{"as_of":"2026-08-20T10:56:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:e08297f5f91a570ca8460906992ea977127bf35f267245669202aaccd775950b","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":5,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":5,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+00:00","state":"measured"},{"denominator":5,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":5,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T17:30:45.901546Z","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":"2026-07-04T08:39:41.824297Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2402.17124","last_updated":"2024-09-08T19:17:32Z","snapshot_observed_at":"2026-08-16T14:15:05.169192Z","submitted_at":"2024-02-27T01:37:23Z","title":"Fact-and-Reflection (FaR) Improves Confidence Calibration of Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17124","snapshot_observed_at":"2026-08-11T20:37:55.268171Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.05563","last_updated":"2025-07-01T22:08:39Z","snapshot_observed_at":"2026-08-16T14:41:18.143042Z","submitted_at":"2024-12-07T06:56:01Z","title":"A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions","version":2},"reference_index":250,"source":"pdf_text","source_observed_at":"2026-08-11T20:37:55.268171Z"},"links":{"cited_paper":"/paper/2402.17124","citing_paper":"/paper/2412.05563"},"observation_digest":"sha256:c69188ff7d259322c77dc199c51418ebd96cec59c39c7aa6418fe87e654fbe1d","observation_id":"8381c13f-ac54-464f-aeb3-c4d2b76af840","resolution":{"observed_at":"2026-08-11T20:37:55.268171Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17124","last_updated":"2024-09-08T19:17:32Z","snapshot_observed_at":"2026-08-16T14:15:05.169192Z","submitted_at":"2024-02-27T01:37:23Z","title":"Fact-and-Reflection (FaR) Improves Confidence Calibration of Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17124","snapshot_observed_at":"2026-08-15T17:30:45.901546Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.12040","last_updated":"2025-08-16T13:29:35Z","snapshot_observed_at":"2026-08-19T08:33:51.851826Z","submitted_at":"2025-08-16T13:29:35Z","title":"Mind the Generation Process: Fine-Grained Confidence Estimation During LLM Generation","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-15T17:30:45.901546Z"},"links":{"cited_paper":"/paper/2402.17124","citing_paper":"/paper/2508.12040"},"observation_digest":"sha256:80445d199a04f2782e28fa1099192d7e382888a8201ea1747989b6d7b2181799","observation_id":"7c4668c2-75cf-4fe2-9060-2be8339dc018","resolution":{"observed_at":"2026-08-15T17:30:45.901546Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17124","last_updated":"2024-09-08T19:17:32Z","snapshot_observed_at":"2026-08-16T14:15:05.169192Z","submitted_at":"2024-02-27T01:37:23Z","title":"Fact-and-Reflection (FaR) Improves Confidence Calibration of Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17124","snapshot_observed_at":"2026-08-03T15:42:27.932287Z","title":"Fact-and-reflection (far) improves confidence calibration of large language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2512.16189","last_updated":"2026-06-09T06:01:03Z","snapshot_observed_at":"2026-08-19T02:36:04.813192Z","submitted_at":"2025-12-18T05:23:47Z","title":"Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation","version":4},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-03T15:42:27.932287Z"},"links":{"cited_paper":"/paper/2402.17124","citing_paper":"/paper/2512.16189"},"observation_digest":"sha256:6ccee4b052da4cfc5055d3516a22ffb488e75489a5af8b524223ee9374760102","observation_id":"6396faf8-9ad8-4286-94ec-d08842517c3f","resolution":{"observed_at":"2026-08-03T15:42:27.932287Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17124","last_updated":"2024-09-08T19:17:32Z","snapshot_observed_at":"2026-08-16T14:15:05.169192Z","submitted_at":"2024-02-27T01:37:23Z","title":"Fact-and-Reflection (FaR) Improves Confidence Calibration of Large Language Models","version":2},"cited_work":{"arxiv_id":"2402.17124","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.17124","snapshot_observed_at":"2026-07-04T08:39:41.824297Z","title":"Fact-and-Reflection Improves Confidence Calibration of Large Language Models,","venue":null,"work_id":"b3e89665-c8a5-4a4a-82ef-a642fae15c8f","year":2024},"citing_paper":{"arxiv_id":"2606.21937","last_updated":"2026-06-20T08:13:31Z","snapshot_observed_at":"2026-08-14T10:54:41.360384Z","submitted_at":"2026-06-20T08:13:31Z","title":"Latent Confidence Alignment for LLM Self-Assessment","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-06-26T11:21:33.744610Z"},"links":{"cited_paper":"/paper/2402.17124","citing_paper":"/paper/2606.21937"},"observation_digest":"sha256:69e13a62abf72ba07c3ccd04b3fa5879eeaae7c2d9a437901612b97fa99c3beb","observation_id":"c5291457-784b-488c-afb4-e71495d4e387","resolution":{"observed_at":"2026-07-04T08:39:41.825824Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17124","last_updated":"2024-09-08T19:17:32Z","snapshot_observed_at":"2026-08-16T14:15:05.169192Z","submitted_at":"2024-02-27T01:37:23Z","title":"Fact-and-Reflection (FaR) Improves Confidence Calibration of Large Language Models","version":2},"cited_work":{"arxiv_id":"2402.17124","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2402.17124","snapshot_observed_at":"2026-07-04T08:39:41.824297Z","title":"Fact-and-Reflection Improves Confidence Calibration of Large Language Models,","venue":null,"work_id":"b3e89665-c8a5-4a4a-82ef-a642fae15c8f","year":2024},"citing_paper":{"arxiv_id":"2607.01612","last_updated":"2026-07-02T02:29:33Z","snapshot_observed_at":"2026-08-18T20:04:49.596902Z","submitted_at":"2026-07-02T02:29:33Z","title":"Scaling with Confidence: Calibrating Confidence of LLMs for Adaptive Test Time Scaling","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-07-03T14:49:33.364596Z"},"links":{"cited_paper":"/paper/2402.17124","citing_paper":"/paper/2607.01612"},"observation_digest":"sha256:01296777903f01f474ce1209efdc0fe7ed01f0a954f7eaf97a7dd6b6de1d3c85","observation_id":"ba1adce1-d3c0-4f51-bb9a-47dc7e4c2d9e","resolution":{"observed_at":"2026-07-03T14:58:32.556599Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2402.17124/citation-record","integrity":"/paper/2402.17124/integrity","json":"/paper/2402.17124/citation-record.json","paper":"/paper/2402.17124"},"outbound":[],"paper":{"arxiv_id":"2402.17124","last_updated":"2024-09-08T19:17:32Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-16T14:15:05.169192Z","submitted_at":"2024-02-27T01:37:23Z","title":"Fact-and-Reflection (FaR) Improves Confidence Calibration of Large Language Models"},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2402.17124."}