{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:JT6CZRXUCFDS76DYUP4KRROIOY","short_pith_number":"pith:JT6CZRXU","schema_version":"1.0","canonical_sha256":"4cfc2cc6f411472ff878a3f8a8c5c8760468e936cfcfae82ff58ec351b1e82cc","source":{"kind":"arxiv","id":"2408.01433","version":1},"attestation_state":"computed","paper":{"title":"Evaluating and Enhancing Trustworthiness of LLMs in Perception Tasks","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.ET"],"primary_cat":"cs.CV","authors_text":"Beatriz Cabrero-Daniel, Christian Berger, Malsha Ashani Mahawatta Dona, Yinan Yu","submitted_at":"2024-07-18T20:58:03Z","abstract_excerpt":"Today's advanced driver assistance systems (ADAS), like adaptive cruise control or rear collision warning, are finding broader adoption across vehicle classes. Integrating such advanced, multimodal Large Language Models (LLMs) on board a vehicle, which are capable of processing text, images, audio, and other data types, may have the potential to greatly enhance passenger comfort. Yet, an LLM's hallucinations are still a major challenge to be addressed. In this paper, we systematically assessed potential hallucination detection strategies for such LLMs in the context of object detection in visi"},"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":"2408.01433","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-07-18T20:58:03Z","cross_cats_sorted":["cs.ET"],"title_canon_sha256":"cf3dfee03662a4f95b3b5a0451df2825f72b20403dd99912d36cb6ded22018a4","abstract_canon_sha256":"6fa61f2273a087314674a3ec46c5148794cd5e914ae3984eb4eaf2ab8b3e3a9c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:51:57.742384Z","signature_b64":"5dMA/19imN8XflfhRaiXOb7Q0I8lr1T4pTA0D09yweB6NuJVr6Bmj1aAORe7Q/LBhWtiA4TQqYZ9VPE7S+RmAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4cfc2cc6f411472ff878a3f8a8c5c8760468e936cfcfae82ff58ec351b1e82cc","last_reissued_at":"2026-07-05T08:51:57.741933Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:51:57.741933Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Evaluating and Enhancing Trustworthiness of LLMs in Perception Tasks","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.ET"],"primary_cat":"cs.CV","authors_text":"Beatriz Cabrero-Daniel, Christian Berger, Malsha Ashani Mahawatta Dona, Yinan Yu","submitted_at":"2024-07-18T20:58:03Z","abstract_excerpt":"Today's advanced driver assistance systems (ADAS), like adaptive cruise control or rear collision warning, are finding broader adoption across vehicle classes. Integrating such advanced, multimodal Large Language Models (LLMs) on board a vehicle, which are capable of processing text, images, audio, and other data types, may have the potential to greatly enhance passenger comfort. Yet, an LLM's hallucinations are still a major challenge to be addressed. In this paper, we systematically assessed potential hallucination detection strategies for such LLMs in the context of object detection in visi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.01433","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/2408.01433/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":"2408.01433","created_at":"2026-07-05T08:51:57.741987+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.01433v1","created_at":"2026-07-05T08:51:57.741987+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.01433","created_at":"2026-07-05T08:51:57.741987+00:00"},{"alias_kind":"pith_short_12","alias_value":"JT6CZRXUCFDS","created_at":"2026-07-05T08:51:57.741987+00:00"},{"alias_kind":"pith_short_16","alias_value":"JT6CZRXUCFDS76DY","created_at":"2026-07-05T08:51:57.741987+00:00"},{"alias_kind":"pith_short_8","alias_value":"JT6CZRXU","created_at":"2026-07-05T08:51:57.741987+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.00991","citing_title":"Large Language Models in Transportation Systems Management and Operations: From Text Reasoning to Multi-modal Decision Support","ref_index":40,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JT6CZRXUCFDS76DYUP4KRROIOY","json":"https://pith.science/pith/JT6CZRXUCFDS76DYUP4KRROIOY.json","graph_json":"https://pith.science/api/pith-number/JT6CZRXUCFDS76DYUP4KRROIOY/graph.json","events_json":"https://pith.science/api/pith-number/JT6CZRXUCFDS76DYUP4KRROIOY/events.json","paper":"https://pith.science/paper/JT6CZRXU"},"agent_actions":{"view_html":"https://pith.science/pith/JT6CZRXUCFDS76DYUP4KRROIOY","download_json":"https://pith.science/pith/JT6CZRXUCFDS76DYUP4KRROIOY.json","view_paper":"https://pith.science/paper/JT6CZRXU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.01433&json=true","fetch_graph":"https://pith.science/api/pith-number/JT6CZRXUCFDS76DYUP4KRROIOY/graph.json","fetch_events":"https://pith.science/api/pith-number/JT6CZRXUCFDS76DYUP4KRROIOY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JT6CZRXUCFDS76DYUP4KRROIOY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JT6CZRXUCFDS76DYUP4KRROIOY/action/storage_attestation","attest_author":"https://pith.science/pith/JT6CZRXUCFDS76DYUP4KRROIOY/action/author_attestation","sign_citation":"https://pith.science/pith/JT6CZRXUCFDS76DYUP4KRROIOY/action/citation_signature","submit_replication":"https://pith.science/pith/JT6CZRXUCFDS76DYUP4KRROIOY/action/replication_record"}},"created_at":"2026-07-05T08:51:57.741987+00:00","updated_at":"2026-07-05T08:51:57.741987+00:00"}