Large language models (LLMs) and agentic workflows are revolutionising science, driving advances in literature discovery, data analysis, coding, and cross-domain synthesis. This session explores both the opportunities and the challenges of applying these tools where LLMs are rapidly becoming essential research instruments.
We invite discussions on safety, integrity, and governance. Key themes include specialised guardrails against hallucinations and bias, rigorous evaluation with uncertainty quantification, reproducibility, data governance, and human-in-the-loop approaches. Presentations addressing ethical, fairness, and responsible AI practices are also encouraged. Equally important are contributions showcasing real-world applications such as taxonomy, data curation, data discovery, data analysis, forecasting, and disaster response. Domain-adapted models and end to end systems are particularly welcome.
By combining case studies with lessons learned, this session fosters collaboration between AI researchers and domain scientists, offering best practices for integrating LLMs into broader scientific decision-making tasks.