{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:L6RMZMQGO4DI7EHRSUVDFVXFZ2","short_pith_number":"pith:L6RMZMQG","schema_version":"1.0","canonical_sha256":"5fa2ccb20677068f90f1952a32d6e5ce87b9d068344e3eee0598abcfc10d67ff","source":{"kind":"arxiv","id":"2311.06769","version":1},"attestation_state":"computed","paper":{"title":"Learning Predictive Safety Filter via Decomposition of Robust Invariant Set","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.SY"],"primary_cat":"eess.SY","authors_text":"Changliu Liu, Chuxiong Hu, Weiye Zhao, Zeyang Li","submitted_at":"2023-11-12T08:11:28Z","abstract_excerpt":"Ensuring safety of nonlinear systems under model uncertainty and external disturbances is crucial, especially for real-world control tasks. Predictive methods such as robust model predictive control (RMPC) require solving nonconvex optimization problems online, which leads to high computational burden and poor scalability. Reinforcement learning (RL) works well with complex systems, but pays the price of losing rigorous safety guarantee. This paper presents a theoretical framework that bridges the advantages of both RMPC and RL to synthesize safety filters for nonlinear systems with state- and"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2311.06769","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SY","submitted_at":"2023-11-12T08:11:28Z","cross_cats_sorted":["cs.LG","cs.SY"],"title_canon_sha256":"3aa3c05890c2ecf9b8cc56b6db781de94409fb4af46684d262aa01c9b458bae8","abstract_canon_sha256":"f630e88173e2d0c6905c775bf14439dc1d9f3a3b6173cd56e80c5221ce70d8f3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:12:03.252404Z","signature_b64":"o4cPA9IuRvxNO1vtoYX+a7L7dchSDnv+WdgZgNKmTztL900sWkEzvF0y2PyqzkAlTmroPbcJfmS8UYyIQM8mBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5fa2ccb20677068f90f1952a32d6e5ce87b9d068344e3eee0598abcfc10d67ff","last_reissued_at":"2026-07-05T07:12:03.251942Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:12:03.251942Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Predictive Safety Filter via Decomposition of Robust Invariant Set","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.SY"],"primary_cat":"eess.SY","authors_text":"Changliu Liu, Chuxiong Hu, Weiye Zhao, Zeyang Li","submitted_at":"2023-11-12T08:11:28Z","abstract_excerpt":"Ensuring safety of nonlinear systems under model uncertainty and external disturbances is crucial, especially for real-world control tasks. Predictive methods such as robust model predictive control (RMPC) require solving nonconvex optimization problems online, which leads to high computational burden and poor scalability. Reinforcement learning (RL) works well with complex systems, but pays the price of losing rigorous safety guarantee. This paper presents a theoretical framework that bridges the advantages of both RMPC and RL to synthesize safety filters for nonlinear systems with state- and"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.06769","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2311.06769/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2311.06769","created_at":"2026-07-05T07:12:03.251998+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.06769v1","created_at":"2026-07-05T07:12:03.251998+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.06769","created_at":"2026-07-05T07:12:03.251998+00:00"},{"alias_kind":"pith_short_12","alias_value":"L6RMZMQGO4DI","created_at":"2026-07-05T07:12:03.251998+00:00"},{"alias_kind":"pith_short_16","alias_value":"L6RMZMQGO4DI7EHR","created_at":"2026-07-05T07:12:03.251998+00:00"},{"alias_kind":"pith_short_8","alias_value":"L6RMZMQG","created_at":"2026-07-05T07:12:03.251998+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2505.09067","citing_title":"Solving Reach- and Stabilize-Avoid Problems Using Discounted Reachability","ref_index":10,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/L6RMZMQGO4DI7EHRSUVDFVXFZ2","json":"https://pith.science/pith/L6RMZMQGO4DI7EHRSUVDFVXFZ2.json","graph_json":"https://pith.science/api/pith-number/L6RMZMQGO4DI7EHRSUVDFVXFZ2/graph.json","events_json":"https://pith.science/api/pith-number/L6RMZMQGO4DI7EHRSUVDFVXFZ2/events.json","paper":"https://pith.science/paper/L6RMZMQG"},"agent_actions":{"view_html":"https://pith.science/pith/L6RMZMQGO4DI7EHRSUVDFVXFZ2","download_json":"https://pith.science/pith/L6RMZMQGO4DI7EHRSUVDFVXFZ2.json","view_paper":"https://pith.science/paper/L6RMZMQG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.06769&json=true","fetch_graph":"https://pith.science/api/pith-number/L6RMZMQGO4DI7EHRSUVDFVXFZ2/graph.json","fetch_events":"https://pith.science/api/pith-number/L6RMZMQGO4DI7EHRSUVDFVXFZ2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L6RMZMQGO4DI7EHRSUVDFVXFZ2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L6RMZMQGO4DI7EHRSUVDFVXFZ2/action/storage_attestation","attest_author":"https://pith.science/pith/L6RMZMQGO4DI7EHRSUVDFVXFZ2/action/author_attestation","sign_citation":"https://pith.science/pith/L6RMZMQGO4DI7EHRSUVDFVXFZ2/action/citation_signature","submit_replication":"https://pith.science/pith/L6RMZMQGO4DI7EHRSUVDFVXFZ2/action/replication_record"}},"created_at":"2026-07-05T07:12:03.251998+00:00","updated_at":"2026-07-05T07:12:03.251998+00:00"}