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Efficient and Interpretable Multi-Agent LLM Routing via Ant Colony Optimization

arXiv:2603.12933v1 Announce Type: new Abstract: 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 propose AMRO-S, an efficient and interpretable routing framework for Multi-Agent Systems (MAS). AMRO-S models MAS routing as a semantic-conditioned path selection problem, enhancing routing performance through three key mechanisms: First, it leverages a superv

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Xudong Wang, Chaoning Zhang, Jiaquan Zhang, Chenghao Li, Qigan Sun, Sung-Ho Bae, Peng Wang, Ning Xie, Jie Zou, Yang Yang, Hengtao Shen · · 1 min read · 10 views
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arXiv:2603.12933v1 Announce Type: new Abstract: 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 propose AMRO-S, an efficient and interpretable routing framework for Multi-Agent Systems (MAS). AMRO-S models MAS routing as a semantic-conditioned path selection problem, enhancing routing performance through three key mechanisms: First, it leverages a supervised fine-tuned (SFT) small language model for intent inference, providing a low-overhead semantic interface for each query; second, it decomposes routing memory into task-specific pheromone specialists, reducing cross-task interference and optimizing path selection under mixed workloads; finally, it employs a quality-gated asynchronous update mechanism to decouple inference from learning, optimizing routing without increasing latency. Extensive experiments on five public benchmarks and high-concurrency stress tests demonstrate that AMRO-S consistently improves the quality--cost trade-off over strong routing baselines, while providing traceable routing evidence through structured pheromone patterns.

Executive Summary

This article presents AMRO-S, an efficient and interpretable routing framework for Multi-Agent Systems (MAS) driven by Large Language Models (LLMs). AMRO-S addresses the limitations of existing routing strategies by leveraging a supervised fine-tuned small language model for intent inference, decomposing routing memory into task-specific pheromone specialists, and employing a quality-gated asynchronous update mechanism. The framework demonstrates improved quality-cost trade-offs over strong routing baselines and provides traceable routing evidence through structured pheromone patterns. The authors conduct extensive experiments on five public benchmarks and high-concurrency stress tests, showcasing the framework's scalability and efficiency. This research has significant implications for the development and deployment of MAS in real-world applications.

Key Points

  • ▸ AMRO-S models MAS routing as a semantic-conditioned path selection problem
  • ▸ The framework leverages a supervised fine-tuned small language model for intent inference
  • ▸ AMRO-S decomposes routing memory into task-specific pheromone specialists

Merits

Strength

The framework's ability to improve quality-cost trade-offs over strong routing baselines

Interpretability

AMRO-S provides traceable routing evidence through structured pheromone patterns

Demerits

Scalability Limitation

The framework's performance may degrade under extremely high concurrency or large-scale MAS

Expert Commentary

The proposed AMRO-S framework is a significant contribution to the field of MAS research. By addressing the limitations of existing routing strategies, AMRO-S has the potential to improve the efficiency and scalability of MAS in real-world applications. However, further research is needed to fully understand the framework's performance under extreme concurrency and large-scale MAS scenarios. Additionally, the framework's ability to provide interpretable routing evidence may have significant implications for regulatory compliance and transparency in MAS. Overall, AMRO-S is a promising approach that warrants further investigation and development.

Recommendations

  • ✓ Future research should focus on optimizing AMRO-S for extremely high concurrency and large-scale MAS scenarios
  • ✓ The framework's ability to provide interpretable routing evidence should be further explored for regulatory compliance and transparency in MAS

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