{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:PWT65XSMTQHOZFE3EQLY54MO6L","short_pith_number":"pith:PWT65XSM","schema_version":"1.0","canonical_sha256":"7da7eede4c9c0eec949b24178ef18ef2c31d8067300d26dac068004d3ec5621f","source":{"kind":"arxiv","id":"2606.14438","version":2},"attestation_state":"computed","paper":{"title":"CADET: Physics-Grounded Causal Auditing and Training-Free Deconfounding of End-to-End Driving Planners","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Zikun Guo","submitted_at":"2026-06-12T13:19:47Z","abstract_excerpt":"End-to-end (E2E) autonomous-driving planners trained by imitation are prone to statistical shortcuts: they associate scene elements that merely co-occur with expert actions (a roadside object, a building facade) with driving decisions, rather than the variables that causally determine them. Such causal confusion silently compromises reliability in long-tail scenarios, and it is difficult to detect, because prevailing open-loop metrics (L2 displacement and collision rate) are dominated by ego status and do not indicate whether a planner depends on spurious cues. Existing remedies based on causa"},"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":"2606.14438","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-06-12T13:19:47Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"cc255afa95bce00ea482250d2ed43976c7caebb83ab61a5931a81b6db2033419","abstract_canon_sha256":"b9f2587d36c2a2a005bc646bdea5848b970d55476aca68167ce486db01e3315d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-19T16:10:00.886498Z","signature_b64":"4LmGTLUOhO8bMzjFJ7auhZ0IwC8XMAvO1mWWftNDCR5qk0rB7LKE8NmzohbaMT8xMZfwqGdPSfZDX463I9Q1Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7da7eede4c9c0eec949b24178ef18ef2c31d8067300d26dac068004d3ec5621f","last_reissued_at":"2026-06-19T16:10:00.886158Z","signature_status":"signed_v1","first_computed_at":"2026-06-19T16:10:00.886158Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CADET: Physics-Grounded Causal Auditing and Training-Free Deconfounding of End-to-End Driving Planners","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Zikun Guo","submitted_at":"2026-06-12T13:19:47Z","abstract_excerpt":"End-to-end (E2E) autonomous-driving planners trained by imitation are prone to statistical shortcuts: they associate scene elements that merely co-occur with expert actions (a roadside object, a building facade) with driving decisions, rather than the variables that causally determine them. Such causal confusion silently compromises reliability in long-tail scenarios, and it is difficult to detect, because prevailing open-loop metrics (L2 displacement and collision rate) are dominated by ego status and do not indicate whether a planner depends on spurious cues. Existing remedies based on causa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.14438","kind":"arxiv","version":2},"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/2606.14438/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":"2606.14438","created_at":"2026-06-19T16:10:00.886212+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.14438v2","created_at":"2026-06-19T16:10:00.886212+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.14438","created_at":"2026-06-19T16:10:00.886212+00:00"},{"alias_kind":"pith_short_12","alias_value":"PWT65XSMTQHO","created_at":"2026-06-19T16:10:00.886212+00:00"},{"alias_kind":"pith_short_16","alias_value":"PWT65XSMTQHOZFE3","created_at":"2026-06-19T16:10:00.886212+00:00"},{"alias_kind":"pith_short_8","alias_value":"PWT65XSM","created_at":"2026-06-19T16:10:00.886212+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/PWT65XSMTQHOZFE3EQLY54MO6L","json":"https://pith.science/pith/PWT65XSMTQHOZFE3EQLY54MO6L.json","graph_json":"https://pith.science/api/pith-number/PWT65XSMTQHOZFE3EQLY54MO6L/graph.json","events_json":"https://pith.science/api/pith-number/PWT65XSMTQHOZFE3EQLY54MO6L/events.json","paper":"https://pith.science/paper/PWT65XSM"},"agent_actions":{"view_html":"https://pith.science/pith/PWT65XSMTQHOZFE3EQLY54MO6L","download_json":"https://pith.science/pith/PWT65XSMTQHOZFE3EQLY54MO6L.json","view_paper":"https://pith.science/paper/PWT65XSM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.14438&json=true","fetch_graph":"https://pith.science/api/pith-number/PWT65XSMTQHOZFE3EQLY54MO6L/graph.json","fetch_events":"https://pith.science/api/pith-number/PWT65XSMTQHOZFE3EQLY54MO6L/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PWT65XSMTQHOZFE3EQLY54MO6L/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PWT65XSMTQHOZFE3EQLY54MO6L/action/storage_attestation","attest_author":"https://pith.science/pith/PWT65XSMTQHOZFE3EQLY54MO6L/action/author_attestation","sign_citation":"https://pith.science/pith/PWT65XSMTQHOZFE3EQLY54MO6L/action/citation_signature","submit_replication":"https://pith.science/pith/PWT65XSMTQHOZFE3EQLY54MO6L/action/replication_record"}},"created_at":"2026-06-19T16:10:00.886212+00:00","updated_at":"2026-06-19T16:10:00.886212+00:00"}