{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:QDATM47IOUBCIF3P5WLGZ4A2BZ","short_pith_number":"pith:QDATM47I","schema_version":"1.0","canonical_sha256":"80c13673e8750224176fed966cf01a0e4a2445ac2305f22964ec6be43a77acaa","source":{"kind":"arxiv","id":"2410.04986","version":3},"attestation_state":"computed","paper":{"title":"Finding Safety Violations of AI-Enabled Control Systems through the Lens of Synthesized Proxy Programs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Bowen Xu, David Lo, DongGyun Han, Dongsun Kim, Jieke Shi, Junda He, Zhou Yang","submitted_at":"2024-10-07T12:34:20Z","abstract_excerpt":"Given the increasing adoption of modern AI-enabled control systems, ensuring their safety and reliability has become a critical task in software testing. One prevalent approach to testing control systems is falsification, which aims to find an input signal that causes the control system to violate a formal safety specification using optimization algorithms. However, applying falsification to AI-enabled control systems poses two significant challenges: (1)~it requires the system to execute numerous candidate test inputs, which can be time-consuming, particularly for systems with AI models that "},"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":"2410.04986","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2024-10-07T12:34:20Z","cross_cats_sorted":[],"title_canon_sha256":"df61656745efeca6b370eefe5ecd03cbfb6d55cfbdb58d57c340c59100bf18f1","abstract_canon_sha256":"5e637e50926f5520a190fd5599934d8ba362de6a280cf03480b68603f7749e7b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:02:52.936521Z","signature_b64":"CXIl8bwHxjDlMVsTTH0e/WtLeKjf7tcK6KHNEwcxmowqANZTugEtxgvaEg/QBVXmyGsvEpcMxQGj8KYUKrKoCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"80c13673e8750224176fed966cf01a0e4a2445ac2305f22964ec6be43a77acaa","last_reissued_at":"2026-07-05T10:02:52.935977Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:02:52.935977Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Finding Safety Violations of AI-Enabled Control Systems through the Lens of Synthesized Proxy Programs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.SE","authors_text":"Bowen Xu, David Lo, DongGyun Han, Dongsun Kim, Jieke Shi, Junda He, Zhou Yang","submitted_at":"2024-10-07T12:34:20Z","abstract_excerpt":"Given the increasing adoption of modern AI-enabled control systems, ensuring their safety and reliability has become a critical task in software testing. One prevalent approach to testing control systems is falsification, which aims to find an input signal that causes the control system to violate a formal safety specification using optimization algorithms. However, applying falsification to AI-enabled control systems poses two significant challenges: (1)~it requires the system to execute numerous candidate test inputs, which can be time-consuming, particularly for systems with AI models that "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.04986","kind":"arxiv","version":3},"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/2410.04986/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":"2410.04986","created_at":"2026-07-05T10:02:52.936039+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.04986v3","created_at":"2026-07-05T10:02:52.936039+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.04986","created_at":"2026-07-05T10:02:52.936039+00:00"},{"alias_kind":"pith_short_12","alias_value":"QDATM47IOUBC","created_at":"2026-07-05T10:02:52.936039+00:00"},{"alias_kind":"pith_short_16","alias_value":"QDATM47IOUBCIF3P","created_at":"2026-07-05T10:02:52.936039+00:00"},{"alias_kind":"pith_short_8","alias_value":"QDATM47I","created_at":"2026-07-05T10:02:52.936039+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.08504","citing_title":"MoDitector: Module-Directed Testing for Autonomous Driving Systems","ref_index":39,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QDATM47IOUBCIF3P5WLGZ4A2BZ","json":"https://pith.science/pith/QDATM47IOUBCIF3P5WLGZ4A2BZ.json","graph_json":"https://pith.science/api/pith-number/QDATM47IOUBCIF3P5WLGZ4A2BZ/graph.json","events_json":"https://pith.science/api/pith-number/QDATM47IOUBCIF3P5WLGZ4A2BZ/events.json","paper":"https://pith.science/paper/QDATM47I"},"agent_actions":{"view_html":"https://pith.science/pith/QDATM47IOUBCIF3P5WLGZ4A2BZ","download_json":"https://pith.science/pith/QDATM47IOUBCIF3P5WLGZ4A2BZ.json","view_paper":"https://pith.science/paper/QDATM47I","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.04986&json=true","fetch_graph":"https://pith.science/api/pith-number/QDATM47IOUBCIF3P5WLGZ4A2BZ/graph.json","fetch_events":"https://pith.science/api/pith-number/QDATM47IOUBCIF3P5WLGZ4A2BZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QDATM47IOUBCIF3P5WLGZ4A2BZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QDATM47IOUBCIF3P5WLGZ4A2BZ/action/storage_attestation","attest_author":"https://pith.science/pith/QDATM47IOUBCIF3P5WLGZ4A2BZ/action/author_attestation","sign_citation":"https://pith.science/pith/QDATM47IOUBCIF3P5WLGZ4A2BZ/action/citation_signature","submit_replication":"https://pith.science/pith/QDATM47IOUBCIF3P5WLGZ4A2BZ/action/replication_record"}},"created_at":"2026-07-05T10:02:52.936039+00:00","updated_at":"2026-07-05T10:02:52.936039+00:00"}