This is one of the most effective ways to improve your agentic workflows. If you are building computer-use agents, this one is worth your time. Task Model Induction takes a raw recording of someone working, just screenshots and mouse and keyboard events, and turns it into a symbolic model of how the work was actually done. The hard part is that real recordings are multi-threaded. People switch between goals mid-task. TMI first discovers the latent tasks inside an unconstrained trace and separates them, hitting 0.974 agreement against ground-truth groupings. Each recovered task then gets two things. A hierarchical objective model of how the goal decomposes, and a procedure model of the control flow that organized execution. It reconstructs 74.9% of observed execution steps, and skills derived from these task models lift held-out task accuracy by 30.0% over the strongest workflow induction baseline. Passive traces are sitting on most work laptops already. This work just shows how to mine them into auditable, reusable skills. Paper: Track more trending AI papers in our academy: