RESEARCH · RESEARCH · #2323
M-Plan: closed-loop multi-agent framework integrating long- and short-term memory for LLM agents
M-Plan is a closed-loop multi-agent system (MAS) framework that combines long-term and short-term memory via global-local and past-current task-specific guidance. The paper (arXiv:2610.27032v1) describes guidance generation during execution, local hints when agents are confused, post-task memory optimization for future retrieval, and reports improved accuracy and efficiency on multi-turn benchmarks including ALFWorld, WebShop, and PDDL compared to several baselines.
KEY POINTS
- M-Plan is a closed-loop multi-agent system (MAS) framework that combines long-term and short-term memory via global-local and past-current task-specific guidance.
- The paper (arXiv:2610.27032v1) describes guidance generation during execution, local hints when agents are confused, post-task memory optimization for future retrieval, and reports improved accuracy and efficiency on multi-turn benchmarks including ALFWorld, WebShop, and PDDL compared to several baselines.
- M-Plan addresses LLM agents' long-horizon weaknesses by combining memory arrangement and task-specific guidance, which materially improves multi-turn task performance and retrieval efficiency.
WHY IT MATTERS
M-Plan addresses LLM agents' long-horizon weaknesses by combining memory arrangement and task-specific guidance, which materially improves multi-turn task performance and retrieval efficiency.