1 citations · 1 across the 9 of their papers we have counts for
10 papers
Coupled Harness--Model Evolution for Scientific Equation Discovery
Pengze Li, Jiaquan Zhang, Encheng Su +8
Large language models (LLMs) are increasingly used to discover scientific equations from experimental data. Such a discovery system has two parts that must work together: a library…
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…
DeepResearch Arena: The First Exam of LLMs' Research Abilities via Seminar-Grounded Tasks
Haiyuan Wan, Chen Yang, Junchi Yu +10
Deep research agents have attracted growing attention for their potential to orchestrate multi-stage research workflows, spanning literature synthesis, methodological design, and e…
Mimicking the Physicist's Eye:A VLM-centric Approach for Physics Formula Discovery
Jiaqi Liu, Songning Lai, Pengze Li +12
Automated discovery of physical laws from observational data in the real world is a grand challenge in AI. Current methods, relying on symbolic regression or LLMs, are limited to u…
Intern-S1: A Scientific Multimodal Foundation Model
Lei Bai, Zhongrui Cai, Yuhang Cao +173
In recent years, a plethora of open-source foundation models have emerged, achieving remarkable progress in some widely attended fields, with performance being quite close to that…
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…
From Sequence to Structure: Uncovering Substructure Reasoning in Transformers
Xinnan Dai, Kai Yang, Jay Revolinsky +4
Recent studies suggest that large language models (LLMs) possess the capability to solve graph reasoning tasks. Notably, even when graph structures are embedded within textual desc…
Kolmogorov-Arnold Network for Gene Regulatory Network Inference
Tsz Pan Tong, Aoran Wang, George Panagopoulos +1
Gene regulation is central to understanding cellular processes and development, potentially leading to the discovery of new treatments for diseases and personalized medicine. Infer…
Examining the Source of Defects from a Mechanical Perspective for 3D Anomaly Detection
Hanzhe Liang, Aoran Wang, Jie Zhou +3
In this paper, we explore a novel approach to 3D anomaly detection (AD) that goes beyond merely identifying anomalies based on structural characteristics. Our primary perspective i…
Integrating Optimal Transport and Structural Inference Models for GRN Inference from Single-cell Data
Tsz Pan Tong, Aoran Wang, George Panagopoulos +1
We introduce a novel gene regulatory network (GRN) inference method that integrates optimal transport (OT) with a deep-learning structural inference model. Advances in next-generat…