KRAIG: Knowledge-Guided RNA Adaptive Inference and Generation under Biological and Protocol Uncertainty

RNA therapeutic design requires a sequence of coupled decisions under heterogeneous biological evidence, assay-dependent supervision, and protocol shift: selecting an admissible therapeutic mechanism, generating and prioritizing candidate oligonucleotides, and determining whether a prediction is sufficiently supported to act upon. Existing approaches typically optimize these decisions independently and propagate point predictions even when the evidence required by a downstream model is unavailable. We formulate RNA therapeutic design as compositional selective inference and introduce KRAIG, an end-to-end framework in which every stage communicates an evidence certificate rather than an unconditional decision. KRAIG first converts typed biological evidence into dependence-robust mechanism intervals and returns a certified mechanism, an admissible set, or abstention. This certificate governs a capability-aware design component that activates mechanism-conditioned generation, within-experiment ranking, and conformal candidate selection only when their modality-specific validity, data, and calibration requirements are satisfied. A support-selective protocol component permits residual adaptation only within supported protocol regions and otherwise exactly recovers an invariant predictor. Under controlled experiments with known latent mechanism scores, certification incurs zero selective error when it returns a singleton, whereas forced point-decision baselines err on 15.7–21.9% of near-tie cases. Across three supported therapeutic modalities, constrained decoding increases chemistry-valid generation from 0–88.8% to 100%. Modality-stratified conformal selection attains 0.92 coverage at 0.90 nominal coverage in the adequately powered stratum while withholding unsupported guarantees elsewhere. Learned components are evaluated under a frozen five-seed protocol with leakage-resistant splits and paired controls. By composing certification, generation constraints, calibrated selection, specialist routing, and invariant fallback, KRAIG makes the decision to abstain or defer an operational part of RNA therapeutic design.