CERA-MoA: Co-Evolving Routing Mechanisms with Continually Learning LLM Agents
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 sema...
| Authors | Jiaxuan Jiang, Liyuan He, Zhixuan Fang |
|---|---|
| Primary category | cs.AI |
| Published | 16 September 2026 |
| Last updated at source | 16 September 2026 |
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