IntentScore: Intent-Conditioned Action Evaluation for Computer-Use Agents
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 rele
Record details
Published: 6 April 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Agents & autonomy · Environment
Retrieved: 14 July 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
Aligning Agents via Planning: A Benchmark for Trajectory-Level Reward Modeling
arXiv · 9 April 2026
CONSCIENTIA: Can LLM Agents Learn to Strategize? Emergent Deception and Trust in a Multi-Agent NYC Simulation
arXiv · 10 April 2026
Semantic Rate-Distortion for Bounded Multi-Agent Communication: Capacity-Derived Semantic Spaces and the Communication Cost of Alignment
arXiv · 10 April 2026
RAAP: Retrieval-Augmented Affordance Prediction with Cross-Image Action Alignment
arXiv · 31 March 2026
OOM-RL: Out-of-Money Reinforcement Learning Market-Driven Alignment for LLM-Based Multi-Agent Systems
arXiv · 13 April 2026
SCORP: Scene-Consistent Multi-agent Diffusion Planning with Stable Online Reinforcement Post-Training for Cooperative Driving
arXiv · 13 April 2026
How to cite this record
ethics.ai (6 April 2026), “IntentScore: Intent-Conditioned Action Evaluation for Computer-Use Agents,” evidence record 6296, https://ethics.ai/record/6296 (originally published by arXiv).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.