arXiv:2609.24362v1 Announce Type: new
Abstract: Sandboxed computer environments support multi-step reasoning with tools, executable programs, and persistent files, yet their extension from language m...
By Hexiong Yang, Mingrui Chen, Jie Cao, Ran He
arXiv:2606. 13239v1 Announce Type: cross Abstract: Existing computer-use agents remain fundamentally limited in professional software manipulation: GUI-based agents suffer from fragile visual grounding and long-horizon error accumulation, while API-basedapproaches struggle with heterogeneous protocols and inaccessible commercial interfaces.
By Jiaxin Ai, Tao Hu, Xuemeng Yang, Shu Zou, Hairong Zhang, Daocheng Fu, Yu Yang, Hongbin Zhou, Nianchen Deng, Pinlong Cai, Zhongyuan Wang, Botian Shi, Kaipeng Zhang, Licheng Wen
arXiv:2607. 29586v1 Announce Type: cross Abstract: The Abstraction and Reasoning Corpus (ARC) tests whether a model can infer an unseen transformation from a few input-output examples and apply it to a new grid.
By Binnan Liu, Yechi Ma, Tian Xie, Wei Hua
The paper surveys Multimodal Code Intelligence, focusing on tasks where code is generated, edited, refined, or reasoned about under visually grounded inputs such as screenshots, charts, and videos. It categorizes the field by the role of code—rendered artifact, editable structure, intermediate reasoning trace, or executable tool interface—and organizes benchmarks into four domains: Graphical User Interface, Scientific Visualization, Structured Graphics, and Frontier Tasks and Frameworks. The authors argue that reliable evaluation must include evidence of semantics and interaction beyond visual fidelity, and propose four verification-centered research directions to advance the field toward evidence-grounded executable systems.
By Xuanle Zhao, Qiushi Sun, Jingyu Xiao, Xuexin Liu, Haoyue Yang, Qiaosheng Chen, Xianzhen Luo, Jing Huang, Yufeng Zhong, Lei Chen, Shuai Fu, Zhenlin Wei, Jinhe Bi, Lei Jiang, Haibo Qiu, Siqi Yang, Peng Shi, Jian Hu, Zhixiong Zeng
OmniHarness is a framework that enables generalizable visual generation by learning symbolic policies from verified executions. It abstracts shared procedures and applicability conditions, allowing these policies to be instantiated, adapted, and composed for new tasks while keeping model parameters fixed. The system uses intermediate verification for refinement, self-directed inquiry to generate practice tasks, and continuous feedback to expand capabilities, achieving strong results on multiple benchmarks and outperforming baselines on Creative tasks.
By Xu Xu (Beihang University), Jinxiu Liu (The Chinese University of Hong Kong), Zhangbo Qiao (Beihang University), Jiaxing Lu (Beihang University), Xiangyu Zhang (Beihang University), Yubin Gu (National University of Singapore), Fangwei Ning (Beihang University), Yan Shi (Beihang University)
Code4Scene is a benchmark that evaluates coding agents on constructing and editing 3D scenes in Unreal Engine. It tests agents on two tasks: construction, where they must build a scene from open‑ended language, and editing, where they must recover a target scene from reference images while preserving everything else. The benchmark measures task fulfillment, artifact integrity, and physical validity, revealing that construction and editing performance are correlated but not interchangeable, with agents struggling most with spatial composition and precise edits.
By Xiaokang Ye, Siddhant Hitesh Mantri, Zimeng Chen, Edward Zhang, Zhaoxu Zheng, Yuanheng Li, Yizhao Chen, Tianyang Huang, Lianhui Qin