AI

M-Plan: Comprehensive Planning For LLM Agents with Memory Arrangement

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Briefings

M-Plan is a closed-loop multi-agent system framework designed to improve long-horizon task performance in LLM agents by integrating task-specific guidance with long-term memory. The system combines global-local guidance to construct short-term memory during execution and optimizes task information for long-term storage after completion. Experiments on ALFWorld, Webshop, and PDDL tasks show M-Plan outperforms baselines in both accuracy and efficiency across diverse models.

Key points

• M-Plan is a closed-loop multi-agent system framework that integrates long-term and short-term memory to enhance multi-turn task performance.
• Agents receive task-specific milestones and local hints only when confusion arises, which form global-local guidance to construct short-term memory.

Context

Existing multi-agent systems often rely on knowledge reuse or summarization, which is constrained by direct retrieval without adaptation. M-Plan addresses these challenges by combining global-local guidance with task-specific milestones to build a comprehensive framework distinct from other memory works.

Limitations

Evidence covers only multi-turn tasks including ALFWorld, Webshop, and PDDL across diverse models. Performance metrics are not provided, and the study does not specify the exact nature of the 'noise' in retrieved memory or the specific pre-processing steps applied.

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