keyword-gated

This paper introduces CAMeR, a memory retention framework for LLM agents that combines keyword-gated hybrid activation with adaptive weight dynamics, and presents CAMeR-Bench, a benchmark for evaluating adaptive memory retention. Experiments show that hybrid symbolic-neural gating improves retention gaps and retrieval efficiency compared to embedding-only or time-driven baselines.