MemVista: Domain-Adaptive User Memory Evolution for LLM Recommendation Agents

When a compact user memory discards useful history, improving the downstream recommendation policy cannot directly recover that evidence. We study whether memory facets, instructions about which historical distinctions to retain, are a useful target of recommendation learning. ***Which historical distinctions are worth preserving depends on the domain***, even after matching profile token budget and the total size of banks of domain-specific facets. We introduce MemVista to ***learn memory facets automatically from recommendation errors***. Facets configure evidence extraction and profile synthesis, while the recommendation prompt remains fixed and never receives them. Evaluation covers twelve domains and six backbones. With GLM, MemVista outperforms Facets from pooled errors by 4.4 percentage points and Shuffled facets with matched total storage by 5.8 points. Target-aware diagnosis improves over Adaptive facets w/o correctness by 10.7 percentage points and Adaptive facets w/o target by 9.0 points. Frozen facets also improve direct summarization and an existing hierarchical memory manager without further search. A blinded, length-controlled exchange with strong reference memories directly tests the value of the additional retained evidence. Together, these results establish the empirical value of supervised content selection beyond domain priors, memory architecture, and ranking-policy adaptation.