Meta记忆代理通过解决行为状态衰减将性能提升57%

meta ai researchers identified behavioral state decay as the cause of agent failure in long tasks, leading to forgotten constraints and repeated error rediscovery. their solution is a secondary memory agent that reviews a sliding window of steps, updates a structured memory bank, and decides whether to remind the primary action agent about past failures or risks. testing with claude opus as the memory coach and claude sonnet as the worker agent boosted success rates by 57% on terminal-bench and 16% on tau2-bench. the system functions as a plug-and-play layer, requiring no modifications to existing agents.