3 citations · 6 across the 24 of their papers we have counts for
30 papers
GRAM: Correcting Frozen Time-Series Foundation Models via Graph-Retrieved Amplitude Memory
Xiaoyun Yu, Xiangfei Qiu, Yonggui Huang +7
Time-series foundation models (TSFMs) enable zero-shot forecasting through large-scale cross-domain pretraining, while retrieval augmentation further improves their performance by…
SymbolicLM: Training Language Models as Symbolic Regressors
Jun Yao, Yingfan Hua, Ruikun Li +4
Large Language Models (LLMs) have shown promising capabilities in scientific reasoning, yet scientific discovery ultimately requires deriving precise laws directly from observation…
SciForge: An AI-Native, Multimodal Workbench for Scientific Discovery
SciForge Team, Zhangyang Gao, Minghao Fang +10
Scientific work increasingly spans heterogeneous artifacts -- papers, code, datasets, scientific file formats, model outputs, figures, manuscripts, and team decisions -- yet genera…
Trans2Occ: Voxel Occupancy Estimation and Grasp for Transparent Objects from Simulation to Reality
Yixuan Yang, Sha Zhang, Rui Li +12
Transparent objects remain challenging for robotic perception due to unreliable depth sensing caused by refraction and reflection. While prior approaches rely on multi-view reconst…
Owl-AuraID 1.0: An Intelligent System for Autonomous Scientific Instrumentation and Scientific Data Analysis
Han Deng, Anqi Zou, Hanling Zhang +14
Scientific discovery increasingly depends on high-throughput characterization, yet automation is hindered by proprietary GUIs and the limited generalizability of existing API-based…
Charting Empirical Laws for LLM Fine-Tuning in Scientific Multi-Discipline Learning
Lintao Wang, Zhuqiang Lu, Yilin Zhu +6
While large language models (LLMs) have achieved strong performance through fine-tuning within individual scientific domains, their learning dynamics in multi-disciplinary contexts…
SciDataCopilot: An Agentic Data Preparation Framework for AGI-driven Scientific Discovery
Jiyong Rao, Yicheng Qiu, Jiahui Zhang +29
The current landscape of AI for Science (AI4S) is predominantly anchored in large-scale textual corpora, where generative AI systems excel at hypothesis generation, literature sear…
NAS-LoRA: Empowering Parameter-Efficient Fine-Tuning for Visual Foundation Models with Searchable Adaptation
Renqi Chen, Haoyang Su, Shixiang Tang
The Segment Anything Model (SAM) has emerged as a powerful visual foundation model for image segmentation. However, adapting SAM to specific downstream tasks, such as medical and a…
ARCHE: A Novel Task to Evaluate LLMs on Latent Reasoning Chain Extraction
Pengze Li, Jiaqi Liu, Junchi Yu +5
Large language models (LLMs) are increasingly used in scientific domains. While they can produce reasoning-like content via methods such as chain-of-thought prompting, these output…
Finetuning Large Language Model as an Effective Symbolic Regressor
Yingfan Hua, Ruikun Li, Jun Yao +5
Deriving governing equations from observational data, known as Symbolic Regression (SR), is a cornerstone of scientific discovery. Large Language Models, (LLMs) have shown promise…
MicroRCA-Agent: Microservice Root Cause Analysis Method Based on Large Language Model Agents
Pan Tang, Shixiang Tang, Huanqi Pu +2
This paper presents MicroRCA-Agent, an innovative solution for microservice root cause analysis based on large language model agents, which constructs an intelligent fault root cau…
Interleaving Reasoning for Better Text-to-Image Generation
Wenxuan Huang, Shuang Chen, Zheyong Xie +15
Unified multimodal understanding and generation models recently have achieve significant improvement in image generation capability, yet a large gap remains in instruction followin…
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…
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…
Dynamic Knowledge Exchange and Dual-diversity Review: Concisely Unleashing the Potential of a Multi-Agent Research Team
Weilun Yu, Shixiang Tang, Yonggui Huang +7
Scientific progress increasingly relies on effective collaboration among researchers, a dynamic that large language models (LLMs) have only begun to emulate. While recent LLM-based…
Position: Intelligent Science Laboratory Requires the Integration of Cognitive and Embodied AI
Sha Zhang, Suorong Yang, Tong Xie +18
Scientific discovery has long been constrained by human limitations in expertise, physical capability, and sleep cycles. The recent rise of AI scientists and automated laboratories…
PhysUniBench: A Multi-Modal Physics Reasoning Benchmark at Undergraduate Level
Lintao Wang, Encheng Su, Jiaqi Liu +11
Physics problem-solving is a challenging domain for AI models, requiring integration of conceptual understanding, mathematical reasoning, and interpretation of physical diagrams. E…
Scientists' First Exam: Probing Cognitive Abilities of MLLM via Perception, Understanding, and Reasoning
Yuhao Zhou, Yiheng Wang, Xuming He +26
Scientific discoveries increasingly rely on complex multimodal reasoning based on information-intensive scientific data and domain-specific expertise. Empowered by expert-level sci…
AI-Driven Automation Can Become the Foundation of Next-Era Science of Science Research
Renqi Chen, Haoyang Su, Shixiang Tang +7
The Science of Science (SoS) explores the mechanisms underlying scientific discovery, and offers valuable insights for enhancing scientific efficiency and fostering innovation. Tra…
MLLM-based Discovery of Intrinsic Coordinates and Governing Equations from High-Dimensional Data
Ruikun Li, Yan Lu, Shixiang Tang +2
Discovering governing equations from scientific data is crucial for understanding the evolution of systems, and is typically framed as a search problem within a candidate equation…
Adept: Annotation-Denoising Auxiliary Tasks with Discrete Cosine Transform Map and Keypoint for Human-Centric Pretraining
Weizhen He, Yunfeng Yan, Shixiang Tang +4
Human-centric perception is the core of diverse computer vision tasks and has been a long-standing research focus. However, previous research studied these human-centric tasks indi…
CMT: A Cascade MAR with Topology Predictor for Multimodal Conditional CAD Generation
Jianyu Wu, Yizhou Wang, Xiangyu Yue +5
While accurate and user-friendly Computer-Aided Design (CAD) is crucial for industrial design and manufacturing, existing methods still struggle to achieve this due to their over-s…