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

Mixture-of-Agents (MoA) architectures improve inference-time scaling by organizing multiple LLM agents into layered reasoning pipelines. However, existing MoA variants struggle to sustain gains as depth increases, exhibiting degradation, early plateauing, or saturation on most benchmarks. 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. ReM-MoA consistently outperforms prior MoA variants under both depth and width scaling on five reasoning benchmarks spanning math, formal logic, code, knowledge, and commonsense. As depth grows, ReM-MoA widens the lead and gains nearly three times as much accuracy per additional token as any baseline, establishing structured cross-layer reasoning memory as a key missing mechanism for scalable MoA inference.