MemMA: Coordinating the Memory Cycle through Multi-Agent Reasoning and In-Situ Self-Evolution
arXiv:2603.18718v1 Announce Type: new Abstract: Memory-augmented LLM agents maintain external memory banks to support long-horizon interaction, yet most existing systems treat construction, retrieval, and utilization as isolated subroutines. This creates two coupled challenges: strategic blindness on the forward path of the memory cycle, where construction and retrieval are driven by local heuristics rather than explicit strategic reasoning, and sparse, delayed supervision on the backward path, where downstream failures rarely translate into direct repairs of the memory bank. To address these challenges, we propose MemMA, a plug-and-play multi-agent framework that coordinates the memory cycle along both the forward and backward paths. On the forward path, a Meta-Thinker produces structured guidance that steers a Memory Manager during construction and directs a Query Reasoner during iterative retrieval. On the backward path, MemMA introduces in-situ self-evolving memory construction, w
arXiv:2603.18718v1 Announce Type: new Abstract: Memory-augmented LLM agents maintain external memory banks to support long-horizon interaction, yet most existing systems treat construction, retrieval, and utilization as isolated subroutines. This creates two coupled challenges: strategic blindness on the forward path of the memory cycle, where construction and retrieval are driven by local heuristics rather than explicit strategic reasoning, and sparse, delayed supervision on the backward path, where downstream failures rarely translate into direct repairs of the memory bank. To address these challenges, we propose MemMA, a plug-and-play multi-agent framework that coordinates the memory cycle along both the forward and backward paths. On the forward path, a Meta-Thinker produces structured guidance that steers a Memory Manager during construction and directs a Query Reasoner during iterative retrieval. On the backward path, MemMA introduces in-situ self-evolving memory construction, which synthesizes probe QA pairs, verifies the current memory, and converts failures into repair actions before the memory is finalized. Extensive experiments on LoCoMo show that MemMA consistently outperforms existing baselines across multiple LLM backbones and improves three different storage backends in a plug-and-play manner. Our code is publicly available at https://github.com/ventr1c/memma.
Executive Summary
This article proposes MemMA, a multi-agent framework that coordinates the memory cycle of memory-augmented LLM agents. MemMA addresses the challenges of strategic blindness and sparse supervision in the memory cycle by introducing a Meta-Thinker, a Memory Manager, a Query Reasoner, and in-situ self-evolving memory construction. The framework is evaluated on LoCoMo, demonstrating improved performance across multiple LLM backbones and storage backends. The authors' contribution is a plug-and-play solution that enhances the memory cycle without requiring significant modifications to existing systems. This work has implications for the development of more sophisticated LLMs and their applications in AI research.
Key Points
- ▸ MemMA is a multi-agent framework that coordinates the memory cycle
- ▸ The framework addresses strategic blindness and sparse supervision
- ▸ In-situ self-evolving memory construction is introduced for repair actions
Merits
Strength
MemMA provides a plug-and-play solution that enhances the memory cycle without requiring significant modifications to existing systems. The framework's modular design allows for easy integration with various LLM backbones and storage backends.
Demerits
Limitation
The framework's performance may be sensitive to the quality of the Meta-Thinker's guidance and the Memory Manager's decisions, which could impact the overall effectiveness of MemMA.
Expert Commentary
MemMA's contribution to the field of AI research is significant, as it addresses a critical challenge in the development of memory-augmented LLM agents. The framework's plug-and-play design and modular architecture make it a valuable tool for researchers and practitioners alike. However, the framework's performance may be sensitive to the quality of the Meta-Thinker's guidance and the Memory Manager's decisions, which could impact the overall effectiveness of MemMA. To further evaluate the framework's potential, it would be beneficial to conduct more extensive experiments and explore its applications in various domains.
Recommendations
- ✓ Future research should focus on exploring the applications of MemMA in various domains, including question answering, language translation, and text summarization.
- ✓ The authors should investigate the potential of MemMA in addressing the challenges of strategic blindness and sparse supervision in other areas of AI research.
