Go to ICLR 2025 Workshop Proposals homepageTowards Agentic AI for Science: Hypothesis Generation, Comprehension, Quantification, and Validation
AI models for science have the potential to harness large datasets, accelerate scientific discoveries and transform numerous fields. Through this workshop, our mission is to foster interdisciplinary collaboration to develop fully autonomous AI systems, addressing challenges like benchmark datasets, human-AI collaboration, robust tools and methods for validating AI outputs, and trustworthiness. By tackling these issues, we can unlock AI's transformative potential in research. In this workshop, themed Agentic AI for Science, we will explore these critical topics and welcome diverse perspectives. We will focus on integrating agentic AI systems to enhance scientific discovery while upholding rigorous standards. For AI to contribute effectively, it must generate novel hypotheses, comprehend their applications, quantify testing resources, and validate feasibility through well-designed experiments. This workshop serves as a vital forum for collaboration and knowledge-sharing aimed at redefining the landscape of scientific discovery.