When and Why Does Multi-Agent Debate Fail and Does It Really Underperform?

Bo Han; Gang Niu; James Cheng; Masashi Sugiyama; Yongqiang Chen

REVIEW 4 major objections 5 minor 2 cited by

Competitive multi-agent debate underperforms single LLMs because debaters game the judge; a collaborative protocol restores the gains and beats both.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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2026-08-04 08:19 UTC pith:UA7BKEKI

load-bearing objection Plausible, useful empirical protocol for LLM error detection, but the theory is hand-wavy and the protocol comparison is confounded by bundled mechanisms. the 4 major comments →

arxiv 2510.20963 v2 pith:UA7BKEKI submitted 2025-10-23 cs.LG

When and Why Does Multi-Agent Debate Fail and Does It Really Underperform?

Yongqiang Chen , Gang Niu , James Cheng , Bo Han , Masashi Sugiyama This is my paper

classification cs.LG

keywords multi-agent debateerror detectionlarge language modelsscalable oversightdebate hackingzero-sum gamecollaborative debatelog-likelihood ratio

verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The paper claims that the reason multi-agent debate underperforms a single LLM in error detection is not the debate itself but its incentive structure: when debaters are rewarded for winning rather than for being right, they game the judge with fake evidence, overconfident phrasing, and fallacious arguments. It proves that in competitive zero-sum debate with optimal debaters and a Bayes-optimal judge, the transcripts add no information beyond the initial responses, so the debate cannot improve on the no-debate baseline. It then proposes ColMAD, a collaborative non-zero-sum protocol with quote verification, self-auditing, and confidence calibration, and shows it beats competitive debate by up to 19% and improves on single-agent detection. The central implication is that protocol design, not the number of agents, decides whether multi-agent debate helps or hurts.

Core claim

The paper's central claim is that competitive multi-agent debate (CopMAD) degenerates into a cheap-talk game whose minimax error equals the no-debate Bayes risk R0: under optimal adversarial debaters, the messages become conditionally independent of the true label given the initial responses (M ⊥ Y | X0), so Vcomp = R0 and the debate provides no new information to the judge. In contrast, collaborative debate (ColMAD) achieves Vcomad ≤ R0, with strict improvement whenever the messages carry conditional information about the label, I(Y; M | X0) > 0. On three error-detection tasks, CopMAD drops performance below single-agent baselines by up to 15% in F2, while ColMAD improves over single-agent

What carries the argument

The carrying object is the debate protocol's incentive structure, formalized as zero-sum versus non-zero-sum games with a Bayes-optimal judge who aggregates log-likelihood ratios. Proposition 2.2 shows that in zero-sum CopMAD the Nash-equilibrium transcripts satisfy M ⊥ Y | X0, so the debate's LLR contribution vanishes and Vcomp = R0. Proposition 2.3 shows collaborative debate preserves informative messages, giving Vcomad ≤ R0, with strict inequality exactly when I(Y; M | X0) > 0. ColMAD implements this with three prompting mechanisms: verified-quote evidence, self-audit of failure modes, and confidence calibration.

Load-bearing premise

The proof of Proposition 2.2 (Appendix A.2, step ii) asserts without derivation that optimal adversarial debaters produce messages conditionally independent of the true label given the initial responses, M ⊥ Y | X0; if debate transcripts ever leak truth-relevant information, the conclusion Vcomp = R0 is not automatic.

What would settle it

Test whether competitive-debate transcripts from real LLM pairs carry information about the true error label beyond the initial responses: if a judge who reads the transcripts outperforms a judge who ignores them, or if I(Y; M | X0) > 0 can be measured on real transcripts, then the conditional-independence premise of Proposition 2.2 is violated and the minimax explanation for CopMAD's collapse needs revision.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

Previous reports that multi-agent debate underperforms single-agent methods are explained by zero-sum debate hacking, not by a fundamental ceiling on multi-agent deliberation.
A cheap change to the prompting protocol — collaborative instead of competitive instructions, plus quote verification and self-auditing — flips debate from harmful to helpful for error detection.
The theoretical result Vcomp = R0 implies that, under optimal adversarial debaters, competitive debate cannot beat the no-debate baseline; observed gains in persuasive-debate studies must come from non-optimal debaters or from truth leaking through stance assignment.
ColMAD yields explanations more aligned with human rationales than CopMAD, which matters for scalable oversight where a weak evaluator must judge a strong model.
Because gains are largest for pairs of LLMs from different families, error detection improves most when collaborating agents have decorrelated error patterns.

Where Pith is reading between the lines

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

If the proof's step M ⊥ Y | X0 fails empirically, competitive debate would sometimes beat the baseline; the theory's scope would then be bounded to idealized perfectly optimal debaters rather than real LLMs.
The ColMAD recipe (verified quotes, self-audit, confidence calibration) could transfer to other test-time-scaling uses such as self-correction and fact-checking, where the same hacking failure modes arise.
A direct test of the theory is to measure I(Y; M | X0) on real debate transcripts; if it is positive in competitive settings, the minimax premise of Proposition 2.2 is the part that does not hold.
The oracle-collaboration analysis implies an upper bound on ColMAD's gains: the ceiling is set by how often the two models err simultaneously, so adding a third agent with decorrelated errors is a natural next step.

Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

Plausible, useful empirical protocol for LLM error detection, but the theory is hand-wavy and the protocol comparison is confounded by bundled mechanisms.

read the letter

Colleague,

The real contribution of arXiv:2510.20963 is practical: ColMAD, a collaborative multi-agent debate protocol for error detection in LLM responses, and the empirical result that it beats both competitive MAD and single-agent baselines on ReaLMistake. I think that result is probably true in the narrow sense—the F1 numbers in Tables 1 and 2 are consistent across model pairs and tasks, and the oracle-collaboration analysis (Fig 2) is a good sanity check that different models make complementary mistakes. The paper also does a decent job of demonstrating CopMAD's failure mode, debate hacking, and offers a cheap, concrete fix: quote verification, self-audit, and confidence calibration. That is useful for people working on scalable oversight.

The soft spots are where the paper overreaches. The theoretical justification for why CopMAD fails, Proposition 2.2, rests on an asserted conditional independence M ⊥ Y | X0 in the proof. That is exactly the claim at issue—if adversarial messages can carry any true signal, the minimax value is not necessarily R0. The theory does not do the work the paper wants; the empirical comparison is the load-bearing part and it is under-specified. There is no reproduced CopMAD prompt, and ColMAD bundles the collaborative instruction with three extra mechanisms. The striking result (GPT-4o-mini judge + DeepSeek-R1 debater, F1 76.8 vs 32.3) could be entirely due to quote verification filtering out unverifiable claims, not to the collaborative incentive. An ablation isolating the collaborative instruction is missing.

The numbers have no error bars or significance tests on 140-160 samples; a few-point gap over single-agent is within plausible noise. The paper should include self-consistency and other strong single-agent baselines. And the title/abstract mismatch (posted title is about when MAD fails; the paper is actually about a new protocol) needs fixing; the abstract itself says both 'up to 10 percentage points' and 'by 19%.'

That said, the core empirical direction holds up better than the stress-test note suggests. The M⊥Y concern is real, but it attacks the theory, not the tables; the tables still say something even if the proof is wrong. The missing ablation and missing prompts are fixable in revision. I would not desk-reject this. It deserves a serious referee, with the understanding that the theory section likely needs to be cut or rewritten and the empirical section needs more rigor.

Who should read it: anyone doing prompt-level work on LLM error detection or multi-agent evaluation protocols. I would probably cite the empirical result with a caveat.

For peer review: send it out, but expect heavy revision.

Referee Report

4 major / 5 minor

Summary. The paper studies why multi-agent debate (MAD) often underperforms single-agent LLM error detection and proposes a collaborative protocol, ColMAD. It argues that competitive MAD (CopMAD) is susceptible to “debate hacking” because zero-sum incentives induce misleading, overconfident, or fake-evidence claims, and that consensus-seeking schemes filter out informative disagreements. The authors formalize this with Proposition 2.2 (CopMAD’s minimax value equals the no-debate Bayes risk) and Proposition 2.3 (collaborative debate can improve on it). Empirically, on the ReaLMistake benchmark (three error-detection tasks, GPT-4 and Llama-2 responses), they report that ColMAD outperforms CopMAD by large margins in several configurations and occasionally improves over the best single-agent baseline. The paper includes detailed prompts, an algorithm, and deterministic model settings.

Significance. If the empirical results are robust, the paper identifies incentive structure as the key factor in whether MAD helps or harms LLM error detection, and it proposes a practical prompting method that could be valuable for scalable oversight. The strengths are the use of an external benchmark, the reproducible temperature-0 settings, detailed prompts and algorithm in the appendix, and the large CopMAD-versus-ColMAD gaps in configurations such as GPT4o-mini with DeepSeek-R1 (Table 1: F1 76.77 vs 32.28). However, the central causal claim—that the non-zero-sum framing itself drives the improvement—is weakened by (i) a flawed proof of Proposition 2.2 that assumes the very conditional independence that needs to be established, and (ii) an implementation that bundles several additional mechanisms beyond the collaborative instruction without ablations.

major comments (4)

[Appendix A.2, Eq. (12)] The proof of Proposition 2.2 asserts, without derivation, that optimal adversarial debaters yield M ⊥ Y | X0. This is the entire content of the lower bound Vcomp ≥ R0; without it, competitive messages could be informative about the label and the minimax value could be strictly below R0. The assumption is implausible when each debater defends its own initial prediction, since the assigned stance is then correlated with the true label. Please derive the conditional independence from explicit game-theoretic assumptions (e.g., lying costs, message spaces) or substantially revise the proposition. As written, the theoretical argument that CopMAD cannot help is not established.
[Sec. 2.3 / Appendix C, Tables 1-2] ColMAD differs from CopMAD not only in the collaborative instruction but also in three additional components: exact-match quote verification, self-auditing, and confidence calibration. The CopMAD baseline prompt from Kenton et al. is not reproduced in the appendix, so the reader cannot verify that the only difference is the zero-sum versus non-zero-sum objective. The largest observed gains occur when a strong persuasive debater (DeepSeek-R1) is paired with a weak judge (GPT4o-mini) — the configuration where filtering fabricated quotes would have the largest effect. Without ablations that add or remove these components (and without testing the same mechanisms under a competitive instruction), the paper’s central claim that the collaborative incentive is what makes MAD effective is not supported.
[Sec. 4.2, Tables 1-2] No error bars, confidence intervals, or significance tests are reported. With n = 140–160 per task (Table 4), point differences of a few F1 units may be within sampling noise. For example, Table 1 (GPT4o-mini + DeepSeek-R1) shows ColMAD average F1 76.77 versus single-agent DeepSeek-R1 75.37, but on fine-grained fact verification ColMAD is 76.77 versus the single-agent 79.77. Please report bootstrap confidence intervals and a paired significance test (e.g., McNemar or an exact binomial test) for the headline ColMAD-vs-CopMAD and ColMAD-vs-best-single-agent comparisons.
[Abstract / Sec. 4.2] The abstract states that ColMAD “significantly outperforms previous competitive MAD by 19%,” but the metric and basis for this number are not defined. The tables show highly variable absolute F1 differences across configurations—from about 2.7 points (Llama3.1-70B + GPT4o-mini) to more than 44 points (GPT4o-mini + DeepSeek-R1). The claimed “non-trivial improvements over single-agent” results are also inconsistent across tasks, and the improvement is small when compared with the best single agent in several rows. Please specify the aggregation, the relative-versus-absolute interpretation, and the statistical basis for the headline percentage.

minor comments (5)

[Abstract / Introduction] The abstract introduces consensus-seeking MAD (CosMAD) and claims it “filters out informative disagreements,” but the body text does not define CosMAD or include it in the experiments. The abstract should be aligned with the scope of the paper, or a section on CosMAD should be added.
[Algorithm 1, lines 5 and 9] The transcript is constructed as (m(0)_A, m(0)_A) instead of (m(0)_A, m(0)_B); the second component should be B's message. The same typo appears in line 9. Also, the notation “M e = {M(t)}^t−1_{t=0}” is unclear and should be replaced with a clearer set construction.
[Appendix A.1, Table 3] The notation is inconsistent: y is used for the true label, Y for a random variable of predictions, and ŷ for individual predictions, but the table and the main text sometimes conflate these. Please clean up the symbol definitions.
[Sec. 2.1, Eq. (4) and Fig. 2] The “oracle collaboration” formula is not clearly defined: it should state that the judge’s error y_J is taken as the error of the better agent when the two initial predictions disagree, and that the min over K is applied per sample. The current notation is ambiguous and should be clarified.
[Sec. 4.1, Baselines] The Ensemble baseline that “randomly chooses” a label on disagreement gives a random expected F1 that depends on the label distribution, and the variance is not reported. It would be better to use a fixed tie-break or report the expected performance with its standard deviation.

Circularity Check

1 steps flagged

Theoretical Prop. 2.2 assumes the conclusion that debate messages are uninformative; empirical ColMAD claim remains externally benchmarked and non-circular.

specific steps

self definitional [Appendix A.2, step (ii), Eqs. (12)-(13)]
"Since we do not impose any limits on the capabilities of the debater agents, they will try to present the evidence as most useful for the respective answer as they can. More formally, the optimal strategies for debaters A and B will yield the following M⊥ ⊥Y|X0."

Prop. 2.2's conclusion is Vcomp=R0, i.e., competitive debate adds no information over X0. The proof's only lower-bound step stipulates M⊥Y|X0, which is exactly the no-additional-information condition: under it, the judge's total LLR (Eq. 7) collapses to Λ0(X0), so the Bayes risk is R0. No derivation from the zero-sum game or from debater utilities is given, so the theorem is the assumption restated as a result.

full rationale

The empirical core is self-contained: ColMAD and CopMAD are evaluated on the external ReaLMistake benchmark with no parameters fitted to the test data, so the headline comparison does not reduce to a fit. The quote-verification mechanism is borrowed from Kenton et al. (2024), but that is an external prior work, not a self-citation, and the central claim does not depend on the present authors' own earlier results. However, the theoretical justification that CopMAD cannot help (Prop. 2.2) is circular: Appendix A.2 assumes M⊥Y|X0 without derivation, and that conditional independence is precisely the 'debate adds no information' conclusion being proved. Because the paper's theoretical case for the competitive-protocol failure rests on this asserted independence, the derivation chain has one question-begging step. The unablated bundled prompt design (quote verification, self-auditing, calibration) is a potential confound for isolating the collaborative-instruction effect, but that is an experimental-control concern, not circularity.

Axiom & Free-Parameter Ledger

1 free parameters · 4 axioms · 0 invented entities

No parameters are fitted to the benchmark data; the protocol has one hand-picked hyperparameter (debate rounds T=2). The main theory rests on an unproved conditional-independence assertion in Appendix A.2. No novel physical/formal entities are introduced.

free parameters (1)

number of debate rounds T = 2
Chosen by hand following prior MAD practice (Section 4.3); robustness to 1-5 rounds shown in Fig. 6, so it does not drive the conclusion.

axioms (4)

domain assumption The judge follows a Bayes test on the total log-likelihood ratio (Assumption 2.1)
Assumption 2.1/Eq. (1)-(3) models the judge as an optimal Bayesian decision maker; in practice the judge is an LLM with limited reasoning.
standard math Debate messages are drawn from conditional likelihood models p_i(m_i|y,x) with bounded log-likelihood ratios |l_i| ≤ L_i
Section 2.2, pre-Prop 2.2; used to bound persuasion budget.
ad hoc to paper Optimal adversarial debaters yield messages conditionally independent of Y given X0 (M ⊥ Y | X0)
Appendix A.2 proof step (ii) asserts this without derivation; it is the crux of Prop 2.2.
domain assumption LLM debaters follow the assigned collaborative/competitive prompts and behave as rational game-theoretic agents
The mapping from prompts to agent behavior is assumed; the paper's behavioral evidence (Fig. 4) is qualitative.

pith-pipeline@v1.3.0-alltime-deepseek · 21150 in / 11576 out tokens · 125501 ms · 2026-08-04T08:19:56.654359+00:00 · methodology

0 comments

read the original abstract

Multi-agent debate (MAD) was proposed as a promising approach for ensembling the wisdom of multiple large language models (LLMs) to improve reasoning and provide effective supervision to superhuman LLMs. However, increasing empirical evidence suggests that MAD may not outperform or even significantly underperform single-agent approaches (SA), raising doubts about the benefits of MAD. In this work, we investigate this issue by analyzing the incentive structures of popular MAD paradigms: (i) competitive MAD (CopMAD) where agents compete by holding opposing positions; (ii) consensus-seeking MAD (CosMAD) where agents are driven to seek consensus. We show that both paradigms suffer from debate hacking: CopMAD reduces to a cheap-talk game, where agents produce misleading messages to win the game, while CosMAD filters out informative disagreements for premature consensus. Consequently, agents in both CopMAD and CosMAD fail to jointly resolve the ambiguity and seek the truth. To this end, we introduce ColMAD, a collaborative protocol that reframes MAD as a non-zero-sum game to encourage agents to provide informative while truthful messages. Through extensive benchmarking on challenging tasks such as error detection, we show that ColMAD significantly outperforms previous MAD protocols up to 10 percentage points. Under the same budgets, ColMAD effectively brings non-trivial improvements over SA methods, implying that the protocol design is critical to realizing the potential of MAD.

Figures

Figures reproduced from arXiv: 2510.20963 by Bo Han, Gang Niu, James Cheng, Masashi Sugiyama, Yongqiang Chen.

**Figure 2.** Figure 2: Error reductions of prevalent LLMs in detecting errors of [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Figure 3: Pitfalls of previous MAD protocols. Under previous MAD schemes, such as CopMAD or SoM, the debate results are lower than any of the LLMs involved in the debate in most cases. In contrast, ColMAD significantly improves CopMAD and outperforms the use of a single LLM. results from ReaLMistake. The ReaLMistake benchmark contains 3 objective error detection tasks: (i) math word problem generation (Math problem)… view at source ↗

**Figure 4.** Figure 4: Illustration of the debate hacking issue in CopMAD. We observe three typical debate hacking behaviors: (i) Fake evidence that dishonest debaters misinterpret the requirements of the task; (ii) Overconfident claims that dishonest debaters use an overconfident tone to mislead the judge; and (iii) Fallacious argument that dishonest debaters turn the focus to side and meaningless points. The proof of Propositi… view at source ↗

**Figure 5.** Figure 5: (a) shows the results in F2 scores of ColMAD performance under different combinations of LLMs, where the diagonal line shows the single-agent performance. (b) shows the rate of alignment to the ground-truth explanations given by CopMAD and ColMAD, where “ML-” refers to the combination of GPT4o-mini and Llama3.1-70B, and “LD-” refers to the combination of Llama3.1-70B and DeepSeek-R1. ColMAD yields more rea… view at source ↗

**Figure 6.** Figure 6: ColMAD is generically robust to the number of rounds for debate. different LLMs. Furthermore, the improvements of ColMAD are general and robust across different combinations of LLMs that differ relatively large in their capabilities. For example, when combining Llama3.1-70B and Mistral-7B-v0.3, as Mistral-7B-v0.3 is relatively weak, both CopMAD and especially the Ensemble method will be biased, while ColMA… view at source ↗

**Figure 7.** Figure 7: Error reductions of prevalent LLMs in detecting errors of [PITH_FULL_IMAGE:figures/full_fig_p015_7.png] view at source ↗

**Figure 8.** Figure 8: Error reductions of prevalent LLMs in detecting errors of [PITH_FULL_IMAGE:figures/full_fig_p016_8.png] view at source ↗

discussion (0)

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

Collaborative Disagreement Resolution for Scalable Oversight
cs.CY 2026-06 unverdicted novelty 6.0

Disagreement resolution achieves 62.1% accuracy for non-expert models in identifying truth, outperforming standard debate at 49.2% by shifting from adversarial persuasion to collaborative consensus-building.
AIVV: Neuro-Symbolic LLM Agent-Integrated Verification and Validation for Trustworthy Autonomous Systems
cs.AI 2026-04 unverdicted novelty 5.0

AIVV deploys LLM agents in a council to semantically validate anomalies in time-series data against natural-language requirements, automating human-in-the-loop verification for autonomous systems.

Reference graph

Works this paper leans on

12 extracted references · 3 linked inside Pith · cited by 2 Pith papers

[1]

Decisive tests that flip the result if they fail (spec mismatch, contradiction, counterexample)
[2]

quote”. . . “quote

Verified quotes (“quote”. . . “quote”→ may become “v quote”) grounded in the task’s general/specific requirements
[3]

Novel, non-redundant contributions; penalties for repetition or unsupported claims
[4]

Accurate calibration and willingness to adopt valid opponent points. </quote-system> <problem>Here is the model input and the model response...</problem> <transcript>Here is the transcript of the debate so far...</transcript> <thinking> Now produce your next turn. Follow these steps in your ”thinking”, then write only the ”argument”:
[5]

quote”. . . “quote

Prepare a one-sentence update note describing any change in your view. </thinking> <argument> - Position: state which answer you defend and why in one sentence. - Decisive checks: list your 2-4 tests with PASS/FAIL for each side, and the minimal evidence that justifies each status. - Evidence: map 2-4 “quote”. . . “quote” to specific claims you make (no d...
[8]

Identify decisive tests: 2-4 concrete checks that would settle {answer defending} vs the alternative (e.g., requirement violation, logical contradiction, factual mismatch)
[9]

If a test fails on your side, plan a revision or boundary condition

Run each test against BOTH sides (yours and opponent’s). If a test fails on your side, plan a revision or boundary condition
[10]

”/quote” (no repetition; aim for new quotes)

Extract 2-4 atomic requirements and cite them via ”quote”. . . ”/quote” (no repetition; aim for new quotes)
[11]

Steelman Opponent: find their strongest correct point and either integrate it or delimit its scope
[2022]

In summary/Conclusion

(Cited on page 7) OpenAI. Gpt-4o mini technical report. https://openai.com/index/ gpt-4o-mini-advancing-cost-efficient-intelligence/ , 2024. (Cited on page 7) Meta AI. Introducing llama 3.1: Our most capable models to date. https://ai.meta.com/ blog/meta-llama-3-1/, 2024. Accessed: 2024-07-23. (Cited on page 7) Albert Q. Jiang, Alexandre Sablayrolles, Ant...

Pith/arXiv arXiv 2024
[2023]

Christiano, and Dario Amodei

(Cited on pages 4 and 6) Geoffrey Irving, Paul F. Christiano, and Dario Amodei. AI safety via debate.arXiv preprint arXiv:1805.00899, 2018b. (Cited on pages 4 and 6) Andries P. Smit, Paul Duckworth, Nathan Grinsztajn, Kale ab Tessera, Thomas D. Barrett, and Arnu Pretorius. Should we be going mad? a look at multi-agent debate strategies for llms. In Intern...

Pith/arXiv arXiv 2024
[2024]

Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou

(Cited on page 6) Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. Self-consistency improves chain of thought reasoning in language models. InInternational Conference on Learning Representations, 2023. (Cited on page 6) Collin Burns, Pavel Izmailov, Jan Hendrik Kirchner, Bowen Baker, Leo Ga...

Pith/arXiv arXiv 2023

[1] [1]

Decisive tests that flip the result if they fail (spec mismatch, contradiction, counterexample)

[2] [2]

quote”. . . “quote

Verified quotes (“quote”. . . “quote”→ may become “v quote”) grounded in the task’s general/specific requirements

[3] [3]

Novel, non-redundant contributions; penalties for repetition or unsupported claims

[4] [4]

Accurate calibration and willingness to adopt valid opponent points. </quote-system> <problem>Here is the model input and the model response...</problem> <transcript>Here is the transcript of the debate so far...</transcript> <thinking> Now produce your next turn. Follow these steps in your ”thinking”, then write only the ”argument”:

[5] [5]

quote”. . . “quote

Prepare a one-sentence update note describing any change in your view. </thinking> <argument> - Position: state which answer you defend and why in one sentence. - Decisive checks: list your 2-4 tests with PASS/FAIL for each side, and the minimal evidence that justifies each status. - Evidence: map 2-4 “quote”. . . “quote” to specific claims you make (no d...

[6] [8]

Identify decisive tests: 2-4 concrete checks that would settle {answer defending} vs the alternative (e.g., requirement violation, logical contradiction, factual mismatch)

[7] [9]

If a test fails on your side, plan a revision or boundary condition

Run each test against BOTH sides (yours and opponent’s). If a test fails on your side, plan a revision or boundary condition

[8] [10]

”/quote” (no repetition; aim for new quotes)

Extract 2-4 atomic requirements and cite them via ”quote”. . . ”/quote” (no repetition; aim for new quotes)

[9] [11]

Steelman Opponent: find their strongest correct point and either integrate it or delimit its scope

[10] [2022]

In summary/Conclusion

(Cited on page 7) OpenAI. Gpt-4o mini technical report. https://openai.com/index/ gpt-4o-mini-advancing-cost-efficient-intelligence/ , 2024. (Cited on page 7) Meta AI. Introducing llama 3.1: Our most capable models to date. https://ai.meta.com/ blog/meta-llama-3-1/, 2024. Accessed: 2024-07-23. (Cited on page 7) Albert Q. Jiang, Alexandre Sablayrolles, Ant...

Pith/arXiv arXiv 2024

[11] [2023]

Christiano, and Dario Amodei

(Cited on pages 4 and 6) Geoffrey Irving, Paul F. Christiano, and Dario Amodei. AI safety via debate.arXiv preprint arXiv:1805.00899, 2018b. (Cited on pages 4 and 6) Andries P. Smit, Paul Duckworth, Nathan Grinsztajn, Kale ab Tessera, Thomas D. Barrett, and Arnu Pretorius. Should we be going mad? a look at multi-agent debate strategies for llms. In Intern...

Pith/arXiv arXiv 2024

[12] [2024]

Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou

(Cited on page 6) Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V Le, Ed H. Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. Self-consistency improves chain of thought reasoning in language models. InInternational Conference on Learning Representations, 2023. (Cited on page 6) Collin Burns, Pavel Izmailov, Jan Hendrik Kirchner, Bowen Baker, Leo Ga...

Pith/arXiv arXiv 2023