AIBullisharXiv – CS AI · Feb 277/106
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Hierarchical LLM-Based Multi-Agent Framework with Prompt Optimization for Multi-Robot Task Planning
Researchers developed a hierarchical multi-agent LLM framework that significantly improves multi-robot task planning by combining natural language processing with classical PDDL planners. The system uses prompt optimization and meta-learning to achieve success rates of up to 95% on compound tasks, outperforming previous state-of-the-art methods by substantial margins.
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