arXiv:2604.05157v2 Announce Type: replace Abstract: Computer-Use Agents (CUAs) leverage large language models to execute GUI operations on desktop environments, yet they generate actions without evaluating action quality, leading to irreversible errors that cascade through subsequent steps. We propose IntentScore, a plan-aware reward model that learns to score candidate actions from 398K offline GUI interaction steps spanning three operating systems. IntentScore trains with two complementary objectives: contrastive alignment for state-action relevance and margin ranking for action correctness.
The rapid development and deployment of Computer-Use Agents (CUAs) necessitate robust mechanisms for evaluating action quality to improve reliability and prevent cascading errors.
This development addresses a critical weakness in current AI agents, improving their reliability and reducing the risks associated with autonomous execution of complex tasks in real-world computer environments.
AI agents can now more effectively evaluate and correct their actions, leading to more stable and trustworthy autonomous systems capable of performing intricate GUI operations.
- · AI agent developers
- · Enterprise software users
- · Automation platforms
- · Operating system providers
- · Manual IT support roles
- · Inefficient automation tools
Improved reliability and expanded capabilities of AI agents for complex computer tasks.
Accelerated adoption of AI agents across various industries, leading to significant productivity gains and workflow automation.
Increased demand for robust, explainable AI, shifting focus towards 'intelligent' rather than merely 'autonomous' systems.
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Read at arXiv cs.AI