G-Dreamer: Long-Term Memory Evolution and Management for LLM Agents

Large language model (LLM) agents increasingly rely on external memory to support inference-time adaptation over long-term interaction. However, accumulating more experience does not necessarily sustain memory utility. As interaction continues, useful experience can become buried in a growing memory store, while redundant, outdated, or incorrect knowledge remains available for retrieval. Consequently, memory may perform well during early accumulation yet degrade substantially in later stages, even when its overall performance remains competitive. We refer to this phenomenon as *long-term degradation*. Mitigating such degradation requires maintaining an effective memory state that continuously incorporates new evidence, remains compact enough for reliable retrieval, and revises knowledge as its validity changes. Moreover, while concrete cases and abstract patterns provide complementary forms of experience, effective compression requires preserving their provenance to avoid losing supporting evidence. We introduce **G-Dreamer**, a framework for long-term memory evolution and management for LLM agents. During wake, G-Dreamer continuously collects trajectory-grounded cases and their outcomes. In the background, sleep-time lifecycle management performs consolidation to extract reusable patterns, selective forgetting to retire redundant experience while preserving provenance, and reconsolidation to revise patterns when later evidence challenges them. Adaptive scheduling coordinates these operations according to the evolving memory state and lifecycle stage. Experiments on long-horizon interactive tasks show that G-Dreamer mitigates long-term degradation, achieving stronger performance with a smaller active memory state at little additional maintenance cost. Code: [anonymous/G-Dreamer](https://anonymous.4open.science/r/G-Dreamer-DCB6/).