Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2508.21148

A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers

Ming Hu·Chenglong Ma·Wei Li·Wanghan Xu·Jiamin Wu·Jucheng Hu·Tianbin Li·Guohang Zhuang·Jiaqi Liu·Yingzhou Lu·Ying Chen·Chaoyang Zhang·Cheng Tan·Jie Ying·Guocheng Wu·Shujian Gao·Pengcheng Chen·Jiashi Lin·Haitao Wu·Lulu Chen·Fengxiang Wang·Yuanyuan Zhang·Xiangyu Zhao·Feilong Tang·Encheng Su·Junzhi Ning·Xinyao Liu·Ye Du·Changkai Ji·Pengfei Jiang·Cheng Tang·Ziyan Huang·Jiyao Liu·Jiaqi Wei·Yuejin Yang·Xiang Zhang·Guangshuai Wang·Yue Yang·Huihui Xu·Ziyang Chen·Yizhou Wang·Chen Tang·Jianyu Wu·Yuchen Ren·Siyuan Yan·Zhonghua Wang·Zhongxing Xu·Shiyan Su·Shangquan Sun·Runkai Zhao·Zhisheng Zhang·Dingkang Yang·Jinjie Wei·Jiaqi Wang·Jiahao Xu·Jiangtao Yan·Wenhao Tang·Hongze Zhu·Yu Liu·Fudi Wang·Yiqing Shen·Yuanfeng Ji·Yanzhou Su·Tong Xie·Hongming Shan·Chun-Mei Feng·Zhi Hou·Diping Song·Lihao Liu·Yanyan Huang·Lequan Yu·Bin Fu·Shujun Wang·Xiaomeng Li·Xiaowei Hu·Yun Gu·Ben Fei·Benyou Wang·Yuewen Cao·Minjie Shen·Jie Xu·Haodong Duan·Fang Yan·Hongxia Hao·Jielan Li·Jiajun Du·Yanbo Wang·Imran Razzak·Zhongying Deng·Chi Zhang·Lijun Wu·Conghui He·Zhaohui Lu·Jinhai Huang·Wenqi Shao·Yihao Liu·Siqi Luo·Yi Xin·Xiaohong Liu·Fenghua Ling
View original source ↗Read source document ↗Export metadata ↓

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.

Explore related subjects

cs.CLcs.AI

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ming Hu, Chenglong Ma, Wei Li, Wanghan Xu, Jiamin Wu, Jucheng Hu, Tianbin Li, Guohang Zhuang, Jiaqi Liu, Yingzhou Lu, Ying Chen, Chaoyang Zhang, Cheng Tan, Jie Ying, Guocheng Wu, Shujian Gao, Pengcheng Chen, Jiashi Lin, Haitao Wu, Lulu Chen, Fengxiang Wang, Yuanyuan Zhang, Xiangyu Zhao, Feilong Tang, Encheng Su, Junzhi Ning, Xinyao Liu, Ye Du, Changkai Ji, Pengfei Jiang, Cheng Tang, Ziyan Huang, Jiyao Liu, Jiaqi Wei, Yuejin Yang, Xiang Zhang, Guangshuai Wang, Yue Yang, Huihui Xu, Ziyang Chen, Yizhou Wang, Chen Tang, Jianyu Wu, Yuchen Ren, Siyuan Yan, Zhonghua Wang, Zhongxing Xu, Shiyan Su, Shangquan Sun, Runkai Zhao, Zhisheng Zhang, Dingkang Yang, Jinjie Wei, Jiaqi Wang, Jiahao Xu, Jiangtao Yan, Wenhao Tang, Hongze Zhu, Yu Liu, Fudi Wang, Yiqing Shen, Yuanfeng Ji, Yanzhou Su, Tong Xie, Hongming Shan, Chun-Mei Feng, Zhi Hou, Diping Song, Lihao Liu, Yanyan Huang, Lequan Yu, Bin Fu, Shujun Wang, Xiaomeng Li, Xiaowei Hu, Yun Gu, Ben Fei, Benyou Wang, Yuewen Cao, Minjie Shen, Jie Xu, Haodong Duan, Fang Yan, Hongxia Hao, Jielan Li, Jiajun Du, Yanbo Wang, Imran Razzak, Zhongying Deng, Chi Zhang, Lijun Wu, Conghui He, Zhaohui Lu, Jinhai Huang, Wenqi Shao, Yihao Liu, Siqi Luo, Yi Xin, Xiaohong Liu, Fenghua Ling. 2025-10-18. A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers. https://arxiv.org/abs/2508.21148

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

SafetyFlow: An Agent-Flow System for Automated LLM Safety Benchmarking

The rapid proliferation of large language models (LLMs) has intensified the requirement for reliable safety evaluation to uncover model vulnerabilities. To this end, numerous LLM safety evaluation benchmarks are proposed. However, existing benchmarks generally rely on labor-intensive manual curation, which causes excessive time and resource consumption. They also exhibit significant redundancy and limited difficulty. To alleviate these problems, we introduce SafetyFlow, the first agent-flow system designed to automate the construction of LLM safety benchmarks. SafetyFlow can automatically build a comprehensive safety benchmark in only four days without any human intervention by orchestrating seven specialized agents, significantly reducing time and resource cost. Equipped with versatile tools, the agents of SafetyFlow ensure process and cost controllability while integrating human expertise into the automatic pipeline. The final constructed dataset, SafetyFlowBench, contains 23,446 queries with low redundancy and strong discriminative power. Our contribution includes the first fully automated benchmarking pipeline and a comprehensive safety benchmark. We evaluate the safety of 49 advanced LLMs on our dataset and conduct extensive experiments to validate our efficacy and efficiency.

cs.CL↗
arXiv2026

Geometric Uncertainty for Detecting and Correcting Hallucinations in LLMs

Large language models are known to hallucinate, generating linguistically plausible but incorrect answers to questions. Uncertainty quantification has been proposed as a strategy to detect such behaviour, but existing methods lack a unified framework to assess reliability at both the prompt and answer level. We introduce a geometric framework which quantifies language model uncertainty at both levels by explicitly modelling a prompt-conditioned semantic distribution in answer embedding space. Our approach is black-box and sampling-based; we generate multiple answers per prompt, and use archetypal analysis to estimate a geometric support for the answer distribution. At the prompt level, we approximate the distribution entropy to quantify uncertainty; for each individual answer, we then use notions of atypicality to assess its reliability relative to the batch. We employ our framework to not only detect hallucinations but correct them, by selecting the batch example deemed most reliable. Experiments show that our framework performs comparably to or better than prior methods on short form question-answering datasets, and achieves superior results on medical datasets where hallucinations carry particularly critical risks. Beyond pure performance, we suggest the theoretical grounding of our work provides support for semantic distributions as useful objects of study for language model uncertainty.

cs.CL↗
arXiv2026

Calibrated Confidence Expression for Radiology Report Generation

Safe deployment of Large Vision-Language Models (LVLMs) in radiology report generation requires not only accurate predictions but also clinically interpretable indicators of when outputs should be thoroughly reviewed, enabling selective radiologist verification and reducing the risk of hallucinated findings influencing clinical decisions. One intuitive approach to this is verbalized confidence, where the model explicitly states its certainty. However, current state-of-the-art language models are often overconfident, and research on calibration in multimodal settings such as radiology report generation is limited. To address this gap, we introduce ConRad (Confidence Calibration for Radiology Reports), a reinforcement learning framework for fine-tuning medical LVLMs to produce calibrated verbalized confidence estimates alongside radiology reports. We study two settings: a single report-level confidence score and a sentence-level variant assigning a confidence to each claim. Both are trained using the GRPO algorithm with reward functions based on the logarithmic scoring rule, which incentivizes truthful self-assessment by penalizing miscalibration and guarantees optimal calibration under reward maximization. Experimentally, ConRad substantially improves calibration and outperforms competing methods. In a clinical evaluation we show that ConRad's report level scores are well aligned with clinicians' judgment. By highlighting full reports or low-confidence statements for targeted review, ConRad can support safer clinical integration of AI-assistance for report generation.

cs.CL↗