PagedMem: Learning to Remember at the Right Granularity for Self-Evolving Agents

Despite the increasing reliance of LLM agents on persistent memory, whether past experience improves or degrades their performance depends on the granularity at which it is remembered. Memory that is over-broad provides no actionable guidance; memory that is over-strict constrains or even misleads the agent when the current task is inconsistent with it. Although injected text is formally soft guidance, an over-strict procedure acts as a hard behavioral prior: it suppresses exploration and triggers negative transfer when the current environment diverges from the source trajectory. Memory injection can therefore degrade rather than improve performance when the stored experience is more specific than the current execution supports, even if the memory content itself is correct. We introduce PagedMem, a self-evolving memory system that treats representation granularity and exposure granularity as separable design variables. Unlike prior methods that fix the granularity of memory at construction and inject retrieved content wholesale, PagedMem keeps multiple levels of abstraction available and progressively exposes the level that execution currently requires. It consolidates free-form reflections into semantic pages with three levels: a short header, an open-ended summary, and provenance-linked supporting records. We conduct extensive experiments on ALFWorld with three model backbones. On every backbone, PagedMem improves success and efficiency over ReAct, AWM, and MemP and substantially reduces invalid actions. These results show that representing and using memory at the right granularity can further improve agents’ ability to adapt and generalize.