KnitNet: Learning to Search at Inference Time for Exact RNA Inverse Design

RNA inverse design seeks nucleotide sequences that fold into specified target structures. Existing methods rely on learned generators that can miss exact structural matches, especially beyond their training distributions, or on costly search over a vast, multimodal sequence space. Search is further complicated by globally coupled nucleotide interactions, as correcting one pairing error can create new errors beyond a fixed repair scope. To address this, we introduce KnitNet, an inference-time scaling framework that learns from verifier feedback to refine sequences for exact fold-back matching. Starting from candidates generated for a target secondary structure, KnitNet uses mismatched folds to infer nucleotide dependencies and adapt its joint repair strategy, while an online surrogate learns to predict which edits are promising. This makes search progressively more informed and compute-efficient. Crucially, KnitNet generalizes across RNA structural regimes by changing only the optimization signal to match the verifier required by each setting. Across five secondary-structure benchmarks, KnitNet increases exact solve rates by an average of 21 percentage points over continued sampling with 70% less time-to-solve. On OpenKnot57 specifically, KnitNet is the first method to achieve exact computational matches for all complex, pseudoknotted structures. Beyond 2D, KnitNet improves 3D structural self-consistency over per-target RL by 7.8% while being 39.3% faster, demonstrating generalization and effective inference-time scaling across RNA design regimes.