{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:VZF3LIWXXIRUKJE3YJZD3EBNSD","short_pith_number":"pith:VZF3LIWX","schema_version":"1.0","canonical_sha256":"ae4bb5a2d7ba2345249bc2723d902d90e3373f92e5309fcb3df07e25cf3f2c0b","source":{"kind":"arxiv","id":"2407.08582","version":3},"attestation_state":"computed","paper":{"title":"On the Universal Truthfulness Hyperplane Inside LLMs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Junteng Liu, Junxian He, Shiqi Chen, Yu Cheng","submitted_at":"2024-07-11T15:07:26Z","abstract_excerpt":"While large language models (LLMs) have demonstrated remarkable abilities across various fields, hallucination remains a significant challenge. Recent studies have explored hallucinations through the lens of internal representations, proposing mechanisms to decipher LLMs' adherence to facts. However, these approaches often fail to generalize to out-of-distribution data, leading to concerns about whether internal representation patterns reflect fundamental factual awareness, or only overfit spurious correlations on the specific datasets. In this work, we investigate whether a universal truthful"},"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":"2407.08582","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-07-11T15:07:26Z","cross_cats_sorted":[],"title_canon_sha256":"0e87bcf6af4137897442676fb45ff3c335ecb755f9cdc334acfd6d381d23cc90","abstract_canon_sha256":"ea1aecc8abb214c170fa710c8ff3dd3fcc4d7337a92671ce1e40576fd103561a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:54:02.873475Z","signature_b64":"O0a9Rh0QByd07x3CVFYMGbyl3ntIc6y8/6OGQMjC80nedRuVS8Wc6/xxnl8FZdoA6w5I4vMGOAaX/y7CeKcpBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ae4bb5a2d7ba2345249bc2723d902d90e3373f92e5309fcb3df07e25cf3f2c0b","last_reissued_at":"2026-07-05T09:54:02.873070Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:54:02.873070Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On the Universal Truthfulness Hyperplane Inside LLMs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Junteng Liu, Junxian He, Shiqi Chen, Yu Cheng","submitted_at":"2024-07-11T15:07:26Z","abstract_excerpt":"While large language models (LLMs) have demonstrated remarkable abilities across various fields, hallucination remains a significant challenge. Recent studies have explored hallucinations through the lens of internal representations, proposing mechanisms to decipher LLMs' adherence to facts. However, these approaches often fail to generalize to out-of-distribution data, leading to concerns about whether internal representation patterns reflect fundamental factual awareness, or only overfit spurious correlations on the specific datasets. In this work, we investigate whether a universal truthful"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.08582","kind":"arxiv","version":3},"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/2407.08582/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":"2407.08582","created_at":"2026-07-05T09:54:02.873129+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.08582v3","created_at":"2026-07-05T09:54:02.873129+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.08582","created_at":"2026-07-05T09:54:02.873129+00:00"},{"alias_kind":"pith_short_12","alias_value":"VZF3LIWXXIRU","created_at":"2026-07-05T09:54:02.873129+00:00"},{"alias_kind":"pith_short_16","alias_value":"VZF3LIWXXIRUKJE3","created_at":"2026-07-05T09:54:02.873129+00:00"},{"alias_kind":"pith_short_8","alias_value":"VZF3LIWX","created_at":"2026-07-05T09:54:02.873129+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.03998","citing_title":"Toward Better Generalisation in Uncertainty Estimators: Leveraging Data-Agnostic Features","ref_index":19,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VZF3LIWXXIRUKJE3YJZD3EBNSD","json":"https://pith.science/pith/VZF3LIWXXIRUKJE3YJZD3EBNSD.json","graph_json":"https://pith.science/api/pith-number/VZF3LIWXXIRUKJE3YJZD3EBNSD/graph.json","events_json":"https://pith.science/api/pith-number/VZF3LIWXXIRUKJE3YJZD3EBNSD/events.json","paper":"https://pith.science/paper/VZF3LIWX"},"agent_actions":{"view_html":"https://pith.science/pith/VZF3LIWXXIRUKJE3YJZD3EBNSD","download_json":"https://pith.science/pith/VZF3LIWXXIRUKJE3YJZD3EBNSD.json","view_paper":"https://pith.science/paper/VZF3LIWX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.08582&json=true","fetch_graph":"https://pith.science/api/pith-number/VZF3LIWXXIRUKJE3YJZD3EBNSD/graph.json","fetch_events":"https://pith.science/api/pith-number/VZF3LIWXXIRUKJE3YJZD3EBNSD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VZF3LIWXXIRUKJE3YJZD3EBNSD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VZF3LIWXXIRUKJE3YJZD3EBNSD/action/storage_attestation","attest_author":"https://pith.science/pith/VZF3LIWXXIRUKJE3YJZD3EBNSD/action/author_attestation","sign_citation":"https://pith.science/pith/VZF3LIWXXIRUKJE3YJZD3EBNSD/action/citation_signature","submit_replication":"https://pith.science/pith/VZF3LIWXXIRUKJE3YJZD3EBNSD/action/replication_record"}},"created_at":"2026-07-05T09:54:02.873129+00:00","updated_at":"2026-07-05T09:54:02.873129+00:00"}