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REVIEW 3 major objections 1 minor 143 cited by

Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate

T0 review · 3 major / 1 minor · reviewed 2026-05-13 · grok-4.3

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Pith's one-line read Large language models overcome stuck reasoning by having multiple agents argue tit-for-tat under a judge instead of reflecting alone.

desk verdict MAD gives a practical empirical boost to LLM reasoning by using tit-for-tat debate, but the judge's bias controls are still thin. read the letter →

arxiv 2305.19118 v4 pith:656DEUNA submitted 2023-05-30 cs.CL

Tian Liang , Zhiwei He , Wenxiang Jiao , Xing Wang , Yan Wang , Rui Wang , Yujiu Yang , Shuming Shi
show 1 more author
Zhaopeng Tu
This is my paper · ORCID
classification cs.CL
keywords largelanguagemodelsmulti-agentdebatedivergentthinkingdegenerationofthoughtself-reflectionreasoningtaskscommonsensetranslationarithmetic
checked against Foundation.LawOfExistenceFoundation.DiscretenessForcing
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

Self-reflection causes LLMs to lock into an initial answer once they gain confidence, even when that answer is wrong, because later reflection fails to produce genuinely new ideas. The paper introduces a Multi-Agent Debate framework in which separate LLM agents present opposing arguments in a back-and-forth exchange while a judge LLM oversees the process and selects a final solution. Experiments on commonsense machine translation and counter-intuitive arithmetic reasoning show that this debate setup produces better results than reflection-based methods. The authors also find that debate performance depends on stopping at an adaptive point and keeping the level of disagreement moderate rather than extreme.

What carries the argument

The Multi-Agent Debate process in which LLM agents generate opposing arguments in a tit-for-tat dynamic and a separate judge LLM synthesizes them into a final answer.

What would settle it

Apply the same MAD setup to the reported datasets and obtain accuracy no higher than self-reflection baselines, or observe the judge consistently favoring one agent's first position regardless of counter-arguments.

Watch

Extended reading notes

Core claim

The Multi-Agent Debate framework encourages divergent thinking in LLMs by placing multiple agents in a tit-for-tat argumentative state, with a judge managing the exchange to reach a final solution, thereby addressing the Degeneration-of-Thought problem that limits self-reflection on tasks requiring deep contemplation.

Load-bearing premise

The judge LLM can evaluate and combine the agents' arguments fairly without itself becoming stuck in an initial view.

Editorial extensions

If this is right

  • MAD improves performance over self-reflection on commonsense machine translation and counter-intuitive arithmetic reasoning.
  • Effective MAD requires an adaptive stopping point for the debate and only a modest level of tit-for-tat intensity.
  • Using different LLMs for agents versus judge can produce biased synthesis.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The same debate structure could be tested on other tasks that reward considering multiple perspectives, such as planning or creative writing.
  • If the judge bias problem is confirmed, replacing the judge with a human or a rule-based aggregator becomes a direct next step.
  • Scaling the number of agents beyond the small groups tested here might increase the chance of surfacing overlooked alternatives.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

3 major / 1 minor

Summary. The paper identifies the Degeneration-of-Thought (DoT) problem in self-reflection methods for LLMs on complex reasoning tasks. It proposes a Multi-Agent Debate (MAD) framework in which multiple agents engage in tit-for-tat arguments managed by a judge LLM to produce a final solution. Experiments on commonsense machine translation and counter-intuitive arithmetic reasoning datasets are reported to demonstrate effectiveness, with additional analyses on adaptive debate length and tit-for-tat intensity.

Significance. If the results hold under tighter controls, the MAD framework provides a concrete procedural approach to mitigating DoT and encouraging divergent thinking in LLMs. The open-sourced code and empirical evaluation on two challenging tasks constitute a useful contribution to the study of LLM reasoning strategies.

major comments (3)
  1. [Abstract and Experiments] The central claim requires that the judge LLM synthesizes the debate without inheriting DoT bias. The manuscript notes unfairness when different LLMs are used for agents but does not report controls that hold the judge model fixed while varying agent diversity or initial stance strength (Abstract; Experiments section).
  2. [Experiments] Statistical significance, exact baseline implementations, prompt sensitivity, and judge-bias controls are not detailed, leaving the reported gains on the two datasets difficult to interpret or reproduce (Experiments section).
  3. [Analyses] The claim that an adaptive break and modest tit-for-tat level are required for good performance lacks quantitative thresholds or effect-size tables showing how performance degrades outside those regimes (Analyses section).
minor comments (1)
  1. All prompts and exact debate templates should be included in an appendix to support reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We appreciate the referee's detailed feedback on our manuscript. We believe the suggested revisions will significantly strengthen the paper by providing more rigorous controls and quantitative analyses. Below we respond point-by-point to the major comments.

read point-by-point responses

  1. Referee: [Abstract and Experiments] The central claim requires that the judge LLM synthesizes the debate without inheriting DoT bias. The manuscript notes unfairness when different LLMs are used for agents but does not report controls that hold the judge model fixed while varying agent diversity or initial stance strength (Abstract; Experiments section).

    Authors: We thank the referee for highlighting this important aspect. While we observed that using different LLMs for agents can lead to unfair judgments, we agree that explicit controls holding the judge fixed are necessary to isolate the effect of agent diversity. In the revised manuscript, we will include additional experiments where the judge model is fixed (e.g., using GPT-4 as judge) and systematically vary the agent models and the strength of initial stances. This will provide clearer evidence that the judge synthesizes without inheriting DoT bias. revision: yes

  2. Referee: [Experiments] Statistical significance, exact baseline implementations, prompt sensitivity, and judge-bias controls are not detailed, leaving the reported gains on the two datasets difficult to interpret or reproduce (Experiments section).

    Authors: We acknowledge the need for more rigorous reporting. In the revision, we will provide: (1) statistical significance tests (e.g., p-values from paired t-tests or bootstrap) for the performance gains; (2) exact prompt templates and baseline implementations with links to code; (3) analysis of prompt sensitivity by varying key prompt elements; and (4) additional judge-bias controls as mentioned above. These details will be added to the Experiments section to enhance reproducibility. revision: yes

  3. Referee: [Analyses] The claim that an adaptive break and modest tit-for-tat level are required for good performance lacks quantitative thresholds or effect-size tables showing how performance degrades outside those regimes (Analyses section).

    Authors: We agree that quantitative support would strengthen this claim. We will add effect-size tables and plots in the Analyses section showing performance as a function of debate length (number of rounds) and tit-for-tat intensity levels. This will include thresholds where performance degrades, such as when debate continues too long or tit-for-tat is too aggressive, leading to degeneration. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity in derivation chain

full rationale

The paper defines the MAD framework as a procedural multi-agent interaction with a judge, without any equations, fitted parameters, or mathematical derivations. Central claims rest on empirical results from two external datasets (commonsense machine translation and counter-intuitive arithmetic reasoning) compared to baselines, with no reduction of outputs to inputs by construction. No self-citation chains, uniqueness theorems, or ansatzes are invoked in a load-bearing manner for the core argument; the DoT observation and MAD proposal are presented as independent contributions evaluated externally.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

The central claim rests on the domain assumption that structured disagreement among LLM instances produces net gains in reasoning quality; no free parameters or invented entities are introduced.

assumptions (2)
  • domain assumption Multiple LLM agents in tit-for-tat debate can generate novel thoughts that a single agent cannot produce via self-reflection.
    Invoked to justify why the framework overcomes DoT; appears in the motivation and method description.
  • domain assumption An LLM judge can reliably select the best solution from the debate transcript.
    Required for the final output step; noted as potentially problematic when different LLMs are used.

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Cite this review

Pith. "Pith review of Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate." pith.science (2026). https://pith.science/paper/656DEUNA

@misc{pith2026230519118,
  author       = {Pith},
  title        = {Pith review of: Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/656DEUNA}},
  note         = {Machine review of arXiv:2305.19118}
}
read the original abstract

Modern large language models (LLMs) like ChatGPT have shown remarkable performance on general language tasks but still struggle on complex reasoning tasks, which drives the research on cognitive behaviors of LLMs to explore human-like problem-solving strategies. Along this direction, one representative strategy is self-reflection, which asks an LLM to refine the solution with the feedback generated by itself iteratively. However, our study shows that such reflection-style methods suffer from the Degeneration-of-Thought (DoT) problem: once the LLM has established confidence in its solutions, it is unable to generate novel thoughts later through reflection even if its initial stance is incorrect. To address the DoT problem, we propose a Multi-Agent Debate (MAD) framework, in which multiple agents express their arguments in the state of "tit for tat" and a judge manages the debate process to obtain a final solution. Clearly, our MAD framework encourages divergent thinking in LLMs which would be helpful for tasks that require deep levels of contemplation. Experiment results on two challenging datasets, commonsense machine translation and counter-intuitive arithmetic reasoning, demonstrate the effectiveness of our MAD framework. Extensive analyses suggest that the adaptive break of debate and the modest level of "tit for tat" state are required for MAD to obtain good performance. Moreover, we find that LLMs might not be a fair judge if different LLMs are used for agents. Code is available at https://github.com/Skytliang/Multi-Agents-Debate.

Discussion (0). Continue with ORCID to comment.

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Pith tools

Reviewed May 13, 2026 · model on record in the stance chip above.