A Reasoning-Informed Verifier-Guided Editing Agent for RNA Inverse Folding

RNA function follows from RNA structure, so designing sequences that reliably fold into a prescribed shape is central to RNA therapeutics and synthetic biology. Inverse folding is difficult because the sequence-to-structure map is highly degenerate and rugged, candidate quality is observable only after folding, and optimizing against a single folding oracle invites overfitting. Existing designers emit a sequence in one open-loop pass, provide no rationale for individual nucleotide choices, and accept candidates through hand-tuned thresholds. We present RIVET, which recasts RNA inverse folding as verifier-guided, model-based reinforcement learning over interpretable edits. Instead of emitting a sequence directly, RIVET repairs one through a propose, imagine, select, verify, and refold loop built from four modules. SKETCH is a reasoning policy that proposes typed, biophysically grounded edits with explicit rationales and is trained by group-relative policy optimization. ECHO is a learned world model that ranks edits without folding. SCOUT forwards only the most promising edits to costly verification. GAUGE is an energy-based verifier that turns folding feasibility into a calibrated dense reward and conformal accept-reject gate. Two guarantees make the loop safe: potential-based shaping provably cannot bias the design objective, and the gate bounds false acceptance at a chosen rate. The same loop supports tertiary-structure design, secondary self-consistency, and binding-conditioned design for RNA-ligand and RNA-RBP targets. Under a multi-tool protocol whose reporting predictors are disjoint from the loop-internal folder, RIVET attains state-of-the-art native recovery of 56.3%, the best tertiary and secondary self-consistency, and generalizes robustly to held-out RNA-Puzzles and CASP15 targets. Our code is available at https://anonymous.4open.science/r/RIVET-4517/.