Amortized Bayesian Experimental Design with In-Context Knowledge Conditioning

Key points

IMBUE accepts two forms of external knowledge without retraining, user-specified priors and observations from previous instances, encoded as additional input tokens.
A learned reliability filter scores each external token against the current observations and excludes tokens with a threshold of 0.5 or below from computation.
A naive-concatenation baseline that receives previous instances only at deployment falls below the original policy in both regimes.
On CES with K = 5 relevant instances, IMBUE reached an sPCE of 13.93, and with irrelevant instances it stayed near its K = 0 score of 13.46 at 13.14.
On Location Finding with a strong prior, IMBUE attained ALINE’s final sPCE several steps earlier in the rollout.

External research summaries. These are not HDATF publications or measured product results.