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Paper Citation Record · LEDGER

The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models

As of 19 August 2026, this Paper Citation Record lists 100 of 129 outbound references and 0 inbound Pith citation observations for arXiv:2606.22792.

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

pith.paper-citation-record.v1
2606.22792 v1

Coverage vector

measured 100 of 129 reference resolution

Typed states for the displayed outbound observations.

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measured 100 of 100 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

100 of 129 outbound references displayed

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  • verified fuzzy0
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External citation measurements

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Outbound references

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models Unresolved cited work

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This paper cites K., Pleiss, G., Zemel, R.

The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models K., Pleiss, G., Zemel, R

Reference 2

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models CSS: Contrastive Semantic Similarity for Uncertainty Quantification of LLMs

Reference 3

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This paper cites Prover agent: An agent-based framework for formal mathematical proofs.

The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models Prover agent: An agent-based framework for formal mathematical proofs

Reference 4

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models N., Belém, C

Reference 5

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models F., Yaldiz, D

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This paper cites Uncertainty quantification in fine-tuned LLMs using LoRA ensembles.

The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models Uncertainty quantification in fine-tuned LLMs using LoRA ensembles

Reference 7

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models Unresolved cited work

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models Cycles of Thought: Measuring LLM Confidence through Stable Explanations

Reference 9

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models Unresolved cited work

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models Unresolved cited work

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models Unresolved cited work

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models Unresolved cited work

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models and Mueller, J

Reference 14

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models Evaluating Large Language Models Trained on Code

Reference 15

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models

Reference 16

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models Uncertainty Quantification of Large Language Models through Multi-Dimensional Responses

Reference 17

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models and Li, Y

Reference 18

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models J., Gibbs, I., and Candès, E

Reference 19

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models Training Verifiers to Solve Math Word Problems

Reference 20

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models LLM Uncertainty Quantification through Directional Entailment Graph and Claim Level Response Augmentation

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models arXiv preprint arXiv:2510.04108 , year=

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models and Goadrich, M

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models DeepSeek-V3 Technical Report

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models Unsupervisedqualityestimationforneuralmachinetranslation.Transactions of the Association for Computational Linguistics, 8:539–555

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models and Ghahramani, Z

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models LLM Factoscope: Uncovering LLMs' Factual Discernment through Inner States Analysis

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Reference 39

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Reference 40

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Reference 41

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models and Waegeman, W

Reference 42

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models S., and Zettlemoyer, L

Reference 43

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models V., Laga, H., Boussaïd, F., Buntine, W

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models and Yuki, A

Reference 45

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models Language Models (Mostly) Know What They Know

Reference 46

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models Scaling Laws for Neural Language Models

Reference 47

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models and Gal, Y

Reference 48

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models Divergent Ensemble Networks: Enhancing Uncertainty Estimation with Shared Representations and Independent Branching

Reference 49

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMs

Reference 50

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models Conformal Prediction with Large Language Models for Multi-Choice Question Answering

Reference 51

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models Unresolved cited work

Reference 52

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models Unresolved cited work

Reference 53

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models Unresolved cited work

Reference 54

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Reference 55

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models Language Model Uncertainty Quantification with Attention Chain

Reference 56

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Reference 57

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models Unresolved cited work

Reference 58

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models Unresolved cited work

Reference 59

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models Unresolved cited work

Reference 60

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models Unresolved cited work

Reference 61

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models Uncertainty Estimation and Quantification for LLMs: A Simple Supervised Approach

Reference 62

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models F., Chao, L

Reference 63

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models Unresolved cited work

Reference 64

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 65

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models The Llama 3 Herd of Models

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models T., Yamada, Y., Hu, S., Foerster, J., Ha, D., and Clune, J

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models J., Izmailov, P., Garipov, T., Vetrov, D

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models and Gales, M

Reference 69

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Reference 70

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This paper cites Evaluating Uncertainty Quantification in End-to-End Autonomous Driving Control.

The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models Evaluating Uncertainty Quantification in End-to-End Autonomous Driving Control

Reference 71

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Reference 72

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models and Xin, M

Reference 73

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Reference 74

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Reference 75

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Reference 76

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This paper cites GPT-4 Technical Report.

The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models GPT-4 Technical Report

Reference 77

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models Not all uncertainty is alike: volatility, stochasticity, and exploration

Reference 78

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models Semanticdensity: Uncertaintyquantificationforlargelanguage models through confidence measurement in semantic space

Reference 79

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models H., Jaakkola, T

Reference 80

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This paper cites Qwen3 Technical Report.

The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models Qwen3 Technical Report

Reference 81

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Reference 82

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Reference 83

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Reference 84

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models Agents of Chaos

Reference 85

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models Unresolved cited work

Reference 86

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Reference 87

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models Z., and Majumdar, A

Reference 88

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models S., Saha, S., Kattakinda, P., and Feizi, S

Reference 89

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This paper cites LACIE: Listener-Aware Finetuning for Confidence Calibration in Large Language Models.

The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models LACIE: Listener-Aware Finetuning for Confidence Calibration in Large Language Models

Reference 90

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models Unresolved cited work

Reference 91

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Reference 92

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models and Lopez-Paz, D

Reference 93

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models Unresolved cited work

Reference 94

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models Unresolved cited work

Reference 95

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models Unresolved cited work

Reference 96

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models Unresolved cited work

Reference 97

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models B., Grishchenkov, K., Panchenko, A., Baldwin, T., Nakov, P., Panov, M., and Shelmanov, A

Reference 98

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models Unresolved cited work

Reference 99

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Observation 3226492f-5c5d-4e02-b787-a59b86e43a7b · outbound

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The Origins of Stochasticity: Comprehensive Investigations on Uncertainty Quantification for Large Language Models Unresolved cited work

Reference 100

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Pith citing papers

No inbound Pith citation observations are available.