Learning What to Remember: A Cognitively Grounded Multi-Factor Value Model for Agentic Memory
This work addresses the challenge of effective memory management for long-horizon large language model agents operating under constrained memory budgets, requiring principled decisions on encoding, forgetting, and retrieval. The authors propose the first cognitively inspired, multi-factor memory valuation model, formulating a linear value function grounded in seven interpretable factors—including emotional intensity and goal relevance—and employ a gradient-free optimizer to automatically learn factor weights that jointly govern memory policies. Evaluated under blind settings with no knowledge of future queries, the approach significantly outperforms single-factor, uniformly weighted, and recency-based baselines on both the LongMemEval benchmark and synthetic tasks, retaining 77.0% of gold evidence across 479 test cases compared to 65.7% for the best baseline, while yielding highly interpretable learned weights.