# Bayesian Experimental Design

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.

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.

For debris fragments too small to catalog, radar detection is coupled to a Bayesian population model that picks the next elevation after each observation. Across 300 simulated worlds with 24 observations each, service-weighted adaptive pointing left 41.2% less uncertainty than fixed pointing, 15.4% less than random pointing and 5.35% less than a preplanned multi-angle schedule; service weighting itself added only 1.55% over unweighted adaptation, with a 95% interval spanning zero.

For debris fragments too small to catalog, radar detection is coupled to a Bayesian population model that picks the next elevation after each observation. Across 300 simulated worlds with 24 observations each, service-weighted adaptive pointing left 41.2% less uncertainty than fixed pointing, 15.4% less than random pointing and 5.35% less than a preplanned multi-angle schedule; service weighting itself added only 1.55% over unweighted adaptation, with a 95% interval spanning zero.