Virtual Scientists V2 serves as a modular AI agent framework tailored for scientific research. It defines multiple virtual scientists—Chemist, Physicist, Biologist, and Data Scientist—each equipped with domain-specific knowledge and tool integrations. These agents utilize LangChain to orchestrate API calls to sources like Semantic Scholar, ArXiv, and web search, enabling automated literature retrieval, contextual analysis, and data extraction. Users script tasks by specifying research objectives; agents autonomously gather papers, summarize methodologies and results, propose experimental protocols, generate hypotheses, and produce structured reports. The framework supports plugins for custom tools and workflows, promoting extensibility. By automating repetitive research tasks, Virtual Scientists V2 accelerates insight generation and reduces manual effort across multidisciplinary projects.

arXiv Viz provides a visual method for exploring and understanding the comprehensive database of research papers available on arXiv. This tool aims to make it easier for researchers, students, and enthusiasts to quickly grasp the key ideas, trends, and connections in academic literature across various fields such as physics, mathematics, computer science, and more. By transforming the traditional text-based search and browsing experience into an intuitive visual interface, arXiv Viz enhances the way users interact with and comprehend scholarly articles.