Dual-Track Flow Matching for Generative RNA Base Pair Prediction

RNA molecules can adopt multiple base-pairing states, yet most learning-based predictors return a single structure per sequence. We introduce **DTFlow**, a dual-track flow framework that unifies prediction and generation of sparse binary base-pair matrices (BPMs), including non-canonical interactions. A pretrained sequence track and a pair track exchange information through attention at nucleotide-pair resolution. Two time step variables condition this exchange and, together with the input states, gate prediction, conditional BPM generation or auxiliary masked sequence reconstruction within the same architecture. Prediction and generation share a BPM endpoint readout and objective, enabling predictive training to initialise generation from limited structural supervision. We evaluate this framework with **RNA-MultiBP**, which preserves multiple observed BPMs per sequence and excludes target-related sequence groups from training and validation through sequence-, structure- and family-informed clustering. On 56 CASP and RNA-Puzzles sequences, we assess reference coverage and candidate fidelity by comparing predicted and observed base pairs. DTFlow's single-output predictor improves reference coverage over the RiNALMo baseline by 11.1 percentage points, while approaching RNAfold and ProbKnot. With 16 candidates, its generative model improves coverage over retrained RNADiffFold by 9.6 percentage points at the same candidate count. On the 26 multi-reference sequences, generating 16 candidates improves coverage over the single predictive output by 3.1 percentage points. Conditioning trRosettaRNA2 on DTFlow samples brings predictions of a riboswitch closer to deposited conformers than conditioning on the deposited BPMs themselves. These results demonstrate competitive prediction and improved candidate-set coverage within a unified framework under limited structural supervision and controlled sequence separation. Code is available at [https://anonymous.4open.science/r/DTFlow-3C0D/](https://anonymous.4open.science/r/DTFlow-3C0D/).