agent/papers/items/2026-2606-24437-rem-moa-reasoning-memory-sustains-mixture-of-agents-scaling.md

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

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type: paper title: "ReM-MoA: Reasoning Memory Sustains Mixture-of-Agents Scaling" authors: Heng Ping, Arijit Bhattacharjee, Peiyu Zhang, Shixuan Li, Wei Yang, Ali Jannesari, Nesreen Ahmed, Paul Bogdan year: 2026 venue: arXiv url: https://arxiv.org/abs/2606.24437 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-06-23 updated_at: 2026-06-23 status: queued relevance: high topics:

• agent-evaluation
• memory
• multi-agent
• reasoning methods:

benchmarks:

models:

datasets:

• cs.AI related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 17 collection_queries: multi-agent-llm

One-line Takeaway

Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.

Why Collected

• matched queries: multi-agent-llm
• inferred topics: agent-evaluation, memory, multi-agent, reasoning
• arXiv categories: cs.AI
• collection score: 17

Review Checklist

• Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
• Does it include a benchmark, dataset, code, or reproducible experimental setup?
• Should it be promoted from queued to skimmed or summarized?

Links

• arXiv: https://arxiv.org/abs/2606.24437