Rethinking Memory Mechanisms of Foundation Agents in the Second Half: A Survey
🤖 AI Summary
This work addresses the challenges faced by foundational agents in long-horizon, dynamic, and user-dependent environments—particularly context explosion and sustained information management—which necessitate efficient memory mechanisms to enhance practical utility. The paper proposes the first unified three-dimensional framework that integrates internal and external memory, five cognitive mechanisms, and a dual-agent/user-centric perspective, offering a systematic structuring of research on agent memory. Drawing on a comprehensive review of hundreds of studies published before 2026, the framework synthesizes insights from memory modeling, cognitive science, and agent architecture to clarify memory operation strategies, evaluation benchmarks, and learning methodologies. It not only delineates structured pathways in existing research but also identifies key open problems, thereby providing a theoretical foundation and directional guidance for the future design of intelligent agent memory systems.
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
foundation agents
memory mechanisms
long-horizon environments
context explosion
real-world utility
Innovation
Methods, ideas, or system contributions that make the work stand out.
memory mechanisms
foundation agents
cognitive architecture
long-horizon reasoning
agent memory evaluation
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