arxiv:2609.18779
Published on Sep 16
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Jiaxuan on Sep 17
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Abstract
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.
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Paper author Paper submitter 6 days ago
Most MoA systems freeze routing while agents learn — or freeze agents while the router is tuned. CERA-MoA closes that loop: a familiarity-based router and N LoRA agents co-evolve with RL. Mid-layer hidden states estimate each agent’s competence without full rollouts; cumulative-threshold routing then activates the smallest capable subset. Code : https://github.com/michaeljiang0530/CERA-MoA
5 days ago
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