Multi-Agent Cooperative Planning and Execution Framework for Distributed LLM Reasoning Systems
Keywords:
multi-agent systems, large language models, distributed reasoning, cooperative planning, execution framework, system governance, socio-technical infrastructureAbstract
The rapid scaling of large language models has introduced significant challenges in reasoning coherence, computational efficiency, and operational robustness when deployed in real-world, distributed environments. This paper proposes a comprehensive multi-agent cooperative planning and execution framework designed to address these challenges by decomposing complex reasoning tasks into subtasks that are allocated across a network of specialized LLM agents. The framework integrates hierarchical planning with decentralized execution, enabling agents to dynamically coordinate through structured communication protocols and shared memory architectures. Emphasis is placed on structural trade-offs between centralized orchestration and autonomous agent decision-making, including considerations of latency, fault tolerance, and alignment. The paper further examines governance mechanisms for ensuring fairness and accountability in multi-agent systems, as well as infrastructure requirements for sustainable deployment at scale. A case illustration involving a distributed medical diagnosis system demonstrates the practical applicability of the proposed architecture. The discussion extends to policy implications, including regulatory frameworks for agent oversight and data sovereignty. By synthesizing insights from distributed systems, artificial intelligence, and socio-technical infrastructure research, this work contributes a systems-level perspective on the design of cooperative LLM reasoning platforms that are both performant and ethically grounded.
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