The authors call this "behavioral state decay." The state that guides the agent's decisions gets scattered across a growing task history. It can become buried deep in the context window or fall out of it altogether. Even when the information remains in the transcript or context window, it may no longer reliably shape the agent's behavior. Meta AI says simply giving agents access to longer histories doesn't solve the problem.
Deciding when memory matters
Existing memory systems focus on storing, updating, and retrieving information. According to the paper, that works well for personalization and recall across sessions. But agents working through a task face another problem. The system must decide when a memory is useful enough to bring back. Too few reminders lead to repeated mistakes, while too many add latency, consume tokens, and distract the agent from its current work.
This goes beyond summarization: a summarizer just decides what information to retain where Meta's system decides whether a stored execution state should influence the agent's next move. Since failure modes vary widely by task, a fixed summarization rule can't make that call reliably.
A second agent decides when to speak up
