{"as_of":"2026-08-07T17:52:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7fc595deeeff47a10eac48b8bf20f4cd73ecd07669e9e966357f4b8cf211bcfd","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T11:23:45.108804Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-18T10:22:33.270277Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2402.18048","last_updated":"2024-02-28T04:56:21Z","snapshot_observed_at":"2026-08-04T05:17:17.897641Z","submitted_at":"2024-02-28T04:56:21Z","title":"Characterizing Truthfulness in Large Language Model Generations with Local Intrinsic Dimension","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.18048","snapshot_observed_at":"2026-08-07T11:23:45.108804Z","title":"Characterizing truthfulness in large language model generations with local intrinsic dimension,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.02696","last_updated":"2025-06-03T09:44:28Z","snapshot_observed_at":"2026-08-07T11:15:44.312106Z","submitted_at":"2025-06-03T09:44:28Z","title":"Shaking to Reveal: Perturbation-Based Detection of LLM Hallucinations","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-07T11:23:45.108804Z"},"links":{"cited_paper":"/paper/2402.18048","citing_paper":"/paper/2506.02696"},"observation_digest":"sha256:6f3faced9239ec91597495fbf8d72828d1fa5302e54a9297df1858918d10fcca","observation_id":"83fc8e36-c515-4ea8-8b13-6fd684cbbffb","resolution":{"observed_at":"2026-08-07T11:23:45.108804Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.18048","last_updated":"2024-02-28T04:56:21Z","snapshot_observed_at":"2026-08-04T05:17:17.897641Z","submitted_at":"2024-02-28T04:56:21Z","title":"Characterizing Truthfulness in Large Language Model Generations with Local Intrinsic Dimension","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.18048","snapshot_observed_at":"2026-08-04T13:52:12.846283Z","title":"Characterizing truthfulness in large language model generations with local intrinsic dimension.arXiv:2402.18048, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.24770","last_updated":"2026-07-27T11:18:04Z","snapshot_observed_at":"2026-08-04T21:27:21.169952Z","submitted_at":"2025-09-29T13:37:12Z","title":"Neural Message-Passing on Attention Graphs for Hallucination Detection","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-04T13:52:12.846283Z"},"links":{"cited_paper":"/paper/2402.18048","citing_paper":"/paper/2509.24770"},"observation_digest":"sha256:2fd2d20873e12aeb4f6e9a765cc7a0f46ec95b249863058589a59dd8b23db2fc","observation_id":"078e2312-3137-4546-9d68-36e378522202","resolution":{"observed_at":"2026-08-04T13:52:12.846283Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.18048","last_updated":"2024-02-28T04:56:21Z","snapshot_observed_at":"2026-08-04T05:17:17.897641Z","submitted_at":"2024-02-28T04:56:21Z","title":"Characterizing Truthfulness in Large Language Model Generations with Local Intrinsic Dimension","version":1},"cited_work":{"arxiv_id":"2402.18048","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.18048","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Characterizing truthfulness in large language model generations with local intrinsic dimension.arXiv preprint arXiv:2402.18048","venue":null,"work_id":"1265b775-1264-4f16-b798-23479da51adb","year":null},"citing_paper":{"arxiv_id":"2510.01105","last_updated":"2026-05-08T13:22:44Z","snapshot_observed_at":"2026-07-06T22:31:24.978726Z","submitted_at":"2025-10-01T16:50:57Z","title":"Geometric Analysis of Neural Regression Collapse via Intrinsic Dimension","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-18T10:22:28.096542Z"},"links":{"cited_paper":"/paper/2402.18048","citing_paper":"/paper/2510.01105"},"observation_digest":"sha256:82111c3a3a1012e1e1ebea77278c357af27f70e53ae838182afeacba311946b9","observation_id":"69835344-27f4-4230-9f58-17f9b218a052","resolution":{"observed_at":"2026-05-18T10:22:33.273011Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.18048","last_updated":"2024-02-28T04:56:21Z","snapshot_observed_at":"2026-08-04T05:17:17.897641Z","submitted_at":"2024-02-28T04:56:21Z","title":"Characterizing Truthfulness in Large Language Model Generations with Local Intrinsic Dimension","version":1},"cited_work":{"arxiv_id":"2402.18048","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.18048","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Characterizing truthfulness in large language model generations with local intrinsic dimension.arXiv preprint arXiv:2402.18048","venue":null,"work_id":"1265b775-1264-4f16-b798-23479da51adb","year":null},"citing_paper":{"arxiv_id":"2605.05668","last_updated":"2026-05-07T04:45:52Z","snapshot_observed_at":"2026-07-06T23:18:17.333660Z","submitted_at":"2026-05-07T04:45:52Z","title":"Large Vision-Language Models Get Lost in Attention","version":1},"reference_index":74,"source":"arxiv_source","source_observed_at":"2026-05-08T11:54:01.224588Z"},"links":{"cited_paper":"/paper/2402.18048","citing_paper":"/paper/2605.05668"},"observation_digest":"sha256:1f85a95bcefc1e50d45883e03957e1cbb83f0970ca3634aa8194d8ecdada1cba","observation_id":"635985ab-d5ac-4a52-86d2-2c5fcd355c6a","resolution":{"observed_at":"2026-05-11T19:26:10.184826Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2402.18048/citation-record","integrity":"/paper/2402.18048/integrity","json":"/paper/2402.18048/citation-record.json","paper":"/paper/2402.18048"},"outbound":[],"paper":{"arxiv_id":"2402.18048","last_updated":"2024-02-28T04:56:21Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-04T05:17:17.897641Z","submitted_at":"2024-02-28T04:56:21Z","title":"Characterizing Truthfulness in Large Language Model Generations with Local Intrinsic Dimension"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2402.18048."}