This survey organizes the scattered literature on LLM agent memory into a three-stage evolutionary framework: Storage (saving interaction traces), Reflection (refining and summarizing those traces), and Experience (abstracting general patterns from trajectories). The authors argue current work is fragmented between engineering and cognitive perspectives, and they propose design principles for building memory systems that enable long-range consistency, adaptation to dynamic environments, and continual learning. They highlight proactive exploration and cross-trajectory abstraction as transformative mechanisms in the Experience stage.
Main takeaways:
- Memory in LLM agents evolves through Storage → Reflection → Experience, moving from raw trajectory logs to abstracted reusable knowledge.
- Three core drivers push this evolution: need for long-range consistency, handling dynamic environments, and achieving continual learning.
- The frontier "Experience" stage involves proactive exploration (trying new things to learn) and cross-trajectory abstraction (generalizing across past episodes).
- Current research is fragmented; this framework offers a unified lens and design roadmap.
- The paper synthesizes engineering and cognitive science views into a coherent evolutionary perspective.