Open Source
Open-source initiatives are democratizing access to and accelerating advancements in various scientific and technological fields by fostering collaboration and reproducibility. Current research focuses on developing open-source tools and datasets across diverse domains, including AI models (like LLMs and specialized models for tasks such as medical diagnosis or autonomous driving), simulation environments for robotics and network analysis, and benchmarks for evaluating model performance. This open approach significantly benefits the scientific community by enabling wider participation, facilitating rigorous validation, and accelerating the translation of research into practical applications across numerous sectors.
Papers
FinRobot: An Open-Source AI Agent Platform for Financial Applications using Large Language Models
Hongyang Yang, Boyu Zhang, Neng Wang, Cheng Guo, Xiaoli Zhang, Likun Lin, Junlin Wang, Tianyu Zhou, Mao Guan, Runjia Zhang, Christina Dan Wang
SolNet: Open-source deep learning models for photovoltaic power forecasting across the globe
Joris Depoortere, Johan Driesen, Johan Suykens, Hussain Syed Kazmi
ORBIT-Surgical: An Open-Simulation Framework for Learning Surgical Augmented Dexterity
Qinxi Yu, Masoud Moghani, Karthik Dharmarajan, Vincent Schorp, William Chung-Ho Panitch, Jingzhou Liu, Kush Hari, Huang Huang, Mayank Mittal, Ken Goldberg, Animesh Garg
ImplicitAVE: An Open-Source Dataset and Multimodal LLMs Benchmark for Implicit Attribute Value Extraction
Henry Peng Zou, Vinay Samuel, Yue Zhou, Weizhi Zhang, Liancheng Fang, Zihe Song, Philip S. Yu, Cornelia Caragea