{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:GZEZPV4H6BPUPFRNTVPN7HRSI3","short_pith_number":"pith:GZEZPV4H","canonical_record":{"source":{"id":"2605.16752","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2026-05-16T02:09:02Z","cross_cats_sorted":[],"title_canon_sha256":"a288d22f04da6f6be8558d1e04735cf954c5e8c949d84210c21eced33a1863b1","abstract_canon_sha256":"b1134a159868011ba6b84f99eecd7446ca9b50b9776e6b5f3f3688a92fb8cb3a"},"schema_version":"1.0"},"canonical_sha256":"364997d787f05f47962d9d5edf9e3246fb37793d5f344a231310fff12b2cd046","source":{"kind":"arxiv","id":"2605.16752","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.16752","created_at":"2026-05-20T00:03:19Z"},{"alias_kind":"arxiv_version","alias_value":"2605.16752v1","created_at":"2026-05-20T00:03:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.16752","created_at":"2026-05-20T00:03:19Z"},{"alias_kind":"pith_short_12","alias_value":"GZEZPV4H6BPU","created_at":"2026-05-20T00:03:19Z"},{"alias_kind":"pith_short_16","alias_value":"GZEZPV4H6BPUPFRN","created_at":"2026-05-20T00:03:19Z"},{"alias_kind":"pith_short_8","alias_value":"GZEZPV4H","created_at":"2026-05-20T00:03:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:GZEZPV4H6BPUPFRNTVPN7HRSI3","target":"record","payload":{"canonical_record":{"source":{"id":"2605.16752","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2026-05-16T02:09:02Z","cross_cats_sorted":[],"title_canon_sha256":"a288d22f04da6f6be8558d1e04735cf954c5e8c949d84210c21eced33a1863b1","abstract_canon_sha256":"b1134a159868011ba6b84f99eecd7446ca9b50b9776e6b5f3f3688a92fb8cb3a"},"schema_version":"1.0"},"canonical_sha256":"364997d787f05f47962d9d5edf9e3246fb37793d5f344a231310fff12b2cd046","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:03:19.803635Z","signature_b64":"knyM19zWNisWgjfKZS/+UpLhO2AcjsNXXbLvWpM4AtPvTAAbGqGaGhpvsslxstLWhpEvjMqcofJGrVwv/RrlBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"364997d787f05f47962d9d5edf9e3246fb37793d5f344a231310fff12b2cd046","last_reissued_at":"2026-05-20T00:03:19.802768Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:03:19.802768Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.16752","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":"4M3+l6Kdu3upHPGsYT3U0EYzbUJt5qRMHNdAxxZbgQeui1c9sV8p4GrEvhInMA7hbW0HO3TS8vKUVZ5ZatKqCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T14:02:11.638352Z"},"content_sha256":"c27972fdefbc18c94744f85f6fc6a57adc75ad77181f6016ae09b9ad6c0a947b","schema_version":"1.0","event_id":"sha256:c27972fdefbc18c94744f85f6fc6a57adc75ad77181f6016ae09b9ad6c0a947b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:GZEZPV4H6BPUPFRNTVPN7HRSI3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Data-Driven Linear Quadratic Control Using Output-Feedback via Non-Minimal Realization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"An augmented system from Kreisselmeier's adaptive filter recovers the optimal state-feedback gain for the original plant in data-driven LQ control.","cross_cats":[],"primary_cat":"math.OC","authors_text":"Bowen Yi, Hai Lin, Panos J. Antsaklis, Weijian Li","submitted_at":"2026-05-16T02:09:02Z","abstract_excerpt":"In this paper, we investigate a continuous-time linear quadratic control problem for systems with unknown matrices, where only input-output data are available. We propose an output-feedback learning framework based on a canonical nonminimal realization constructed through Kreisselmeier's adaptive filter. The filter admits an observer interpretation, which leads to an augmented system that preserves the input-output response of the realization and provides accessible state trajectories. We show that the optimal gain of this augmented system explicitly recovers the optimal gain associated with t"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"We show that the optimal gain of this augmented system explicitly recovers the optimal gain associated with the canonical non-minimal realization, and hence achieves the optimal state-feedback solution of the original plant.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The Kreisselmeier's adaptive filter admits an observer interpretation that leads to an augmented system preserving the input-output response of the realization and providing accessible state trajectories (abstract, paragraph describing the filter and augmented system).","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Presents a data-driven value iteration algorithm for output-feedback LQR that recovers the optimal state-feedback gain via a non-minimal realization constructed from Kreisselmeier's adaptive filter.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"An augmented system from Kreisselmeier's adaptive filter recovers the optimal state-feedback gain for the original plant in data-driven LQ control.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"aa2eda354fcfabde937e24eba323e0b2ef62efbc9bf05402f1fbb844cf888f5f"},"source":{"id":"2605.16752","kind":"arxiv","version":1},"verdict":{"id":"40d859f6-9a0c-403f-9295-9419306fa391","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T21:24:02.318760Z","strongest_claim":"We show that the optimal gain of this augmented system explicitly recovers the optimal gain associated with the canonical non-minimal realization, and hence achieves the optimal state-feedback solution of the original plant.","one_line_summary":"Presents a data-driven value iteration algorithm for output-feedback LQR that recovers the optimal state-feedback gain via a non-minimal realization constructed from Kreisselmeier's adaptive filter.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The Kreisselmeier's adaptive filter admits an observer interpretation that leads to an augmented system preserving the input-output response of the realization and providing accessible state trajectories (abstract, paragraph describing the filter and augmented system).","pith_extraction_headline":"An augmented system from Kreisselmeier's adaptive filter recovers the optimal state-feedback gain for the original plant in data-driven LQ control."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.16752/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_title_agreement","ran_at":"2026-05-19T21:31:19.374176Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T21:31:13.943247Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T19:01:56.325707Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T18:33:26.456689Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"e057eceb5bbcaf211efe38fd7a8b1e08939964d32ec27a489adf33f8dc6f36f5"},"references":{"count":32,"sample":[{"doi":"","year":2018,"title":"R. S. Sutton and A. G. Barto,Reinforcement Learning: An Introduction, 2nd ed. Cambridge, MA: MIT Press, 2018","work_id":"c22bd12f-59aa-4a67-b420-22a6f5074e90","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":1974,"title":"Beyond regression: New tools for prediction and analysis in the behavioral sciences,","work_id":"4b623f50-8dae-4db4-a61f-707b2f8af725","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2021,"title":"Deep reinforcement learning for autonomous driving: A survey,","work_id":"3e2aa56c-848c-4167-a6a3-207a7187988b","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2013,"title":"Reinforcement learning in robotics: A survey,","work_id":"7a5b5887-b9a8-4f0a-b783-b0361171714f","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2023,"title":"Data-driven control based on the behavioral approach from theory to applications in power systems,","work_id":"e8e4c66c-0ce7-429d-b77e-9e7eb07fd438","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":32,"snapshot_sha256":"97c31a494d621f4cae195550278c0757fcbf128119f16d6641244b4e8bbab818","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":"40d859f6-9a0c-403f-9295-9419306fa391"},"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":"8Yf9hWPic9u1rLSQ9MK6S9sKPhx81LeZjv4m5hGLhDZI4FAMhHZ+LlxVV/uRbTCW0O5E8dgHJu10g/jU5B/4Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T14:02:11.639508Z"},"content_sha256":"659b5d640b9db957109bc52ce97ac55257bd7064f9549908efca4e31292612ee","schema_version":"1.0","event_id":"sha256:659b5d640b9db957109bc52ce97ac55257bd7064f9549908efca4e31292612ee"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GZEZPV4H6BPUPFRNTVPN7HRSI3/bundle.json","state_url":"https://pith.science/pith/GZEZPV4H6BPUPFRNTVPN7HRSI3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GZEZPV4H6BPUPFRNTVPN7HRSI3/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":"2026-05-28T14:02:11Z","links":{"resolver":"https://pith.science/pith/GZEZPV4H6BPUPFRNTVPN7HRSI3","bundle":"https://pith.science/pith/GZEZPV4H6BPUPFRNTVPN7HRSI3/bundle.json","state":"https://pith.science/pith/GZEZPV4H6BPUPFRNTVPN7HRSI3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GZEZPV4H6BPUPFRNTVPN7HRSI3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:GZEZPV4H6BPUPFRNTVPN7HRSI3","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":"b1134a159868011ba6b84f99eecd7446ca9b50b9776e6b5f3f3688a92fb8cb3a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2026-05-16T02:09:02Z","title_canon_sha256":"a288d22f04da6f6be8558d1e04735cf954c5e8c949d84210c21eced33a1863b1"},"schema_version":"1.0","source":{"id":"2605.16752","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.16752","created_at":"2026-05-20T00:03:19Z"},{"alias_kind":"arxiv_version","alias_value":"2605.16752v1","created_at":"2026-05-20T00:03:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.16752","created_at":"2026-05-20T00:03:19Z"},{"alias_kind":"pith_short_12","alias_value":"GZEZPV4H6BPU","created_at":"2026-05-20T00:03:19Z"},{"alias_kind":"pith_short_16","alias_value":"GZEZPV4H6BPUPFRN","created_at":"2026-05-20T00:03:19Z"},{"alias_kind":"pith_short_8","alias_value":"GZEZPV4H","created_at":"2026-05-20T00:03:19Z"}],"graph_snapshots":[{"event_id":"sha256:659b5d640b9db957109bc52ce97ac55257bd7064f9549908efca4e31292612ee","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":"We show that the optimal gain of this augmented system explicitly recovers the optimal gain associated with the canonical non-minimal realization, and hence achieves the optimal state-feedback solution of the original plant."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The Kreisselmeier's adaptive filter admits an observer interpretation that leads to an augmented system preserving the input-output response of the realization and providing accessible state trajectories (abstract, paragraph describing the filter and augmented system)."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"Presents a data-driven value iteration algorithm for output-feedback LQR that recovers the optimal state-feedback gain via a non-minimal realization constructed from Kreisselmeier's adaptive filter."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"An augmented system from Kreisselmeier's adaptive filter recovers the optimal state-feedback gain for the original plant in data-driven LQ control."}],"snapshot_sha256":"aa2eda354fcfabde937e24eba323e0b2ef62efbc9bf05402f1fbb844cf888f5f"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"doi_title_agreement","ran_at":"2026-05-19T21:31:19.374176Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T21:31:13.943247Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T19:01:56.325707Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T18:33:26.456689Z","status":"skipped","version":"1.0.0"}],"endpoint":"/pith/2605.16752/integrity.json","findings":[],"snapshot_sha256":"e057eceb5bbcaf211efe38fd7a8b1e08939964d32ec27a489adf33f8dc6f36f5","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this paper, we investigate a continuous-time linear quadratic control problem for systems with unknown matrices, where only input-output data are available. We propose an output-feedback learning framework based on a canonical nonminimal realization constructed through Kreisselmeier's adaptive filter. The filter admits an observer interpretation, which leads to an augmented system that preserves the input-output response of the realization and provides accessible state trajectories. We show that the optimal gain of this augmented system explicitly recovers the optimal gain associated with t","authors_text":"Bowen Yi, Hai Lin, Panos J. Antsaklis, Weijian Li","cross_cats":[],"headline":"An augmented system from Kreisselmeier's adaptive filter recovers the optimal state-feedback gain for the original plant in data-driven LQ control.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2026-05-16T02:09:02Z","title":"Data-Driven Linear Quadratic Control Using Output-Feedback via Non-Minimal Realization"},"references":{"count":32,"internal_anchors":0,"resolved_work":32,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"R. S. Sutton and A. G. Barto,Reinforcement Learning: An Introduction, 2nd ed. Cambridge, MA: MIT Press, 2018","work_id":"c22bd12f-59aa-4a67-b420-22a6f5074e90","year":2018},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Beyond regression: New tools for prediction and analysis in the behavioral sciences,","work_id":"4b623f50-8dae-4db4-a61f-707b2f8af725","year":1974},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Deep reinforcement learning for autonomous driving: A survey,","work_id":"3e2aa56c-848c-4167-a6a3-207a7187988b","year":2021},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Reinforcement learning in robotics: A survey,","work_id":"7a5b5887-b9a8-4f0a-b783-b0361171714f","year":2013},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Data-driven control based on the behavioral approach from theory to applications in power systems,","work_id":"e8e4c66c-0ce7-429d-b77e-9e7eb07fd438","year":2023}],"snapshot_sha256":"97c31a494d621f4cae195550278c0757fcbf128119f16d6641244b4e8bbab818"},"source":{"id":"2605.16752","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-19T21:24:02.318760Z","id":"40d859f6-9a0c-403f-9295-9419306fa391","model_set":{"reader":"grok-4.3"},"one_line_summary":"Presents a data-driven value iteration algorithm for output-feedback LQR that recovers the optimal state-feedback gain via a non-minimal realization constructed from Kreisselmeier's adaptive filter.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"An augmented system from Kreisselmeier's adaptive filter recovers the optimal state-feedback gain for the original plant in data-driven LQ control.","strongest_claim":"We show that the optimal gain of this augmented system explicitly recovers the optimal gain associated with the canonical non-minimal realization, and hence achieves the optimal state-feedback solution of the original plant.","weakest_assumption":"The Kreisselmeier's adaptive filter admits an observer interpretation that leads to an augmented system preserving the input-output response of the realization and providing accessible state trajectories (abstract, paragraph describing the filter and augmented system)."}},"verdict_id":"40d859f6-9a0c-403f-9295-9419306fa391"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:c27972fdefbc18c94744f85f6fc6a57adc75ad77181f6016ae09b9ad6c0a947b","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":"b1134a159868011ba6b84f99eecd7446ca9b50b9776e6b5f3f3688a92fb8cb3a","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2026-05-16T02:09:02Z","title_canon_sha256":"a288d22f04da6f6be8558d1e04735cf954c5e8c949d84210c21eced33a1863b1"},"schema_version":"1.0","source":{"id":"2605.16752","kind":"arxiv","version":1}},"canonical_sha256":"364997d787f05f47962d9d5edf9e3246fb37793d5f344a231310fff12b2cd046","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"364997d787f05f47962d9d5edf9e3246fb37793d5f344a231310fff12b2cd046","first_computed_at":"2026-05-20T00:03:19.802768Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:03:19.802768Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"knyM19zWNisWgjfKZS/+UpLhO2AcjsNXXbLvWpM4AtPvTAAbGqGaGhpvsslxstLWhpEvjMqcofJGrVwv/RrlBw==","signature_status":"signed_v1","signed_at":"2026-05-20T00:03:19.803635Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.16752","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c27972fdefbc18c94744f85f6fc6a57adc75ad77181f6016ae09b9ad6c0a947b","sha256:659b5d640b9db957109bc52ce97ac55257bd7064f9549908efca4e31292612ee"],"state_sha256":"0de47be650095c6ed9582ff03b26e930c7265fe3fc10f73650f371550c9fd36a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"A0kEPMh4qyqkDm10SthZPqUiT/7B58LH+cz0OBcIP0DbduX2mdl4hc1KGNRZSBNtUybbCRLdmz0UQrMJYNNzBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T14:02:11.643153Z","bundle_sha256":"4b46a9cec2b7f17ac991ff9eb4ab7b64ce242c6032b732f5f712ed1a5b005bbc"}}