Can small buffers stabilize learning router queues?
Learning in Strategic Queuing Systems with Small Buffers
This paper studies how simple learning algorithms used by routers (agents) in a network can efficiently distribute packets (tasks) to servers (resources) with limited buffer capacity. The key finding is that even with small buffers at each server, a small constant factor increase in total server capacity, compared to a perfectly coordinated system, is enough to keep the system stable when routers use learning algorithms. This is relevant to LLM-based multi-agent systems as it demonstrates that decentralized, learning-based agents can effectively manage shared resources even with limitations like buffering constraints, hinting at robust and scalable multi-agent coordination possibilities in web applications.