mlsanityData to 7 Oct 2026

Robust Conformal Consensus: Multi-Agent LLM-as-a-Judge Interval Evaluation with Conformal Prediction

arXiv 2609.06367 ↗First posted 6 Sep 2026cs.LG cs.AIcited by 0

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

LLM-as-a-Judge has emerged as a promising paradigm for evaluating natural language generation. However, the uncertainty associated with such evaluations remains largely unexplored, which limits their reliability in real-world applications. Although conformal prediction offers a principled framework for uncertainty quantification, existing approaches typically apply it to a single LLM judge, overlooking the variability introduced by using different LLM evaluators. In this work, we propose a robust uncertainty estimation framework for multi-agent LLM-as-a-Judge evaluation. Our approach constructs conformal prediction intervals for LLM-based scores from multiple LLMs. By considering intervals from different LLM judges, we obtain more stable and reliable uncertainty estimates. Extensive experiments demonstrate that our method produces valid prediction intervals with coverage guarantees, and that interval-based aggregation across multiple judges leads to more stable evaluation outcomes.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 35 of its 64 references that are in the map. First posted on arXiv 6 Sep 2026.

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References · 35

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  3. 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,205
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  12. 2024Benchmarking LLMs via Uncertainty Quantificationcited by 124
23 more
  1. 2024SaySelf: Teaching LLMs to Express Confidence with Self-Reflective Rationalescited by 102
  2. 2024Trust or Escalate: LLM Judges with Provable Guarantees for Human Agreementcited by 99
  3. 2024Can Large Language Models Faithfully Express Their Intrinsic Uncertainty in Words?cited by 85
  4. 2021Conformal Prediction using Conditional Histogramscited by 77
  5. 2023MR-GSM8K: A Meta-Reasoning Benchmark for Large Language Model Evaluationcited by 57
  6. 2024Conformal Prediction for Natural Language Processing: A Surveycited by 52
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  8. 2022Improving Trustworthiness of AI Disease Severity Rating in Medical Imaging with Ordinal Conformal Prediction Setscited by 38
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  11. 2025Analyzing Uncertainty of LLM-as-a-Judge: Interval Evaluations with Conformal Predictioncited by 26
  12. 2021Locally Valid and Discriminative Prediction Intervals for Deep Learning Modelscited by 24
  13. 2020KompaRe: A Knowledge Graph Comparative Reasoning Systemcited by 20
  14. 2024Logic Query of Thoughts: Guiding Large Language Models to Answer Complex Logic Queries with Knowledge Graphscited by 20
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  17. 2024Conformal Prediction via Regression-as-Classificationcited by 18
  18. 2024Black-box Uncertainty Quantification Method for LLM-as-a-Judgecited by 18
  19. 2025Graph-O1 : Monte Carlo Tree Search with Reinforcement Learning for Text-Attributed Graph Reasoningcited by 7
  20. 2025A Cost-Effective LLM-based Approach to Identify Wildlife Trafficking in Online Marketplacescited by 3
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  22. 2026Neural-Symbolic Logic Query Answering in Non-Euclidean Spacecited by 2
  23. 2026Prompt-tuning with Attribute Guidance for Low-resource Entity Matchingcited by 2

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  3. 2026DiscoUQ: Structured Disagreement Analysis for Uncertainty Quantification in LLM Agent Ensembles
  4. 2026Uncertainty Quantification for LLM Agents: A Taxonomy, an Evaluation Protocol, and an Empirical Study
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  6. 2026From Uncertain Judgments to Calibrated Rankings: Conformal Elo Estimation for LLM Evaluation
  7. 2026CollabEval: Enhancing LLM-as-a-Judge via Multi-Agent Collaboration
  8. 2024Rethinking Uncertainty Estimation in LLMs: A Principled Single-Sequence Measure