IntentScore: Intent-Conditioned Action Evaluation for Computer-Use Agents
📰 ArXiv cs.AI
IntentScore is a plan-aware reward model that evaluates action quality for Computer-Use Agents to prevent irreversible errors
Action Steps
- Collect offline GUI interaction steps to train the IntentScore model
- Train IntentScore with complementary objectives to learn action evaluation
- Integrate IntentScore into Computer-Use Agents to score candidate actions
- Evaluate and refine IntentScore using feedback from GUI operations
Who Needs to Know This
AI engineers and researchers working on Computer-Use Agents can benefit from IntentScore to improve the reliability of their systems, and software engineers can apply this concept to develop more robust GUI automation tools
Key Insight
💡 IntentScore learns to score candidate actions to prevent irreversible errors in Computer-Use Agents
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🤖 IntentScore: a plan-aware reward model to evaluate action quality for Computer-Use Agents 📊
Key Takeaways
IntentScore is a plan-aware reward model that evaluates action quality for Computer-Use Agents to prevent irreversible errors
Full Article
Title: IntentScore: Intent-Conditioned Action Evaluation for Computer-Use Agents
Abstract:
arXiv:2604.05157v1 Announce Type: new 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 objecti
Abstract:
arXiv:2604.05157v1 Announce Type: new 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 objecti
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