Topology-Aware Coordination: Dynamic Rewiring for Error Containment

TL;DR: If errors snowball more in certain network topologies, why not let agents reshape their connections on the fly to stop error spread? This project explores adaptive agent graph topologies that reconfigure in response to detected coordination gaps or error signals.

Research Question: Can dynamically adjusting multi-agent system topology in response to real-time error metrics reduce error amplification and improve coordination efficiency compared to static topologies?

Hypothesis: Allowing agents to adapt their communication graph—temporarily centralizing, decentralizing, or forming hybrid subgroups—when error propagation is detected, will significantly suppress error amplification and improve overall performance, especially in heterogeneous task settings.

Experiment Plan: Implement a multi-agent system where agents monitor local error metrics and can propose/accept rewiring of their communication links. Define triggers for topology adaptation (e.g., local error spikes, coordination failures). Compare static (fixed) topologies versus adaptive ones on standard and stress-test benchmarks. Measure error amplification, task completion, and recovery from coordination breakdowns. Analyze how frequently and under what conditions topology adaptation is invoked, and its effect on system stability.

References:

• Yang, Y., Chai, H., Shao, S., Song, Y., Qi, S., Rui, R., & Zhang, W. (2025). AgentNet: Decentralized Evolutionary Coordination for LLM-based Multi-Agent Systems. arXiv.org.
• Wang, Q., Wang, T., Tang, Z., Li, Q., Chen, N., Liang, J., & He, B. (2024). MegaAgent: A Large-Scale Autonomous LLM-based Multi-Agent System Without Predefined SOPs. Annual Meeting of the Association for Computational Linguistics.
• Li, D., Tan, Z., Qian, P., Li, Y., Chaudhary, K. S., Hu, L., & Shen, J. (2024). SMoA: Improving Multi-agent Large Language Models with Sparse Mixture-of-Agents. Pacific-Asia Conference on Knowledge Discovery and Data Mining.