The research of artificial intelligence is undergoing a paradigm shift from prioritizing model innovations over benchmark scores towards emphasizing problem definition and rigorous real-world evaluation. As the field enters the "second half," the central challenge becomes real utility in long-horizon, dynamic, and user-dependent environments, where agents face context explosion and must continuously accumulate, manage, and selectively reuse large volumes of information across extended interactions. Memory, with hundreds of papers released this year, therefore emerges as the critical solution to fill the utility gap. In this survey, we provide a unified view of foundation agent memory along three dimensions: memory substrate (internal and external), cognitive mechanism (episodic, semantic, sensory, working, and procedural), and memory subject (agent- and user-centric). We then analyze how memory is instantiated and operated under different agent topologies and highlight learning policies over memory operations. Finally, we review evaluation benchmarks and metrics for assessing memory utility, and outline various open challenges and future directions.

翻译：人工智能研究正经历从优先考虑模型创新与基准测试分数，向强调问题定义与严格现实世界评估的范式转变。随着领域进入"下半场"，核心挑战在于智能体在长周期、动态且用户依赖的环境中实现真正的效用，这些环境面临情境爆炸，智能体必须在长期交互中持续积累、管理并有选择地重用海量信息。记忆，作为填补效用鸿沟的关键解决方案，今年已有数百篇相关论文发表。本综述从三个维度提供了基础智能体记忆的统一视图：记忆载体（内部与外部）、认知机制（情景、语义、感觉、工作与程序记忆）以及记忆主体（以智能体为中心和以用户为中心）。随后，我们分析了记忆在不同智能体拓扑结构下的实例化与运作方式，并重点探讨了记忆操作的学习策略。最后，我们回顾了评估记忆效用的基准测试与度量方法，并概述了当前面临的各种开放挑战与未来研究方向。