EPIC is a preference-aligned memory construction framework for on-device RAG that selectively retains preference-relevant information and aligns retrieval toward user preferences, achieving 18.79 pp higher accuracy with 2,404× lower memory usage and 32.17× lower latency than the best baseline.
Retrieval-Augmented Generation (RAG) on personal devices requires managing memory under tight resource budgets while aligning with user-specific preferences. Existing approaches focus on volume-based memory management, retaining information based on recency or frequency, without considering what users actually prefer. We propose EPIC, a preference-aligned memory construction framework that selectively retains preference-relevant information and aligns retrieval toward user preferences. EPIC first identifies preference-relevant information from user interactions and constructs a compact preference-aligned memory. During retrieval, EPIC prioritizes memory entries that align with inferred user preferences. Experiments on four benchmarks show that EPIC improves preference-following accuracy by 18.79 percentage points on average while reducing memory usage by 2,404× and retrieval latency by 32.17× compared with the strongest baseline.