@inproceedings{shayegani2025just,
author = {Shayegani, Erfan and Hines, Keegan and Dong, Yue and Abu-Ghazaleh, Nael B. and Lutz, Roman and Whitehead, Spencer and Balachandran, Vidhisha and Nushi, Besmira and Vineet, Vibhav},
title = {Just Do It!? Computer-Use Agents Exhibit Blind Goal-Directedness},
booktitle = {ICLR 2026},
year = {2025},
month = {October},
abstract = {Computer-Use Agents (CUAs) are an increasingly deployed class of agents that take actions on GUIs to accomplish user goals. In this paper, we show that CUAs consistently exhibit Blind Goal-Directedness (BGD): a bias to pursue goals regardless of feasibility, safety, reliability, or context. We characterize three prevalent patterns of BGD: (i) lack of contextual reasoning, (ii) assumptions and decisions under ambiguity, and (iii) contradictory or infeasible goals. We develop BLIND-ACT, a benchmark of 90 tasks capturing these three patterns. Built on OSWorld, BLIND-ACT provides realistic environments and employs LLM-based judges to evaluate agent behavior, achieving 93.75% agreement with human annotations. We use BLIND-ACT to evaluate nine frontier models, including Claude Sonnet and Opus 4, Computer-Use-Preview, and GPT-5, observing high average BGD rates (80.8%) across them. We show that BGD exposes subtle risks that arise even when inputs are not directly harmful. While prompting-based interventions lower BGD levels, substantial risk persists, highlighting the need for stronger training- or inference-time interventions. Qualitative analysis reveals observed failure modes: execution-first bias (focusing on how to act over whether to act), thought-action disconnect (execution diverging from reasoning), and request-primacy (justifying actions due to user request). Identifying BGD and introducing BLIND-ACT establishes a foundation for future research on studying and mitigating this fundamental risk and ensuring safe CUA deployment.},
url = {http://approjects.co.za/?big=en-us/research/publication/just-do-it-computer-use-agents-exhibit-blind-goal-directedness/},
}