Document-level translation remains one of the most challenging tasks for large language models, which are constrained by limited context windows that impede global cohesion, while simultaneously suffering from redundant contextual information that degrades translation quality. To address this, we propose a human-like long document translation agent called Loong, which leverages a 3E memory module (Essence-Exemplar-Entity) to store summaries, sentence pairs, and entity records as historical context. Instead of passively attending to all history, Loong performs deep reasoning to adaptively identify the optimal context for translation guidance. Loong optimizes its context policy through reinforcement learning, utilizing preference data derived from its own sampled observe-and-act reasoning trajectories. Empirical evaluations demonstrate that Loong achieves substantial translation quality improvements in English $\Leftrightarrow$ Chinese, German, and French directions, with average gains of up to 13.0 points across the three evaluation metrics. Furthermore, Loong exhibits strong generalization across domains and robustness against contextual noise, while maintaining remarkable stability in ultra-long document translation. Our code is released at https://github.com/YutongWang1216/LoongDocMT.

翻译：文档级翻译仍然是大型语言模型面临的最具挑战性的任务之一，其受限于有限的上下文窗口而阻碍全局连贯性，同时又因冗余的上下文信息而降低翻译质量。为此，我们提出了一种名为Loong的类人长文档翻译智能体，它利用3E记忆模块（本质-范例-实体）存储摘要、句对和实体记录作为历史上下文。与被动关注所有历史信息不同，Loong通过深度推理自适应地识别用于翻译指导的最优上下文。Loong通过强化学习优化其上下文策略，利用从其自身采样的观察-行动推理轨迹中获得的偏好数据。实证评估表明，Loong在英⇄中、德、法三种语言方向上的翻译质量显著提升，在三个评估指标上平均提升高达13.0分。此外，Loong在跨领域和对抗上下文噪声方面表现出强大的泛化能力和鲁棒性，同时在超长文档翻译中保持出色的稳定性。我们的代码已在https://github.com/YutongWang1216/LoongDocMT开源。