Inducing Task Models from Computer-Use Traces
Score
76首次提出无需预设任务的并发任务模型归纳方法，突破了既有工作流的限制。
方法设计严谨，实验覆盖内在与外在评估，对比基线充分。
衍生的技能可显著提升下游任务准确率，对智能体学习与组织审计有直接应用价值。
契合当前计算机使用智能体热门方向，且该论文刚发布，时效性好。
来自arXiv AI/CL/LG，属于权威预印本平台，论文质量可信。
Base 84.5 × T2 Source weight ×0.9 = 76
Curation line 70Selected
Why selected
The paper presents a novel method with strong experiments, showing clear gains in task separation and downstream skills, though further validation of generalization in real-world scenarios is needed.
AI commentary
「This paper is worth watching because it tackles a key pain point when agents enter real work: how to extract auditable structured knowledge from messy traces. For those working on agent learning or enterprise process automation, TMI offers a promising path, and the experimental numbers are impressive—though it's worth examining how it scales to complex real-world settings.」
Quick overview
This paper introduces Task Model Induction (TMI), a framework for extracting symbolic, auditable, and reusable task models from naturalistic computer-use traces recorded passively. Unlike existing methods that assume a given task or single workflow, TMI discovers latent tasks in unconstrained traces, disentangles concurrent activity, and induces for each task a hierarchical objective model and a procedure model of control flow. On controlled human and agent trajectories, TMI recovers interleaved tasks with 0.974 agreement against ground-truth groupings and reconstructs 74.9% of observed execution steps; downstream, skills derived from TMI's task models improve held-out task accuracy by 30.0% over the strongest baseline.
- TMI automatically discovers and disentangles concurrent tasks from raw traces without assuming tasks or workflows.
- It builds hierarchical objective models and procedure models of control flow for each latent task.
- In controlled experiments, it achieves 0.974 agreement on task grouping and 74.9% step reconstruction.
- Skills derived from TMI's task models improve downstream task accuracy by 30.0% over the strongest baseline.
Full text
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