Sina Shahandeh

This talk explores the application of autonomous agents to complex scientific discovery tasks, moving beyond simpler coding puzzles or optimization problems. It highlights the necessity for agents to engage with real-world measurement data and employ a scientific method, including hypothesis generation, model implementation, and learning from failures. The core thesis is that significant advancements in agent performance for scientific tasks stem from formulating accurate hypotheses about physical systems and correctly implementing them.