#computer-use agents

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4 papers match

cs.AI2026

OSReward: Instituting Standardized Evaluation for Cross-Platform Computer-Use Reward Models

Qiushi Sun, Kanzhi Cheng, Yian Wang +20

The paper introduces OSReward, a benchmark for evaluating vision-language model judges that assess computer-using agent trajectories, and presents open reward models (OS‑Shepherd)…

#computer-use agents#vision-language models#reward modeling#benchmark

cs.AI2026

Rethinking Inference-Time Scaling in Local Computer-Use Agents: Failure Modes and Compute Tradeoffs

Woongkyu Lee, Jungwook Choi

The paper empirically studies how different inference-time scaling strategies affect the performance and failure modes of locally deployed autonomous computer-use agents under hard…

#autonomous agents#computer-use agents#inference scaling#resource-constrained models

cs.AI2026

Echoverse: Deep, Evolving Environments for Training Computer-Use Agents at Scale

Yash Pandya, Sahil Gupta, Sarthak Harne +10

Echoverse introduces a pipeline that compiles specifications into deep, stateful synthetic applications for training computer-use agents, using a co‑evolution loop that repairs env…

#computer-use agents#synthetic environments#stateful applications#co-evolution training

cs.AI2026

How Benchmarks Mis-Score Computer-Use Agents

Zihan Dong, Zhiyuan Ma, Zekun Wang +5

The paper examines how current benchmarks for computer-use agents often give inaccurate scores due to issues in task design, trajectory observation, scoring, and reporting, and pro…

#computer-use agents#benchmark evaluation#reliability#error analysis

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