SciAgent: Tool-augmented Language Models for Scientific Reasoning
Microsoft（微软公司）；University of California, Santa Barbara（加利福尼亚大学圣巴巴拉分校）；Tsinghua University（清华大学）；Singapore Management University（新加坡管理大学）；Nanyang Technological University（南洋理工大学）
Scientific reasoning poses an excessive challenge for even the most advanced Large Language Models (LLMs). To make this task more practical and solvable for LLMs, we introduce a new task setting named tool-augmented scientific reasoning. This setting supplements LLMs with scalable toolsets, and shifts the focus from pursuing an omniscient problem solver to a proficient tool-user. To facilitate the research of such setting, we construct a tool-augmented training corpus named MathFunc which encompasses over 30,000 samples and roughly 6,000 tools. Building on MathFunc, we develop SciAgent to retrieve, understand and, if necessary, use tools for scientific problem solving. Additionally, we craft a benchmark, SciToolBench, spanning five scientific domains to evaluate LLMs' abilities with tool assistance. Extensive experiments on SciToolBench confirm the effectiveness of SciAgent. Notably, SciAgent-Mistral-7B surpasses other LLMs with the same size by more than 13% in absolute accuracy. Furthermore, SciAgent-DeepMath-7B shows much superior performance than ChatGPT.
SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning
Massachusetts Institute of Technology（麻省理工学院）；Schwarzman College of Computing（计算学院）；Center for Computational Science and Engineering（计算科学与工程中心）；Laboratory for Atomistic and Molecular Mechanics (LAMM)（原子与分子力学实验室）
SciRerankBench: Benchmarking Rerankers Towards Scientific Retrieval-Augmented Generation with LLMs
Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）；University of California, Los Angeles（加州大学洛杉矶分校）；Peking University（北京大学）
Graph Your Way to Inspiration: Integrating Co-Author Graphs with Retrieval-Augmented Generation for Large Language Model Based Scientific Idea Generation
School of Computing Newcastle University（计算学院新castle大学）
MuISQA: Multi-Intent Retrieval-Augmented Generation for Scientific Question Answering
Zhongke Zidong Taichu (Beijing)（中科紫东太初（北京））；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Wuhan AI Research（武汉人工智能研究院）
SparkRA: A Retrieval-Augmented Knowledge Service System Based on Spark Large Language Model
iFLYTEK Research（讯飞研究院）；University of Science and Technology of China（中国科学技术大学）；Harbin Institute of Technology（哈尔滨工业大学）；National Science Library, Chinese Academy of Sciences（中国科学院国家科学图书馆）
SciToolAgent: A Knowledge Graph-Driven Scientific Agent for Multi-Tool Integration
College of Computer Science and Technology（计算机科学与技术学院）；Zhejiang University（浙江大学）；Zhejiang Key Laboratory of Intelligent Manufacturing for Functional Chemicals（功能化学品智能制造重点实验室）；ZJU-Hangzhou Global Scientific and Technological Innovation Center（浙大杭州国际科学技术创新中心）；ZJU-UIUC Institute（浙大UIUC研究院）；The Polytechnic Institute（技术学院）；State Key Laboratory of Ocean Sensing（海洋感知国家重点实验室）
Attention Expansion: Enhancing Keyphrase Extraction from Long Documents with Attention-Augmented Contextualized Embeddings
Institute for Research in Technology, ICAI School of Engineering, Comillas Pontifical University（技术研究所，ICAI工程学院，科米利亚斯宗座大学）；DD-AIM, Senior Machine Learning Researcher（DD-AIM，高级机器学习研究员）
CoAuthorAI: A Human in the Loop System For Scientific Book Writing
Kexin Technology, Beijing（凯欣科技，北京）；Institute for Sustainable Industries and Liveable Cities, Victoria University（可持续产业与宜居城市研究所，维多利亚大学）；School of Information Technology, Deakin University（信息科技学院，德坎大学）
OpenScholar: Synthesizing Scientific Literature with Retrieval-augmented LMs
University of Washington（华盛顿大学）；Allen Institute for AI（艾伦人工智能研究所）；University of Illinois, Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Carnegie Mellon University（卡内基梅隆大学）；Meta；University of North Carolina, Chapel Hill（北卡罗来纳大学教堂山分校）；Stanford University（斯坦福大学）
Benchmarking Retrieval-Augmented Generation for Chemistry
Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校Siebel计算与数据科学学院）；National Library of Medicine, National Institutes of Health（美国国立卫生研究院国家医学图书馆）
MOT-SR: Multi-Objective Tool-Augmented Scientific Equation Discovery with Large Language Models
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of the Chinese Academy of Sciences（中国科学院大学）；School of Advanced Interdisciplinary Sciences（先进交叉科学学院）；School of Astronomy and Space Science, University of the Chinese Academy of Sciences（中国科学院大学天文与空间科学学院）
MeasHalu: Mitigation of Scientific Measurement Hallucinations for Large Language Models with Enhanced Reasoning
Shenzhen Key Laboratory for High Performance Data Mining（深圳高性能数据挖掘重点实验室）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院，中国科学院）；Artificial Intelligence Research Institute, Shenzhen University of Advanced Technology（深圳先进技术大学人工智能研究院）
Shanghai AI Laboratory（上海人工智能实验室）；Harbin Institute of Technology（哈尔滨工业大学）；Fudan University（复旦大学）；CUHK（香港中文大学）；Tsinghua University（清华大学）；CUHK-Shenzhen（香港中文大学深圳校区）；UESTC（电子科技大学）；USTC（中国科学技术大学）
ScIRGen: Synthesize Realistic and Large-Scale RAG Dataset for Scientific Research
The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））；Institute of Tibetan Plateau Research, Chinese Academy of Sciences（中国科学院青藏高原研究所）；Lanzhou University（兰州大学）；College of Resources and Environment, University of Chinese Academy of Sciences（中国科学院大学资源与环境学院）
LLM-Based Scientific Peer Review: Methods, Benchmarks, and Reliability Challenges
L3S Research Center, Leibniz University Hannover（莱布尼茨汉诺威大学L3S研究中心）；Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School（布伦瑞克工业大学与汉诺威医学院彼得·L·赖歇茨医学信息学研究所）；Lower Saxony Center for AI and Causal Methods in Medicine (CAIMed)（下萨克森州医学人工智能与因果方法中心（CAIMed））
Enhancing Agentic Autonomous Scientific Discovery with Vision-Language Model Capabilities
Department of Physics, University of Cambridge, Cambridge, United Kingdom（剑桥大学物理系）；Kavli Institute for Cosmology, University of Cambridge, Cambridge, United Kingdom（剑桥大学卡弗利天文研究所）；Department of Physics and Astronomy, Haverford College, 370 Lancaster Avenue, Haverford, PA 19041, USA（哈弗福德学院物理与天文学系）；Division of Physics, Mathematics and Astronomy, California Institute of Technology, Pasadena, CA 91125, USA（加州理工学院物理、数学与天文学系）；Institute of Astronomy, University of Cambridge, Cambridge, United Kingdom（剑桥大学天文研究所）
Multimodal Deep Learning for Scientific Imaging Interpretation
Science and Technology Division, Corning Incorporated（康宁公司科技事业部）；Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University（康奈尔大学罗伯特·弗雷德里克·史密斯化学与生物分子工程学院）
本文提出多模态深度学习框架 GlassLLaVA，结合论文图文数据与 GPT-4，实现玻璃材料 SEM 图像的类人解释、特征识别和缺陷检测，并建立可对照研究答案的评估指标。
