How Much Is a Memory Worth? Efficient Credit Assignment for Memory Admission Under Unknown Demand

Language-model agents use persistent memory to carry information across interactions, but limited storage requires deciding which memories to retain before future questions arrive. Existing admission policies rank memories using combinations of factors such as recency and confidence, or novelty relative to what is already stored. A memory's contribution to answering, however, depends on both the questions and the other available memories. Learning an admission policy from this contribution requires assigning credit when memories overlap or depend on one another. Here, we measure the utility of a set of memories by its reduction of a frozen reader's codelength for reference answers, then use the Shapley value to assign credit to individual memories. A policy, which we call Shapley Credit Assignment Policy (**SCAP**), predicts the resulting credit at write time from candidate and resident records using one regression objective over frozen embeddings. On held-out LongMemEval questions and supporting sessions, SCAP retains greater utility than the selections made using A-MAC and SAGE across capacities, with larger advantages at higher capacity pressure. The advantage also persists with a reader from another model family without retraining the policy. These findings support a simple approach to memory admission in which the learning target follows from the utility of the stored information.