A Survey of Scientific Large Language Models: From Data Foundations to
  Agent Frontiers
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.
68b5534b851c6e7b001ecb3f
Ming Hu
68b5534b851c6e7b001ecb40
Chenglong Ma
68b5534b851c6e7b001ecb41
Wei Li
68b5534b851c6e7b001ecb42
Wanghan Xu
claimed_verified
2025-09-01T13:51:06.482Z
65f3f43fc9940817ca9a427b
https://cdn-avatars.huggingface.co/v1/production/uploads/65f3f43fc9940817ca9a427b/02NN3XjSsbgWDhjrJWtVL.jpeg
Wanghan Xu
user
CoCoOne
68b5534b851c6e7b001ecb43
Jiamin Wu
68b5534b851c6e7b001ecb44
Jucheng Hu
68b5534b851c6e7b001ecb45
Tianbin Li
68b5534b851c6e7b001ecb46
Guohang Zhuang
68b5534b851c6e7b001ecb47
Jiaqi Liu
claimed_verified
2025-09-01T13:51:04.066Z
684ff37fa383bc5d6b0ff77f
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/0JPr-cd_rxQz3k6rmzBOF.png
JiaqiLiu
user
JiaaqiLiu
68b5534b851c6e7b001ecb48
Yingzhou Lu
68b5534b851c6e7b001ecb49
Ying Chen
68b5534b851c6e7b001ecb4a
Chaoyang Zhang
68b5534b851c6e7b001ecb4b
Cheng Tan
68b5534b851c6e7b001ecb4c
Jie Ying
claimed_verified
2025-09-02T07:56:40.879Z
667a5f53f922286634baea35
/avatars/c87868159c4e0a4602463e1f2c72639e.svg
jie ying
user
yj12869741
68b5534b851c6e7b001ecb4d
Guocheng Wu
claimed_verified
2025-09-02T07:56:59.392Z
66dedef483b31d8cf886efc5
/avatars/5c8a6918894a377065f4dda46f1cb83c.svg
WUGUOCHENG
user
KUOCHENG
68b5534b851c6e7b001ecb4e
Shujian Gao
68b5534b851c6e7b001ecb4f
Pengcheng Chen
68b5534b851c6e7b001ecb50
Jiashi Lin
68b5534b851c6e7b001ecb51
Haitao Wu
68b5534b851c6e7b001ecb52
Lulu Chen
68b5534b851c6e7b001ecb53
Fengxiang Wang
68b5534b851c6e7b001ecb54
Yuanyuan Zhang
68b5534b851c6e7b001ecb55
Xiangyu Zhao
68b5534b851c6e7b001ecb56
Feilong Tang
68b5534b851c6e7b001ecb57
Encheng Su
claimed_verified
2025-09-02T07:56:48.617Z
6784a79c5ffcc118504ed99c
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/eL4kAIp_oi9Q7si5--uVl.png
Encheng Su
user
EncSU
68b5534b851c6e7b001ecb58
Junzhi Ning
68b5534b851c6e7b001ecb59
Xinyao Liu
68b5534b851c6e7b001ecb5a
Ye Du
68b5534b851c6e7b001ecb5b
Changkai Ji
68b5534b851c6e7b001ecb5c
Cheng Tang
68b5534b851c6e7b001ecb5d
Huihui Xu
68b5534b851c6e7b001ecb5e
Ziyang Chen
68b5534b851c6e7b001ecb5f
Ziyan Huang
68b5534b851c6e7b001ecb60
Jiyao Liu
68b5534b851c6e7b001ecb61
Pengfei Jiang
68b5534b851c6e7b001ecb62
Yizhou Wang
claimed_verified
2025-09-26T12:23:45.264Z
65dc5e84139bc4eee375a839
https://cdn-avatars.huggingface.co/v1/production/uploads/65dc5e84139bc4eee375a839/jXun3fxP3uXoA7TnZ3dGI.jpeg
Yizhou Wang
user
Cohesion98
68b5534b851c6e7b001ecb63
Chen Tang
68b5534b851c6e7b001ecb64
Jianyu Wu
claimed_verified
2025-09-26T12:23:48.677Z
65cd955637be1841d0b75397
/avatars/9f39725cb42d9276899fd58a91468d6a.svg
Jianyu Wu
user
Uanu
68b5534b851c6e7b001ecb65
Yuchen Ren
68b5534b851c6e7b001ecb66
Siyuan Yan
68b5534b851c6e7b001ecb67
Zhonghua Wang
68b5534b851c6e7b001ecb68
Zhongxing Xu
68b5534b851c6e7b001ecb69
Shiyan Su
68b5534b851c6e7b001ecb6a
Shangquan Sun
68b5534b851c6e7b001ecb6b
Runkai Zhao
68b5534b851c6e7b001ecb6c
Zhisheng Zhang
68b5534b851c6e7b001ecb6d
Yu Liu
68b5534b851c6e7b001ecb6e
Fudi Wang
68b5534b851c6e7b001ecb6f
Yuanfeng Ji
68b5534b851c6e7b001ecb70
Yanzhou Su
68b5534b851c6e7b001ecb71
Hongming Shan
68b5534b851c6e7b001ecb72
Chunmei Feng
68b5534b851c6e7b001ecb73
Jiahao Xu
68b5534b851c6e7b001ecb74
Jiangtao Yan
68b5534b851c6e7b001ecb75
Wenhao Tang
claimed_verified
2025-09-16T09:43:40.279Z
649010619852e20da842e84c
/avatars/5016c566d804d1a00fccbba69059e2ef.svg
Wenhao
user
Dearcat
68b5534b851c6e7b001ecb76
Diping Song
68b5534b851c6e7b001ecb77
Lihao Liu
68b5534b851c6e7b001ecb78
Yanyan Huang
68b5534b851c6e7b001ecb79
Lequan Yu
68b5534b851c6e7b001ecb7a
Bin Fu
68b5534b851c6e7b001ecb7b
Shujun Wang
68b5534b851c6e7b001ecb7c
Xiaomeng Li
68b5534b851c6e7b001ecb7d
Xiaowei Hu
68b5534b851c6e7b001ecb7e
Yun Gu
68b5534b851c6e7b001ecb7f
Ben Fei
68b5534b851c6e7b001ecb80
Zhongying Deng
68b5534b851c6e7b001ecb81
Benyou Wang
68b5534b851c6e7b001ecb82
Yuewen Cao
68b5534b851c6e7b001ecb83
Minjie Shen
68b5534b851c6e7b001ecb84
Haodong Duan
68b5534b851c6e7b001ecb85
Jie Xu
68b5534b851c6e7b001ecb86
Yirong Chen
68b5534b851c6e7b001ecb87
Fang Yan
68b5534b851c6e7b001ecb88
Hongxia Hao
68b5534b851c6e7b001ecb89
Jielan Li
68b5534b851c6e7b001ecb8a
Jiajun Du
68b5534b851c6e7b001ecb8b
Yanbo Wang
68b5534b851c6e7b001ecb8c
Imran Razzak
68b5534b851c6e7b001ecb8d
Chi Zhang
68b5534b851c6e7b001ecb8e
Lijun Wu
68b5534b851c6e7b001ecb8f
Conghui He
68b5534b851c6e7b001ecb90
Zhaohui Lu
68b5534b851c6e7b001ecb91
Jinhai Huang
68b5534b851c6e7b001ecb92
Yihao Liu
68b5534b851c6e7b001ecb93
Fenghua Ling
68b5534b851c6e7b001ecb94
Yuqiang Li
68b5534b851c6e7b001ecb95
Aoran Wang
claimed_verified
2025-09-02T07:56:56.867Z
666839475ddf78d3bc5b80f4
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/j4OBskJ_GMS2QGE_nEuEJ.jpeg
Aoran Wang
user
RalfWang
68b5534b851c6e7b001ecb96
Qihao Zheng
68b5534b851c6e7b001ecb97
Nanqing Dong
claimed_verified
2025-09-26T12:23:51.512Z
68385a89ecf59de6ef0510ca
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/6qZ0KijBOhFri0ue3T1pB.jpeg
Nanqing Dong
user
eveningdong
68b5534b851c6e7b001ecb98
Tianfan Fu
68b5534b851c6e7b001ecb99
Dongzhan Zhou
68b5534b851c6e7b001ecb9a
Yan Lu
68b5534b851c6e7b001ecb9b
Wenlong Zhang
68b5534b851c6e7b001ecb9c
Jin Ye
68b5534b851c6e7b001ecb9d
Jianfei Cai
68b5534b851c6e7b001ecb9e
Wanli Ouyang
68b5534b851c6e7b001ecb9f
Yu Qiao
68b5534b851c6e7b001ecba0
Zongyuan Ge
68b5534b851c6e7b001ecba1
Shixiang Tang
68b5534b851c6e7b001ecba2
Junjun He
68b5534b851c6e7b001ecba3
Chunfeng Song
68b5534b851c6e7b001ecba4
Lei Bai
68b5534b851c6e7b001ecba5
Bowen Zhou
A Survey of Scientific Large Language Models: From Data Foundations to
  Agent Frontiers
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.
Sci-LLMs are evolving through a co-development with scientific data, addressing unique challenges like multimodal and domain-specific information, and are moving towards autonomous, closed-loop systems in scientific research.
Scientific Large Language Models Sci-LLMs data-centric synthesis unified taxonomy hierarchical model scientific knowledge multimodal cross-scale domain-specific pre-/post-training datasets heterogeneous multi-scale uncertainty-laden domain invariance cross-modal reasoning benchmark datasets process-oriented assessments semi-automated annotation pipelines expert validation closed-loop systems autonomous agents living knowledge base trustworthy AI scientific discovery