Semantic Routing Improves Multi-Agent System Efficiency
Researchers Xudong Wang et al. (submitted Mar 13, 2026) propose AMRO-S, an interpretable routing framework for LLM-driven multi-agent systems that models routing as a semantic-conditioned path selection problem. It uses a supervised fine-tuned small language model for intent inference, task-specific pheromone specialists, and quality-gated asynchronous updates. Experiments on five public benchmarks and high-concurrency stress tests show AMRO-S improves the quality–cost trade-off versus strong baselines.
Key Points
- 1Introduces AMRO-S: SFT small LLM intent inference, task-specific pheromone specialists, and quality-gated asynchronous updates.
- 2Reduces inference cost and latency while improving routing interpretability under mixed intents and high concurrency.
- 3Enables scalable, traceable MAS routing, improving quality–cost trade-offs across five benchmarks and stress tests.
Scoring Rationale
Strong novel routing method with actionable experiments, limited by single-source preprint status without peer review.
Sources
Public references used for this report.
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