AcademiClaw: When Students Set Challenges for AI Agents
Published in arXiv preprint, 2026
AcademiClaw examines the academic-level capabilities of AI agents through tasks contributed by university students, including homework, research projects, competitions, and personal projects that current agents struggle to solve effectively.
Highlights
- Curates 80 complex bilingual tasks from 230 student-submitted candidates.
- Runs tasks in isolated Docker sandboxes with multidimensional scoring rubrics and a safety audit.
- Diagnoses capability boundaries across domains, including tasks that require GPU execution and full-stack debugging.
Recommended citation: Junjie Yu, Pengrui Lu, Weiye Si, Hongliang Lu, Jiabao Wu, Kaiwen Tao, Kun Wang, Lingyu Yang, Qiran Zhang, Xiuting Guo, Xuanyu Wang, Yang Wang, et al. (2026). "AcademiClaw: When Students Set Challenges for AI Agents." arXiv:2605.02661.
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