RecMem: Recurrence-based Memory Consolidation for Efficient and Effective Long-Running LLM Agents

ArXi:2605.16045v1 Announce Type: cross Memory systems often organize user-agent interactions as retrievable external memory and are crucial for long-running agents by overcoming the limited context windows of LLMs. However, existing memory systems invoke LLMs to process every incoming interaction for memory extraction, and such an eager memory consolidation scheme leads to substantial token consumption. To tackle this problem, we propose RecMem by rethinking when memory consolidation should be conducted.