AtomMem : Learnable Dynamic Agentic Memory with Atomic Memory Operation

• URL: http://arxiv.org/abs/2601.08323v1
• Date: Tue, 13 Jan 2026 08:22:28 GMT
• Title: AtomMem : Learnable Dynamic Agentic Memory with Atomic Memory Operation
• Authors: Yupeng Huo, Yaxi Lu, Zhong Zhang, Haotian Chen, Yankai Lin,
• Abstract summary: We propose AtomMem, which reframes memory management as a dynamic decision-making problem.<n>By combining supervised fine-tuning with reinforcement learning, AtomMem learns an autonomous, task-aligned policy to orchestrate memory behaviors.<n> Experimental results across 3 long-context benchmarks demonstrate that the trained AtomMem-8B consistently outperforms prior static-workflow memory methods.
• Score: 40.1709026042412
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Equipping agents with memory is essential for solving real-world long-horizon problems. However, most existing agent memory mechanisms rely on static and hand-crafted workflows. This limits the performance and generalization ability of these memory designs, which highlights the need for a more flexible, learning-based memory framework. In this paper, we propose AtomMem, which reframes memory management as a dynamic decision-making problem. We deconstruct high-level memory processes into fundamental atomic CRUD (Create, Read, Update, Delete) operations, transforming the memory workflow into a learnable decision process. By combining supervised fine-tuning with reinforcement learning, AtomMem learns an autonomous, task-aligned policy to orchestrate memory behaviors tailored to specific task demands. Experimental results across 3 long-context benchmarks demonstrate that the trained AtomMem-8B consistently outperforms prior static-workflow memory methods. Further analysis of training dynamics shows that our learning-based formulation enables the agent to discover structured, task-aligned memory management strategies, highlighting a key advantage over predefined routines.

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