{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:TOVKLSHANBNYORQL27UTDEMLTH","short_pith_number":"pith:TOVKLSHA","schema_version":"1.0","canonical_sha256":"9baaa5c8e0685b87460bd7e931918b99eaf0b68ff59ac02b2a6f764563a80d76","source":{"kind":"arxiv","id":"2008.10581","version":3},"attestation_state":"computed","paper":{"title":"Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Aman Sinha, John Duchi, Matthew O'Kelly, Russ Tedrake","submitted_at":"2020-08-24T17:46:27Z","abstract_excerpt":"Learning-based methodologies increasingly find applications in safety-critical domains like autonomous driving and medical robotics. Due to the rare nature of dangerous events, real-world testing is prohibitively expensive and unscalable. In this work, we employ a probabilistic approach to safety evaluation in simulation, where we are concerned with computing the probability of dangerous events. We develop a novel rare-event simulation method that combines exploration, exploitation, and optimization techniques to find failure modes and estimate their rate of occurrence. We provide rigorous gua"},"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":"2008.10581","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-08-24T17:46:27Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"2d8b66113618dbeb0ea40db49486b0d730930f6bc287c6a619c68faed8b1fa2c","abstract_canon_sha256":"e3be540b799ca3421217145035b5e0ea68da5c7c9b76a80099785388084fc427"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:04:01.618283Z","signature_b64":"rZPVYOMCy9wn4SBQtM5twLS9fDbqAIFbYvVIdJm9IuT3rShlHWVL/prVz+mbRp432LCnIJwXTVijqe8O701bDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9baaa5c8e0685b87460bd7e931918b99eaf0b68ff59ac02b2a6f764563a80d76","last_reissued_at":"2026-07-05T03:04:01.617845Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:04:01.617845Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Aman Sinha, John Duchi, Matthew O'Kelly, Russ Tedrake","submitted_at":"2020-08-24T17:46:27Z","abstract_excerpt":"Learning-based methodologies increasingly find applications in safety-critical domains like autonomous driving and medical robotics. Due to the rare nature of dangerous events, real-world testing is prohibitively expensive and unscalable. In this work, we employ a probabilistic approach to safety evaluation in simulation, where we are concerned with computing the probability of dangerous events. We develop a novel rare-event simulation method that combines exploration, exploitation, and optimization techniques to find failure modes and estimate their rate of occurrence. We provide rigorous gua"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2008.10581","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/2008.10581/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":"2008.10581","created_at":"2026-07-05T03:04:01.617917+00:00"},{"alias_kind":"arxiv_version","alias_value":"2008.10581v3","created_at":"2026-07-05T03:04:01.617917+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2008.10581","created_at":"2026-07-05T03:04:01.617917+00:00"},{"alias_kind":"pith_short_12","alias_value":"TOVKLSHANBNY","created_at":"2026-07-05T03:04:01.617917+00:00"},{"alias_kind":"pith_short_16","alias_value":"TOVKLSHANBNYORQL","created_at":"2026-07-05T03:04:01.617917+00:00"},{"alias_kind":"pith_short_8","alias_value":"TOVKLSHA","created_at":"2026-07-05T03:04:01.617917+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/TOVKLSHANBNYORQL27UTDEMLTH","json":"https://pith.science/pith/TOVKLSHANBNYORQL27UTDEMLTH.json","graph_json":"https://pith.science/api/pith-number/TOVKLSHANBNYORQL27UTDEMLTH/graph.json","events_json":"https://pith.science/api/pith-number/TOVKLSHANBNYORQL27UTDEMLTH/events.json","paper":"https://pith.science/paper/TOVKLSHA"},"agent_actions":{"view_html":"https://pith.science/pith/TOVKLSHANBNYORQL27UTDEMLTH","download_json":"https://pith.science/pith/TOVKLSHANBNYORQL27UTDEMLTH.json","view_paper":"https://pith.science/paper/TOVKLSHA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2008.10581&json=true","fetch_graph":"https://pith.science/api/pith-number/TOVKLSHANBNYORQL27UTDEMLTH/graph.json","fetch_events":"https://pith.science/api/pith-number/TOVKLSHANBNYORQL27UTDEMLTH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TOVKLSHANBNYORQL27UTDEMLTH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TOVKLSHANBNYORQL27UTDEMLTH/action/storage_attestation","attest_author":"https://pith.science/pith/TOVKLSHANBNYORQL27UTDEMLTH/action/author_attestation","sign_citation":"https://pith.science/pith/TOVKLSHANBNYORQL27UTDEMLTH/action/citation_signature","submit_replication":"https://pith.science/pith/TOVKLSHANBNYORQL27UTDEMLTH/action/replication_record"}},"created_at":"2026-07-05T03:04:01.617917+00:00","updated_at":"2026-07-05T03:04:01.617917+00:00"}