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”，核心挑战在于实现智能体在长周期、动态且用户依赖环境中的实际效用。在此类环境中，智能体面临情境爆炸，必须在长期交互中持续积累、管理并选择性重用海量信息。记忆——今年已有数百篇相关论文发表——因此成为填补效用鸿沟的关键解决方案。本综述从三个维度提供基础智能体记忆的统一视角：记忆载体（内部与外部）、认知机制（情景记忆、语义记忆、感知记忆、工作记忆与程序记忆）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，并重点探讨记忆操作的学习策略。最后，我们回顾评估记忆效用的基准测试与度量标准，并展望当前面临的开放挑战与未来研究方向。