A Usage-Reinforced Decay Engine for AI Agent Memory Using the Ebbinghaus Forgetting Curve

Short summary

The author proposes a memory engine for LLM-based AI agents that prioritizes importance over recency, applying the Ebbinghaus forgetting curve to decay unused context while reinforcing frequently accessed information. This addresses a core limitation of standard context windows that retain only the newest data. The approach aims to make agent memory more human-like and efficient.

• •Standard context windows retain newest info, not most important
• •Applies Ebbinghaus forgetting curve to build usage-reinforced decay engine
• •Targets AI agent memory that prioritizes importance over recency

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