{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:CW3N3MMXD4FHO5K2PHB45ADTQF","short_pith_number":"pith:CW3N3MMX","schema_version":"1.0","canonical_sha256":"15b6ddb1971f0a77755a79c3ce8073814f1b1f7496fa3215485200ef329094d5","source":{"kind":"arxiv","id":"2411.19809","version":1},"attestation_state":"computed","paper":{"title":"Q-learning-based Model-free Safety Filter","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.SY","eess.SY"],"primary_cat":"cs.RO","authors_text":"Carmel Majidi, Guanya Shi, Guo Ning Sue, John Dolan, Richard Desatnik, Yogita Choudhary","submitted_at":"2024-11-29T16:16:59Z","abstract_excerpt":"Ensuring safety via safety filters in real-world robotics presents significant challenges, particularly when the system dynamics is complex or unavailable. To handle this issue, learning-based safety filters recently gained popularity, which can be classified as model-based and model-free methods. Existing model-based approaches requires various assumptions on system model (e.g., control-affine), which limits their application in complex systems, and existing model-free approaches need substantial modifications to standard RL algorithms and lack versatility. This paper proposes a simple, plugi"},"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":"2411.19809","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2024-11-29T16:16:59Z","cross_cats_sorted":["cs.AI","cs.SY","eess.SY"],"title_canon_sha256":"f55b3d77fe1f0b8eb4dded25bbec6ede7768e39e2d078c03040e46831ec7a96d","abstract_canon_sha256":"6a173d2b705ec484a6c0135b3b139775c60e036f4f2348531c5439a0090fe715"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:42:08.699038Z","signature_b64":"6i+iK8VCZh8DtMfnvqn8O0b7IZcO+COAeZWmVp5LF2VYoQmvYP33TbrzKOeJH+1qS3pGHWv9DShxRPU8kdyTBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"15b6ddb1971f0a77755a79c3ce8073814f1b1f7496fa3215485200ef329094d5","last_reissued_at":"2026-07-05T09:42:08.698463Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:42:08.698463Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Q-learning-based Model-free Safety Filter","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.SY","eess.SY"],"primary_cat":"cs.RO","authors_text":"Carmel Majidi, Guanya Shi, Guo Ning Sue, John Dolan, Richard Desatnik, Yogita Choudhary","submitted_at":"2024-11-29T16:16:59Z","abstract_excerpt":"Ensuring safety via safety filters in real-world robotics presents significant challenges, particularly when the system dynamics is complex or unavailable. To handle this issue, learning-based safety filters recently gained popularity, which can be classified as model-based and model-free methods. Existing model-based approaches requires various assumptions on system model (e.g., control-affine), which limits their application in complex systems, and existing model-free approaches need substantial modifications to standard RL algorithms and lack versatility. This paper proposes a simple, plugi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.19809","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/2411.19809/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":"2411.19809","created_at":"2026-07-05T09:42:08.698527+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.19809v1","created_at":"2026-07-05T09:42:08.698527+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.19809","created_at":"2026-07-05T09:42:08.698527+00:00"},{"alias_kind":"pith_short_12","alias_value":"CW3N3MMXD4FH","created_at":"2026-07-05T09:42:08.698527+00:00"},{"alias_kind":"pith_short_16","alias_value":"CW3N3MMXD4FHO5K2","created_at":"2026-07-05T09:42:08.698527+00:00"},{"alias_kind":"pith_short_8","alias_value":"CW3N3MMX","created_at":"2026-07-05T09:42:08.698527+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.15693","citing_title":"Verifiable Safety Q-Filters via Hamilton-Jacobi Reachability and Multiplicative Q-Networks","ref_index":16,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CW3N3MMXD4FHO5K2PHB45ADTQF","json":"https://pith.science/pith/CW3N3MMXD4FHO5K2PHB45ADTQF.json","graph_json":"https://pith.science/api/pith-number/CW3N3MMXD4FHO5K2PHB45ADTQF/graph.json","events_json":"https://pith.science/api/pith-number/CW3N3MMXD4FHO5K2PHB45ADTQF/events.json","paper":"https://pith.science/paper/CW3N3MMX"},"agent_actions":{"view_html":"https://pith.science/pith/CW3N3MMXD4FHO5K2PHB45ADTQF","download_json":"https://pith.science/pith/CW3N3MMXD4FHO5K2PHB45ADTQF.json","view_paper":"https://pith.science/paper/CW3N3MMX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.19809&json=true","fetch_graph":"https://pith.science/api/pith-number/CW3N3MMXD4FHO5K2PHB45ADTQF/graph.json","fetch_events":"https://pith.science/api/pith-number/CW3N3MMXD4FHO5K2PHB45ADTQF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CW3N3MMXD4FHO5K2PHB45ADTQF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CW3N3MMXD4FHO5K2PHB45ADTQF/action/storage_attestation","attest_author":"https://pith.science/pith/CW3N3MMXD4FHO5K2PHB45ADTQF/action/author_attestation","sign_citation":"https://pith.science/pith/CW3N3MMXD4FHO5K2PHB45ADTQF/action/citation_signature","submit_replication":"https://pith.science/pith/CW3N3MMXD4FHO5K2PHB45ADTQF/action/replication_record"}},"created_at":"2026-07-05T09:42:08.698527+00:00","updated_at":"2026-07-05T09:42:08.698527+00:00"}