Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T18:52:21.870977Z
Paper Citation Record · LEDGER
Simple Yet Effective: An Information-Theoretic Approach to Multi-LLM Uncertainty Quantification
As of 8 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 3 inbound Pith citation observations for arXiv:2507.07236.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-04T11:17:36.724299Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T16:31:07.324052Z
Reference resolution
16 of 16 outbound references displayed
- verified exact1
- verified fuzzy8
- unresolved6
- parse uncertain0
- malformed identifier1
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 6c32e97f-873c-48fd-bad4-23b0eb920198 · outbound
This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.
Simple Yet Effective: An Information-Theoretic Approach to Multi-LLM Uncertainty Quantification DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:52:21.827990Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 575f07fc-31a8-4e0a-8c34-0656f2e25761 · outbound
This paper cites Uncertainty-Aware Fusion: An Ensemble Framework for Mitigating Hallucinations in Large Language Models.
Simple Yet Effective: An Information-Theoretic Approach to Multi-LLM Uncertainty Quantification Uncertainty-Aware Fusion: An Ensemble Framework for Mitigating Hallucinations in Large Language Models
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:52:21.831035Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1bc8dd51-444f-4b12-888f-0f7386d513c6 · outbound
This paper cites InPro- ceedings of the 1st Workshop on Uncertainty-Aware NLP (UncertaiNLP 2024), pages 1–14.
Simple Yet Effective: An Information-Theoretic Approach to Multi-LLM Uncertainty Quantification InPro- ceedings of the 1st Workshop on Uncertainty-Aware NLP (UncertaiNLP 2024), pages 1–14
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:52:22.158499Z
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 8215e9b7-f25c-4c5f-afee-451c5376f1df · outbound
This paper cites an unresolved cited work.
Simple Yet Effective: An Information-Theoretic Approach to Multi-LLM Uncertainty Quantification Unresolved cited work
Reference 7
- Resolution
- unresolved
- raw_fallback, observed 2026-08-06T18:52:22.149849Z
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 e8e1a6be-de5f-498e-b329-637c751fdd07 · outbound
This paper cites InProceedings of the 2024 Conference on Empiri- cal Methods in Natural Language Processing, pages 21635–21645.
Simple Yet Effective: An Information-Theoretic Approach to Multi-LLM Uncertainty Quantification InProceedings of the 2024 Conference on Empiri- cal Methods in Natural Language Processing, pages 21635–21645
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:52:22.141843Z
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 8ebaff8b-f357-47f0-b837-9c9e3912522d · outbound
This paper cites InFindings of the Association for Computational Linguistics: EMNLP 2024, pages 2520–2537.
Simple Yet Effective: An Information-Theoretic Approach to Multi-LLM Uncertainty Quantification InFindings of the Association for Computational Linguistics: EMNLP 2024, pages 2520–2537
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:52:22.133333Z
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 439cef45-c767-4b51-be13-bc239e17a4ef · outbound
This paper cites InProceedings of the 1st Workshop on Uncertainty- Aware NLP (UncertaiNLP 2024), pages 114–126.
Simple Yet Effective: An Information-Theoretic Approach to Multi-LLM Uncertainty Quantification InProceedings of the 1st Workshop on Uncertainty- Aware NLP (UncertaiNLP 2024), pages 114–126
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:52:22.124521Z
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 ff8aac71-cc80-4959-bae3-377126929bae · outbound
This paper cites Gemini: A Family of Highly Capable Multimodal Models.
Simple Yet Effective: An Information-Theoretic Approach to Multi-LLM Uncertainty Quantification Gemini: A Family of Highly Capable Multimodal Models
Reference 11
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:52:21.855490Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4fed6c7e-345f-46bf-9a50-9849a02c1518 · outbound
This paper cites Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement.
Simple Yet Effective: An Information-Theoretic Approach to Multi-LLM Uncertainty Quantification Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:52:21.864474Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5346393a-507f-4a63-ae5b-c9844228f1c9 · outbound
This paper cites an unresolved cited work.
Simple Yet Effective: An Information-Theoretic Approach to Multi-LLM Uncertainty Quantification Unresolved cited work
Reference 16
- Resolution
- verified exact
- raw_fallback, observed 2026-08-06T18:52:21.973181Z
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 1f143bf9-5ddc-4355-830d-11d6fcea4d59 · outbound
This paper cites stronger.
Simple Yet Effective: An Information-Theoretic Approach to Multi-LLM Uncertainty Quantification stronger
Reference 20
- Resolution
- malformed identifier
- raw_fallback, observed 2026-08-06T18:52:22.046598Z
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 7258e6fc-ed61-4374-88d0-9e5c3886f52f · outbound
This paper cites InProceedings of the ACM conference on health, inference, and learning, pages 222–235.
Simple Yet Effective: An Information-Theoretic Approach to Multi-LLM Uncertainty Quantification InProceedings of the ACM conference on health, inference, and learning, pages 222–235
Reference 2020
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:52:22.115561Z
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 94be80a5-769a-463f-b1ff-8ba9f7151231 · outbound
This paper cites InFindings of the Association for Computational Linguistics: EMNLP 2022, pages 7273–7284.
Simple Yet Effective: An Information-Theoretic Approach to Multi-LLM Uncertainty Quantification InFindings of the Association for Computational Linguistics: EMNLP 2022, pages 7273–7284
Reference 2022
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:52:22.106082Z
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 808904fe-6fc7-43b2-b620-7f22c9db17be · outbound
This paper cites Mistral 7B.
Simple Yet Effective: An Information-Theoretic Approach to Multi-LLM Uncertainty Quantification Mistral 7B
Reference 2023
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:52:21.837172Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c2bd1653-3842-46ce-a41b-e381e13151d5 · outbound
This paper cites InPro- ceedings of the 2024 Conference on Empirical Meth- ods in Natural Language Processing, pages 284–312.
Simple Yet Effective: An Information-Theoretic Approach to Multi-LLM Uncertainty Quantification InPro- ceedings of the 2024 Conference on Empirical Meth- ods in Natural Language Processing, pages 284–312
Reference 2024
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:52:22.167372Z
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 4e7c0b16-f906-4fe5-a47e-4ab5a0808ee2 · outbound
This paper cites InFindings of the Association for Computa- tional Linguistics: NAACL 2025, pages 7512–7523, Albuquerque, New Mexico.
Simple Yet Effective: An Information-Theoretic Approach to Multi-LLM Uncertainty Quantification InFindings of the Association for Computa- tional Linguistics: NAACL 2025, pages 7512–7523, Albuquerque, New Mexico
Reference 2025
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T18:52:22.176548Z
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.
Pith citing papers
Observation b8c570dd-d43a-4150-b0d1-544d1c28b749 · 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 Simple Yet Effective: An Information-Theoretic Approach to Multi-LLM Uncertainty Quantification
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T11:17:36.724299Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 13736281-8064-466e-9ecf-428e80a650ef · inbound
Zero-Shot Confidence Estimation for Small LLMs: When Supervised Baselines Aren't Worth Training cites this paper.
Zero-Shot Confidence Estimation for Small LLMs: When Supervised Baselines Aren't Worth Training Simple Yet Effective: An Information-Theoretic Approach to Multi-LLM Uncertainty Quantification
Reference 14
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T16:31:07.330866Z
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 be319ab9-38de-41b7-8d8d-8097f285e634 · inbound
Uncertainty-Aware Trust Estimation for Multi-LLM Systems via Structured Expert Judgement cites this paper.
Uncertainty-Aware Trust Estimation for Multi-LLM Systems via Structured Expert Judgement Simple Yet Effective: An Information-Theoretic Approach to Multi-LLM Uncertainty Quantification
Reference 2
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T07:46:08.627235Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.