Can LLMs infer network topology in multi-agent systems?
Graph Attention Inference of Network Topology in Multi-Agent Systems*
August 29, 2024
https://arxiv.org/pdf/2408.15449-
This research introduces a new method to predict the underlying network structure ("who's connected to whom") in a group of interacting agents (like in a multi-agent AI system) by analyzing their behavior over time. They achieve this without needing any prior knowledge of how the agents are connected or even the specific rules governing their behavior.
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Importantly for LLM-based multi-agent systems, this method demonstrates:
- Learning network topology implicitly: Instead of directly deciphering the connections, the model learns them indirectly while simultaneously getting better at predicting agent behavior. This is key for LLMs, as their internal workings are often opaque.
- Handling complex, dynamic interactions: The method works for both simple (linear) and more realistic (non-linear) interactions between agents, which is crucial for capturing the nuances of LLMs communicating.
- Potential for real-time adaptation: While not directly addressed in the paper, the core idea of learning from behavior opens possibilities for systems that continuously adapt to evolving relationships between LLMs, leading to more organic and robust multi-agent collaboration.