Researchers introduced CERA-MoA, a Mixture-of-Agents framework that co-evolves query routing with continually learning LLM agents, addressing the disconnect in current MoA paradigms where routing and agent fine-tuning are treated as separate processes. The work targets the limitation that routing strategies cannot adapt to evolving agent capabilities during post-training.
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 pre
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