Paper Citation Record · LEDGER

Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 17 inbound Pith citation observations for arXiv:2403.04696.

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

pith.paper-citation-record.v1
2403.04696 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 17 of 17 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 17 of 17 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:44:24.160411Z

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

0
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 aa55868c-a52d-492e-b5f2-66c42287102c · inbound

Unconditional Truthfulness: Learning Unconditional Uncertainty of Large Language Models cites this paper.

Unconditional Truthfulness: Learning Unconditional Uncertainty of Large Language Models Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-23T22:13:30.067578Z

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 e0c5699e-b1c9-49a7-868f-13f1a0afff05 · 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 Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-23T02:52:26.004352Z

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 cea4da7e-4122-4535-adb4-41a36fac4f3d · 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 Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-22T23:22:15.368639Z

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 f374183d-fad9-4b7f-93dd-d5fcc8c8f14c · inbound

Will It Still Be True Tomorrow? Multilingual Evergreen Question Classification to Improve Trustworthy QA cites this paper.

Will It Still Be True Tomorrow? Multilingual Evergreen Question Classification to Improve Trustworthy QA Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T13:44:24.160411Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b38c3eba-8063-457a-8b6e-d0e271d4e1a8 · inbound

ChemAU: Harness the Reasoning of LLMs in Chemical Research with Adaptive Uncertainty Estimation cites this paper.

ChemAU: Harness the Reasoning of LLMs in Chemical Research with Adaptive Uncertainty Estimation Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T11:56:45.011459Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab522ab3-6853-492d-9b96-6cd507d73f33 · inbound

Reasoning about Uncertainty: Do Reasoning Models Know When They Don't Know? cites this paper.

Reasoning about Uncertainty: Do Reasoning Models Know When They Don't Know? Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T23:28:21.087470Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61b19f3e-5f90-4b1b-be0d-f8a68e56d401 · inbound

Can LLMs Make (Personalized) Access Control Decisions? cites this paper.

Can LLMs Make (Personalized) Access Control Decisions? Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-17T05:34:05.019296Z

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 523fdf26-5125-433a-b69a-5fbe03aba79c · inbound

Entropy Sentinel: Probing Entropy Traces for LLM Monitoring cites this paper.

Entropy Sentinel: Probing Entropy Traces for LLM Monitoring Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T10:48:02.988632Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a8887f34-3364-4c51-aaf6-c57cb55bea9f · inbound

Entropy Sentinel: Probing Entropy Traces for LLM Monitoring cites this paper.

Entropy Sentinel: Probing Entropy Traces for LLM Monitoring Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T06:25:48.469518Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce37e73b-ff2c-487e-a705-3e9b5503222f · inbound

Filling the Gaps: Selective Knowledge Augmentation for LLM Recommenders cites this paper.

Filling the Gaps: Selective Knowledge Augmentation for LLM Recommenders Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-11T06:31:01.672201Z

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 a846132c-4ab9-4e2b-81bc-ee12af37c637 · inbound

IUQ: Interrogative Uncertainty Quantification for Long-Form Large Language Model Generation cites this paper.

IUQ: Interrogative Uncertainty Quantification for Long-Form Large Language Model Generation Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-10T11:45:21.479548Z

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 5cef7ed5-2446-411f-a260-aefa173ccc0e · inbound

LLMs Uncertainty Quantification via Adaptive Conformal Semantic Entropy cites this paper.

LLMs Uncertainty Quantification via Adaptive Conformal Semantic Entropy Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-11T17:11:08.337039Z

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 5da50856-a51f-4a7a-8818-24a7ec2a12b6 · inbound

Estimating the Black-box LLM Uncertainty with Distribution-Aligned Adversarial Distillation cites this paper.

Estimating the Black-box LLM Uncertainty with Distribution-Aligned Adversarial Distillation Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification

Reference 56

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:46:15.089326Z

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 efc4cd03-976c-4728-a3f6-b97c09f238c4 · inbound

Confidence-Aware Alignment Makes Reasoning LLMs More Reliable cites this paper.

Confidence-Aware Alignment Makes Reasoning LLMs More Reliable Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:55:54.024672Z

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 100b2f2e-0173-4afa-b30e-b27b1d9c2c95 · inbound

Sanity Checks for Long-Form Hallucination Detection cites this paper.

Sanity Checks for Long-Form Hallucination Detection Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-12T08:06:28.370563Z

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 2b9caaf7-7647-4cc0-970d-cf5d248247ec · inbound

The CRISTAL Method: Neurosymbolic analysis from AI-synthesized world models cites this paper.

The CRISTAL Method: Neurosymbolic analysis from AI-synthesized world models Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification

Reference 9

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T06:44:18.980998Z

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 36434fc6-c957-47af-8a21-b0b2efa3278f · inbound

VecFontLLM: Anchor-Guided Direct Synthesis of Chinese Vector Fonts cites this paper.

VecFontLLM: Anchor-Guided Direct Synthesis of Chinese Vector Fonts Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-01T18:40:02.959247Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.