{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:W3SR3RHLVKOE6S3W4H6LLTROGU","short_pith_number":"pith:W3SR3RHL","schema_version":"1.0","canonical_sha256":"b6e51dc4ebaa9c4f4b76e1fcb5ce2e3523e508a6f084bfd418378a3463cfbf0f","source":{"kind":"arxiv","id":"2508.21228","version":1},"attestation_state":"computed","paper":{"title":"Decoding Memories: An Efficient Pipeline for Self-Consistency Hallucination Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Dan Lu, Feiyi Wang, Junqi Yin, Weizhi Gao, Xiaorui Liu","submitted_at":"2025-08-28T21:39:53Z","abstract_excerpt":"Large language models (LLMs) have demonstrated impressive performance in both research and real-world applications, but they still struggle with hallucination. Existing hallucination detection methods often perform poorly on sentence-level generation or rely heavily on domain-specific knowledge. While self-consistency approaches help address these limitations, they incur high computational costs due to repeated generation. In this paper, we conduct the first study on identifying redundancy in self-consistency methods, manifested as shared prefix tokens across generations, and observe that non-"},"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":"2508.21228","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-08-28T21:39:53Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"94d1951049a86fbf959303327fefb17b22164215ed2e1e414cb4ae4a6a6d1896","abstract_canon_sha256":"1bc6ec18dc56d4c8c431b4a2ce35ce5bea48a8c1132db61782d59a7207ec280a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:01:30.465819Z","signature_b64":"J+hxl+yj5Gb42Ub7HEsIPXhaRRi2UngoHoRcBgPt/vzyryGkdns4Ft9kJ0E3FgQbIUbNMfbPELxMM3lO6MwqDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b6e51dc4ebaa9c4f4b76e1fcb5ce2e3523e508a6f084bfd418378a3463cfbf0f","last_reissued_at":"2026-07-05T12:01:30.465269Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:01:30.465269Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Decoding Memories: An Efficient Pipeline for Self-Consistency Hallucination Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Dan Lu, Feiyi Wang, Junqi Yin, Weizhi Gao, Xiaorui Liu","submitted_at":"2025-08-28T21:39:53Z","abstract_excerpt":"Large language models (LLMs) have demonstrated impressive performance in both research and real-world applications, but they still struggle with hallucination. Existing hallucination detection methods often perform poorly on sentence-level generation or rely heavily on domain-specific knowledge. While self-consistency approaches help address these limitations, they incur high computational costs due to repeated generation. In this paper, we conduct the first study on identifying redundancy in self-consistency methods, manifested as shared prefix tokens across generations, and observe that non-"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.21228","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/2508.21228/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":"2508.21228","created_at":"2026-07-05T12:01:30.465340+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.21228v1","created_at":"2026-07-05T12:01:30.465340+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.21228","created_at":"2026-07-05T12:01:30.465340+00:00"},{"alias_kind":"pith_short_12","alias_value":"W3SR3RHLVKOE","created_at":"2026-07-05T12:01:30.465340+00:00"},{"alias_kind":"pith_short_16","alias_value":"W3SR3RHLVKOE6S3W","created_at":"2026-07-05T12:01:30.465340+00:00"},{"alias_kind":"pith_short_8","alias_value":"W3SR3RHL","created_at":"2026-07-05T12:01:30.465340+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.20500","citing_title":"Efficient Test-Time Inference via Deterministic Exploration of Truncated Decoding Trees","ref_index":4,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/W3SR3RHLVKOE6S3W4H6LLTROGU","json":"https://pith.science/pith/W3SR3RHLVKOE6S3W4H6LLTROGU.json","graph_json":"https://pith.science/api/pith-number/W3SR3RHLVKOE6S3W4H6LLTROGU/graph.json","events_json":"https://pith.science/api/pith-number/W3SR3RHLVKOE6S3W4H6LLTROGU/events.json","paper":"https://pith.science/paper/W3SR3RHL"},"agent_actions":{"view_html":"https://pith.science/pith/W3SR3RHLVKOE6S3W4H6LLTROGU","download_json":"https://pith.science/pith/W3SR3RHLVKOE6S3W4H6LLTROGU.json","view_paper":"https://pith.science/paper/W3SR3RHL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.21228&json=true","fetch_graph":"https://pith.science/api/pith-number/W3SR3RHLVKOE6S3W4H6LLTROGU/graph.json","fetch_events":"https://pith.science/api/pith-number/W3SR3RHLVKOE6S3W4H6LLTROGU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/W3SR3RHLVKOE6S3W4H6LLTROGU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/W3SR3RHLVKOE6S3W4H6LLTROGU/action/storage_attestation","attest_author":"https://pith.science/pith/W3SR3RHLVKOE6S3W4H6LLTROGU/action/author_attestation","sign_citation":"https://pith.science/pith/W3SR3RHLVKOE6S3W4H6LLTROGU/action/citation_signature","submit_replication":"https://pith.science/pith/W3SR3RHLVKOE6S3W4H6LLTROGU/action/replication_record"}},"created_at":"2026-07-05T12:01:30.465340+00:00","updated_at":"2026-07-05T12:01:30.465340+00:00"}