Seeing, Maintaining, and Learning: The Evolution of Multimodal Agent Memory
- Published
- Source
- arXiv
- Paper number
- 1210
- Field
- Memory / Agents
- arXiv ID
- 2610.19366
Key points
- The survey organizes memory along two axes: Axis I covers what representation retained experience takes, and Axis II covers how memory state and management policy change during operation.
- The management axis has three tiers: T1 reuses fixed memory, T2 updates memory through a stable policy, and T3 evolves the management policy itself using feedback.
- Analysis of 297 architectures showed that T2 Dynamic Memory is the most common with 204 architectures (68.7%), while T3 Self-Evolving Memory accounts for only 17 (5.7%).
- Hybrid representations form the largest category with 139 architectures (46.8%), and designs keeping explicit records together with source evidence are widely used.
- Of the 17 T3 Self-Evolving Memory architectures, 13 are cross-represented systems, and no memory-policy evolution case exists with only latent or only parametric representations.
- Across 82 benchmarks, cross-modal and long-range evidence are well evaluated, but only 7 and 10 benchmarks directly evaluate conflicting-memory handling and unanswerable queries, respectively.
Paper links
External research summaries. These are not HDATF publications or measured product results.