Task Model Induction boosts agent workflow accuracy by 30%
dair_ai · x · 2026-08-23
Task Model Induction (TMI) is presented as an effective method to improve agentic workflows, especially for computer-use agents. TMI takes raw recordings (screenshots and input events) and converts them into symbolic models of the work process. It handles multi-threaded real-world traces by discovering and separating latent tasks with 0.974 agreement against ground-truth. It reconstructs 74.9% of observed execution steps and improves held-out task accuracy by 30.0% over the strongest workflow baselines.
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