{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:M72SKI4KBZODWWRAKMCJGZMLA3","short_pith_number":"pith:M72SKI4K","canonical_record":{"source":{"id":"2605.16754","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SY","submitted_at":"2026-05-16T02:09:16Z","cross_cats_sorted":["cs.SY"],"title_canon_sha256":"195c5934030c775484a50e46d80685468ef451d58f311de60fd78558065a5a54","abstract_canon_sha256":"dd223013e3cc28839aa67c5cb51204ec68717bc848995fdb63b5a67a5be8e36e"},"schema_version":"1.0"},"canonical_sha256":"67f525238a0e5c3b5a20530493658b06e2376f9afdad89ebf682b10900cec6c9","source":{"kind":"arxiv","id":"2605.16754","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.16754","created_at":"2026-05-20T00:03:19Z"},{"alias_kind":"arxiv_version","alias_value":"2605.16754v1","created_at":"2026-05-20T00:03:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.16754","created_at":"2026-05-20T00:03:19Z"},{"alias_kind":"pith_short_12","alias_value":"M72SKI4KBZOD","created_at":"2026-05-20T00:03:19Z"},{"alias_kind":"pith_short_16","alias_value":"M72SKI4KBZODWWRA","created_at":"2026-05-20T00:03:19Z"},{"alias_kind":"pith_short_8","alias_value":"M72SKI4K","created_at":"2026-05-20T00:03:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:M72SKI4KBZODWWRAKMCJGZMLA3","target":"record","payload":{"canonical_record":{"source":{"id":"2605.16754","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SY","submitted_at":"2026-05-16T02:09:16Z","cross_cats_sorted":["cs.SY"],"title_canon_sha256":"195c5934030c775484a50e46d80685468ef451d58f311de60fd78558065a5a54","abstract_canon_sha256":"dd223013e3cc28839aa67c5cb51204ec68717bc848995fdb63b5a67a5be8e36e"},"schema_version":"1.0"},"canonical_sha256":"67f525238a0e5c3b5a20530493658b06e2376f9afdad89ebf682b10900cec6c9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:03:19.899989Z","signature_b64":"JlXsKuhbVrhlQN+w1m/npbgppVbHTNtZnVIE5NU7qADSfPTHefbj9QfZwicPUmfOVX0f72POkMCl1Tzfxd2/Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"67f525238a0e5c3b5a20530493658b06e2376f9afdad89ebf682b10900cec6c9","last_reissued_at":"2026-05-20T00:03:19.898866Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:03:19.898866Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.16754","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":"2026-05-20T00:03:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jouij8oIxlHzxkTGzHfh/RRAlhbVgZBEcfYix8YjjCqZ8mVQUGlmnbKg6E5BDOJhh1ks5FyeMLigWGcyCW/CBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T22:28:49.519415Z"},"content_sha256":"4bb3eb6d34f3c3056149f1372655a2de597e740dafddf0927dc3dc2e4a26e112","schema_version":"1.0","event_id":"sha256:4bb3eb6d34f3c3056149f1372655a2de597e740dafddf0927dc3dc2e4a26e112"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:M72SKI4KBZODWWRAKMCJGZMLA3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Stable Fiber-Koopman Residual Dynamics for Environment-Constrained Robust Control","license":"http://creativecommons.org/licenses/by/4.0/","headline":"A fiber-bundle Koopman model with contraction residuals certifies stability for environment-varying vehicle control.","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Syed Pouladi","submitted_at":"2026-05-16T02:09:16Z","abstract_excerpt":"Learning-based dynamical models face a persistent tension between expressiveness and formal guarantees: richer model classes improve predictive accuracy, but their stability properties are typically verified only empirically, if at all.\n  This paper proposes \\emph{Stable Fiber-Koopman Residual Dynamics} (SFKD), a unified framework that simultaneously addresses environment-aware geometric consistency, latent-space stability certification, and bounded residual perturbation propagation.\n  Concretely, SFKD constructs a fiber bundle latent manifold whose fibers encode environment-specific dynamics;"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Theoretical analysis establishes ISS of the latent dynamics and a finite ultimate bound on tracking error. Numerical experiments demonstrate a 31% reduction in tracking RMSE, a 44% improvement in control smoothness, and near-zero latent stability violation rate across environment-switching scenarios.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That a contraction-constrained residual neural network can capture unmodeled nonlinear effects while still admitting an explicit input-to-state stability certificate without loss of expressiveness or violation of the fiber-bundle geometry.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"SFKD combines a fiber-bundle latent manifold, environment-conditioned Koopman operators, and contraction-constrained residuals to certify input-to-state stability while improving path-tracking performance under variable conditions.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A fiber-bundle Koopman model with contraction residuals certifies stability for environment-varying vehicle control.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"80717cf60c9cd974d8521c08576d650bbc6fa272db72017e8889914ee360534d"},"source":{"id":"2605.16754","kind":"arxiv","version":1},"verdict":{"id":"6e19c7f2-d663-410e-a38b-5d6c62682ff3","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T21:39:14.534075Z","strongest_claim":"Theoretical analysis establishes ISS of the latent dynamics and a finite ultimate bound on tracking error. Numerical experiments demonstrate a 31% reduction in tracking RMSE, a 44% improvement in control smoothness, and near-zero latent stability violation rate across environment-switching scenarios.","one_line_summary":"SFKD combines a fiber-bundle latent manifold, environment-conditioned Koopman operators, and contraction-constrained residuals to certify input-to-state stability while improving path-tracking performance under variable conditions.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That a contraction-constrained residual neural network can capture unmodeled nonlinear effects while still admitting an explicit input-to-state stability certificate without loss of expressiveness or violation of the fiber-bundle geometry.","pith_extraction_headline":"A fiber-bundle Koopman model with contraction residuals certifies stability for environment-varying vehicle control."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.16754/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_title_agreement","ran_at":"2026-05-19T22:01:19.802150Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T21:51:05.095259Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T19:01:56.324190Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T18:33:26.455273Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"767b44b4f13fa0c3618d88e297e80cd187c8a3d85dab40654ec19d88853d87f1"},"references":{"count":18,"sample":[{"doi":"","year":1931,"title":"Hamiltonian systems and transformation in Hilbert space,","work_id":"524f25f5-4df4-4385-8a91-f74139d9984a","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2018,"title":"Linear predictors for nonlinear dynamical sys- tems: Koopman operator meets model predictive control","work_id":"fd409937-1bb5-4b25-922a-957b1e15792a","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"On input-to- state stability verification of identified models obtained by Koopman operator,","work_id":"e7061d89-e85b-4e83-af15-16ebbaa24466","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"ICODE: Modeling dynam- ical systems with extrinsic input information,","work_id":"6a680ae2-6710-4ead-bdbb-bcb6e65c67b4","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"Learning Dynamics under Environmental Constraints via Measurement-Induced Bundle Structures,","work_id":"547ce436-2aec-49cd-ba0b-2955dd6327a0","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":18,"snapshot_sha256":"86d8aa372459075aefbecc020003b848929c47f9a9372666174e8f4c654b77a8","internal_anchors":1},"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":"6e19c7f2-d663-410e-a38b-5d6c62682ff3"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:03:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vgSvfMECfESjf3Szpk930bK7FJPCDPBVmWLxMubrc8TIy0YMk+KMgkPVOO1cDV+eo5PYddhZ/lEnb7sJV4J/Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T22:28:49.520836Z"},"content_sha256":"5ccdf7433a4e932bbec80bd520a3f49051a341613a057e3728f16b9e56f10cfc","schema_version":"1.0","event_id":"sha256:5ccdf7433a4e932bbec80bd520a3f49051a341613a057e3728f16b9e56f10cfc"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/M72SKI4KBZODWWRAKMCJGZMLA3/bundle.json","state_url":"https://pith.science/pith/M72SKI4KBZODWWRAKMCJGZMLA3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/M72SKI4KBZODWWRAKMCJGZMLA3/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":"2026-05-31T22:28:49Z","links":{"resolver":"https://pith.science/pith/M72SKI4KBZODWWRAKMCJGZMLA3","bundle":"https://pith.science/pith/M72SKI4KBZODWWRAKMCJGZMLA3/bundle.json","state":"https://pith.science/pith/M72SKI4KBZODWWRAKMCJGZMLA3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/M72SKI4KBZODWWRAKMCJGZMLA3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:M72SKI4KBZODWWRAKMCJGZMLA3","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":"dd223013e3cc28839aa67c5cb51204ec68717bc848995fdb63b5a67a5be8e36e","cross_cats_sorted":["cs.SY"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SY","submitted_at":"2026-05-16T02:09:16Z","title_canon_sha256":"195c5934030c775484a50e46d80685468ef451d58f311de60fd78558065a5a54"},"schema_version":"1.0","source":{"id":"2605.16754","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.16754","created_at":"2026-05-20T00:03:19Z"},{"alias_kind":"arxiv_version","alias_value":"2605.16754v1","created_at":"2026-05-20T00:03:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.16754","created_at":"2026-05-20T00:03:19Z"},{"alias_kind":"pith_short_12","alias_value":"M72SKI4KBZOD","created_at":"2026-05-20T00:03:19Z"},{"alias_kind":"pith_short_16","alias_value":"M72SKI4KBZODWWRA","created_at":"2026-05-20T00:03:19Z"},{"alias_kind":"pith_short_8","alias_value":"M72SKI4K","created_at":"2026-05-20T00:03:19Z"}],"graph_snapshots":[{"event_id":"sha256:5ccdf7433a4e932bbec80bd520a3f49051a341613a057e3728f16b9e56f10cfc","target":"graph","created_at":"2026-05-20T00:03:19Z","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":"Theoretical analysis establishes ISS of the latent dynamics and a finite ultimate bound on tracking error. Numerical experiments demonstrate a 31% reduction in tracking RMSE, a 44% improvement in control smoothness, and near-zero latent stability violation rate across environment-switching scenarios."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That a contraction-constrained residual neural network can capture unmodeled nonlinear effects while still admitting an explicit input-to-state stability certificate without loss of expressiveness or violation of the fiber-bundle geometry."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"SFKD combines a fiber-bundle latent manifold, environment-conditioned Koopman operators, and contraction-constrained residuals to certify input-to-state stability while improving path-tracking performance under variable conditions."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A fiber-bundle Koopman model with contraction residuals certifies stability for environment-varying vehicle control."}],"snapshot_sha256":"80717cf60c9cd974d8521c08576d650bbc6fa272db72017e8889914ee360534d"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"doi_title_agreement","ran_at":"2026-05-19T22:01:19.802150Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T21:51:05.095259Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T19:01:56.324190Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T18:33:26.455273Z","status":"skipped","version":"1.0.0"}],"endpoint":"/pith/2605.16754/integrity.json","findings":[],"snapshot_sha256":"767b44b4f13fa0c3618d88e297e80cd187c8a3d85dab40654ec19d88853d87f1","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Learning-based dynamical models face a persistent tension between expressiveness and formal guarantees: richer model classes improve predictive accuracy, but their stability properties are typically verified only empirically, if at all.\n  This paper proposes \\emph{Stable Fiber-Koopman Residual Dynamics} (SFKD), a unified framework that simultaneously addresses environment-aware geometric consistency, latent-space stability certification, and bounded residual perturbation propagation.\n  Concretely, SFKD constructs a fiber bundle latent manifold whose fibers encode environment-specific dynamics;","authors_text":"Syed Pouladi","cross_cats":["cs.SY"],"headline":"A fiber-bundle Koopman model with contraction residuals certifies stability for environment-varying vehicle control.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SY","submitted_at":"2026-05-16T02:09:16Z","title":"Stable Fiber-Koopman Residual Dynamics for Environment-Constrained Robust Control"},"references":{"count":18,"internal_anchors":1,"resolved_work":18,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Hamiltonian systems and transformation in Hilbert space,","work_id":"524f25f5-4df4-4385-8a91-f74139d9984a","year":1931},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Linear predictors for nonlinear dynamical sys- tems: Koopman operator meets model predictive control","work_id":"fd409937-1bb5-4b25-922a-957b1e15792a","year":2018},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"On input-to- state stability verification of identified models obtained by Koopman operator,","work_id":"e7061d89-e85b-4e83-af15-16ebbaa24466","year":2025},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"ICODE: Modeling dynam- ical systems with extrinsic input information,","work_id":"6a680ae2-6710-4ead-bdbb-bcb6e65c67b4","year":2025},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Learning Dynamics under Environmental Constraints via Measurement-Induced Bundle Structures,","work_id":"547ce436-2aec-49cd-ba0b-2955dd6327a0","year":2025}],"snapshot_sha256":"86d8aa372459075aefbecc020003b848929c47f9a9372666174e8f4c654b77a8"},"source":{"id":"2605.16754","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-19T21:39:14.534075Z","id":"6e19c7f2-d663-410e-a38b-5d6c62682ff3","model_set":{"reader":"grok-4.3"},"one_line_summary":"SFKD combines a fiber-bundle latent manifold, environment-conditioned Koopman operators, and contraction-constrained residuals to certify input-to-state stability while improving path-tracking performance under variable conditions.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A fiber-bundle Koopman model with contraction residuals certifies stability for environment-varying vehicle control.","strongest_claim":"Theoretical analysis establishes ISS of the latent dynamics and a finite ultimate bound on tracking error. Numerical experiments demonstrate a 31% reduction in tracking RMSE, a 44% improvement in control smoothness, and near-zero latent stability violation rate across environment-switching scenarios.","weakest_assumption":"That a contraction-constrained residual neural network can capture unmodeled nonlinear effects while still admitting an explicit input-to-state stability certificate without loss of expressiveness or violation of the fiber-bundle geometry."}},"verdict_id":"6e19c7f2-d663-410e-a38b-5d6c62682ff3"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:4bb3eb6d34f3c3056149f1372655a2de597e740dafddf0927dc3dc2e4a26e112","target":"record","created_at":"2026-05-20T00:03:19Z","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":"dd223013e3cc28839aa67c5cb51204ec68717bc848995fdb63b5a67a5be8e36e","cross_cats_sorted":["cs.SY"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SY","submitted_at":"2026-05-16T02:09:16Z","title_canon_sha256":"195c5934030c775484a50e46d80685468ef451d58f311de60fd78558065a5a54"},"schema_version":"1.0","source":{"id":"2605.16754","kind":"arxiv","version":1}},"canonical_sha256":"67f525238a0e5c3b5a20530493658b06e2376f9afdad89ebf682b10900cec6c9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"67f525238a0e5c3b5a20530493658b06e2376f9afdad89ebf682b10900cec6c9","first_computed_at":"2026-05-20T00:03:19.898866Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:03:19.898866Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JlXsKuhbVrhlQN+w1m/npbgppVbHTNtZnVIE5NU7qADSfPTHefbj9QfZwicPUmfOVX0f72POkMCl1Tzfxd2/Bg==","signature_status":"signed_v1","signed_at":"2026-05-20T00:03:19.899989Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.16754","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4bb3eb6d34f3c3056149f1372655a2de597e740dafddf0927dc3dc2e4a26e112","sha256:5ccdf7433a4e932bbec80bd520a3f49051a341613a057e3728f16b9e56f10cfc"],"state_sha256":"193dfcd957523ebd7440886c9ca7536e231b0d0e9a0f2c8d4ecd6ad59ff6871d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fIFPLcyMRtU0WoD4rhhgLUEPl9IGZc62QhWtKig2EKiSfIstBlcOLh3bDWhAgPWDcnwWameqEhPQaaibUK5aCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T22:28:49.527474Z","bundle_sha256":"68a23c962cf918ef5929fa965059994d3f3d95899d6ffb7a89bef30d75c50c5f"}}