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
Published: 2025-08-28 18:30:52

Abstract: 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.