Paper Citation Record · LEDGER

Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2406.15627.

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

pith.paper-citation-record.v1
2406.15627 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:58:51.084865Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

2
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 1491bb5e-82c4-4402-91d9-364507b451f7 · inbound

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models cites this paper.

Token-Level Density-Based Uncertainty Quantification Methods for Eliciting Truthfulness of Large Language Models Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:52:26.374817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation d251edab-b608-494f-a935-f2be67cd40d8 · inbound

Self-Reported Confidence of Large Language Models in Gastroenterology: Analysis of Commercial, Open-Source, and Quantized Models cites this paper.

Self-Reported Confidence of Large Language Models in Gastroenterology: Analysis of Commercial, Open-Source, and Quantized Models Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-22T23:22:15.462036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f02da17c-9c2c-4c0b-a8b7-5efdf4d9e4ba · inbound

Reconsidering LLM Uncertainty Estimation Methods in the Wild cites this paper.

Reconsidering LLM Uncertainty Estimation Methods in the Wild Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T11:58:51.084865Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af6c187f-24a0-4896-ac8b-3c50cae8ba4b · inbound

ExploreGS: Explorable 3D Scene Reconstruction with Virtual Camera Samplings and Diffusion Priors cites this paper.

ExploreGS: Explorable 3D Scene Reconstruction with Virtual Camera Samplings and Diffusion Priors Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-05T23:01:47.458095Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c5c2de1-0ddd-402d-8dac-46eedc6e182d · inbound

On-the-Fly Input Adaptation for Reliable Code Intelligence cites this paper.

On-the-Fly Input Adaptation for Reliable Code Intelligence Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-20T05:03:03.842932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e8b4f44b-c8ba-498a-b7ce-e32397c4bbf0 · inbound

When to Answer and When to Defer: A Decision Framework for Reliable Code Predictions cites this paper.

When to Answer and When to Defer: A Decision Framework for Reliable Code Predictions Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-20T04:58:05.225993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.