Computational framework demonstrates reduced communication overhead in multi-agent AI systems, indicating improved scalability for collaborative reasoning tasks.
Multi-agent AI systems, in which multiple learning agents coordinate to solve a shared task, often rely on exchanging rich communication messages to share observations, intentions, or partial reasoning — but this communication can become a significant computational and bandwidth bottleneck as the number of agents or the complexity of shared information grows. This project proposes a framework for efficient multi-agent reasoning through learned communication compression, in which agents learn compact, task-relevant message representations that preserve the information necessary for effective coordination while substantially reducing communication overhead compared to exchanging full, uncompressed observations or reasoning traces.
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Adishree Gupta (2026) studied this question.