IMBUE enables amortized Bayesian experimental design to incorporate external deployment knowledge via in-context tokens and a reliability filter, accelerating early information gain with reliable inputs while maintaining baseline performance otherwise.
Cross-model frozen-memory extraction shows target-aligned readers matter more than frozen tables, with dual-layer readers nearly closing reuse gaps and compatible interfaces enabling direct utility.
FICBO pretrains a feedback-aware transformer to condition on both optimization history and unreliable auxiliary feedback, estimating source reliability in context to improve black-box query selection.