arXiv:2608. 13708v1 Announce Type: cross Abstract: Automatically generating textbook-grounded assessment items can reduce science teachers' workload, but existing retrieval-augmented generation (RAG) systems rely on flat retrieval, support only single-question generation, lack safeguards against weak evidence, and are ill-suited to low-resource, board-exam-structured curricula.
By Fatema Tuj Johora Faria, Mukaffi Bin Moin, M. F. Mridha, Jubayer Al Mahmud
arXiv:2607. 16989v1 Announce Type: cross Abstract: Introduction.
By Mohammad Arvan, Amber E. Osterholt, Bailee Rue, Yuvaneswaren Ramakrishnan Sureshbabu, Krishna Riteshkumar Patel, Rebecca T. Feinstein, Bethany C. Bray, Niranjan S. Karnik
The paper introduces SPRA (Structured Paper Representation for Agents), a comprehensive representation of AI research papers that organizes key research elements—Problems, Contributions, Components, ExperimentSetups, Measures, and Findings—into typed relations for easier querying, traversal, and reuse. SPRA includes paper-level citation relations that capture dependencies, component usage, and comparisons, and it has been instantiated across more than one million AI papers, yielding millions of research units and internal relations. Empirical evaluations show that SPRA improves source-conditioned scientific QA and directed cross-paper reasoning over passage retrieval while significantly reducing input and output token counts, demonstrating its practicality for scientific workflows.
By Yutong Leng, Haojiong Chen, Fukai Shang, Xiaoyang Wang, Mindong Liu, Runyuan Ma, Jiantao Qiu
arXiv:2607. 05682v1 Announce Type: new Abstract: LLM systems for scientific discovery increasingly assist with ideation, literature synthesis, experiment planning, and report generation, but the first research question they propose can remain difficult to audit: it may sound plausible without exposing the mechanism, falsifier, or assumption that a scientist should inspect.
By Yufeng Wang
arXiv:2605.29522v2 Announce Type: replace
Abstract: As scientific literature grows rapidly and research increasingly involves AI agents, automated survey generation has become a key capability for bo...
By Ziyue Yang, Da Ma, Hanqi Li, Zijian Wang, Tiancheng Huang, Zijian Hu, Chenrun Wang, Yunzhe Zhang, Xiaobao Wu, Kai Yu, Lu Chen
arXiv:2607. 14178v1 Announce Type: new Abstract: Recent advances in Large Language Models have fueled autonomous AI agents capable of tackling complex scientific tasks, yet existing automated research systems remain predominantly focused on empirically driven domains with quantitative benchmarks, leaving theory-driven discovery, particularly in mathematically grounded disciplines requiring rigorous proofs and synthesis of domain knowledge, largely underexplored.
By Yutong He, Daibo Li, Guohong Li, Jiahe Geng, Zhengyang Huang, Can Ren, Zekun Zhang, Yifan Liu, Shuchen Zhu, Hengrui Zhang, Boao Kong, Ming Sun, Shu Li, Chenyi Li, Jiang Hu, Kun Yuan, Zaiwen Wen, Pingwen Zhang
Sciπvot is a 248‑case benchmark that evaluates how well large language model (LLM) agents perform scientific judgment during autonomous research. The benchmark is built from six months of real‑world experiments where senior researchers ran LLM agents on 26 projects across six scientific fields and repeatedly corrected the agents’ decisions. In each case, the agent must choose between its original plan and a human‑corrected alternative using only the project record available before the correction, allowing a direct comparison of LLM and human performance under varying reasoning effort and record access.
By Youran Sun, Xingyu Ren, Chugang Yi, Kejia Zhang, Di Wu, Yingkang Cao, Allen Zhou, Jianping Ye, Suying Liu, Jiaxuan Guo, Jianda Du, Weicheng Ye, Kecheng Liu, Hefan Zhang, Haixin Wang, Jie Xiao, Eric Yang, Haizhao Yang
arXiv:2609.21841v1 Announce Type: new
Abstract: Frontier language models now produce professional deliverables that expert graders judge to match human work on a substantial share of economically val...
By Abbas Raza Ali, Muhammad Ajmal Siddiqui, Moona Zahid
EduAgentQG is a multi‑agent framework for generating personalized mathematics questions that explicitly controls diversity and aligns with educational objectives. It operates through a closed‑loop cycle of planning, writing, evaluation, refinement, and checking, using fine‑grained evaluation to ensure logical correctness, solvability, and alignment with knowledge concepts, difficulty, grade level, and core competencies. The authors built a benchmark of 10,273 questions across Grades 1‑9 and demonstrated that EduAgentQG outperforms existing methods in diversity, objective consistency, and win rate.
By Rui Jia, Min Zhang, Fengrui Liu, Bo Jiang, Kun Kuang, Zhongxiang Dai
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
Autonomous scientific research agents are increasingly applied to end-to-end scientific workflows, including literature review, data analysis, experimentation, and report generation. However, open-end...
arXiv:2610.32441v1 Announce Type: new
Abstract: Large language model (LLM) agents are increasingly capable of supporting scientific workflows, but their capabilities across diverse research activitie...
By Tianyun Zhong, Wei Wang, Jialin Li, Jianhong Tu, Liyao Li, Chuqiao Kuang, Tianshi Zheng, Songchi Zhou, Weiqi Zhai, Qile Jin, Jiajia Li, Jiasheng Zheng, Boxi Cao, Yaojie Lu, Hongyu Lin, Shiwei Ye, Bing Zhao, Hu Wei, Lin Qu, Xianpei Han