Paper Citation Record · LEDGER

Benchmarking LLMs via Uncertainty Quantification

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2401.12794.

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

pith.paper-citation-record.v1
2401.12794 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T19:15:25.711690Z

measured 0 of 1 external citation measurements

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 2026-08-10T06:31:04.303077+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 2026-08-10T06:31:04.303077+00:00.

Observation e7e3174e-72ef-4043-8cf8-490a5ba224f2 · inbound

Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey cites this paper.

Towards Trustworthy Retrieval Augmented Generation for Large Language Models: A Survey Benchmarking LLMs via Uncertainty Quantification

Reference 219

Resolution: unresolved; no resolver link, observed 2026-08-08T19:15:25.711690Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e00f366-438d-44fa-abdc-e8e600095721 · inbound

Learning Conformal Abstention Policies for Adaptive Risk Management in Large Language and Vision-Language Models cites this paper.

Learning Conformal Abstention Policies for Adaptive Risk Management in Large Language and Vision-Language Models Benchmarking LLMs via Uncertainty Quantification

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-08T18:23:05.750075Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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.