TMI方法能够从计算机使用痕迹中诱导出任务模型，为任务执行提供更准确的指导，比现有方法更有效
自然计算机使用痕迹是获取日常工作模型的有价值资源。TMI方法能够发现未约束痕迹中的潜在任务，并为每个潜在任务诱导一个任务模型，包括递归目标分解的层次目标模型和控制流程的流程模型。TMI在控制的人类和代理轨迹上，与真实分组有0.974的一致性，并重建了74.9%的观察到的执行步骤。技能提升使任务准确率比最强基线高30.0%
Inducing Task Models from Computer-Use Traces
Naturalistic computer-use traces, passively recorded screenshots and mouse or keyboard actions, are a valuable resource for deriving symbolic, auditable, and reusable models of how everyday work is done. Such models matter as computer-use agents enter real work, where agents need to learn how tasks are actually performed, and organizations need to audit and reuse that knowledge. However, inducing such task models is challenging, as activity is observed only as low-level events and real-world work is multi-threaded with interleaved goals. Existing methods assume a given task or a single workflow, and produce step-level summaries rather than structured task models. We introduce Task Model Induction (TMI), which (i) discovers the latent tasks in an unconstrained trace, disentangling concurrent activity, and (ii) for each latent task, induces a task model pairing a hierarchical objective model of recursive goal decomposition with a procedure model of the control flow that organized the execution. Intrinsically, on controlled human and agent trajectories, TMI recovers interleaved tasks with 0.974 agreement against ground-truth groupings and reconstructs 74.9% of the observed execution steps, far more than the strongest workflow induction baseline. Extrinsically, skills derived from TMI's task models improve held-out task accuracy by 30.0% over the strongest baseline.