SENTRY-BD: Bilevel Measurement Design for Cross-Context Perturbation Recovery

We introduce Sentry-BD, a framework that learns which genes to measure by optimizing cross-context perturbation recovery after refitting the predictor. Compact panels could extend perturbation screens across cell states and time points, but useful measurements must predict the endpoint and remain informative alongside the other selected genes. We formulate panel learning as a budget-constrained bilevel problem: a shared gene ordering determines context-grouped measurement weights, an analytic inner model fits the response map, and disjoint query perturbations train the design. An exact gene-utility derivative accounts for decoder refitting; a signed response loss complements magnitude reconstruction. One training run produces nested panels at three budgets. On an independent confirmation cohort of 1,699 primary CD4 perturbations, a 128-gene panel recovers a 10,282-gene endpoint with 1.711% lower MSE than a representation-matched PERSIST adaptation (95% CI: 1.492–1.945%). The reduction is 3.498% over 1,676 K562-to-RPE1 perturbations. Five training seeds and component controls show that learning through query prediction provides the main gain. The framework connects measurement allocation to the response prediction task through a differentiable, computationally compact design objective.