where $\text{EM}(\cdot)$ denotes the matching function, $y_{\text{pred}}$ denotes the predicted answer, and $y_{\text{gold}}$ denotes the ground-truth answer. This design avoids the need to manually annotate individual memory operations. Instead, the memory manager learns which operations are beneficial by directly optimizing their impact on downstream task performance.
After optimizing the memory manager, the agent can dynamically maintain its memory during future interactions. However, learning effective memory operations is only the first step toward experience-driven agent improvement. A key challenge is how to evaluate the long-term utility of accumulated memories. Existing memory optimization methods typically define rewards based on downstream task performance, i.e., whether the retrieved memories help the agent solve the current task. However, the usefulness of a memory is not always reflected immediately. For example, a failure case collected from one interaction may not improve the current response, but it can help the agent avoid similar mistakes in future tasks. As a result, we can consider memory as a core component of agent learning rather than a passive storage module \citep{xu-etal:a-mem}. In this way, instead of treating memory updates as isolated operations for individual tasks, we can view memory management as an ongoing learning process, where agents gradually organize past experiences into reusable knowledge.
