EvoMem: Type-Adaptive Retrieval over Versioned Memory for Self-Evolving LLM Agents

Long-term memory enables LLM agents to retain user-specific information across extended interactions. However, maintaining evolving user attributes requires identifying their current values and their historical states at specified times. Existing approaches either retrieve outdated values from an insufficiently updated knowledge base or compress observations into summaries that obscure revision histories. Moreover, most systems tightly couple memory storage and retrieval, thereby imposing a fixed access path regardless of query requirements. To address these limitations, we introduce EvoMem, a memory architecture that supports both current-state and historical reasoning by decoupling versioned storage from query-adaptive retrieval. EvoMem preserves complete histories of user attributes without committing them to a single retrieval structure, and dynamically selects and ranks evidence according to the query type. This design poses two key challenges. Different queries require distinct retrieval mechanisms, while retaining all attribute versions may cause obsolete information to compete with current values and lead to incorrect retrieval. EvoMem addresses these challenges through type-adaptive retrieval and self-evolving memory consolidation. Type-adaptive retrieval selects appropriate memory structures and retrieval paths for factual, time-sensitive, and multi-session reasoning queries. Self-evolving memory consolidation integrates Versioned Knowledge Evolution (VKE), which preserves timestamped attribute versions, with Error-Driven Pattern Learning (EDPL), which extracts reusable patterns from prior retrieval errors to continually refine ranking policies. Experiments on LongMemEval and LoCoMo demonstrate that EvoMem consistently outperforms strong baselines under predicted query-type routing. In particular, on LoCoMo, EvoMem exceeds the strongest baseline by $3.6$ points in QA accuracy and $7.1$ points in token-level F1. The source code is available at https://anonymous.4open.science/r/Evomem/.