Molecular optimization beyond experience summaries: the agent writes and revises causal hypotheses as it experiments, lifting mean AUC@10 across five molecular tasks from 0.250 to 0.279
Large language models can now construct and keep revising scientific principles during a discovery run, rather than leaving that translation to human experts. Doing it by hand needs domain expertise, time and effort, and bottlenecks discovery. Principia compares related inputs and their observed outcomes to write causal hypotheses, clusters similar inputs to induce recurring principles, then revises them with new observations. Across molecular optimization, protein engineering, gene perturbation and molecular property prediction, it beat baselines using internal knowledge, experience summaries or web retrieval; on five molecular optimization tasks it averaged AUC@10 0.279 versus 0.250 for the experience-summary baseline.