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Paper Citation Record · LEDGER

Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs

As of 8 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 1 inbound Pith citation observation for arXiv:2506.07448.

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

pith.paper-citation-record.v1
2506.07448 v1

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:41:36.470295Z

measured 39 of 39 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-25T04:25:26.710488Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-25T04:26:38.059561Z

Reference resolution

38 of 38 outbound references displayed

  • verified exact3
  • verified fuzzy10
  • unresolved24
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c43875b2-29e4-4b11-afb8-20e0615b0179 · outbound

This paper cites Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference.

Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference

Reference 4

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 5c92569c-d722-4489-818a-e7cb5a9f6878 · outbound

This paper cites InProceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs InProceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Reference 6

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verified fuzzy
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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.

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Observation 880196aa-131a-4498-b753-a0bc673f37ff · outbound

This paper cites Humanity's Last Exam.

Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs Humanity's Last Exam

Reference 7

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 1d45a82d-1e66-4362-bf73-8d96be17ba0c · outbound

This paper cites Bayesian Active Learning for Classification and Preference Learning.

Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs Bayesian Active Learning for Classification and Preference Learning

Reference 9

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 9374f3c4-ad41-44ce-9aee-ff1b8bb6cb81 · outbound

This paper cites InProceedings of the 56th Annual ACM Symposium on Theory of Computing.

Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs InProceedings of the 56th Annual ACM Symposium on Theory of Computing

Reference 11

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 653d634c-28e7-4abb-9c91-b58906b2f58b · outbound

This paper cites ACM64: 31–32.

Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs ACM64: 31–32

Reference 12

Resolution
verified exact
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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.

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Observation 58816b68-b7e5-419b-8284-91c49a142666 · outbound

This paper cites Large Language Models Must Be Taught to Know What They Don't Know.

Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs Large Language Models Must Be Taught to Know What They Don't Know

Reference 13

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation ef8fb29b-1e83-448d-837c-d40f43572e9f · outbound

This paper cites Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMs.

Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs Semantic Entropy Probes: Robust and Cheap Hallucination Detection in LLMs

Reference 15

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation ce55169b-4557-4a5a-8cf7-f5f21d83520e · outbound

This paper cites Contextualized Sequence Likelihood: Enhanced Confidence Scores for Natural Language Generation.

Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs Contextualized Sequence Likelihood: Enhanced Confidence Scores for Natural Language Generation

Reference 17

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unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 8b2544d5-d54f-430b-abc5-691b486dcc93 · outbound

This paper cites an unresolved cited work.

Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs Unresolved cited work

Reference 19

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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.

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Observation 203fa63d-6a43-4973-8a07-d5e546df5721 · outbound

This paper cites Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems.

Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems

Reference 20

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 2bea6591-c579-4e73-aca2-8cb4f19054c0 · outbound

This paper cites The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery.

Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery

Reference 21

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 5dec3fb1-275b-453f-9942-c784217cfcd5 · outbound

This paper cites Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity.

Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity

Reference 22

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unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation bdbe6dad-af29-4264-811d-6f76fea5cc3f · outbound

This paper cites In International Conference on Learning Representations.

Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs In International Conference on Learning Representations

Reference 23

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verified fuzzy
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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.

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Observation 11d65d56-1c19-4841-9829-b425ed329315 · outbound

This paper cites ISSN 2377-3766.

Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs ISSN 2377-3766

Reference 24

Resolution
verified exact
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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.

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Observation 9cc3872a-f9e4-4642-a28e-fc989e8adffe · outbound

This paper cites Statistical Science39.

Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs Statistical Science39

Reference 26

Resolution
verified exact
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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.

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Observation c3aadda2-17d2-42a1-9dd3-36e1ceac14e7 · outbound

This paper cites Rethinking Aleatoric and Epistemic Uncertainty.

Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs Rethinking Aleatoric and Epistemic Uncertainty

Reference 27

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Source-reported events for the cited work

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Observation e8a5c11f-d97f-4194-a98b-45ab81348617 · outbound

This paper cites InProceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs InProceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Reference 28

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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.

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Observation de102e1e-e8fc-4161-966b-15947d9d6de1 · outbound

This paper cites URL https://proceedings.neurips.cc/paper/2017/hash/2650d6089a6d640c5e85b 2b88265dc2b-Abstract.html18, 19 Kiureghian AD, Ditlevsen O.

Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs URL https://proceedings.neurips.cc/paper/2017/hash/2650d6089a6d640c5e85b 2b88265dc2b-Abstract.html18, 19 Kiureghian AD, Ditlevsen O

Reference 30

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Source-reported events for the cited work

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Observation dec64333-3645-4474-a007-0e13624a73bc · outbound

This paper cites InProceedings of the 2023 Conference on Empirical Methods in Natural Language Processing.

Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs InProceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Reference 31

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

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Observation b6d3b22e-2822-4b53-a191-f3c8eaf9de08 · outbound

This paper cites ArXiv:2502.13069 [cs].

Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs ArXiv:2502.13069 [cs]

Reference 32

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Observation b8fe737f-c529-4b38-b246-6d210f4b4caf · outbound

This paper cites URL https://proceedings.neurips.cc/paper/2020/hash/1457c0d6bfcb4967418bf b8ac142f64a-Abstract.html1, 6 Carlini N.

Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs URL https://proceedings.neurips.cc/paper/2020/hash/1457c0d6bfcb4967418bf b8ac142f64a-Abstract.html1, 6 Carlini N

Reference 33

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Observation 222f61ac-c543-4893-926e-309007b96865 · outbound

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

Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs On Subjective Uncertainty Quantification and Calibration in Natural Language Generation

Reference 34

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Observation 56ce8a57-e9c6-4f32-aa79-41991f66241a · outbound

This paper cites InThe Twelfth International Conference on Learning Representations.

Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs InThe Twelfth International Conference on Learning Representations

Reference 35

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Source-reported events for the cited work

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Observation 6368131e-d007-4305-af4a-4b5fb5428b65 · outbound

This paper cites InProceedings of the 2023 CHI Conference on Human Factors in Computing Systems.

Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs InProceedings of the 2023 CHI Conference on Human Factors in Computing Systems

Reference 36

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 63b4753c-adf1-44d1-ada1-67c9a5b73d38 · outbound

This paper cites Mitigating LLM Hallucinations via Conformal Abstention.

Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs Mitigating LLM Hallucinations via Conformal Abstention

Reference 37

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Observation bef57448-cca0-47ea-b904-4d327b6b65fa · outbound

This paper cites Clarify When Necessary: Resolving Ambiguity Through Interaction with LMs.

Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs Clarify When Necessary: Resolving Ambiguity Through Interaction with LMs

Reference 38

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Source-reported events for the cited work

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Observation a1fcb81f-1314-4bf3-8c8f-daffe68d9d19 · outbound

This paper cites Calibrate Before Use: Improving Few-Shot Performance of Language Models.

Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs Calibrate Before Use: Improving Few-Shot Performance of Language Models

Reference 39

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation aeb8fd59-1de3-416f-9b46-fe4d069c8657 · outbound

This paper cites tuning knob.

Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs tuning knob

Reference 40

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verified fuzzy
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Source-reported events for the cited work

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Observation f41bbf3f-6642-4120-91a2-1e4467caa731 · outbound

This paper cites ISSN 0003-4851, 2168-8990.

Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs ISSN 0003-4851, 2168-8990

Reference 1956

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 73ed59e9-e476-4a8d-ab79-972d446e086a · outbound

This paper cites ISSN 0003-4851, 2168-8990.

Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs ISSN 0003-4851, 2168-8990

Reference 1962

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Source-reported events for the cited work

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Observation 3659eab5-3dc8-407e-aafd-023968550b20 · outbound

This paper cites InPerspectives on Thinking, Judging and Decision Making: A Tribute to Karl Halvor Teigen.

Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs InPerspectives on Thinking, Judging and Decision Making: A Tribute to Karl Halvor Teigen

Reference 2011

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Observation c5c699d6-f323-44e5-9334-4a39f6379299 · outbound

This paper cites InProceedings of The 33rd International Conference on Machine Learning.

Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs InProceedings of The 33rd International Conference on Machine Learning

Reference 2016

Resolution
verified fuzzy
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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.

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Observation ef7dd376-df69-4afc-980b-73413778f6ca · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs Evaluating Large Language Models Trained on Code

Reference 2021

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation b7bcca92-58c1-4532-af4b-d4bc1b9ae4aa · outbound

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

Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs Language Models (Mostly) Know What They Know

Reference 2022

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unresolved
no resolver link, observed 2026-08-07T05:41:36.309140Z

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source=pdf_text observed=2026-08-07T05:41:36.309140Z digest=sha256:71bb96d9cb482c13e800776cd5393ede1fd369c8a6d238acf239f678316729b4

Observation 760f1797-588f-47c1-ae57-08338cc0e9bd · outbound

This paper cites Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models.

Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Reference 2023

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unresolved
no resolver link, observed 2026-08-07T05:41:36.420906Z

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source=pdf_text observed=2026-08-07T05:41:36.420906Z digest=sha256:55eba833c55aeded579bf3d8fabe35ee25e6a552cad59472e2f0c32d5c4023d7

Observation 8567b534-5231-4a94-b1b8-b8e01e88dd6e · outbound

This paper cites ArXiv:2406.04306 [cs].

Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs ArXiv:2406.04306 [cs]

Reference 2024

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unresolved
no resolver link, observed 2026-08-07T05:41:36.257358Z

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source=pdf_text observed=2026-08-07T05:41:36.257358Z digest=sha256:7f700b0a8281164f3ceb5ad90655531fc7774fb486d51c429630e9d81b5b6cc9

Observation 2d6f6084-d62c-4e3d-b145-780925008f8e · outbound

This paper cites Evolution and The Knightian Blindspot of Machine Learning.

Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs Evolution and The Knightian Blindspot of Machine Learning

Reference 2025

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unresolved
no resolver link, observed 2026-08-07T05:41:36.341103Z

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source=pdf_text observed=2026-08-07T05:41:36.341103Z digest=sha256:2df34690195ceee7795807f91b75cfa7e9f25280805be2c3435c55203bdd7852

Pith citing papers

Observation b1b996e1-0663-4857-80e5-1e96c4e075e6 · inbound

When Planning Fails Despite Correct Execution: On Epistemic Calibration for LLM-Based Multi-Agent Systems cites this paper.

When Planning Fails Despite Correct Execution: On Epistemic Calibration for LLM-Based Multi-Agent Systems Extending Epistemic Uncertainty Beyond Parameters Would Assist in Designing Reliable LLMs

Reference 14

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verified exact
arxiv_id, observed 2026-05-25T04:26:38.062182Z

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

source=arxiv_source observed=2026-05-25T04:25:26.710488Z digest=sha256:f013f7c6cf04b469e81ac42da8fbd3b1a4f2e7adce43c2603949c078d9dbc13f