"2003.03485","doi":"10.48550/arxiv.2003.03485","is_internal_anchor":true,"ref_index":5,"title":"Neural Operator: Graph Kernel Network for Partial Differential Equations","work_id":"00a591bc-6cad-477c-be12-26d5623f625d","year":2003}],"snapshot_sha256":"cf113538a3701cc4b2216e4e5025d4cf988f42bcc2ef4ec12adb2d1da20b3dc8"},"source":{"id":"2605.15881","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-19T19:18:53.854982Z","id":"c92a5bc2-5f17-4150-b899-9978fe9d13c2","model_set":{"reader":"grok-4.3"},"one_line_summary":"Symplectic Neural Operators preserve symplectic structure for learning infinite-dimensional Hamiltonian PDEs and deliver improved long-term energy stability in theory and experiments.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Symplectic neural operators preserve structure to guarantee long-term stability in infinite-dimensional Hamiltonian systems.","strongest_claim":"We provide a theoretical characterization of their symplecticity and establish a rigorous long-term stability result based on the combination of symplectic structure preservation and learning accuracy.","weakest_assumption":"The learned operator must approximate the true dynamics with sufficient accuracy in addition to preserving symplecticity for the long-term stability result to hold in practice."}},"verdict_id":"c92a5bc2-5f17-4150-b899-9978fe9d13c2"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:4fc4261281ea9dea49a95409dd67e6f944e9cf6f8230d4fa049764fd3f45efeb","target":"record","created_at":"2026-05-20T00:01:23Z","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":"8a53d9ab972d05c6c95cb82df04211ba1a686b2aa8fee112517645009be35d82","cross_cats_sorted":["cs.AI","physics.comp-ph"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.DS","submitted_at":"2026-05-15T11:58:15Z","title_canon_sha256":"df6be4044fab410f1205af66f1b279719a66bc092680df80382854979bdc7290"},"schema_version":"1.0","source":{"id":"2605.15881","kind":"arxiv","version":1}},"canonical_sha256":"d7530ebdd0e798d0184999c476550b657bdaea47ea2dfa312e81a394d1677efd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d7530ebdd0e798d0184999c476550b657bdaea47ea2dfa312e81a394d1677efd","first_computed_at":"2026-05-20T00:01:23.434387Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:01:23.434387Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"mu0Ouqy42tvIAaNfN5GM6IukuFqQwmA6tWOlSxqWA+3T7c3scgqZ4SsM/deLiBm/idmByNe6wK7dkK9VhZZLBQ==","signature_status":"signed_v1","signed_at":"2026-05-20T00:01:23.435250Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.15881","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4fc4261281ea9dea49a95409dd67e6f944e9cf6f8230d4fa049764fd3f45efeb","sha256:047f0c4325e3680a5d0f748f423e7d1b0c7d9081e11d7aa2657eda387dee5387"],"state_sha256":"731e141531833a5eb28b7d620b4da2a51cb226b2115af62b751b8dda289e1a0c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"g8QwGDbhKwUr0GOJlpVh/n8YL7pWrSARUexL6ty1rMnnPIenJ7Duq7OS4NNYzwJUHUlvgUdmBDcd2s8dPb19CA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T04:14:38.080214Z","bundle_sha256":"c0a13328a7475ff601671099de5c2ac83f148b3b64ef0662fe309099ac098139"}}