Paper Citation Record · LEDGER

From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered

As of 7 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 8 inbound Pith citation observations for arXiv:2506.07461.

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

pith.paper-citation-record.v1
2506.07461 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:38:27.633983Z

measured 38 of 38 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T20:19:44.382744Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T04:06:35.144894Z

Reference resolution

30 of 30 outbound references displayed

verified exact2
verified fuzzy6
unresolved21
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bf372bc8-3355-4f9a-abb5-e240226bad11 · outbound

This paper cites Conformal Prediction under Levy-Prokhorov Distribution Shifts: Robustness to Local and Global Perturbations.

From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered Conformal Prediction under Levy-Prokhorov Distribution Shifts: Robustness to Local and Global Perturbations

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:24.716390Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d83e1d5-af15-4fc9-a118-7e0bbd1aeae0 · outbound

This paper cites Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., and Vaughan, J.

From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., and Vaughan, J

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:38:30.232921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fea5095f-e75c-4a34-9ac4-a00738a9e10f · outbound

This paper cites Quantifying Uncertainty in Answers from any Language Model and Enhancing their Trustworthiness.

From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered Quantifying Uncertainty in Answers from any Language Model and Enhancing their Trustworthiness

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:25.085909Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71725e05-6d86-4712-b327-e41ce82ffb4c · outbound

This paper cites Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language Models.

From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:25.328625Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca126e62-af19-4b2c-bd8e-c6ad1a35c473 · outbound

This paper cites an unresolved cited work.

From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:38:30.085185Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 63009957-b26f-4902-a95e-7fe479886908 · outbound

This paper cites V ., Zhang, Y ., Luss, R., Doshi-Velez, F., and Dhurandhar, A.

From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered V ., Zhang, Y ., Luss, R., Doshi-Velez, F., and Dhurandhar, A

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:38:29.937323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5c39eeed-d8f3-4fc9-b730-162dd3e2cbe7 · outbound

This paper cites Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders.

From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered Bridging Language and Items for Retrieval and Recommendation: Benchmarking LLMs as Semantic Encoders

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:26.099120Z

Source-reported events for the cited work

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Observation 61ca351b-9fcc-4c3b-860b-0ac29f87aec0 · outbound

This paper cites Confidence Under the Hood: An Investigation into the Confidence-Probability Alignment in Large Language Models.

From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered Confidence Under the Hood: An Investigation into the Confidence-Probability Alignment in Large Language Models

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:38:28.130453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3efbbf97-ecac-4788-8450-7ce1fec3262b · outbound

This paper cites an unresolved cited work.

From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered Unresolved cited work

Reference 15

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:38:29.778566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2475d0c7-9fc0-49f6-a163-34de5c1ca0fa · outbound

This paper cites Uncertainty Estimation and Quantification for LLMs: A Simple Supervised Approach.

From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered Uncertainty Estimation and Quantification for LLMs: A Simple Supervised Approach

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:26.436174Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f0b10dbb-6461-4bda-922b-802b466d79dc · outbound

This paper cites Multi-group Uncertainty Quantification for Long-form Text Generation.

From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered Multi-group Uncertainty Quantification for Long-form Text Generation

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:26.518075Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97784c1e-7c48-4296-bc2f-fd88f15244a1 · outbound

This paper cites an unresolved cited work.

From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered Unresolved cited work

Reference 18

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:38:29.506265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fa5c0aa9-7cd4-484f-8e0d-9b572216a1b6 · outbound

This paper cites A., Kirschbaum, E., Kasiviswanathan, S., and Ramdas, A.

From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered A., Kirschbaum, E., Kasiviswanathan, S., and Ramdas, A

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:38:29.225315Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3e095e3f-ec05-46ec-aef2-4433c1bf0670 · outbound

This paper cites J., Szlam, A., Dinan, E., and Boureau, Y .-L.

From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered J., Szlam, A., Dinan, E., and Boureau, Y .-L

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:38:29.017152Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e57a6ec7-8e95-4bc4-9380-572c68a3d5ff · outbound

This paper cites CleanCoNLL: A Nearly Noise-Free Named Entity Recognition Dataset.

From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered CleanCoNLL: A Nearly Noise-Free Named Entity Recognition Dataset

Reference 21

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T05:38:27.961274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 089171da-e274-4b6e-b388-563faab853ed · outbound

This paper cites The Dilemma of Uncertainty Estimation for General Purpose AI in the EU AI Act.

From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered The Dilemma of Uncertainty Estimation for General Purpose AI in the EU AI Act

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:27.057398Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9793be62-cfa3-49a7-a933-3ce8ed4b8f93 · outbound

This paper cites an unresolved cited work.

From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:38:28.854793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation edabd43f-d672-47ed-8916-e3672963c7f1 · outbound

This paper cites Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs.

From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:27.285389Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69ff2805-46b2-4cb3-a49c-7c346712d229 · outbound

This paper cites On Verbalized Confidence Scores for LLMs.

From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered On Verbalized Confidence Scores for LLMs

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:27.382257Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2800604e-9a57-4ae1-b5d6-0c6e74e972c5 · outbound

This paper cites an unresolved cited work.

From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered Unresolved cited work

Reference 28

Resolution: unresolved; raw_fallback, observed 2026-08-07T05:38:28.700319Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2f6eaecd-19ee-43e8-b153-037fcb8f6ae5 · outbound

This paper cites Zheng, C., Zhou, H., Meng, F., Zhou, J., and Huang, M.

From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered Zheng, C., Zhou, H., Meng, F., Zhou, J., and Huang, M

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:38:28.504631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 87379cc9-e676-45fa-9c91-4facdfff5089 · outbound

This paper cites Overconfidence is Key: Verbalized Uncertainty Evaluation in Large Language and Vision-Language Models.

From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered Overconfidence is Key: Verbalized Uncertainty Evaluation in Large Language and Vision-Language Models

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:25.518866Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b12233a2-9bc9-43ec-b826-c6de19061980 · outbound

This paper cites • Trainable / learnable response scoring function for LLMs.

From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered • Trainable / learnable response scoring function for LLMs

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:38:28.369241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a0240669-7ea8-46bb-9241-e574773b4d9d · outbound

This paper cites GRACE: A Granular Benchmark for Evaluating Model Calibration against Human Calibration.

From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered GRACE: A Granular Benchmark for Evaluating Model Calibration against Human Calibration

Reference 63

Resolution: verified exact; local_arxiv, observed 2026-08-07T05:38:27.815145Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation dadf7bb8-3dfd-42fe-be69-5e20d9fad8e9 · outbound

This paper cites Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis.

From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis

Reference 1070

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:27.157672Z

Source-reported events for the cited work

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Observation d582a2db-7bc9-4833-a5b1-d32bf3c7f488 · outbound

This paper cites Do LLMs estimate uncertainty well in instruction-following?.

From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered Do LLMs estimate uncertainty well in instruction-following?

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:26.011880Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b851a32c-b487-4a7b-b4ff-a4f29c10ff30 · outbound

This paper cites Preserving Pre-trained Features Helps Calibrate Fine-tuned Language Models.

From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered Preserving Pre-trained Features Helps Calibrate Fine-tuned Language Models

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:25.847025Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d28aa60c-e318-4c31-82bd-b502adec5636 · outbound

This paper cites Language Models (Mostly) Know What They Know.

From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered Language Models (Mostly) Know What They Know

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:26.164304Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c8e6d571-1cd2-4819-b888-b23386e77b0c · outbound

This paper cites Which of These Best Describes Multiple Choice Evaluation with LLMs? A) Forced B) Flawed C) Fixable D) All of the Above.

From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered Which of These Best Describes Multiple Choice Evaluation with LLMs? A) Forced B) Flawed C) Fixable D) All of the Above

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:24.830582Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2cc2581-ef09-47e6-af7e-e38ba75522f6 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered Training Verifiers to Solve Math Word Problems

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-07T05:38:25.196811Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 1601aa2b-3880-42d3-92ce-f2b2d5e50ec7 · inbound

TokUR: Token-Level Uncertainty Estimation for Large Language Model Reasoning cites this paper.

TokUR: Token-Level Uncertainty Estimation for Large Language Model Reasoning From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-05-22T14:01:38.443500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9ab092b9-4ca0-4244-9b20-9c721ca8576a · inbound

PaTAS: A Framework for Trust Propagation in Neural Networks Using Subjective Logic cites this paper.

PaTAS: A Framework for Trust Propagation in Neural Networks Using Subjective Logic From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-03T20:19:44.382744Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 58c3a38f-2980-4952-90a4-8f5c9558d690 · inbound

Learning to Decide with AI Assistance under Human-Alignment cites this paper.

Learning to Decide with AI Assistance under Human-Alignment From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:48:01.037035Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 534c8b39-c697-43e1-917b-a0855af3100e · inbound

Learning to Decide with AI Assistance under Human-Alignment cites this paper.

Learning to Decide with AI Assistance under Human-Alignment From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-07-01T14:15:47.445709Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 411167e9-8093-4bce-88dc-93036d04f761 · inbound

Position: Uncertainty Quantification in LLMs is Just Unsupervised Clustering cites this paper.

Position: Uncertainty Quantification in LLMs is Just Unsupervised Clustering From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered

Reference 113

Resolution: verified exact; arxiv_id, observed 2026-05-20T06:58:06.143097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8d22ed27-1d07-4c78-b95f-f7b0d4ff3a98 · inbound

Evaluating the Relevance of Uncertainty Estimators for LLM Hallucination cites this paper.

Evaluating the Relevance of Uncertainty Estimators for LLM Hallucination From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-06-29T18:33:50.445296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ebb73d27-005d-4413-bb6e-5507c9bd2235 · inbound

Testing LLM Arithmetic Reasoning Generalization with Automatic Numeric-Remapping Attacks cites this paper.

Testing LLM Arithmetic Reasoning Generalization with Automatic Numeric-Remapping Attacks From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-07-02T04:06:35.146616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation e8c5ed4e-fa62-48f5-999c-45e9e533d5e4 · inbound

Clustered Self-Assessment: A Simple yet Effective Method for Uncertainty Quantification in Large Language Models cites this paper.

Clustered Self-Assessment: A Simple yet Effective Method for Uncertainty Quantification in Large Language Models From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-07-02T03:26:29.576673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.