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  "text": "Outstanding paper on computer-using agents.\n\n(bookmark it)\n\nComputer-using agents drive real software through the screen, but they solve every task from scratch. Ask one to repeat a task, and it re-reads the screen and re-reasons every tap, paying the full cost again.\n\nPreAct compiles the first successful run into a small state-machine program, states that check the screen and transitions that act, then replays it directly on later runs. That runs 8.5 to 13x faster with no per-step language-model calls.\n\nReplay stays guarded. At each step, PreAct checks that the screen matches what the program expects before acting, and hands control back to the agent when reality diverges.\n\nWhy does it matter?\n\nMost computer-use costs are repeated reasoning on tasks the agent has already solved. Amortizing that into a replayable program is a clean way to make agents faster the second time.\n\nPaper: https://t.co/kMloX0qC5M\n\nLearn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c",
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