Rohan Paul@rohanpaul_ai · XFluxMem是一种新型AI智能体记忆系统，其核心思想是将记忆视为一个动态连接的网络，而非静态存储。它将事实、过往任务经历与可复用技能作为图中的节点进行存储。执行任务时，FluxMem先收集可能有用的记忆，再根据任务反馈动态修正记忆间的连接关系。此外，系统能将反复成功的任务路径转化为可复用技能。测试显示，该系统在LoCoMo基准上平均准确率达95.06，并在GAIA基准上结合Kimi K2取得了12.73分的性能提升，优于现有记忆系统。
AI agents should treat memory as a changing web of useful connections， not static storage.
Most agent memory systems retrieve old facts as if the past were a filing cabinet.
The paper proposes FluxMem， a memory system that stores facts， past task episodes， and reusable skills as connected pieces in a graph.
When the agent works on a task， FluxMem first gathers likely useful memories， then uses feedback from the task to fix the memory connections by adding missing links， removing bad ones， or rewriting memories at the right level of detail.
Over time， it also turns repeated successful task paths into reusable skills， so the agent does not need to rebuild the same reasoning pattern again and again.