Computer-use agents risk cascading errors by acting without verifying alignment to user intent, a flaw IntentScore aims to fix through plan-aware reward modeling.

The arXiv paper by Chen et al. details training IntentScore on 398,000 offline GUI steps from three OSes using contrastive alignment and margin ranking losses, achieving 97.5% discrimination accuracy and a 6.9-point success rate gain for Agent S3 on the unseen OSWorld benchmark (https://arxiv.org/abs/2604.05157). Original coverage in the abstract understates limitations in real-world distribution shift; it misses explicit ties to process supervision breakthroughs in Lightman et al. (2023), where step-level verifiers outperformed outcome rewards in reducing LLM errors (https://arxiv.org/abs/2310.10621). OSWorld by Xie et al. (2024) previously showed baseline agents fail 75-85% of complex multi-app tasks due to irreversible GUI mistakes, patterns IntentScore directly mitigates yet does not fully quantify for enterprise safety (https://arxiv.org/abs/2404.07972).

Synthesis across these works reveals a systemic reliability gap: current CUAs optimize for action generation but lack decoupled, intent-conditioned critics, allowing visually similar clicks or keystrokes with divergent rationales to propagate. Anthropic's 2024 Claude computer-use deployment echoed these issues with user reports of unintended file operations, underscoring that offline heterogeneous trajectories, as used in IntentScore, offer a scalable path to generalization missing from purely online RL methods. This decoupling of evaluation from generation mirrors scalable oversight needs as agents approach mainstream autonomy.

By embedding planning intent into the action encoder, IntentScore identifies what prior coverage overlooked: success hinges not on benchmark scores alone but on preventing intent drift that compounds in long-horizon desktop tasks, establishing a foundation for verifiable agent loops critical to real-world deployment.