AI RESEARCH
Efficient and Interpretable Multi-Agent LLM Routing via Ant Colony Optimization
arXiv CS.AI
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ArXi:2603.12933v1 Announce Type: new Large Language Model (LLM)-driven Multi-Agent Systems (MAS) have nstrated strong capability in complex reasoning and tool use, and heterogeneous agent pools further broaden the quality--cost trade-off space. Despite these advances, real-world deployment is often constrained by high inference cost, latency, and limited transparency, which hinders scalable and efficient routing.