Efficient and Interpretable Multi-Agent LLM Routing via Ant Colony Optimization
Large Language Model (LLM)-driven Multi-Agent Systems (MAS) have demonstrated 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. Existing routing strategies typically rely on expensive LLM-based selectors or static policies, and offer limited controllability for semantic-aware routing under dynamic loads and mixed intents, often resulting in unstable performance and inefficient resource utilization. To address these limitations, we propos...
| Authors | Xudong Wang, Chaoning Zhang, Jiaquan Zhang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 13 March 2026 |
| Last updated at source | 13 March 2026 |
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