Letta 发布 FrontierMemory，通过下游任务表现而非召回率来评估智能体记忆，聚焦智能体如何从经验中学习。该基准显示 Opus 5.5 位居榜首，开源权重模型 GLM-5.3 在记忆生成上表现同样出色。Elvis Saravia 关注更好的记忆系统与持久化智能体能力如何转化为更高效、更强的 RSI 智能体与 harness。
Recommended reading and a super-interesting benchmark for measuring continual learning capabilities through memory.
Agent memory is largely unsolved. As agents advance in capabilities like long-horizon work, it's unclear which models or harnesses work best, or which memory systems and types to use.
Opus 5.5 at the top is not a surprise. But it's exciting that open-weight models like GLM-5.3 perform so well on memory generation.
My big question is how this (better memory systems and persistent agent capabilities) translates into more performant, efficient RSI agents and harnesses.
Introducing FrontierMemory: a new way to evaluate agent memory through downstream task performance. While existing memory benchmarks focus on recall (saturated via agentic search), FrontierMemory focuses on how agents learn from experience.在 X 查看被引用的帖子
来源：elvis · x.com