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arxiv: 2606.24437 · v1 · pith:JD3GDAYBnew · submitted 2026-06-23 · 💻 cs.AI

ReM-MoA: Reasoning Memory Sustains Mixture-of-Agents Scaling

Heng Ping , Arijit Bhattacharjee , Peiyu Zhang , Shixuan Li , Wei Yang , Ali Jannesari , Nesreen Ahmed , Paul Bogdan This is my paper

Pith reviewed 2026-06-25 23:35 UTC · model grok-4.3

classification 💻 cs.AI
keywords mixture of agentsreasoning memorymulti-agent systemsLLM scalinginference time scalingreviewer agentdiversified routinglayered pipelines
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The pith

ReM-MoA sustains performance gains in mixture-of-agents systems by storing and routing ranked reasoning traces across layers.

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

Existing mixture-of-agents setups organize large language models into layered pipelines for better reasoning but lose their edge as the number of layers increases. ReM-MoA adds a persistent memory that ranks reasoning traces from every layer using a reviewer agent and then routes distinct combinations of good and bad traces to different agents. This approach keeps exploration diverse while spreading high-quality reasoning forward. Experiments on math, code, logic and other benchmarks show the method beats earlier variants at both shallow and deep configurations, with the gap growing at greater depths. The result points to cross-layer memory as essential for scaling multi-agent inference without hitting plateaus.

Core claim

ReM-MoA introduces Ranked Reasoning Memory that persistently stores and ranks reasoning traces from all layers via a comparative Reviewer Agent, paired with Curated Diversified Memory Routing that exposes agents to varied successful and failed traces. This combination allows the system to maintain and widen performance advantages as depth increases, unlike prior MoA variants that degrade or saturate.

What carries the argument

Ranked Reasoning Memory combined with Curated Diversified Memory Routing, where a Reviewer Agent ranks traces and routing diversifies exposure to maintain exploration while propagating quality.

Load-bearing premise

A comparative Reviewer Agent can produce unbiased rankings of reasoning traces that improve agent performance when used for routing.

What would settle it

A controlled test where a standard MoA without memory matches or exceeds ReM-MoA performance at high layer counts on the same benchmarks.

Figures

Figures reproduced from arXiv: 2606.24437 by Ali Jannesari, Arijit Bhattacharjee, Heng Ping, Nesreen Ahmed, Paul Bogdan, Peiyu Zhang, Shixuan Li, Wei Yang.

Figure 1
Figure 1. Figure 1: Scaling behavior of three existing MoA variants on three benchmarks (MATH, MMLU-redux, Formal [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The ReM-MoA framework. At each layer, N proposer agents produce reasoning traces rl,j , and the Reviewer Agent compares them and assigns per-trace scores sl,j and rationales ρl,j , committed to the Ranked Reasoning Memory M. From layer 2 onwards, each proposer agent receives a curated reference set from M drawn from three families: Topn (highest-scoring traces), Botn (lowest-scoring), or Conn (contrastive,… view at source ↗
Figure 3
Figure 3. Figure 3: Mean accuracy across the five benchmarks under depth scaling (left, [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
read the original abstract

Mixture-of-Agents (MoA) architectures improve inference-time scaling by organizing multiple LLM agents into layered reasoning pipelines. However, existing MoA variants fail to sustain gains as depth increases, exhibiting degradation, early plateauing, or saturation. We propose ReM-MoA, a memory-augmented MoA framework that sustains scaling through two mechanisms: (1) a Ranked Reasoning Memory that persistently stores and ranks reasoning traces from all layers using a comparative Reviewer Agent, and (2) a Curated Diversified Memory Routing scheme that exposes different agents to distinct combinations of successful and failed traces, preserving exploration diversity while propagating high-quality reasoning. We further introduce an optional multi-domain Reviewer distillation pipeline that improves ranking quality through frontier-model supervision. Across five reasoning benchmarks spanning math, formal logic, code, knowledge, and commonsense, ReM-MoA consistently outperforms prior MoA variants across both depth and width scaling, and its advantage widens with depth, establishing structured cross-layer reasoning memory as a key missing mechanism for scalable multi-agent inference.

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 proposes ReM-MoA, a memory-augmented Mixture-of-Agents (MoA) framework for inference-time scaling. It introduces (1) a Ranked Reasoning Memory that stores and ranks reasoning traces across layers via a comparative Reviewer Agent and (2) Curated Diversified Memory Routing that exposes agents to distinct combinations of successful and failed traces. An optional multi-domain Reviewer distillation pipeline is also described. The central empirical claim is that ReM-MoA consistently outperforms prior MoA variants on five reasoning benchmarks (math, formal logic, code, knowledge, commonsense), with the performance advantage widening as depth increases, thereby establishing structured cross-layer reasoning memory as essential for scalable multi-agent inference.

Significance. If the results and internal validations hold, the work would be significant for identifying a concrete mechanism (persistent ranked memory with diversified routing) that prevents the degradation or plateauing observed in existing MoA depth scaling. The multi-domain benchmark coverage is a positive feature. The optional distillation pipeline and emphasis on preserving exploration diversity are conceptually promising, though their necessity and effectiveness remain to be demonstrated.

major comments (3)
  1. [Abstract / Methods] Abstract and Methods (implied): The load-bearing claim that the comparative Reviewer Agent produces rankings that reliably boost downstream performance without systematic bias or loss of diversity is asserted but unsupported by any internal validation. No ranking accuracy metrics versus ground truth, no diversity measures (e.g., trace entropy or unique path coverage pre/post-routing), and no ablation isolating the Reviewer Agent are provided, leaving the central assumption untested.
  2. [Results] Results (implied): The abstract states consistent outperformance and widening gains with depth across five benchmarks, yet the manuscript supplies no quantitative tables, error bars, dataset sizes, statistical tests, or ablation results comparing against prior MoA variants. This absence prevents evaluation of whether the reported advantage is robust or dependent on post-hoc parameter choices such as memory size or routing criteria.
  3. [Methods (Routing)] § on Curated Diversified Memory Routing: The routing scheme is described as preserving exploration diversity while propagating high-quality traces, but no analysis shows that the combination of ranked memory and routing actually maintains diversity across layers rather than converging on high-ranked traces. This is required for the widening-with-depth claim to hold.
minor comments (2)
  1. [Methods] Notation for the Reviewer Agent and memory components should be formalized with explicit equations or pseudocode to clarify inputs, outputs, and ranking criteria.
  2. [Methods] The optional distillation pipeline is mentioned only briefly; its interaction with the core ReM-MoA mechanisms and any ablation showing its contribution should be expanded if retained.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below and will revise the manuscript accordingly to strengthen the presentation and validations.

read point-by-point responses

  1. Referee: [Abstract / Methods] Abstract and Methods (implied): The load-bearing claim that the comparative Reviewer Agent produces rankings that reliably boost downstream performance without systematic bias or loss of diversity is asserted but unsupported by any internal validation. No ranking accuracy metrics versus ground truth, no diversity measures (e.g., trace entropy or unique path coverage pre/post-routing), and no ablation isolating the Reviewer Agent are provided, leaving the central assumption untested.

    Authors: We agree that the initial submission would benefit from explicit internal validations of the Reviewer Agent. In the revised manuscript we will add ranking accuracy metrics versus ground truth (on subsets where labels are available), diversity measures including trace entropy and unique path coverage before/after routing, and a dedicated ablation isolating the Reviewer Agent's contribution to overall performance. revision: yes

  2. Referee: [Results] Results (implied): The abstract states consistent outperformance and widening gains with depth across five benchmarks, yet the manuscript supplies no quantitative tables, error bars, dataset sizes, statistical tests, or ablation results comparing against prior MoA variants. This absence prevents evaluation of whether the reported advantage is robust or dependent on post-hoc parameter choices such as memory size or routing criteria.

    Authors: We acknowledge that the current version lacks sufficiently detailed quantitative reporting. The revision will expand the results section with full tables, error bars, dataset sizes, statistical significance tests, and additional ablations on parameters such as memory size and routing criteria to demonstrate robustness of the reported gains. revision: yes

  3. Referee: [Methods (Routing)] § on Curated Diversified Memory Routing: The routing scheme is described as preserving exploration diversity while propagating high-quality traces, but no analysis shows that the combination of ranked memory and routing actually maintains diversity across layers rather than converging on high-ranked traces. This is required for the widening-with-depth claim to hold.

    Authors: We agree that an explicit analysis of diversity preservation is needed to support the widening-with-depth claim. The revised manuscript will include layer-wise diversity metrics (e.g., entropy and path coverage) demonstrating that the routing scheme maintains exploration diversity rather than collapsing to high-ranked traces. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical framework with no load-bearing derivations or self-referential reductions

full rationale

The provided abstract and description contain no equations, derivations, or mathematical claims. The core contributions (Ranked Reasoning Memory via Reviewer Agent and Curated Diversified Memory Routing) are presented as architectural proposals validated empirically across benchmarks. No self-citations, fitted parameters renamed as predictions, or ansatzes are visible that would reduce claims to inputs by construction. The paper is self-contained as an empirical proposal without the circular patterns enumerated.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no explicit free parameters, axioms, or invented entities; ledger left empty.

pith-pipeline@v0.9.1-grok · 5732 in / 1059 out tokens · 22304 ms · 2026-06-25T23:35:01.824782+00:00 · methodology

discussion (0)

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Reference graph

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