[Submitted on 14 Oct 2025 (
[v1](https://arxiv.org/abs/2510.12635v1)), last revised 7 May 2026 (this version, v3)]# Title:Memory as Action: Autonomous Context Curation for Long-Horizon Agentic Tasks
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Abstract:Long-context Large Language Models, despite their expanded capacity, require careful working memory management to mitigate attention dilution during long-horizon tasks. Yet existing approaches rely on external mechanisms that lack awareness of the agent's reasoning state, leading to suboptimal decisions. We propose Memory-as-Action (MemAct), a framework that treats working memory management as learnable policy actions. By formulating context management as in-place editing operations (deletion, insertion), MemAct enables joint optimization of information retention and task performance through end-to-end reinforcement learning. To address the computational challenges of dynamic context updates, we introduce Dynamic Context Policy Optimization, which restores training efficiency without compromising reasoning integrity. Experiments show that MemAct-RL-14B matches the accuracy of models $16\times$ larger while reducing average context length by 51%, with learned strategies that adapt to model capabilities and generalize across task complexities.
Submission history #
From: Yuxiang Zhang [[view email](/show-email/46b9f528/2510.12635)]
**Tue, 14 Oct 2025 15:29:57 UTC (327 KB)**
[[v1]](/abs/2510.12635v1)**Sat, 10 Jan 2026 01:44:56 UTC (374 KB)**
[[v2]](/abs/2510.12635v2)**[v3]** Thu, 7 May 2026 13:18:53 UTC (371 KB)
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