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    <abstract>Large language model (LLM)-based agents increasingly face tasks that require reasoning over extremely long range. A key bottleneck is memory design: most prior approaches treat memory as a static, human-designed component, limiting adaptability. Inspired by biological memory evolution, we propose AtomMem, a framework that exposes a set of atomic memory operations—including summarization and CRUD primitives—as tool-like actions. The agent learns a policy over these operations purely via reinforcement learning, using only the task reward. AtomMem allows the agent to dynamically interleave summarization and CRUD to retain important information while managing memory efficiently. We evaluate AtomMem on streaming long-context understanding and web-navigation benchmarks, demonstrating an average improvement of 8.5 points over static memory workflows and 2.1 points over the current learnable-memory baseline. Ablation studies show that both summarization and CRUD are essential to train agent memory. The project can be accessed at https://github.com/RUCBM/AtomMem.</abstract>
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%0 Conference Proceedings
%T AtomMem: Learnable Dynamic Agentic Memory with Atomic Memory Operation
%A Huo, Yupeng
%A Lu, Yaxi
%A Zhang, Zhong
%A Chen, Haotian
%A Lin, Yankai
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F huo-etal-2026-atommem
%X Large language model (LLM)-based agents increasingly face tasks that require reasoning over extremely long range. A key bottleneck is memory design: most prior approaches treat memory as a static, human-designed component, limiting adaptability. Inspired by biological memory evolution, we propose AtomMem, a framework that exposes a set of atomic memory operations—including summarization and CRUD primitives—as tool-like actions. The agent learns a policy over these operations purely via reinforcement learning, using only the task reward. AtomMem allows the agent to dynamically interleave summarization and CRUD to retain important information while managing memory efficiently. We evaluate AtomMem on streaming long-context understanding and web-navigation benchmarks, demonstrating an average improvement of 8.5 points over static memory workflows and 2.1 points over the current learnable-memory baseline. Ablation studies show that both summarization and CRUD are essential to train agent memory. The project can be accessed at https://github.com/RUCBM/AtomMem.
%U https://aclanthology.org/2026.emnlp-main.1431/
%P 32684-32699

Markdown (Informal)

[AtomMem: Learnable Dynamic Agentic Memory with Atomic Memory Operation](https://aclanthology.org/2026.emnlp-main.1431/) (Huo et al., EMNLP 2026)

• AtomMem: Learnable Dynamic Agentic Memory with Atomic Memory Operation (Huo et al., EMNLP 2026)

ACL

• Yupeng Huo, Yaxi Lu, Zhong Zhang, Haotian Chen, and Yankai Lin. 2026. AtomMem: Learnable Dynamic Agentic Memory with Atomic Memory Operation. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 32684–32699, Budapest, Hungary. Association for Computational Linguistics.