A Survey of Scientific Large Language Models: From Data Foundations to
  Agent Frontiers
Authors: name: Ming Hu, name: Chenglong Ma, name: Wei Li, name: Wanghan Xu, name: Jiamin Wu, name: Jucheng Hu, name: Tianbin Li, name: Guohang Zhuang, name: Jiaqi Liu, name: Yingzhou Lu, name: Ying Chen, name: Chaoyang Zhang, name: Cheng Tan, name: Jie Ying, name: Guocheng Wu, name: Shujian Gao, name: Pengcheng Chen, name: Jiashi Lin, name: Haitao Wu, name: Lulu Chen, name: Fengxiang Wang, name: Yuanyuan Zhang, name: Xiangyu Zhao, name: Feilong Tang, name: Encheng Su, name: Junzhi Ning, name: Xinyao Liu, name: Ye Du, name: Changkai Ji, name: Cheng Tang, name: Huihui Xu, name: Ziyang Chen, name: Ziyan Huang, name: Jiyao Liu, name: Pengfei Jiang, name: Yizhou Wang, name: Chen Tang, name: Jianyu Wu, name: Yuchen Ren, name: Siyuan Yan, name: Zhonghua Wang, name: Zhongxing Xu, name: Shiyan Su, name: Shangquan Sun, name: Runkai Zhao, name: Zhisheng Zhang, name: Yu Liu, name: Fudi Wang, name: Yuanfeng Ji, name: Yanzhou Su, name: Hongming Shan, name: Chunmei Feng, name: Jiahao Xu, name: Jiangtao Yan, name: Wenhao Tang, name: Diping Song, name: Lihao Liu, name: Yanyan Huang, name: Lequan Yu, name: Bin Fu, name: Shujun Wang, name: Xiaomeng Li, name: Xiaowei Hu, name: Yun Gu, name: Ben Fei, name: Zhongying Deng, name: Benyou Wang, name: Yuewen Cao, name: Minjie Shen, name: Haodong Duan, name: Jie Xu, name: Yirong Chen, name: Fang Yan, name: Hongxia Hao, name: Jielan Li, name: Jiajun Du, name: Yanbo Wang, name: Imran Razzak, name: Chi Zhang, name: Lijun Wu, name: Conghui He, name: Zhaohui Lu, name: Jinhai Huang, name: Yihao Liu, name: Fenghua Ling, name: Yuqiang Li, name: Aoran Wang, name: Qihao Zheng, name: Nanqing Dong, name: Tianfan Fu, name: Dongzhan Zhou, name: Yan Lu, name: Wenlong Zhang, name: Jin Ye, name: Jianfei Cai, name: Wanli Ouyang, name: Yu Qiao, name: Zongyuan Ge, name: Shixiang Tang, name: Junjun He, name: Chunfeng Song, name: Lei Bai, name: Bowen Zhou
Organization: Intern Science

Scientific Large Language Models (Sci-LLMs) are transforming how knowledge is
represented, integrated, and applied in scientific research, yet their progress
is shaped by the complex nature of scientific data. This survey presents a
comprehensive, data-centric synthesis that reframes the development of Sci-LLMs
as a co-evolution between models and their underlying data substrate. We
formulate a unified taxonomy of scientific data and a hierarchical model of
scientific knowledge, emphasizing the multimodal, cross-scale, and
domain-specific challenges that differentiate scientific corpora from general
natural language processing datasets. We systematically review recent Sci-LLMs,
from general-purpose foundations to specialized models across diverse
scientific disciplines, alongside an extensive analysis of over 270
pre-/post-training datasets, showing why Sci-LLMs pose distinct demands --
heterogeneous, multi-scale, uncertainty-laden corpora that require
representations preserving domain invariance and enabling cross-modal
reasoning. On evaluation, we examine over 190 benchmark datasets and trace a
shift from static exams toward process- and discovery-oriented assessments with
advanced evaluation protocols. These data-centric analyses highlight persistent
issues in scientific data development and discuss emerging solutions involving
semi-automated annotation pipelines and expert validation. Finally, we outline
a paradigm shift toward closed-loop systems where autonomous agents based on
Sci-LLMs actively experiment, validate, and contribute to a living, evolving
knowledge base. Collectively, this work provides a roadmap for building
trustworthy, continually evolving artificial intelligence (AI) systems that
function as a true partner in accelerating scientific discovery.