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This paper presents a holistic evaluation of memory substrates for memory-augmented LLM agents, finding that no single substrate dominates across all regimes and advocating for adaptive substrate routing to optimize performance in different operating conditions.
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.
This paper proposes AdaTKG, a method for temporal knowledge graph reasoning that uses adaptive memory to refine entity representations dynamically as new interactions occur, improving performance over static baselines.