Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Benchmarking LLMs via Uncertainty Quantification
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2401.12794.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T12:44:08.930215Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-23T19:35:47.071151Z
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 0b6b0dde-0951-4955-9c07-8801e8d687d9 · inbound
Functional-level Uncertainty Quantification for Calibrated Fine-tuning on LLMs cites this paper.
Functional-level Uncertainty Quantification for Calibrated Fine-tuning on LLMs Benchmarking LLMs via Uncertainty Quantification
Reference 20
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T19:35:47.074997Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 2c88540e-9749-46c8-aa8b-8dd0a528c27f · inbound
Multiple Choice Questions: Reasoning Makes Large Language Models (LLMs) More Self-Confident, Especially When They are Wrong cites this paper.
Multiple Choice Questions: Reasoning Makes Large Language Models (LLMs) More Self-Confident, Especially When They are Wrong Benchmarking LLMs via Uncertainty Quantification
Reference 13
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T05:37:36.048475Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation d80b6f44-9edc-4ea3-83ec-098c3230df77 · inbound
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for LLMs cites this paper.
Is Your Model Fairly Certain? Uncertainty-Aware Fairness Evaluation for LLMs Benchmarking LLMs via Uncertainty Quantification
Reference 78
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:44:08.930215Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 58640330-70a4-45ad-9bb3-43527e453b96 · inbound
PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models cites this paper.
PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models Benchmarking LLMs via Uncertainty Quantification
Reference 64
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:12:03.559440Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9e83e0de-6cf3-4c91-9d0f-4027229e3059 · inbound
Uncertainty-Aware Complex Scientific Table Data Extraction cites this paper.
Uncertainty-Aware Complex Scientific Table Data Extraction Benchmarking LLMs via Uncertainty Quantification
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:58:52.234268Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.