Sequential screening policies must use acquired feedback effectively under a fixed experiment budget.
We study acquisition-time residual adaptation: a small ranking correction built from fixed historical features and anchored to base scores recorded before feedback is revealed.
The interface accepts a history-conditioned scorer and a candidate feature table; its primary instantiation augments AssayFormer's ten-dimensional representation with 64 historical directions while retaining the pretrained model.
In Retrospective Evaluation
In retrospective evaluation on 20 official-test screens from 15 recorded sources, a comparison fixed before target-outcome access increased source-macro fraction of hits recovered from 22.58% to 28.61%, exceeding all six prespecified comparators.
A same-feature ordinary ranking head achieved 25.73%. Supplementary strong logistic, rank-fusion, and unprojected controls achieved 26.42%, 26.02%, and 26.78%, respectively.
A Separate Model-Independent Pilot
A separate model-independent pilot on 13 reused development screens reconstructs the features without AssayFormer assets.
Across three seeds, residual adaptation improves random-forest hit recovery from 28.87% to 31.27%, while logistic recovery changes from 30.22% to 29.78%.
The results show effective pretrained-policy adaptation and useful transfer to an independent nonlinear base, with gains that depend on the base model.