Provenance Grounds Trust in Autonomous Science

Summary

Nature Computational Science, Published online: 20 August 2026; doi:10.1038/s43588-026-01035-4 As large language models begin to plan and run experiments on their own, the reflex is to demand that they be interpretable before they are trusted. We argue that what grounds trust in autonomous science is not a view inside the model; it is provenance: a complete, re-openable record of what was reasoned, done and measured that can be audited and set right when it is wrong, making autonomy a corrective for the literature’s biases rather than a threat to rigor. Interpretability remains valuable, but the record is the gateway to trust.