Dynamic Coalition Formation and Communication Pricing in Skill-Based Agentic AI Systems
Applying cooperative game theory to solve the cost and efficiency bottlenecks of multi-agent LLM architectures.
Modern agentic AI systems combine multiple large language model agents with heterogeneous skills, yet most architectures either fix communication in advance or allow full broadcast. Both can be inefficient because token cost, latency, redundancy, and error propagation increase wi...
ArXiv CS.AI
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This research addresses the critical bottleneck of token costs and latency in multi-agent AI systems by applying game-theoretic principles to agent collaboration. By optimizing agent selection, developers can build scalable, resource-efficient architectures that maintain performance without constant, expensive model broadcasts.
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