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.
Scoring Rationale
Strong novel routing method with actionable experiments, limited by single-source preprint status without peer review.
Practice interview problems based on real data
1,500+ SQL & Python problems across 15 industry datasets — the exact type of data you work with.
Try 250 free problems
