FOCUS: Managing Memory Retrieval Space with Reversible Forgetting in LLM Agents

Long term memory enables LLM agents to retain information across interactions and reuse it in later responses. As interactions accumulate, memory stores grow, increasing the amount of information available for retrieval. However, the expanding retrieval space allows weakly related memories to compete with relevant evidence, which makes it harder to identify the information needed for each query. Removing memories can reduce this retrieval space, yet memories that have not been useful for an extended period may become relevant again in the future. To address this problem, we introduce FOCUS, a long term memory framework that uses a reversible memory lifecycle to control retrieval space growth. FOCUS assigns an activation value to each memory and organizes stored memories into an Active Set and a Dormant Set, with activation governing transitions between the two sets. Active memories participate directly in query matching, while Dormant memories remain stored in groups whose summaries are matched to the query to select relevant groups and guide retrieval of their full memory entries. Extensive experiments on LoCoMo and Memora with Qwen3-30B-A3B-Instruct and DeepSeek-V4-Flash show that FOCUS significantly improves long term memory performance over competing methods while maintaining the smallest retrieval space. Notably, in the longest Memora setting, FOCUS maintains a small retrieval space that is only 18.28% of all stored memories after 1,750 sessions.