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 han...
| Authors | Bojie Li |
|---|---|
| Primary category | cs.AI |
| Published | 16 June 2026 |
| Last updated at source | 16 June 2026 |
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