Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
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.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T20:37:55.268171Z
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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+00:00.