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K. Jin

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#artificial intelligence Preprint Oct 2026

OS-Helix: Unifying GUI Interaction and Code Execution in Native Computer-Using Agents

Real desktop automation is neither purely visual nor purely programmatic. Agents must understand user-facing interfaces while efficiently manipulating files, scripts, and system state. However, native GUI/code coordination cannot be obtained by prompt-level tool composition. In our preliminary experiments, naively addi...

Bowen Yang, K. Jin, Zichen Ding et al. · 0 citations
Jul 2026

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

OSReward is introduced, a realistic, high-quality benchmark that evaluates VLM judges on CUA trajectories, and an open corpus of reasoning-annotated trajectory judgments for the CUA community, to close the gap in reliable CUA reward at scale.

Qiushi Sun, Kanzhi Cheng, Yian Wang et al. · 1 citation · ⚡1
#small language model Preprint Aug 2026

Apodex 1.1: Scaling Agentic Intelligence for Complex Work

Apodex 1.1 reaches the leading performance band despite using a substantially smaller model than many frontier systems, and the 35B-parameter Apodex 1.1 Mini further retains strong working capability in a locally deployable form.

B. An, B. Li, B. Wang et al. · 3 citations · ⚡1
#small language model Preprint Aug 2026

Apodex 1.1: Scaling Agentic Intelligence for Complex Work

General-purpose language models can reason and synthesize knowledge, but complex work also requires sustained interaction with files, information sources, and executable code, together with state maintenance, failure recovery, and verifiable delivery. We call this \emph{working capability}: sustained, verifiable progre...

Apodex Team B. An, B. Li, B. Wang et al. · 0 citations

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