[2601.08323] AtomMem : Learnable Dynamic Agentic Memory with Atomic Memory Operation

Computer Science > Artificial Intelligence arXiv:2601.08323 (cs) [Submitted on 13 Jan 2026 (v1), last revised 27 Mar 2026 (this version, v3)] Title:AtomMem : Learnable Dynamic Agentic Memory with Atomic Memory Operation Authors:Yupeng Huo, Yaxi Lu, Zhong Zhang, Haotian Chen, Yankai Lin View a PDF of the paper titled AtomMem : Learnable Dynamic Agentic Memory with Atomic Memory Operation, by Yupeng Huo and 4 other authors View PDF HTML (experimental) 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 ...