{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:2RAZIT43NKWFGZKN6YA5AIEMGE","short_pith_number":"pith:2RAZIT43","schema_version":"1.0","canonical_sha256":"d441944f9b6aac53654df601d0208c3129bd5aba276725e42b3bc5aca367b3a7","source":{"kind":"arxiv","id":"2506.00780","version":1},"attestation_state":"computed","paper":{"title":"Do not Abstain! Identify and Solve the Uncertainty","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Bo Zheng, Jingquan Peng, Jingyu Liu, Tiezheng Ge, Xiaopeng Wu, Xubin Li, Yong Liu","submitted_at":"2025-06-01T02:15:17Z","abstract_excerpt":"Despite the widespread application of Large Language Models (LLMs) across various domains, they frequently exhibit overconfidence when encountering uncertain scenarios, yet existing solutions primarily rely on evasive responses (e.g., \"I don't know\") overlooks the opportunity of identifying and addressing the uncertainty to generate more satisfactory responses. To systematically investigate and improve LLMs' ability of recognizing and addressing the source of uncertainty, we introduce \\textbf{ConfuseBench}, a benchmark mainly focus on three types of uncertainty: document scarcity, limited capa"},"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":"2506.00780","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-06-01T02:15:17Z","cross_cats_sorted":[],"title_canon_sha256":"bf9790b1ce911cf2e4ed462b8c2b3ab8f155f1060aac4c0340e87e5329516e22","abstract_canon_sha256":"0c77fa2210caf10218809018d320960681b6331259622ac865f83f80100caf43"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:13:40.316510Z","signature_b64":"vfe853ydhIJcRhsHCVgIfwmbcY2C+FgCAKlPvznfavxzNRsZvsOLFY6IVlV+ZKm2f0y4VW0JCNWAN+iflxEoCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d441944f9b6aac53654df601d0208c3129bd5aba276725e42b3bc5aca367b3a7","last_reissued_at":"2026-07-05T11:13:40.316046Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:13:40.316046Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Do not Abstain! Identify and Solve the Uncertainty","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Bo Zheng, Jingquan Peng, Jingyu Liu, Tiezheng Ge, Xiaopeng Wu, Xubin Li, Yong Liu","submitted_at":"2025-06-01T02:15:17Z","abstract_excerpt":"Despite the widespread application of Large Language Models (LLMs) across various domains, they frequently exhibit overconfidence when encountering uncertain scenarios, yet existing solutions primarily rely on evasive responses (e.g., \"I don't know\") overlooks the opportunity of identifying and addressing the uncertainty to generate more satisfactory responses. To systematically investigate and improve LLMs' ability of recognizing and addressing the source of uncertainty, we introduce \\textbf{ConfuseBench}, a benchmark mainly focus on three types of uncertainty: document scarcity, limited capa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.00780","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/2506.00780/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":"2506.00780","created_at":"2026-07-05T11:13:40.316102+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.00780v1","created_at":"2026-07-05T11:13:40.316102+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.00780","created_at":"2026-07-05T11:13:40.316102+00:00"},{"alias_kind":"pith_short_12","alias_value":"2RAZIT43NKWF","created_at":"2026-07-05T11:13:40.316102+00:00"},{"alias_kind":"pith_short_16","alias_value":"2RAZIT43NKWFGZKN","created_at":"2026-07-05T11:13:40.316102+00:00"},{"alias_kind":"pith_short_8","alias_value":"2RAZIT43","created_at":"2026-07-05T11:13:40.316102+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/2RAZIT43NKWFGZKN6YA5AIEMGE","json":"https://pith.science/pith/2RAZIT43NKWFGZKN6YA5AIEMGE.json","graph_json":"https://pith.science/api/pith-number/2RAZIT43NKWFGZKN6YA5AIEMGE/graph.json","events_json":"https://pith.science/api/pith-number/2RAZIT43NKWFGZKN6YA5AIEMGE/events.json","paper":"https://pith.science/paper/2RAZIT43"},"agent_actions":{"view_html":"https://pith.science/pith/2RAZIT43NKWFGZKN6YA5AIEMGE","download_json":"https://pith.science/pith/2RAZIT43NKWFGZKN6YA5AIEMGE.json","view_paper":"https://pith.science/paper/2RAZIT43","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.00780&json=true","fetch_graph":"https://pith.science/api/pith-number/2RAZIT43NKWFGZKN6YA5AIEMGE/graph.json","fetch_events":"https://pith.science/api/pith-number/2RAZIT43NKWFGZKN6YA5AIEMGE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2RAZIT43NKWFGZKN6YA5AIEMGE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2RAZIT43NKWFGZKN6YA5AIEMGE/action/storage_attestation","attest_author":"https://pith.science/pith/2RAZIT43NKWFGZKN6YA5AIEMGE/action/author_attestation","sign_citation":"https://pith.science/pith/2RAZIT43NKWFGZKN6YA5AIEMGE/action/citation_signature","submit_replication":"https://pith.science/pith/2RAZIT43NKWFGZKN6YA5AIEMGE/action/replication_record"}},"created_at":"2026-07-05T11:13:40.316102+00:00","updated_at":"2026-07-05T11:13:40.316102+00:00"}