Seeing, Maintaining, and Learning: The Evolution of Multimodal Agent Memory
Abstract
This work proposes a two-axis framework for multimodal agent memory covering information formation and adaptive management to support lifelong learning.
Multimodal models and agents have emerged to enable AI systems to learn from and interact with the real world more like humans by integrating information across multiple modalities. However, enabling multimodal agents to continually acquire useful information from complex, heterogeneous multimodal data remains a significant challenge as they move toward long-horizon tasks and lifelong settings. Addressing this challenge requires effective memory mechanisms that can maintain continuity across sessions, adapt to changing users and environments, and refine memory-management policies through accumulated experience. We introduce a two-axis analytical framework for multimodal agent memory structured around two key questions: how multimodal information is formed and represented (Axis I, Formation and Representation), and how its management adapts with accumulated experience (Axis II, Memory Management). Axis I, Formation and Representation, characterizes what information is retained and how it is encoded (e.g., modality-specific embeddings, textual and structured records, latent and parametric representations) to support subsequent reasoning and action. Axis II, Memory Management, distinguishes three tiers: T1 Static Memory reuses fixed memory; T2 Dynamic Memory updates memory through a stable policy; and T3 Self-Evolving Memory updates memory through an evolving policy. Based on this framework, we synthesize recurring design patterns and derive principles for balancing source fidelity with abstraction, maintaining temporal validity and provenance, grounding memory use in recoverable evidence, and evolving memory policies over time. Furthermore, we discuss the challenges and potential future directions for the memory mechanism of multimodal agents.
Community
How can multimodal agents remember what they perceive, keep that memory useful as the world changes, and learn to manage it better over time?
We are sharing our survey, “Seeing, Maintaining, and Learning: The Evolution of Multimodal Agent Memory.” We organize this growing field around two complementary axes:
🔹 Formation and Representation: How is multimodal information stored—as modality-specific representations, text and structured records, latent or parametric memory, or hybrid representations that preserve links to the original evidence?
🔹 Memory Management: How does memory adapt over time? We distinguish static memory, dynamic memory updated through a stable policy, and self-evolving memory whose management policy improves through experience.
A central distinction is that updating memory content and improving the policy that manages memory are different capabilities. Our framework helps clarify this difference and connects memory design to evidence preservation, temporal consistency, and reliable reasoning across long-horizon tasks.
The survey also covers memory infrastructure, evaluation benchmarks, applications, and open challenges. We hope it serves as a useful guide for researchers and practitioners building multimodal agents.
We welcome feedback, discussion, and suggestions for relevant work!
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