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M-Plan framework enhances LLM agents with integrated long-term and short-term memory

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced M-Plan, a novel closed-loop multi-agent system (MAS) framework designed to enhance the performance of LLM agents on long-horizon and multi-turn tasks. M-Plan integrates both long-term and short-term memory, utilizing a global-local and past-current guidance strategy. This approach allows agents to receive task-specific milestones and local hints when needed, while also optimizing and reorganizing information for future retrieval into long-term memory. Experiments on tasks like ALFWorld and Webshop demonstrated M-Plan's superiority over baseline methods in accuracy and efficiency. AI

IMPACT This framework could improve the efficiency and accuracy of LLM agents in complex, multi-turn tasks.

RANK_REASON The cluster describes a new research paper detailing a novel framework for LLM agents. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

M-Plan framework enhances LLM agents with integrated long-term and short-term memory

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The cluster describes a new research paper detailing a novel framework for LLM agents. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Ziyang Liu, Jiakang Yuan, Jiayuan Fan, Tao Chen ·

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

    arXiv:2610.27032v1 Announce Type: new Abstract: Large language model (LLM)-based agents have recently demonstrated outstanding performance in single-turn tasks, yet they continue to struggle with long-horizon tasks, particularly in environments without prior training. A key limit…

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