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REVIEW 5 major objections 4 minor 2 cited by

RMoA: Optimizing Mixture-of-Agents through Diversity Maximization and Residual Compensation

T0 review · 5 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read RMoA claims that adding ResNet-style residual connections to Mixture-of-Agents—passing inter-layer response differences into the next round—plus embedding-based diverse selection and early stopping, improves benchmark performance while…

desk verdict The architecture is sensible and the cost analysis is useful, but the headline gains are confounded by prompt asymmetry between RMoA and its baselines, so the numbers need tighter controls before they can be believed. read the letter →

arxiv 2505.24442 v1 pith:ODIPMCC4 submitted 2025-05-30 cs.AI

Zhentao Xie , Chengcheng Han , Jinxin Shi , Wenjun Cui , Xin Zhao , Xingjiao Wu , Jiabao Zhao This is my paper
classification cs.AI
keywords Mixture-of-Agentsresiduallearningmulti-agentLLMsystemsdiversityselectionadaptiveterminationinferenceefficiencylargelanguagemodelsreasoning
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

RMoA claims that the main failure mode of Mixture-of-Agents architectures—where several language models propose answers and an aggregator merges them—is not too much collaboration but too little preservation: answers lose information as they pass through layers. Borrowing the residual-connection idea from ResNet, it feeds the difference between consecutive rounds of responses into the next round instead of only the latest answers. To keep that cheap, an embedding model scores pairwise similarity, and a greedy loop selects the K most semantically diverse responses; a Residual Extraction Agent reports what genuinely changed between rounds, and a Residual Aggregation Agent folds those changes into the final answer. An adaptive termination mechanism stops the loop when several rounds show no residual, saving computation once the system has converged. On AlpacaEval 2.0, MATH, CRUX, and MMLU-redux, the paper reports average-score gains over MoA and SMoA for all four backbones, together with roughly 31.88% lower TFLOPs than MoA at the same configuration.

What carries the argument

The carrying mechanism is the residual pair: a Residual Extraction Agent (a prompted LLM that compares the selected responses of consecutive layers and reports only genuine changes, per Eq. 6) and a Residual Aggregation Agent (a prompted LLM that integrates the previous layer's responses with the extracted residual, per Eq. 8). These are supported by three smaller mechanisms: the greedy diversity selection, which builds a similarity matrix $S_{i,j} = \cos(e_i, e_j)$ and iteratively chooses the candidate minimizing $\Phi(i) = \max_{q\in Q} S_{i,q}$, so the K responses passed forward are as semantically far apart as possible; the concatenation scheme $\hat{R}_l = \mathrm{Cat}(\{R_{l-1,x_j}\}, \Delta R_l)$, which attaches the residual to the prior round's responses; and the adaptive termination rule, which stops iteration when the extractor reports 'no change' or 'no update' for the current and $m-1$ preceding layers. The extraction agent is the load-bearing part: it is what converts inter-layer differences into a form the aggregator can use, and its 'Residuals Detected: No' verdict is what triggers early stopping.

What would settle it

Run RMoA on a dataset like MATH500 with round-by-round ground truth, then check the extractor directly: count how often it flags 'Residuals Detected: Yes' when the previous and current answers are identical (false positives) and how often it reports 'No' when a later round changes a correct answer to an incorrect one (false negatives). A high rate of either error would show that the residual signal, not just the aggregation prompt, is doing the reported work.

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Extended reading notes

Core claim

On its own terms, this paper establishes that multi-agent LLM iteration can be made both deeper and cheaper if each layer passes forward not only selected responses but also the residual difference between consecutive response sets. In RMoA, every layer first computes a cosine-similarity matrix over response embeddings and greedily selects K responses whose maximum similarity to the already-selected set is minimized, maximizing diversity. A Residual Extraction Agent then compares current responses with the previous round and outputs a compact statement of hallucinations, detail discrepancies, and genuinely new information, prefixed by 'Residuals Detected: Yes/No'. The next layer and the final Residual Aggregation Agent work from the previous responses plus that residual, preserving incremental information that would otherwise be lost in aggregation. The authors report that this configuration raises average accuracy over MoA and SMoA on all four tested backbones, with the largest gains on mathematical reasoning for small models (e.g., +13.8% for Gemma2-9B-Instruct on MATH) and on code understanding for GPT-4o (+11.57% on CRUX), while cutting computational cost: RMoA uses about 68.83% of MoA's token cost and about 31.88% fewer TFLOPs at the same configuration, and the adaptive stop reduces hallucination rates by 3.77% and 2.95% per round on Qwen2.5-7B and GPT-4o respectively.

Load-bearing premise

The load-bearing premise is that the Residual Extraction Agent reliably finds real differences between consecutive rounds and that a 'Residuals Detected: No' verdict truly means the answers have converged; if the extractor invents differences or misses real corrections, both the information-preservation benefit and the early-stopping benefit weaken.

Editorial extensions

If this is right

  • With RMoA, deeper iterations keep improving scores instead of degrading: on MATH, GPT-4o rises from 79.18% at layer 1 to 81.34% at layer 6, while MoA declines across all tested models.
  • Small open models that lose ground under standard MoA on knowledge-heavy tasks can gain from RMoA: on MMLU-redux, Qwen2.5-7B and Llama3.1-8B improve while MoA drops 3.50 to 7.20 percentage points below their baselines.
  • Adaptive termination gives a measurable efficiency-and-stability dividend: on MATH the reported per-round hallucination rate falls by average margins of 3.77% for Qwen2.5-7B and 2.95% for GPT-4o, and simple tasks such as last-letter concatenation stop after two rounds.
  • The gains persist on stronger models: Qwen2.5-72B gains 7.8% and DeepSeek-V3 gains 4.2% on MATH500, so the residual mechanism complements rather than replaces progress in base-model capability.
  • Deep architectures become more affordable: on MATH500, RMoA's per-turn cost grows by about $15 per turn versus about $37 for MoA, and its eight-layer cumulative cost is about 46.7% of MoA's.

Reading between the lines

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

  • A testable extension would audit the extractor itself: build a set of rounds with known injected answer changes and measure whether the Residual Extraction Agent's 'Residuals Detected' flags track those changes, since the whole early-stopping benefit depends on that precision.
  • The embedding-diversity selector is a generic primitive: the same greedy min-max-similarity loop could be used by other recursive-agent, memory, or retrieval pipelines to decide which prior outputs deserve another pass, independent of residual semantics.
  • The paper's budget comparison implies a design rule the authors do not fully develop: when compute is limited, spend marginal tokens on a stronger aggregator rather than a stronger extractor, since swapping in Qwen2.5-72B as aggregator raised MATH from 52.10 to 80.16 while extractor swaps moved scores by only a few points.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

5 major / 4 minor

Summary. The paper proposes RMoA, a Mixture-of-Agents variant with three components: (i) greedy diversity selection over response embeddings (Eqs. 1–4), (ii) a residual extraction agent and residual aggregation agent that pass inter-layer differences to subsequent layers (Eqs. 5–8), and (iii) an adaptive termination mechanism that stops iteration when the extractor reports no residuals for m consecutive layers (Section 3.2.3). RMoA is evaluated on AlpacaEval 2.0, MATH, CRUX, and MMLU-redux using Gemma2-9B, Qwen2.5-7B, Llama3.1-8B, and GPT-4o, reporting consistent improvements over MoA/SMoA and lower cost/TFLOPs. Ablations, hyperparameter analysis, and cost analyses are provided in Sections 5 and Appendix C.

Significance. The proposed architecture is a natural and potentially useful extension of MoA: it replaces judge-based sparse selection with embedding diversity, adds explicit residual information flow, and introduces early stopping. The paper is unusually thorough in reporting ablations (Table 4), hyperparameter sweeps (Table 2), cost/TFLOP analyses (Figure 5), hallucination rates (Table 10), and robustness to embedding models (Table 7). The code is released, which will help replication. However, the experimental comparison is currently confounded by prompt asymmetry and by test-set hyperparameter selection, so the reported gains cannot yet be attributed to the architecture. If the authors control for these factors, the residual/diversity design could make a modest but valuable contribution to efficient multi-agent inference.

major comments (5)
  1. [Section 4.1 / Appendix A] The headline comparisons in Table 1 are confounded by prompt asymmetry. Section 4.1 states that 'we used the same prompts, sampling temperature, and max_tokens across all datasets,' but Appendix A (Figures 7–10, Table 5) shows that RMoA proposers receive role-playing personas and dataset-specific reasoning prompts (few-shot for MATH, CoT for CRUX and MMLU-redux, zero-shot for AlpacaEval), whereas the MoA/SMoA baselines use prompts sourced from their official GitHub repositories. The text does not state that the same role/reasoning prompts were given to the baseline proposers. Because proposer prompt content directly affects output quality, the reported gains (e.g., +13.8% MATH for Gemma2-9B, +8.11% AlpacaEval for GPT-4o) may be attributable to prompt engineering rather than to diversity selection, residual compensation, or adaptive termination. Please rerun MoA and SMoA with the identical role/reasoning proposer prompts, or explicitly document that the official baseline prompts already contain equivalent conditioning.
  2. [Section 5.1 / Table 2] The diversity parameter K is selected on the same test benchmarks used to report the final results. Table 2 sweeps K over 2–5 on MATH, CRUX, and MMLU-r and then chooses K=3; no held-out validation set or nested selection is described. This makes the reported accuracies optimistic and prevents reading the gains as architecture effects. In addition, Table 1 does not report error bars or repeated-run statistics, so differences of 0.5–2 percentage points (e.g., SMoA vs. RMoA on Qwen2.5-7B MMLU-r: 72.00 vs. 71.80) may be within noise. Please report validation-based K selection and confidence intervals.
  3. [Section 3.2.2 / 3.2.3] The residual delta-R_l in Eq. (6) is not a mathematical difference but the output of a prompted LLM, and the adaptive termination mechanism stops when that same LLM outputs 'Residuals Detected: No.' The paper provides no reliability evidence for this extractor: Appendix C.1 compares LLM judgment with similarity thresholds and variance metrics, but it does not measure agreement with ground-truth residual annotations or human judgments. If the extractor hallucinates residuals, the early-stopping signal is unreliable and the information-preservation benefit is not established. Please add a reliability study (e.g., precision/recall against human-annotated residual labels on a sample of each dataset) and report how often termination decisions agree with a held-out judge.
  4. [Section 4.1 / 4.2 / Table 1] The claim of state-of-the-art performance is not supported by the comparisons shown. Table 1 compares RMoA only with MoA, SMoA, and the base model; no comparison is made with other published multi-agent or prompting methods, nor with published leaderboard numbers for these benchmarks. Additionally, the AlpacaEval 2.0 setup deviates from the benchmark's official protocol: Section 4.1 says gpt-4-1106-preview is the reference model, while Section 4.2 says the evaluation used 'the official GPT-4o-mini as the evaluator.' Scores obtained with a different evaluator are not directly comparable to the AlpacaEval 2.0 leaderboard, so the SOTA claim should be scaled back or supported with a proper protocol.
  5. [Section 3.2.3] The adaptive termination mechanism depends on an integer m ('preceding m consecutive layers'), but the manuscript never reports the value of m used in the experiments. Without this, the cost and hallucination-rate results in Tables 4 and 10 are not reproducible. Please state m for each experiment or provide a sensitivity analysis.
minor comments (4)
  1. [Throughout] There are numerous typos and inconsistent notations, including 'RMOA' vs. 'RMoA', 'Aggreator' in Table 3, 'Framewrok', 'ultilizes', 'traditaional', and 'Addtionally' in Section 2.1. A careful proofread is needed.
  2. [Appendix C.1 / Table 6] The three variance-metric rows in Table 6 all list the threshold as sigma^2 = 1 x 10^-3; presumably these should be different values (e.g., 10^-3, 10^-4, 10^-5). Please correct the table.
  3. [Section 5.1 / Table 4] Table 4's caption says the cost is 'total dollar expenditure of the method across the three datasets' calculated from Together API pricing, while Section 5.2 says 'we are using local inference' and therefore estimates cost from API prices. Please reconcile these statements and clarify whether costs are measured or estimated.
  4. [Section 4.1] The text says 'we build up to 6 layers of RMOA and select 3 responses on Greedy Diversity Embedding Selection,' but Section 5.1 then tunes K over 2–5. Please make the default configuration clear before presenting the hyperparameter analysis.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: RMoA is an empirical architecture paper whose components are heuristics evaluated on external benchmarks, with no prediction reducing to fitted inputs or a self-citation chain.

full rationale

RMoA's claimed contributions are architectural and empirical, not derived by definition. The greedy diversity selection (Eqs. 1-4) is a standard farthest-point heuristic over embedding cosine similarities; it does not by construction determine the benchmark outcomes. The residual extraction (Eq. 6) and aggregation (Eq. 8) are implemented as prompted LLMs, so 'residuals' are outputs of an external model, not quantities constructed to equal the reported performance gains. The adaptive termination criterion is an LLM judgment ('Residuals Detected: No') followed by early stopping; this is a design heuristic, not an identity. The state-of-the-art claims are supported by external benchmarks (AlpacaEval 2.0, MATH, CRUX, MMLU-redux), and no derivation inside the paper reduces a benchmark score to a fitted parameter. The choice K=3 is made from Table 2 on the same benchmarks, but this is a test-set tuning and reporting concern, not a circular reduction: the architecture's outputs are not constructed to equal the benchmark numbers. Self-citations (Jinxin et al., 2023 for role-playing prompts; Xie et al., 2024 and Bai et al., 2024 for cognitive-bias commentary) are contextual and not load-bearing; removing them would not collapse the architecture into its inputs. Concerns about prompt asymmetry between RMoA and baselines, or about hyperparameter selection on test data, are experimental-validity issues rather than circular reductions, and per the review rules they do not raise the circularity score.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The method's main free design choices are K and the termination patience, both tuned on the test benchmarks. The core assumptions are that embedding similarity captures diversity and that the prompted LLM extractor produces faithful residual signals.

free parameters (2)
  • K (number of responses selected by diversity) = 3
    Chosen by ablation on the evaluation benchmarks (Table 2); performance degrades at 4-5, so K=3 is used for all main results.
  • m (number of consecutive no-residual layers before termination) = not stated
    Hyperparameter for adaptive termination; no value is given in the paper, yet it controls the early stopping behavior and cost.
assumptions (3)
  • domain assumption Cosine similarity of BGE-m3 embeddings is a valid proxy for semantic diversity of LLM responses.
    Used in Eq. (1)-(4) to select K responses. If embeddings fail to capture information overlap, diversity selection may discard critical content.
  • ad hoc to paper The Residual Extraction Agent, a prompted LLM, reliably identifies genuine 'residuals' between consecutive response sets.
    Eq. (6) formalizes residual extraction as LLM output. The entire residual compensation and adaptive termination depend on this reliability, which is not independently verified.
  • domain assumption Early stopping based on m consecutive 'no residuals' judgments improves efficiency without sacrificing quality.
    Section 3.2.3. Assumes residual absence indicates convergence; could be false if the extractor misses residuals.

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Cite this review

Pith. "Pith review of RMoA: Optimizing Mixture-of-Agents through Diversity Maximization and Residual Compensation." pith.science (2026). https://pith.science/paper/ODIPMCC4

@misc{pith2026250524442,
  author       = {Pith},
  title        = {Pith review of: RMoA: Optimizing Mixture-of-Agents through Diversity Maximization and Residual Compensation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ODIPMCC4}},
  note         = {Machine review of arXiv:2505.24442}
}
read the original abstract

Although multi-agent systems based on large language models show strong capabilities on multiple tasks, they are still limited by high computational overhead, information loss, and robustness. Inspired by ResNet's residual learning, we propose Residual Mixture-of-Agents (RMoA), integrating residual connections to optimize efficiency and reliability. To maximize information utilization from model responses while minimizing computational costs, we innovatively design an embedding-based diversity selection mechanism that greedily selects responses via vector similarity. Furthermore, to mitigate iterative information degradation, we introduce a Residual Extraction Agent to preserve cross-layer incremental information by capturing inter-layer response differences, coupled with a Residual Aggregation Agent for hierarchical information integration. Additionally, we propose an adaptive termination mechanism that dynamically halts processing based on residual convergence, further improving inference efficiency. RMoA achieves state-of-the-art performance on the benchmarks of across alignment, mathematical reasoning, code generation, and multitasking understanding, while significantly reducing computational overhead. Code is available at https://github.com/mindhunter01/RMoA.

Figures

Figures reproduced from arXiv: 2505.24442 by the authors.

Figure 1
Figure 1. MoA-RMoA Structural Comparison. et al., 2023) coordinates agents with special￾ized knowledge to solve complex tasks, while Theory of Mind (Li et al., 2023b) improves col￾laboration by enabling agents to predict each other’s intentions. Besides, frameworks like MetaGPT (Hong et al., 2023) and Chatdev (Qian et al., 2024) utilize specialized agents for modular tasks, such as programming, while MapCoder (Islam et al., 2… view at source ↗
Figure 2
Figure 2. Overview of Residual Mixture-of-Agents Structure. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Performance comparison of RMoA and MoA across different layer counts on GPT-4o and Qwen2.5-7B-Instruct [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figures from the paper (14 more)
Figure 4
Figure 4. Figure 4: Performance comparison of RMoA and MoA across different layer counts on Llama3.1-8B￾Instruct and Gemma2-9B-Instruct. ers, whereas MoA exhibits varying degrees of decline across all models. This further indi￾cates that MoA may generate hallucinations during the iteratio…
Figure 5
Figure 5. Figure 5: Comparison of Performance Metrics.Figure 5(a) illustrates the token cost incurred by Qwen2.5- [PITH_FULL_IMAGE:figures/full_fig_p009_5.png]
Figure 6
Figure 6. Figure 6: Agent Prompt Design-Part One [PITH_FULL_IMAGE:figures/full_fig_p017_6.png]
Figure 7
Figure 7. Figure 7: Agent Prompt Design - Part two [PITH_FULL_IMAGE:figures/full_fig_p018_7.png]
Figure 8
Figure 8. Figure 8: Agent Prompt Design - Part Three [PITH_FULL_IMAGE:figures/full_fig_p019_8.png]
Figure 9
Figure 9. Figure 9: Agent Prompt Design - Part Four [PITH_FULL_IMAGE:figures/full_fig_p020_9.png]
Figure 10
Figure 10. Figure 10: Agent Prompt Design - Part five [PITH_FULL_IMAGE:figures/full_fig_p021_10.png]
Figure 11
Figure 11. Figure 11: Math Few-shot Prompt-Part One [PITH_FULL_IMAGE:figures/full_fig_p022_11.png]
Figure 12
Figure 12. Figure 12: Math Few-shot Prompt-Part Two [PITH_FULL_IMAGE:figures/full_fig_p023_12.png]
Figure 13
Figure 13. Figure 13: CRUX Chain-of-Thought Prompt [PITH_FULL_IMAGE:figures/full_fig_p024_13.png]
Figure 14
Figure 14. Figure 14: An example to demonstrate Greedy Diversity Embedding Selection and Residual Extraction. [PITH_FULL_IMAGE:figures/full_fig_p025_14.png]
Figure 15
Figure 15. Figure 15: An example to demonstrate Greedy Diversity Embedding Selection and Residual Extraction. [PITH_FULL_IMAGE:figures/full_fig_p026_15.png]
Figure 16
Figure 16. Figure 16: An example to demonstrate Greedy Diversity Embedding Selection and Residual Extraction. [PITH_FULL_IMAGE:figures/full_fig_p027_16.png]
Figure 17
Figure 17. Figure 17: Model Aggregation Case Study for Convex Polygon Side Calculation [PITH_FULL_IMAGE:figures/full_fig_p028_17.png]

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