MACRO-LLM: Spatiotemporal Multi-Agent Collaborative Reasoning and Negotiation Framework скачать в хорошем качестве

MACRO-LLM: Spatiotemporal Multi-Agent Collaborative Reasoning and Negotiation Framework

MACRO-LLM: Spatiotemporal Multi-Agent Collaborative Reasoning and Negotiation Framework The provided text introduces MACRO-LLM, a framework designed to manage decentralized multi-agent collaboration in environments where agents have limited visibility. To overcome spatiotemporal partial observability, the system utilizes three specialized modules: the CoProposer for action planning, the Negotiator for resolving conflicts, and the Introspector for self-reflection. It employs mean-field approximation and semantic gradient descent to compress complex environmental data into actionable linguistic strategies. Experimental results in autonomous driving and pandemic control scenarios show that this method outperforms traditional reinforcement learning and other LLM-based baselines. Ultimately, the framework demonstrates strong scalability and zero-shot generalization, allowing diverse agents to align their short-term actions with long-term global goals. 🔔 Want to master the latest in Artificial Intelligence? Sign up and learn about LLMs, AI Agents, GPT, Gemini, Claude, and cutting-edge AI solutions, with practical and up-to-date content. Link to this video:    • MACRO-LLM: Spatiotemporal Multi-Agent Coll...   #ArtificialIntelligence #MachineLearning #DeepLearning #LanguageModels #Transformers #LLMs