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

1 citations · 1 across the 4 of their papers we have counts for

collaborators

5 papers

cs.AI2026

SciIF: Benchmarking Scientific Instruction Following Towards Rigorous Scientific Intelligence

Encheng Su, Jianyu Wu, Chen Tang +9

As large language models (LLMs) transition from general knowledge retrieval to complex scientific discovery, their evaluation standards must also incorporate the rigorous norms of…

cs.AI2026

SciEvalKit: An Open-source Evaluation Toolkit for Scientific General Intelligence

Yiheng Wang, Yixin Chen, Shuo Li +33

We introduce SciEvalKit, a unified benchmarking toolkit designed to evaluate AI models for science across a broad range of scientific disciplines and task capabilities. Unlike gene…

cs.CL2025

SciReasoner: Laying the Scientific Reasoning Ground Across Disciplines

Yizhou Wang, Chen Tang, Han Deng +29

We present a scientific reasoning foundation model that aligns natural language with heterogeneous scientific representations. The model is pretrained on a 206B-token corpus spanni…

cs.CL20251 cited

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

Ming Hu, Chenglong Ma, Wei Li +117

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 compl…

cs.CV2025

UniSTD: Towards Unified Spatio-Temporal Learning across Diverse Disciplines

Chen Tang, Xinzhu Ma, Encheng Su +6

Traditional spatiotemporal models generally rely on task-specific architectures, which limit their generalizability and scalability across diverse tasks due to domain-specific desi…