A trained experiment-design policy learns to take advice at deployment: Imbue turns expert priors and past experiments into droppable input tokens
Imbue lets an already-trained Bayesian experimental design policy take in external knowledge available only at deployment without retraining: user-specified priors and observations from previous instances of the experiment are encoded as extra input tokens, processed in-context with the accumulated observations by a shared Transformer, and a reliability filter scores each token against those observations and excludes inconsistent ones. Across four benchmarks it accelerates early-stage information acquisition when the external knowledge is reliable and stays close to the no-external baseline when it is not.