{"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"}