TodyComm: Task-Oriented Dynamic Communication for Multi-Round LLM-based Multi-Agent Systems
Researchers have introduced TodyComm, a novel algorithm designed to enhance communication within multi-round Large Language Model (LLM)-based multi-agent systems. Current methods typically rely on fixed communication topologies during inference, which limits effectiveness in dynamic environments where agent roles shift due to adversarial actions, task progression, or bandwidth constraints. TodyComm addresses this by generating behavior-driven collaboration topologies that adapt in real-time at each round. The system optimizes task utility through policy gradient techniques, ensuring efficient and responsive agent interaction. Experimental results across five benchmarks indicate that TodyComm significantly outperforms existing methods in both dynamic adversarial settings and under strict communication budget constraints. The algorithm demonstrates superior task performance while maintaining high token efficiency, scalability, and strong generalizability across varying conditions. This development represents a significant advancement in artificial intelligence, specifically in optimizing collaborative structures for autonomous agents operating in complex, changing environments. The paper, authored by Wenzhe Fan and colleagues, was published on arXiv, contributing to the field of computer science and artificial intelligence by offering a robust solution for dynamic multi-agent coordination.
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TodyComm: Task-Oriented Dynamic Communication for Multi-Round LLM-based Multi-Agent Systems
Researchers have introduced TodyComm, a novel algorithm designed to enhance communication within multi-round Large Language Model (LLM)-based multi-agent systems. Current methods typically rely on fixed communication topologies during inference, which limits effectiveness in dynamic environments where agent roles shift due to adversarial actions, task progression, or bandwidth constraints. TodyComm addresses this by generating behavior-driven collaboration topologies that adapt in real-time at each round. The system optimizes task utility through policy gradient techniques, ensuring efficient and responsive agent interaction. Experimental results across five benchmarks indicate that TodyComm significantly outperforms existing methods in both dynamic adversarial settings and under strict communication budget constraints. The algorithm demonstrates superior task performance while maintaining high token efficiency, scalability, and strong generalizability across varying conditions. This development represents a significant advancement in artificial intelligence, specifically in optimizing collaborative structures for autonomous agents operating in complex, changing environments. The paper, authored by Wenzhe Fan and colleagues, was published on arXiv, contributing to the field of computer science and artificial intelligence by offering a robust solution for dynamic multi-agent coordination.
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