{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:D5AUKWBJKOZM4XBQ3OGK4VILFA","short_pith_number":"pith:D5AUKWBJ","schema_version":"1.0","canonical_sha256":"1f4145582953b2ce5c30db8cae550b2835697d0af5fef2c5f3aec7d3da3c2565","source":{"kind":"arxiv","id":"2603.07306","version":2},"attestation_state":"computed","paper":{"title":"Seeing the Reasoning: How LLM Rationales Influence User Trust and Decision-Making in Factual Verification Tasks","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.HC","authors_text":"Abdallah El Ali, Isao Echizen, Jos A Bosch, Saku Sugawara, Shu Wei, Xin Sun","submitted_at":"2026-03-07T18:21:28Z","abstract_excerpt":"Large Language Models (LLMs) increasingly show reasoning rationales alongside their answers, turning \"reasoning\" into a user-interface element. While step-by-step rationales are typically associated with model performance, how they influence users' trust and decision-making in factual verification tasks remains unclear. We ran an online study (N=68) manipulating three properties of LLM reasoning rationales: presentation format (instant vs. delayed vs. on-demand), correctness (correct vs. incorrect), and certainty framing (none vs. certain vs. uncertain). We found that correct rationales and ce"},"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":"2603.07306","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.HC","submitted_at":"2026-03-07T18:21:28Z","cross_cats_sorted":[],"title_canon_sha256":"e051d3c6512e2d15d2823e286b092d9c06b98bc58c78ee770a7459b3405d9c31","abstract_canon_sha256":"81e9ee82941ba753ec5b76cbb9b34587c8c02aa7c0cd8ca6d46806af2167c62c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-25T01:17:51.576340Z","signature_b64":"GZmL2yNoWsfy5hBIEtlxsOsQ+JMo8/s7TnvzgU9oMCRjwcxphpaf9qTpFVWJ7o6GPQzNWGNTcfvpTs00jsOCDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1f4145582953b2ce5c30db8cae550b2835697d0af5fef2c5f3aec7d3da3c2565","last_reissued_at":"2026-06-25T01:17:51.575904Z","signature_status":"signed_v1","first_computed_at":"2026-06-25T01:17:51.575904Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Seeing the Reasoning: How LLM Rationales Influence User Trust and Decision-Making in Factual Verification Tasks","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.HC","authors_text":"Abdallah El Ali, Isao Echizen, Jos A Bosch, Saku Sugawara, Shu Wei, Xin Sun","submitted_at":"2026-03-07T18:21:28Z","abstract_excerpt":"Large Language Models (LLMs) increasingly show reasoning rationales alongside their answers, turning \"reasoning\" into a user-interface element. While step-by-step rationales are typically associated with model performance, how they influence users' trust and decision-making in factual verification tasks remains unclear. We ran an online study (N=68) manipulating three properties of LLM reasoning rationales: presentation format (instant vs. delayed vs. on-demand), correctness (correct vs. incorrect), and certainty framing (none vs. certain vs. uncertain). We found that correct rationales and ce"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2603.07306","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/2603.07306/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":"2603.07306","created_at":"2026-06-25T01:17:51.575964+00:00"},{"alias_kind":"arxiv_version","alias_value":"2603.07306v2","created_at":"2026-06-25T01:17:51.575964+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2603.07306","created_at":"2026-06-25T01:17:51.575964+00:00"},{"alias_kind":"pith_short_12","alias_value":"D5AUKWBJKOZM","created_at":"2026-06-25T01:17:51.575964+00:00"},{"alias_kind":"pith_short_16","alias_value":"D5AUKWBJKOZM4XBQ","created_at":"2026-06-25T01:17:51.575964+00:00"},{"alias_kind":"pith_short_8","alias_value":"D5AUKWBJ","created_at":"2026-06-25T01:17:51.575964+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2605.15455","citing_title":"Multi-Turn Neural Transparency: Surfacing Neural Activations Improves User Calibration to LLM Behavioral Drift","ref_index":43,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/D5AUKWBJKOZM4XBQ3OGK4VILFA","json":"https://pith.science/pith/D5AUKWBJKOZM4XBQ3OGK4VILFA.json","graph_json":"https://pith.science/api/pith-number/D5AUKWBJKOZM4XBQ3OGK4VILFA/graph.json","events_json":"https://pith.science/api/pith-number/D5AUKWBJKOZM4XBQ3OGK4VILFA/events.json","paper":"https://pith.science/paper/D5AUKWBJ"},"agent_actions":{"view_html":"https://pith.science/pith/D5AUKWBJKOZM4XBQ3OGK4VILFA","download_json":"https://pith.science/pith/D5AUKWBJKOZM4XBQ3OGK4VILFA.json","view_paper":"https://pith.science/paper/D5AUKWBJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2603.07306&json=true","fetch_graph":"https://pith.science/api/pith-number/D5AUKWBJKOZM4XBQ3OGK4VILFA/graph.json","fetch_events":"https://pith.science/api/pith-number/D5AUKWBJKOZM4XBQ3OGK4VILFA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/D5AUKWBJKOZM4XBQ3OGK4VILFA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/D5AUKWBJKOZM4XBQ3OGK4VILFA/action/storage_attestation","attest_author":"https://pith.science/pith/D5AUKWBJKOZM4XBQ3OGK4VILFA/action/author_attestation","sign_citation":"https://pith.science/pith/D5AUKWBJKOZM4XBQ3OGK4VILFA/action/citation_signature","submit_replication":"https://pith.science/pith/D5AUKWBJKOZM4XBQ3OGK4VILFA/action/replication_record"}},"created_at":"2026-06-25T01:17:51.575964+00:00","updated_at":"2026-06-25T01:17:51.575964+00:00"}