Customize Your Memory: Scenario-Adaptive and Evolvable Memory Modeling for LLM Agents

Memory enables large language model (LLM) agents to learn continually across sessions and scenarios. Memory systems have attracted great attention as LLM agents are widely used in coding, office work, and other scenarios. However, memory modeling, i.e. how to represent flat text and what features to extract, is either ignored or hand-crafted and frozen at design time. We argue that memory is a process of identification, extraction, and synthesis, i.e. an offline cognitive capability whose core is memory modeling instead of a flat archive. We formulate the memory system as a modeling-guided optimization problem and present ScenarioModelMem, a scenario-adaptive memory-modeling system. We split memory modeling into a static memory structure and a dynamic memory policy to achieve efficient representation, flexible configuration, and optimizability. A validation-driven evolutionary algorithm refines the dynamic memory policy through error-informed mutation, crossover, and pruning. Under the same experimental configuration as the strongest published systems, ScenarioModelMem achieves state-of-the-art performance on LOCOMO (94.03) and PersonaMem (70.63) with a pre-defined memory policy. Furthermore, we evaluate automatic policy design on PersonaMem. Starting from a basic initial policy, the evolved policy outperforms a carefully crafted human-designed one. Memory modeling thus becomes scenario-adaptive and optimizable, rather than a fixed design choice.