{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:KSWWEQNTFFM5DNWESDHOEN2PWH","short_pith_number":"pith:KSWWEQNT","schema_version":"1.0","canonical_sha256":"54ad6241b32959d1b6c490cee2374fb1f5485780205c48390a113f5c8ac06386","source":{"kind":"arxiv","id":"2606.26922","version":1},"attestation_state":"computed","paper":{"title":"Risk-Aware Selective Multimodal Driver Monitoring with Driver-State World Modeling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Daosheng Qiu, Hao Su, Haozhuang Chi, Shu Long, Wei Zhang, Xinyue Miao, Yongle Dong","submitted_at":"2026-06-25T11:59:21Z","abstract_excerpt":"Continuous driver monitoring in automated vehicles requires low-latency inference while avoiding unsafe decisions under uncertain driver states. Large vision-language models provide broad multimodal priors, but their latency and limited reliability in this setting make them unsuitable as always-on in-cabin monitors. We propose a cost-aware selective inference framework for deployable multimodal driver monitoring. The core system is a lightweight RGB-physiological student that combines in-cabin visual observations with window-level HR/EDA signals, and a learned gate that decides when to accept "},"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":"2606.26922","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2026-06-25T11:59:21Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"346bf1387d6ba5eef14bad9d13123e309d1c036efe6571b7cd9fab9ac36be706","abstract_canon_sha256":"e1a19abbc9216b5d62d8de6150e976ad5f0737d58aa62f65e714ec47f808f9f9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-26T01:16:04.193337Z","signature_b64":"wegu48NigqrpVjKgXeRBMZzHcDCco4D0x737aAJ/ovkCWWTNXMbf3tXG3uRaXUWPiUhg0XxhgLUTMBOdF7ASAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"54ad6241b32959d1b6c490cee2374fb1f5485780205c48390a113f5c8ac06386","last_reissued_at":"2026-06-26T01:16:04.192952Z","signature_status":"signed_v1","first_computed_at":"2026-06-26T01:16:04.192952Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Risk-Aware Selective Multimodal Driver Monitoring with Driver-State World Modeling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.RO","authors_text":"Daosheng Qiu, Hao Su, Haozhuang Chi, Shu Long, Wei Zhang, Xinyue Miao, Yongle Dong","submitted_at":"2026-06-25T11:59:21Z","abstract_excerpt":"Continuous driver monitoring in automated vehicles requires low-latency inference while avoiding unsafe decisions under uncertain driver states. Large vision-language models provide broad multimodal priors, but their latency and limited reliability in this setting make them unsuitable as always-on in-cabin monitors. We propose a cost-aware selective inference framework for deployable multimodal driver monitoring. The core system is a lightweight RGB-physiological student that combines in-cabin visual observations with window-level HR/EDA signals, and a learned gate that decides when to accept "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.26922","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/2606.26922/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":"2606.26922","created_at":"2026-06-26T01:16:04.193006+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.26922v1","created_at":"2026-06-26T01:16:04.193006+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.26922","created_at":"2026-06-26T01:16:04.193006+00:00"},{"alias_kind":"pith_short_12","alias_value":"KSWWEQNTFFM5","created_at":"2026-06-26T01:16:04.193006+00:00"},{"alias_kind":"pith_short_16","alias_value":"KSWWEQNTFFM5DNWE","created_at":"2026-06-26T01:16:04.193006+00:00"},{"alias_kind":"pith_short_8","alias_value":"KSWWEQNT","created_at":"2026-06-26T01:16:04.193006+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/KSWWEQNTFFM5DNWESDHOEN2PWH","json":"https://pith.science/pith/KSWWEQNTFFM5DNWESDHOEN2PWH.json","graph_json":"https://pith.science/api/pith-number/KSWWEQNTFFM5DNWESDHOEN2PWH/graph.json","events_json":"https://pith.science/api/pith-number/KSWWEQNTFFM5DNWESDHOEN2PWH/events.json","paper":"https://pith.science/paper/KSWWEQNT"},"agent_actions":{"view_html":"https://pith.science/pith/KSWWEQNTFFM5DNWESDHOEN2PWH","download_json":"https://pith.science/pith/KSWWEQNTFFM5DNWESDHOEN2PWH.json","view_paper":"https://pith.science/paper/KSWWEQNT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.26922&json=true","fetch_graph":"https://pith.science/api/pith-number/KSWWEQNTFFM5DNWESDHOEN2PWH/graph.json","fetch_events":"https://pith.science/api/pith-number/KSWWEQNTFFM5DNWESDHOEN2PWH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KSWWEQNTFFM5DNWESDHOEN2PWH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KSWWEQNTFFM5DNWESDHOEN2PWH/action/storage_attestation","attest_author":"https://pith.science/pith/KSWWEQNTFFM5DNWESDHOEN2PWH/action/author_attestation","sign_citation":"https://pith.science/pith/KSWWEQNTFFM5DNWESDHOEN2PWH/action/citation_signature","submit_replication":"https://pith.science/pith/KSWWEQNTFFM5DNWESDHOEN2PWH/action/replication_record"}},"created_at":"2026-06-26T01:16:04.193006+00:00","updated_at":"2026-06-26T01:16:04.193006+00:00"}