# CERA-MoA：让路由机制与持续学习 LLM 智能体协同演化

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-09-16 08:00
- AIHOT 分数：34
- AIHOT 链接：https://aihot.news/items/cmu5hmk750655roqogv3rrhyp
- 原文链接：https://arxiv.org/abs/2609.18779

## AI 摘要

CERA-MoA 是一个迭代强化学习框架，让动态路由器与独立智能体策略协同演化，解决现有 Mixture-of-Agents 范式将查询路由与智能体微调割裂的问题。

## 正文

Current Mixture-of-Agents (MoA) paradigms generally treat query routing and agent fine-tuning as separate processes, limiting their ability to respond to evolving agent capabilities. This disconnect prevents routing strategies from adapting to evolving agent capabilities during post-training and prevents agents from achieving synergistic data-driven specialization. To resolve this, we introduce CERA-MoA (Co-Evolving Router with continually learning Agents for Mixture-of-Agents), an iterative reinforcement learning framework where the dynamic router and independent agent policies co-evolve.

We design a predictive familiarity estimator that leverages mid-layer hidden states to evaluate semantic competence among agents, avoiding the overhead of full rollouts. Based on these familiarity scores, a cumulative-threshold adaptive routing mechanism dynamically activates a tailored minimal agent subset, achieving a trade-off between task performance and efficiency. By proactively allocating targeted training samples to agents based on their evolving competence, CERA-MoA promotes capability differentiation. Extensive experiments across various domains demonstrate that CERA-MoA outperforms state-of-the-art static-agent routing and fix-workflow fine-tuning baselines.