Paper Citation Record · LEDGER

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems

As of 11 August 2026, this Paper Citation Record lists 80 of 80 outbound references and 7 inbound Pith citation observations for arXiv:2506.10060.

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

pith.paper-citation-record.v1
2506.10060 v2

Coverage vector

measured 80 of 80 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-19T09:20:12.827871Z

measured 87 of 87 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T11:19:49.639145Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-03T17:28:44.173966Z

Reference resolution

80 of 80 outbound references displayed

verified exact22
verified fuzzy50
unresolved1
parse uncertain1
malformed identifier1
metadata mismatch5

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ca79095b-7db6-4fa5-8645-9f4b0bdfb958 · outbound

This paper cites GPT-4 Technical Report.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems GPT-4 Technical Report

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-05-19T09:22:16.002337Z

Source-reported events for the cited work

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Observation b4633ec9-3f79-4658-924a-6a5caba78de2 · outbound

This paper cites A statistical theory of cold posteriors in deep neural networks.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems A statistical theory of cold posteriors in deep neural networks

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.223047Z

Source-reported events for the cited work

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Observation 0177f880-98d4-47aa-aaa7-704c5c9625f7 · outbound

This paper cites Bie, T., Cao, M., Chen, K., Du, L., Gong, M., Gong, Z., Gu, Y ., Hu, J., Huang, Z., Lan, Z., et al.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Bie, T., Cao, M., Chen, K., Du, L., Gong, M., Gong, Z., Gu, Y ., Hu, J., Huang, Z., Lan, Z., et al

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-05-19T09:22:15.983044Z

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Observation 5759095f-d992-405d-ae81-ff60b7003f7e · outbound

This paper cites Bayesian Theory, volume 405.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Bayesian Theory, volume 405

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.219689Z

Source-reported events for the cited work

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Observation 31b3801f-097c-4a99-80b8-80e15536dc5d · outbound

This paper cites Weight uncertainty in neural network.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Weight uncertainty in neural network

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.209149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 94dfb5d4-d275-45ef-8c77-6e960a7c2d8f · outbound

This paper cites Emergent autonomous scientific research capabilities of large language models.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Emergent autonomous scientific research capabilities of large language models

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-19T21:43:44.843677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 060fb61c-cf59-4c2a-bca7-eab5b7e9eaa9 · outbound

This paper cites Opportunities and Challenges of AI-Driven Customer Service, pages 33–71.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Opportunities and Challenges of AI-Driven Customer Service, pages 33–71

Reference 7

Resolution: verified exact; doi, observed 2026-05-19T09:22:13.961100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 55b7900b-ae78-4766-bf76-c4c5b53c6c10 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Evaluating Large Language Models Trained on Code

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-05-19T09:22:16.008496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 050f1408-c8cf-4ad0-9765-cf2f9441e423 · outbound

This paper cites Trace is the Next AutoDiff: Generative Optimization with Rich Feedback, Execution Traces, and LLMs.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Trace is the Next AutoDiff: Generative Optimization with Rich Feedback, Execution Traces, and LLMs

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.212916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 16929d51-2803-410d-a139-55887ebd9db0 · outbound

This paper cites Aime problems and solutions.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Aime problems and solutions

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.216724Z

Source-reported events for the cited work

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Observation 8ac89577-5801-4448-867b-2d49801cdbd8 · outbound

This paper cites A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers

Reference 11

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Observation f588d314-aae5-4716-8866-40ac5626582c · outbound

This paper cites Laplace redux-effortless Bayesian deep learning.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Laplace redux-effortless Bayesian deep learning

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.176336Z

Source-reported events for the cited work

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Observation 4900f99c-3a11-4147-8209-31963695a0cd · outbound

This paper cites an unresolved cited work.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Unresolved cited work

Reference 13

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Source-reported events for the cited work

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Observation ec6e8b08-7af4-4103-91eb-30e6ea14034c · outbound

This paper cites Sample, don’t search: Rethinking test-time alignment for language models.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Sample, don’t search: Rethinking test-time alignment for language models

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-19T09:22:15.941560Z

Source-reported events for the cited work

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Observation a8a2e341-43ae-406f-a9b2-9d303f68eba1 · outbound

This paper cites QUEST: Quality-aware metropolis-hastings sampling for machine translation.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems QUEST: Quality-aware metropolis-hastings sampling for machine translation

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.043506Z

Source-reported events for the cited work

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Observation 18c24100-24eb-4c7d-9ceb-d7a6d4b35828 · outbound

This paper cites Ober, Florian Wenzel, Gunnar Ratsch, Richard E Turner, Mark van der Wilk, and Laurence Aitchison.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Ober, Florian Wenzel, Gunnar Ratsch, Richard E Turner, Mark van der Wilk, and Laurence Aitchison

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.192889Z

Source-reported events for the cited work

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Observation b2f2e1e4-4ddd-4826-9dfa-24101cdcdbb6 · outbound

This paper cites SPUQ: Perturbation-Based Uncertainty Quantification for Large Language Models.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems SPUQ: Perturbation-Based Uncertainty Quantification for Large Language Models

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-19T09:22:15.976487Z

Source-reported events for the cited work

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Observation 6014652a-e57f-4e8f-97b4-2596947c5c0e · outbound

This paper cites A Confederacy of Models: a Comprehensive Evaluation of LLMs on Creative Writing.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems A Confederacy of Models: a Comprehensive Evaluation of LLMs on Creative Writing

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.061622Z

Source-reported events for the cited work

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Observation 3bdee7e4-02c4-4283-8e99-6d1b6eb55e39 · outbound

This paper cites Improving Uncertainty Quantification in Large Language Models via Semantic Embeddings.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Improving Uncertainty Quantification in Large Language Models via Semantic Embeddings

Reference 19

Resolution: metadata mismatch; arxiv_id, observed 2026-05-19T09:22:15.886714Z

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Observation ac4635bf-1ea3-4daa-9703-a2a8b5d77a06 · outbound

This paper cites Weinberger.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Weinberger

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.026745Z

Source-reported events for the cited work

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Observation d305396e-4de8-4a9a-86e2-2468f634da81 · outbound

This paper cites De- composing uncertainty for large language models through input clarification ensembling.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems De- composing uncertainty for large language models through input clarification ensembling

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.002668Z

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Observation 95b8370f-d732-4fa9-99d2-8b5f4c0d4d73 · outbound

This paper cites Automated Design of Agentic Systems.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Automated Design of Agentic Systems

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-05-19T09:22:15.955244Z

Source-reported events for the cited work

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Observation 9ca38320-2bcf-426b-bc3d-a65e3065ba90 · outbound

This paper cites What Are Bayesian Neural Network Posteriors Really Like? In Proceedings of the 38th International Conference on Machine Learning, volume 139, pages 4629–4640.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems What Are Bayesian Neural Network Posteriors Really Like? In Proceedings of the 38th International Conference on Machine Learning, volume 139, pages 4629–4640

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.164184Z

Source-reported events for the cited work

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Observation c786b5d3-0b7c-4b05-b88c-9ee9047f0e03 · outbound

This paper cites Estimating the hallucination rate of generative AI.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Estimating the hallucination rate of generative AI

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.017993Z

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Observation 9d13bc5e-01d9-4045-8d86-ef9b05cd5323 · outbound

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

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Language Models (Mostly) Know What They Know

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-05-19T09:22:15.880422Z

Source-reported events for the cited work

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Observation 08079ea4-7d55-4c3d-b985-7106b723e84e · outbound

This paper cites On uncertainty, tempering, and data augmentation in bayesian classification.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems On uncertainty, tempering, and data augmentation in bayesian classification

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.010295Z

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Observation f98672d8-ad49-4ae9-ac9e-6c019bbc69e1 · outbound

This paper cites Joshi, Hanna Moazam, Heather Miller, Matei Zaharia, and Christopher Potts.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Joshi, Hanna Moazam, Heather Miller, Matei Zaharia, and Christopher Potts

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.006484Z

Source-reported events for the cited work

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Observation 6b011272-54fc-4811-bd82-08dfb6b7bdfc · outbound

This paper cites Auto-encoding variational Bayes.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Auto-encoding variational Bayes

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.172728Z

Source-reported events for the cited work

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Observation 9c355347-0fea-4f98-a7be-029a4abbbf8e · outbound

This paper cites Being Bayesian, even just a bit, fixes overconfidence in relu networks.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Being Bayesian, even just a bit, fixes overconfidence in relu networks

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.058016Z

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Observation 505203e2-8062-42f1-a473-469478595d0d · outbound

This paper cites Semantic uncertainty: Linguistic invariances for uncertainty estimation in natural language generation.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Semantic uncertainty: Linguistic invariances for uncertainty estimation in natural language generation

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.140376Z

Source-reported events for the cited work

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Observation 9df5bcbf-12ec-487b-8c23-e7acaa3bc192 · outbound

This paper cites Simple and scalable predictive uncertainty estimation using deep ensembles.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Simple and scalable predictive uncertainty estimation using deep ensembles

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.047214Z

Source-reported events for the cited work

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Observation 9be4b414-fa7b-4849-85ac-50029033bac9 · outbound

This paper cites Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.051016Z

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Observation bce37f25-debb-4b99-867f-f6f969aec8bc · outbound

This paper cites Generating with confidence: Uncertainty quantification for black-box large language models.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Generating with confidence: Uncertainty quantification for black-box large language models

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:03.999022Z

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Observation fbc47071-af24-43e3-9562-03ab0db3c823 · outbound

This paper cites Uncertainty Quantification for In-Context Learning of Large Language Models.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Uncertainty Quantification for In-Context Learning of Large Language Models

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-05-19T09:22:15.916798Z

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Observation 6a770bee-a331-480f-afe3-49ebf647d628 · outbound

This paper cites Information Theory, Inference and Learning Algorithms.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Information Theory, Inference and Learning Algorithms

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.168909Z

Source-reported events for the cited work

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Observation c87913a9-c1b6-4e3c-862b-2fd6070a4d68 · outbound

This paper cites Self- Refine: Iterative Refinement with Self-Feedback.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Self- Refine: Iterative Refinement with Self-Feedback

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.022394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 8c69d0f6-ab60-48a6-aedf-81ed2adab8f9 · outbound

This paper cites Manakul, A.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Manakul, A

Reference 37

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Observation d3fa9b46-c592-4ce4-ac8e-122679a23aa9 · outbound

This paper cites On faithfulness and factuality in abstractive summarization.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems On faithfulness and factuality in abstractive summarization

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.029722Z

Source-reported events for the cited work

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Observation 9f148d29-8d71-47e1-9675-32d963161384 · outbound

This paper cites FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.035964Z

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Observation 37ad64c5-5ec1-47d0-82b8-881b2ee3c7bf · outbound

This paper cites Proceedings of the 2023.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Proceedings of the 2023

Reference 40

Resolution: verified exact; doi, observed 2026-05-19T09:22:13.942490Z

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Observation 04752fa0-26b7-46f0-99d6-a025daeec8e6 · outbound

This paper cites Language models with conformal factuality guarantees.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Language models with conformal factuality guarantees

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.155853Z

Source-reported events for the cited work

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Observation 4da31421-c03d-439c-9b44-e63776e5105e · outbound

This paper cites Data augmentation in Bayesian neural networks and the cold posterior effect.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Data augmentation in Bayesian neural networks and the cold posterior effect

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.133164Z

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Observation 6a36ab45-06b4-49d6-9542-459ffec0a768 · outbound

This paper cites Neal.Bayesian Learning for Neural Networks, volume 118 ofLecture Notes in Statistics.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Neal.Bayesian Learning for Neural Networks, volume 118 ofLecture Notes in Statistics

Reference 43

Resolution: verified exact; doi, observed 2026-05-19T09:22:13.954491Z

Source-reported events for the cited work

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Observation aa35143e-a04e-4eac-948a-fcba815229df · outbound

This paper cites Kernel language entropy: Fine-grained uncertainty quantification for LLMs from semantic similarities.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Kernel language entropy: Fine-grained uncertainty quantification for LLMs from semantic similarities

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.184966Z

Source-reported events for the cited work

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Observation c7209b4b-7238-4efd-8395-72c337fee669 · outbound

This paper cites Disentangling the roles of curation, data-augmentation and the prior in the cold posterior effect.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Disentangling the roles of curation, data-augmentation and the prior in the cold posterior effect

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.189362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9729a636-9bf0-486f-a4df-abd29f06e0f5 · outbound

This paper cites Obtaining well calibrated probabilities using bayesian binning.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Obtaining well calibrated probabilities using bayesian binning

Reference 46

Resolution: verified exact; doi, observed 2026-05-19T09:22:13.948196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 59ab5b03-ba62-4a62-9e57-4113a9b891c5 · outbound

This paper cites Semantic density: Uncertainty quantification for large language models through confidence measurement in semantic space.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Semantic density: Uncertainty quantification for large language models through confidence measurement in semantic space

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.110634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 544a7b3e-ce32-4b1c-bb3a-d59c3f55800d · outbound

This paper cites A Scalable Laplace Approximation for Neural Networks.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems A Scalable Laplace Approximation for Neural Networks

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.180218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 50aff046-de85-4c9c-9b05-a0dc68c46226 · outbound

This paper cites A scalable Laplace approximation for neural networks.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems A scalable Laplace approximation for neural networks

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.127856Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 86c375ff-7960-4924-9fee-dad39ef8db32 · outbound

This paper cites The Metropolis-Hastings algorithm.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems The Metropolis-Hastings algorithm

Reference 50

Resolution: verified exact; local_arxiv, observed 2026-05-19T09:22:15.874698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2341d4b7-c1f2-400a-9b21-f2302a619bff · outbound

This paper cites Optimal proposal distributions and adaptive MCMC.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Optimal proposal distributions and adaptive MCMC

Reference 51

Resolution: malformed identifier; raw_fallback, observed 2026-05-19T09:23:04.086382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 69afdeeb-94b7-462d-b6da-03dee8178503 · outbound

This paper cites Mean field theory for sigmoid belief networks.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Mean field theory for sigmoid belief networks

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.077957Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3ba2d168-a5e2-4cf8-ad61-6a812bc28c86 · outbound

This paper cites Agent Laboratory: Using LLM Agents as Research Assistants.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Agent Laboratory: Using LLM Agents as Research Assistants

Reference 53

Resolution: verified exact; local_arxiv, observed 2026-05-19T09:22:15.892515Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 60bf237a-e8a8-47b5-bdb2-a08fe6a443ee · outbound

This paper cites An efficient minibatch acceptance test for metropolis-hastings.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems An efficient minibatch acceptance test for metropolis-hastings

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.082170Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2f89e590-441f-4ddb-a651-847e14f0cd0b · outbound

This paper cites A tutorial on conformal prediction.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems A tutorial on conformal prediction

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.114291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 12d866f7-a449-45b9-ab52-9d4e81c55757 · outbound

This paper cites Springer.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Springer

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.159971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation dd0f5501-d24f-4e05-a506-e637fcf20386 · outbound

This paper cites LoRA ensembles for large language model fine-tuning.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems LoRA ensembles for large language model fine-tuning

Reference 57

Resolution: verified exact; arxiv_id, observed 2026-05-19T09:22:15.948220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d9b0ff3f-35a2-454d-8992-12cb02019d90 · outbound

This paper cites Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.201604Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 7304ac50-3bbf-4e6f-bd22-4b0975c2e187 · outbound

This paper cites Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Codet5: Identifier-aware unified pre-trained encoder-decoder models for code understanding and generation

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.118599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 755f426c-4980-4b25-9170-c6be89aa72b4 · outbound

This paper cites HelpSteer2-Preference: Complementing Ratings with Preferences.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems HelpSteer2-Preference: Complementing Ratings with Preferences

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-05-19T09:22:15.929822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4626bf7d-b2aa-48d6-ae42-45bc1ec3761c · outbound

This paper cites On Subjective Uncertainty Quantification and Calibration in Natural Language Generation.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems On Subjective Uncertainty Quantification and Calibration in Natural Language Generation

Reference 61

Resolution: verified exact; arxiv_id, observed 2026-05-19T09:22:15.962131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 446b6891-c756-41bc-bf9a-f3ab1f12e427 · outbound

This paper cites Jailbroken: How does LLM safety training fail? Advances in Neural Information Processing Systems, 36:80079–80110.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Jailbroken: How does LLM safety training fail? Advances in Neural Information Processing Systems, 36:80079–80110

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.032674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation a5afbbc3-760e-4c3c-90d1-efff001a7b65 · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Chain-of-thought prompting elicits reasoning in large language models

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.197239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 62e5ed4e-0cb3-4a94-a314-773e22ff5983 · outbound

This paper cites Measuring short-form factuality in large language models.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Measuring short-form factuality in large language models

Reference 64

Resolution: verified exact; local_arxiv, observed 2026-05-19T09:22:15.910547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ce614dda-63b9-44ea-b1c0-a8c945f3d5ba · outbound

This paper cites Bayesian Learning via Stochastic Gradient Langevin Dynam- ics.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Bayesian Learning via Stochastic Gradient Langevin Dynam- ics

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.013938Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d38f3e0a-d6ad-46aa-bb8b-4b9398231f86 · outbound

This paper cites an unresolved cited work.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Unresolved cited work

Reference 66

Resolution: parse uncertain; raw_fallback, observed 2026-05-19T09:23:04.054378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation bc96b930-01b1-424a-aeab-af980a2f4932 · outbound

This paper cites Characterizing LLM Abstention Behavior in Science QA with Context Perturbations.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Characterizing LLM Abstention Behavior in Science QA with Context Perturbations

Reference 67

Resolution: verified exact; arxiv_id, observed 2026-05-19T09:22:15.899014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 493ff05b-d8fe-4134-846d-c23f24178f2d · outbound

This paper cites How good is the Bayes posterior in deep neural networks really? In Proceedings of the 37th International Conference on Machine Learning, volume 119, pages 10248–10259.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems How good is the Bayes posterior in deep neural networks really? In Proceedings of the 37th International Conference on Machine Learning, volume 119, pages 10248–10259

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.122860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9a445467-46c1-44e1-aaab-1e39b2b4771c · outbound

This paper cites Intelligent agents: Theory and practice.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Intelligent agents: Theory and practice

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.104947Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 7f38c75f-1bf9-4403-aa9a-620fbde87dae · outbound

This paper cites The rise and potential of large language model based agents: A survey.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems The rise and potential of large language model based agents: A survey

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.205661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation e45dea1c-1134-4581-b733-e018da8ee24b · outbound

This paper cites Hallucination is Inevitable: An Innate Limitation of Large Language Models.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Hallucination is Inevitable: An Innate Limitation of Large Language Models

Reference 71

Resolution: verified exact; local_arxiv, observed 2026-05-19T09:22:15.904746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4cdb6333-c0fc-4bf0-9c6d-519ff1024b60 · outbound

This paper cites The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search

Reference 72

Resolution: verified exact; local_arxiv, observed 2026-05-19T09:22:15.923759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 16615df9-30f0-48bc-9709-f89e8b97cdec · outbound

This paper cites Backdooring instruction-tuned large language models with virtual prompt injection.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Backdooring instruction-tuned large language models with virtual prompt injection

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.136597Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation d5e47119-4219-458e-b88e-e4a903e671a7 · outbound

This paper cites Bayesian low-rank adaptation for large language models.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Bayesian low-rank adaptation for large language models

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.145103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 9a405a36-f65a-4f9c-b2eb-a3ca666d9f7d · outbound

This paper cites On Verbalized Confidence Scores for LLMs.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems On Verbalized Confidence Scores for LLMs

Reference 75

Resolution: verified exact; local_arxiv, observed 2026-05-19T09:22:15.968495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3a2ba4ba-9de4-4ff9-8a0b-e7ff477c1c44 · outbound

This paper cites Optimizing generative AI by backpropagating language model feedback.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Optimizing generative AI by backpropagating language model feedback

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.150493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 3b662838-bed4-4d6d-9015-dc9ca1e16686 · outbound

This paper cites Judging llm-as-a-judge with mt-bench and chatbot arena.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Judging llm-as-a-judge with mt-bench and chatbot arena

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.070877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2117e68a-675e-4e40-81a0-3062ca3dc168 · outbound

This paper cites Large language models are human-level prompt engineers.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Large language models are human-level prompt engineers

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.074284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation ee4d3ecd-3374-4091-ad78-27ed8f69d6a0 · outbound

This paper cites GPTSwarm: Language Agents as Optimizable Graphs.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems GPTSwarm: Language Agents as Optimizable Graphs

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T09:23:04.065584Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 80df81ac-fe22-4c21-b861-2c9cf89ea567 · outbound

This paper cites Universal and Transferable Adversarial Attacks on Aligned Language Models.

Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems Universal and Transferable Adversarial Attacks on Aligned Language Models

Reference 80

Resolution: metadata mismatch; local_arxiv, observed 2026-05-19T09:22:15.935221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

Observation ccf5a398-b0d7-42c6-bfad-70a2b02edb2b · inbound

Don't Pass@k: A Bayesian Framework for Large Language Model Evaluation cites this paper.

Don't Pass@k: A Bayesian Framework for Large Language Model Evaluation Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems

Reference 43

Resolution: verified exact; local_arxiv, observed 2026-05-18T10:06:13.844261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation c486a4f5-dbdd-47f1-912d-fd4e657d1113 · inbound

Correcting Prompt Dependence in LLM Benchmarks: A Bayesian Hierarchical Model with Embedding-Space Clustering cites this paper.

Correcting Prompt Dependence in LLM Benchmarks: A Bayesian Hierarchical Model with Embedding-Space Clustering Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-04T11:19:49.639145Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86eb37f9-d4fa-4adb-8efd-97e1592fb523 · inbound

The Alignment Auditor: A Bayesian Framework for Verifying and Refining LLM Objectives cites this paper.

The Alignment Auditor: A Bayesian Framework for Verifying and Refining LLM Objectives Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems

Reference 2007

Resolution: unresolved; no resolver link, observed 2026-08-04T11:17:36.735976Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0298b269-e203-44eb-942b-b915a1ddc543 · inbound

Calibration, Not Compilation: Detecting and Repairing Misspecified Probabilistic Programs Written by Language Models cites this paper.

Calibration, Not Compilation: Detecting and Repairing Misspecified Probabilistic Programs Written by Language Models Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems

Reference 11

Resolution: metadata mismatch; local_arxiv, observed 2026-07-01T09:45:39.891578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 66143a1a-69a8-4608-9029-74579fc19f18 · inbound

When Can You Debias an LLM Judge? Identifiability Limits, a Test, and Designs for Top-k Ranking cites this paper.

When Can You Debias an LLM Judge? Identifiability Limits, a Test, and Designs for Top-k Ranking Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-07-03T17:28:44.175582Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 47fcfe40-0d87-4f03-a0bc-ee4445b994dd · inbound

When Can You Debias an LLM Judge? Identifiability Limits, a Test, and Designs for Top-k Ranking cites this paper.

When Can You Debias an LLM Judge? Identifiability Limits, a Test, and Designs for Top-k Ranking Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-02T09:07:09.534780Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a10ef0c0-2f7f-4650-a7a4-80781eebb611 · inbound

Diagnosis-Driven Automatic Repair for Agentic Workflow via Symbolic Inference cites this paper.

Diagnosis-Driven Automatic Repair for Agentic Workflow via Symbolic Inference Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-12T06:24:52.086585Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.