Paper Citation Record · LEDGER

Fact-and-Reflection (FaR) Improves Confidence Calibration of Large Language Models

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2402.17124.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2402.17124 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T20:37:55.268171Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T08:39:41.824297Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 8381c13f-ac54-464f-aeb3-c4d2b76af840 · inbound

A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions cites this paper.

A Survey on Uncertainty Quantification of Large Language Models: Taxonomy, Open Research Challenges, and Future Directions Fact-and-Reflection (FaR) Improves Confidence Calibration of Large Language Models

Reference 250

Resolution: unresolved; no resolver link, observed 2026-08-11T20:37:55.268171Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6396faf8-9ad8-4286-94ec-d08842517c3f · inbound

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation cites this paper.

Mitigating hallucinations in healthcare LLMs with granular fact-checking and domain-specific adaptation Fact-and-Reflection (FaR) Improves Confidence Calibration of Large Language Models

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-03T15:42:27.932287Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5291457-784b-488c-afb4-e71495d4e387 · inbound

Latent Confidence Alignment for LLM Self-Assessment cites this paper.

Latent Confidence Alignment for LLM Self-Assessment Fact-and-Reflection (FaR) Improves Confidence Calibration of Large Language Models

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-07-04T08:39:41.825824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ba1adce1-d3c0-4f51-bb9a-47dc7e4c2d9e · inbound

Scaling with Confidence: Calibrating Confidence of LLMs for Adaptive Test Time Scaling cites this paper.

Scaling with Confidence: Calibrating Confidence of LLMs for Adaptive Test Time Scaling Fact-and-Reflection (FaR) Improves Confidence Calibration of Large Language Models

Reference 6

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T14:58:32.556599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.