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arxiv: 2606.10307 · v1 · pith:XOTB67KDnew · submitted 2026-06-09 · 💻 cs.CL

Early-Token Confidence Predicts Reasoning Quality in Multi-Agent LLM Debate

Ali Keramati , Justin Cheok , Jacob Horne , Mark Warschauer This is my paper

Pith reviewed 2026-06-27 13:28 UTC · model grok-4.3

classification 💻 cs.CL
keywords LLM confidencetoken log-probabilitiesmulti-agent debatereasoning qualityessay scoringASAP datasetLLM-as-judge
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The pith

Early-token confidence from decoding predicts reasoning quality better than full-sequence statistics in multi-agent LLM debates.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines whether token-level log-probabilities generated during decoding can serve as a signal for the quality of an LLM's reasoning when no reference answer exists. In a multi-agent debate setup for scoring essays, the confidence expressed in the first few tokens correlates most strongly with the quality ratings later assigned by an LLM judge. This early phase of generation shows the greatest variation across outputs, which makes it more informative than averages taken over the complete response. The correlation is stronger when agents provide supportive reasoning than when they offer adversarial critique. If the pattern holds, systems could gauge reliability after only a short prefix rather than waiting for a full generation and separate evaluation step.

Core claim

In a debate-based framework applied to two ASAP essay datasets, early-token confidence measured by the log-probabilities of the initial generated tokens is the strongest predictor of reasoning quality as scored by LLM judges. This measure outperforms full-sequence log-probability statistics. Log-probability trajectories indicate that heterogeneity peaks at the start of generation. Alignment between confidence and judged quality is systematically higher for supportive agents than for adversarial ones.

What carries the argument

Early-token confidence, the log-probabilities attached to the first few tokens of an LLM's generated response, used as a proxy for reasoning quality.

If this is right

  • Reasoning quality can be estimated from only the opening segment of generation rather than the complete output.
  • Multi-agent systems could monitor early confidence to decide whether to accept, revise, or discard a turn before full completion.
  • Full-sequence confidence statistics add less predictive value, implying later tokens contribute noise to quality estimation.
  • Supportive and adversarial roles require separate calibration because their confidence-quality alignment differs.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same early-token signal might serve as a lightweight filter for low-quality responses in other open-ended generation tasks such as planning or code synthesis.
  • If the pattern generalizes, real-time systems could reduce the number of separate judge calls by using prefix confidence alone.
  • The finding suggests that model uncertainty concentrates at the moment an initial reasoning direction is chosen.

Load-bearing premise

LLM-as-judge rubric scores provide a valid and unbiased ground truth for reasoning quality.

What would settle it

Re-running the debate protocol on the same essay sets but substituting human raters for the LLM judge and testing whether the early-token correlation with quality remains significant.

Figures

Figures reproduced from arXiv: 2606.10307 by Ali Keramati, Jacob Horne, Justin Cheok, Mark Warschauer.

Figure 1
Figure 1. Figure 1: Overview of the proposed multi-agent debate and LLM-as-judge evaluation framework. [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Token-level log-probability trajectories for Advocate and Skeptic responses on Essay Sets 7 (left) and 8 [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Distribution of LLM-as-judge scores across evaluation dimensions. Evidence grounding is highly saturated [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: System instructions for the Advocate agent. [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: System instructions for the Skeptic agent. [PITH_FULL_IMAGE:figures/full_fig_p013_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: System instructions for the Synthesizer-Judge agent. [PITH_FULL_IMAGE:figures/full_fig_p014_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: System instructions for the Meta-Evaluator agent. [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Task prompt provided to the Meta-Evaluator agent. [PITH_FULL_IMAGE:figures/full_fig_p015_8.png] view at source ↗
read the original abstract

Evaluating reasoning quality in multi-agent LLM systems is challenging, especially for open-ended tasks without reference answers. We investigate whether intrinsic confidence signals, token-level log-probabilities from decoding, can predict reasoning quality as assessed by LLM-as-judge evaluation. Using a debate-based essay scoring framework, we compare confidence proxies against rubric-based judge scores across two ASAP essay sets. We find that early-token confidence, particularly within the first few generated tokens, is consistently the strongest predictor of reasoning quality, outperforming full-sequence statistics. Analysis of log-probability trajectories shows that the opening phase of generation is the most heterogeneous and therefore most informative. We also observe a systematic asymmetry between agent roles, with stronger alignment between confidence and quality for supportive reasoning than for adversarial critique. These results suggest that early decoding dynamics provide a lightweight and effective signal for estimating reasoning reliability in multi-agent LLM systems.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript claims that in a multi-agent LLM debate framework for scoring essays on two ASAP datasets, token-level log-probabilities from the first few generated tokens are the strongest predictor of reasoning quality (as measured by LLM-as-judge rubric scores), outperforming full-sequence statistics; log-probability trajectories indicate the opening generation phase is most heterogeneous and informative; and there is a systematic role asymmetry with stronger confidence-quality alignment for supportive reasoning than adversarial critique.

Significance. If the reported correlations prove robust after adding statistical tests, sample-size reporting, and controls for judge-model bias, the result would supply a lightweight intrinsic signal for estimating reasoning reliability in multi-agent LLM systems, potentially reducing reliance on full external evaluations. The trajectory and role-asymmetry observations could also guide protocol design. The absence of these controls in the current version limits immediate applicability.

major comments (3)
  1. [Abstract] Abstract: the central claim that early-token confidence is 'consistently the strongest predictor' and 'outperforms full-sequence statistics' is presented without any reported sample sizes, statistical tests, confidence intervals, or effect sizes across the two ASAP sets, rendering the empirical correlation unsubstantiated.
  2. [Methods] Methods (debate-based essay scoring framework): no calibration of LLM-as-judge rubric scores against human ratings, no inter-judge agreement statistics, and no controls for prompt sensitivity or training-data overlap between generator and judge models are described, which is load-bearing because both confidence signals and judge scores could share surface-feature biases.
  3. [Results] Results (role asymmetry): the reported stronger alignment for supportive vs. adversarial roles lacks any control or ablation for differences in judge-prompt wording between roles, leaving open that the asymmetry is an artifact of the evaluation protocol rather than a property of reasoning quality.
minor comments (2)
  1. [Abstract] The exact token window used for 'early-token' confidence is not numerically defined, complicating replication.
  2. [Results] No mention of how log-probability heterogeneity is quantified in the trajectory analysis.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We appreciate the referee's detailed feedback on our manuscript. We address each major comment below, agreeing to incorporate additional statistical reporting, methodological clarifications, and controls where appropriate to strengthen the claims.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that early-token confidence is 'consistently the strongest predictor' and 'outperforms full-sequence statistics' is presented without any reported sample sizes, statistical tests, confidence intervals, or effect sizes across the two ASAP sets, rendering the empirical correlation unsubstantiated.

    Authors: We acknowledge this omission in the abstract and main text. While the manuscript reports results on two ASAP datasets, we did not include explicit statistical tests or effect sizes in the presented version. In the revision, we will add sample sizes (number of essays per set), Pearson or Spearman correlations with p-values, confidence intervals, and effect sizes (e.g., Cohen's d or r^2) for the comparisons between early-token and full-sequence predictors. This will substantiate the 'consistently strongest' claim. revision: yes

  2. Referee: [Methods] Methods (debate-based essay scoring framework): no calibration of LLM-as-judge rubric scores against human ratings, no inter-judge agreement statistics, and no controls for prompt sensitivity or training-data overlap between generator and judge models are described, which is load-bearing because both confidence signals and judge scores could share surface-feature biases.

    Authors: The referee correctly identifies a limitation in our evaluation setup. The current manuscript does not include calibration against human ratings or inter-judge agreement, nor explicit controls for prompt sensitivity or data overlap. We will revise the Methods section to explicitly state these absences as limitations and discuss potential biases. Additionally, we will add an analysis of prompt sensitivity by varying judge prompts and report any changes in correlations. For training-data overlap, we will note the models used and any known overlaps. revision: partial

  3. Referee: [Results] Results (role asymmetry): the reported stronger alignment for supportive vs. adversarial roles lacks any control or ablation for differences in judge-prompt wording between roles, leaving open that the asymmetry is an artifact of the evaluation protocol rather than a property of reasoning quality.

    Authors: We agree that without controlling for prompt wording differences, the role asymmetry could be influenced by the evaluation protocol. In the revision, we will include an ablation study where we standardize the judge prompts across roles or swap wording to test if the asymmetry persists. If the asymmetry holds, it strengthens the claim; otherwise, we will qualify the finding accordingly. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical correlation between independent measurements

full rationale

The paper reports an empirical analysis correlating early-token log-probabilities (intrinsic to the generating LLM) with separate LLM-as-judge rubric scores on two ASAP essay datasets. No equations, derivations, or parameter-fitting steps are described that would reduce one quantity to a function of the other by construction. No self-citations appear as load-bearing premises, no uniqueness theorems are invoked, and no ansatzes or renamings of known results are presented as novel derivations. The reported asymmetries and trajectory analyses are likewise data-driven observations rather than self-referential definitions. The study is therefore self-contained against external benchmarks and receives the default non-circularity finding.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are described in the abstract; the work is purely observational.

pith-pipeline@v0.9.1-grok · 5679 in / 1101 out tokens · 21731 ms · 2026-06-27T13:28:35.681263+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

41 extracted references · 15 canonical work pages

  1. [1]

    Aho and Jeffrey D

    Alfred V. Aho and Jeffrey D. Ullman , title =. 1972

    1972

  2. [2]

    Publications Manual , year = "1983", publisher =

    1983

  3. [3]

    Chandra and Dexter C

    Ashok K. Chandra and Dexter C. Kozen and Larry J. Stockmeyer , year = "1981", title =. doi:10.1145/322234.322243

    work page doi:10.1145/322234.322243 1981

  4. [4]

    Scalable training of

    Andrew, Galen and Gao, Jianfeng , booktitle=. Scalable training of

  5. [5]

    Dan Gusfield , title =. 1997

    1997

  6. [6]

    Tetreault , title =

    Mohammad Sadegh Rasooli and Joel R. Tetreault , title =. Computing Research Repository , volume =. 2015 , url =

    2015

  7. [7]

    A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data , Volume =

    Ando, Rie Kubota and Zhang, Tong , Issn =. A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data , Volume =. Journal of Machine Learning Research , Month = dec, Numpages =

  8. [8]

    arXiv preprint arXiv:2402.03578 , year=

    LLM multi-agent systems: Challenges and open problems , author=. arXiv preprint arXiv:2402.03578 , year=

    Pith/arXiv arXiv

  9. [9]

    2025 , eprint=

    PlanGEN: A Multi-Agent Framework for Generating Planning and Reasoning Trajectories for Complex Problem Solving , author=. 2025 , eprint=

    2025

  10. [10]

    2023 , eprint=

    AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation , author=. 2023 , eprint=

    2023

  11. [11]

    2023 , eprint=

    Improving Factuality and Reasoning in Language Models through Multiagent Debate , author=. 2023 , eprint=

    2023

  12. [12]

    Crossley and Perpetual Baffour and L

    Scott A. Crossley and Perpetual Baffour and L. Burleigh and Jules King , keywords =. A large-scale corpus for assessing source-based writing quality: ASAP 2.0 , journal =. 2025 , issn =. doi:https://doi.org/10.1016/j.asw.2025.100954 , url =

    work page doi:10.1016/j.asw.2025.100954 2025

  13. [13]

    The Journal of Technology, Learning and Assessment , author=

    An Overview of Automated Scoring of Essays , volume=. The Journal of Technology, Learning and Assessment , author=. 2006 , month=

    2006

  14. [14]

    Large language models and automated essay scoring of English language learner writing: Insights into validity and reliability , journal =

    Austin Pack and Alex Barrett and Juan Escalante , keywords =. Large language models and automated essay scoring of English language learner writing: Insights into validity and reliability , journal =. 2024 , issn =. doi:https://doi.org/10.1016/j.caeai.2024.100234 , url =

    work page doi:10.1016/j.caeai.2024.100234 2024

  15. [15]

    doi:10.5281/zenodo.17196206 , url =

    Keramati, Ali and Warschauer, Mark , title =. doi:10.5281/zenodo.17196206 , url =

    work page doi:10.5281/zenodo.17196206

  16. [16]

    2025 , eprint=

    Talk Isn't Always Cheap: Understanding Failure Modes in Multi-Agent Debate , author=. 2025 , eprint=

    2025

  17. [17]

    and Zhang, Hao and Gonzalez, Joseph E

    Zheng, Lianmin and Chiang, Wei-Lin and Sheng, Ying and Zhuang, Siyuan and Wu, Zhanghao and Zhuang, Yonghao and Lin, Zi and Li, Zhuohan and Li, Dacheng and Xing, Eric P. and Zhang, Hao and Gonzalez, Joseph E. and Stoica, Ion , title =. Proceedings of the 37th International Conference on Neural Information Processing Systems , articleno =. 2023 , publisher =

    2023

  18. [18]

    2025 , eprint=

    Multi-Agent-as-Judge: Aligning LLM-Agent-Based Automated Evaluation with Multi-Dimensional Human Evaluation , author=. 2025 , eprint=

    2025

  19. [19]

    2025 , eprint=

    Self-Evaluating LLMs for Multi-Step Tasks: Stepwise Confidence Estimation for Failure Detection , author=. 2025 , eprint=

    2025

  20. [20]

    2nd AI for Math Workshop @ ICML 2025 , year=

    Scalable Best-of-N Selection for Large Language Models via Self-Certainty , author=. 2nd AI for Math Workshop @ ICML 2025 , year=

    2025

  21. [21]

    Can Large Language Models Be an Alternative to Human Evaluations?

    Chiang, Cheng-Han and Lee, Hung-yi. Can Large Language Models Be an Alternative to Human Evaluations?. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023. doi:10.18653/v1/2023.acl-long.870

    work page doi:10.18653/v1/2023.acl-long.870 2023

  22. [22]

    2025 , eprint=

    Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators , author=. 2025 , eprint=

    2025

  23. [23]

    Automating Theory of Mind Assessment with a LLaMA-3-Powered Chatbot: Enhancing Faux Pas Detection in Autism , year=

    Fallah, Avisa and Keramati, Ali and Nazari, Mohammad Ali and Mirfazeli, Fatemeh Sadat , booktitle=. Automating Theory of Mind Assessment with a LLaMA-3-Powered Chatbot: Enhancing Faux Pas Detection in Autism , year=

  24. [24]

    GPTS core: Evaluate as You Desire

    Fu, Jinlan and Ng, See-Kiong and Jiang, Zhengbao and Liu, Pengfei. GPTS core: Evaluate as You Desire. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024. doi:10.18653/v1/2024.naacl-long.365

    work page doi:10.18653/v1/2024.naacl-long.365 2024

  25. [25]

    G -Eval: NLG Evaluation using Gpt-4 with Better Human Alignment

    Liu, Yang and Iter, Dan and Xu, Yichong and Wang, Shuohang and Xu, Ruochen and Zhu, Chenguang. G -Eval: NLG Evaluation using Gpt-4 with Better Human Alignment. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023. doi:10.18653/v1/2023.emnlp-main.153

    work page doi:10.18653/v1/2023.emnlp-main.153 2023

  26. [26]

    2024 , eprint=

    FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets , author=. 2024 , eprint=

    2024

  27. [27]

    doi: 10.18653/v1/2024.acl-long.511

    Wang, Peiyi and Li, Lei and Chen, Liang and Cai, Zefan and Zhu, Dawei and Lin, Binghuai and Cao, Yunbo and Kong, Lingpeng and Liu, Qi and Liu, Tianyu and Sui, Zhifang. Large Language Models are not Fair Evaluators. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024. doi:10.18653/v1/2024.ac...

    work page doi:10.18653/v1/2024.acl-long.511 2024

  28. [28]

    2024 , eprint=

    Evaluating Large Language Models at Evaluating Instruction Following , author=. 2024 , eprint=

    2024

  29. [29]

    2023 , eprint=

    CoAScore: Chain-of-Aspects Prompting for NLG Evaluation , author=. 2023 , eprint=

    2023

  30. [30]

    Branch-Solve-Merge Improves Large Language Model Evaluation and Generation

    Saha, Swarnadeep and Levy, Omer and Celikyilmaz, Asli and Bansal, Mohit and Weston, Jason and Li, Xian. Branch-Solve-Merge Improves Large Language Model Evaluation and Generation. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024. doi...

    work page doi:10.18653/v1/2024.naacl-long.462 2024

  31. [31]

    2024 , eprint=

    PRD: Peer Rank and Discussion Improve Large Language Model based Evaluations , author=. 2024 , eprint=

    2024

  32. [32]

    2023 , eprint=

    ChatEval: Towards Better LLM-based Evaluators through Multi-Agent Debate , author=. 2023 , eprint=

    2023

  33. [33]

    PRePair: Pointwise Reasoning Enhance Pairwise Evaluating for Robust Instruction-Following Assessments , doi =

    Jeong, Hawon and Park, Chaehun and Hong, Jimin and Choo, Jaegul , year =. PRePair: Pointwise Reasoning Enhance Pairwise Evaluating for Robust Instruction-Following Assessments , doi =

  34. [34]

    R e IFE : Re-evaluating Instruction-Following Evaluation

    Liu, Yixin and Shi, Kejian and Fabbri, Alexander and Zhao, Yilun and Wang, PeiFeng and Wu, Chien-Sheng and Joty, Shafiq and Cohan, Arman. R e IFE : Re-evaluating Instruction-Following Evaluation. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1...

    work page doi:10.18653/v1/2025.naacl-long.610 2025

  35. [35]

    An Empirical Study of LLM -as-a-Judge for LLM Evaluation: Fine-tuned Judge Model is not a General Substitute for GPT -4

    Huang, Hui and Bu, Xingyuan and Zhou, Hongli and Qu, Yingqi and Liu, Jing and Yang, Muyun and Xu, Bing and Zhao, Tiejun. An Empirical Study of LLM -as-a-Judge for LLM Evaluation: Fine-tuned Judge Model is not a General Substitute for GPT -4. Findings of the Association for Computational Linguistics: ACL 2025. 2025. doi:10.18653/v1/2025.findings-acl.306

    work page doi:10.18653/v1/2025.findings-acl.306 2025

  36. [36]

    Calibration of Pre-trained Transformers

    Desai, Shrey and Durrett, Greg. Calibration of Pre-trained Transformers. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020. doi:10.18653/v1/2020.emnlp-main.21

    work page doi:10.18653/v1/2020.emnlp-main.21 2020

  37. [37]

    2022 , eprint=

    Language Models (Mostly) Know What They Know , author=. 2022 , eprint=

    2022

  38. [38]

    2024 , eprint=

    Detecting Hallucinations in Large Language Model Generation: A Token Probability Approach , author=. 2024 , eprint=

    2024

  39. [39]

    A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation

    Liu, Tianyu and Zhang, Yizhe and Brockett, Chris and Mao, Yi and Sui, Zhifang and Chen, Weizhu and Dolan, Bill. A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022. doi:10.18653/v1/2022.acl-long.464

    work page doi:10.18653/v1/2022.acl-long.464 2022

  40. [40]

    S elf C heck GPT : Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models

    Manakul, Potsawee and Liusie, Adian and Gales, Mark. S elf C heck GPT : Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023. doi:10.18653/v1/2023.emnlp-main.557

    work page doi:10.18653/v1/2023.emnlp-main.557 2023

  41. [41]

    When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories

    Mallen, Alex and Asai, Akari and Zhong, Victor and Das, Rajarshi and Khashabi, Daniel and Hajishirzi, Hannaneh. When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023. doi:10.18653/v1/2023...

    work page doi:10.18653/v1/2023.acl-long.546 2023