想让你那个傻傻的屏幕操作AI学会重复干活不重来？PreAct把第一次成功步骤变成程序，后面直接快10倍，还不容易翻车。
PreAct 是一种让计算机使用代理（如屏幕点击、打字）在重复任务上更快的新方法。首次成功时，PreAct 将运行编译为小状态机程序，后续重放直接执行，无需逐步骤调用语言模型，速度提升8.5-13倍。重放时每步检查屏幕状态，若不一致则交回代理处理。在移动、桌面和Web基准上，存储时检查机制确保编译程序可靠，避免错误积累，平均多完成1.75-2.6个任务。
PreAct: Computer-Using Agents that Get Faster on Repeated Tasks
Computer-using agents drive real software through the screen -- clicking and typing -- but they solve every task from scratch: asked to repeat a task, an agent re-reads the screen, re-reasons every tap, and pays the full cost again. We present PreAct, which lets such an agent get faster on tasks it has done before. The first time it succeeds, PreAct compiles the run into a small state-machine program-states that check the screen, transitions that act-and on later runs replays it directly instead of invoking the agent 8.5-13x faster, with no per-step language-model calls. Replay is not blind: at each step PreAct checks that the screen matches what the program expects before acting, and hands control back to the agent the moment something is off. PreAct applies the same discipline when deciding what to keep: a freshly compiled program enters the store only if, re-run from a clean state, an independent evaluator confirms it solved the task-catching programs that replay to their last step yet leave the task undone. Across a mobile, a desktop, and a web benchmark, this store-time check separates repeated runs that improve from ones that degrade as faulty programs accumulate, worth 1.75-2.6 tasks per benchmark, the same direction on all three; a fallback that explores afresh when no program fits brings PreAct level with a strong record-and-replay baseline. We also report what did not matter: prompt wording, runtime guardrails, and whether a language model or a plain embedding retriever selects which program to reuse.