arXiv:2606. 06546v1 Announce Type: new Abstract: Evaluating large language models (LLMs) for education requires measuring how models teach, not only what they know.
By Tao Liu, Ye Lu, Ruohua Zhang, Siyu Song, Wentao Liu, Aimin Zhou, Hao Hao
arXiv:2604. 26962v3 Announce Type: replace-cross Abstract: Education is one of the most promising real-world applications for Large Language Models (LLMs).
By Bingxi Zhao, Jiahao Zhang, Xubin Ren, Zirui Guo, Tianzhe Chu, Yi Ma, Chao Huang
arXiv:2609.38660v1 Announce Type: cross
Abstract: Long-form subtitle translation requires reasoning over discourse and cultural context spanning episodes or entire series, while maintaining consisten...
By Haibo Jin, Xinjie Li, Najmeh Sadoughi, Yang Liu, Yibo Wang, Zhu Liu, Yuzong Liu
The paper introduces OneMillion-Bench (OMB), a benchmark comprising 400 expert-curated tasks across Law, Finance, Industry, Healthcare, and Natural Science. OMB evaluates language agents on complex, real-world scenarios that require authoritative source retrieval, conflict resolution, domain-specific rule application, and constraint decision-making. The benchmark uses a rubric-based scoring system assessing factual accuracy, logical coherence, practical feasibility, and professional compliance to differentiate agent performance on expert-level problems.
By Yang Liu, Jiaqi Li, Jun Bai, Qianyu Yang, Xiaobo Hu, Tao Peng, Zaiyuan Wang, Ran Tian, Jiayun Dong, Chun Zhang, Zixia Jia, Kaiyuan Chen, Yixin Ren, Yang Liu, Yanglihong Xiao, Lingyue Yin, Tiliang Duan, Ge Zhang, Gang Yao, Hao Chen, Yuan Gong, Jianpeng Jiao, Zilong Zheng
arXiv:2607. 13041v1 Announce Type: cross Abstract: Large Language Model (LLM) based AI educational content generation systems are increasingly being developed, yet no standardised benchmark exists to systematically evaluate them.
By Ravidu Suien Rammuni Silva, Ahmad Lotfi, Isibor Kennedy Ihianle, Golnaz Shahtahmassebi, Jordan J. Bird
AgentVidBench is a new multi‑hop video question‑answering benchmark designed to evaluate spatial, temporal, and causal reasoning in multimodal large language models (MLLMs). Unlike existing tests that focus on simple scene queries or global summaries, AgentVidBench includes step‑by‑step solution traces to assess whether agents gather the necessary evidence to justify their answers. Experiments with 12 MLLMs show limited single‑turn performance, but integrating these models into agentic workflows improves both accuracy and trajectory scores, establishing AgentVidBench as a comprehensive testbed for future research on agentic video understanding.
By Seoyeon An, Hyeonseo Jang, Minsu Kim, Chanho Lee, Younghan Park, Kangwook Lee