{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:LZSL35F4RIO5U672KQ2ERESQR5","short_pith_number":"pith:LZSL35F4","schema_version":"1.0","canonical_sha256":"5e64bdf4bc8a1dda7bfa54344892508f45c33bebfe542976164464d77139210c","source":{"kind":"arxiv","id":"2605.18895","version":1},"attestation_state":"computed","paper":{"title":"KG-ASG: Collision-Knowledge-Guided Closed-Loop Adversarial Scenario Generation With Primary-Support Attribution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Cheng Wang, Chen Xiong, Qiang Liu, Yuchen Zhou, Ziwen Wang","submitted_at":"2026-05-17T09:04:48Z","abstract_excerpt":"Safety validation of autonomous driving systems requires high-risk scenario coverage, clear collision semantics, executable trajectories, and attributable multi-vehicle interactions. Existing safety-critical scenario generation methods often rely on low-level trajectory perturbations, collision-proxy optimization, or single-adversary search, which may produce adversarial samples with ambiguous collision causes or uncontrolled multi-vehicle collisions. This paper proposes KG-ASG, a collision-knowledge-guided closed-loop adversarial scenario generation framework with primary-support attribution."},"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":"2605.18895","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2026-05-17T09:04:48Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"41b955b9b1c0398b5e5056c41d00c9447819248292c72544bc5758979ee66e8c","abstract_canon_sha256":"b546b0b167ea5b4ddcec77fe94f4a40949a6406381536a09152133b12a7a6521"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:06:30.981044Z","signature_b64":"zwhd0808G8xQ+sTctu6xYkowGCr9AMSjrg1VuhSPck6W5uzbiqfrxUOj2TVmjO7sF7EFtW0zYhsnr395ziiHAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5e64bdf4bc8a1dda7bfa54344892508f45c33bebfe542976164464d77139210c","last_reissued_at":"2026-05-20T00:06:30.980274Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:06:30.980274Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"KG-ASG: Collision-Knowledge-Guided Closed-Loop Adversarial Scenario Generation With Primary-Support Attribution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Cheng Wang, Chen Xiong, Qiang Liu, Yuchen Zhou, Ziwen Wang","submitted_at":"2026-05-17T09:04:48Z","abstract_excerpt":"Safety validation of autonomous driving systems requires high-risk scenario coverage, clear collision semantics, executable trajectories, and attributable multi-vehicle interactions. Existing safety-critical scenario generation methods often rely on low-level trajectory perturbations, collision-proxy optimization, or single-adversary search, which may produce adversarial samples with ambiguous collision causes or uncontrolled multi-vehicle collisions. This paper proposes KG-ASG, a collision-knowledge-guided closed-loop adversarial scenario generation framework with primary-support attribution."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.18895","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/2605.18895/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":"2605.18895","created_at":"2026-05-20T00:06:30.980434+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.18895v1","created_at":"2026-05-20T00:06:30.980434+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.18895","created_at":"2026-05-20T00:06:30.980434+00:00"},{"alias_kind":"pith_short_12","alias_value":"LZSL35F4RIO5","created_at":"2026-05-20T00:06:30.980434+00:00"},{"alias_kind":"pith_short_16","alias_value":"LZSL35F4RIO5U672","created_at":"2026-05-20T00:06:30.980434+00:00"},{"alias_kind":"pith_short_8","alias_value":"LZSL35F4","created_at":"2026-05-20T00:06:30.980434+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LZSL35F4RIO5U672KQ2ERESQR5","json":"https://pith.science/pith/LZSL35F4RIO5U672KQ2ERESQR5.json","graph_json":"https://pith.science/api/pith-number/LZSL35F4RIO5U672KQ2ERESQR5/graph.json","events_json":"https://pith.science/api/pith-number/LZSL35F4RIO5U672KQ2ERESQR5/events.json","paper":"https://pith.science/paper/LZSL35F4"},"agent_actions":{"view_html":"https://pith.science/pith/LZSL35F4RIO5U672KQ2ERESQR5","download_json":"https://pith.science/pith/LZSL35F4RIO5U672KQ2ERESQR5.json","view_paper":"https://pith.science/paper/LZSL35F4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.18895&json=true","fetch_graph":"https://pith.science/api/pith-number/LZSL35F4RIO5U672KQ2ERESQR5/graph.json","fetch_events":"https://pith.science/api/pith-number/LZSL35F4RIO5U672KQ2ERESQR5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LZSL35F4RIO5U672KQ2ERESQR5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LZSL35F4RIO5U672KQ2ERESQR5/action/storage_attestation","attest_author":"https://pith.science/pith/LZSL35F4RIO5U672KQ2ERESQR5/action/author_attestation","sign_citation":"https://pith.science/pith/LZSL35F4RIO5U672KQ2ERESQR5/action/citation_signature","submit_replication":"https://pith.science/pith/LZSL35F4RIO5U672KQ2ERESQR5/action/replication_record"}},"created_at":"2026-05-20T00:06:30.980434+00:00","updated_at":"2026-05-20T00:06:30.980434+00:00"}