Composing Co-Folding Models with Feynman–Kac Correctors

Feynman–Kac correctors (FKCs) compose pretrained diffusion models by simulating the sum of their scores and correcting the samples with importance weights and sequential Monte Carlo resampling. Diffusion-based all-atom structure predictors are natural candidates for such composition: no single predictor is reliably best, and pooling their outputs helps sampling more than selection because confidence scores are not comparable across models. We introduce FKCFold, which adapts FKCs to independently trained co-folding models that differ in architecture and tokenization but share the same all-atom denoiser parameterization. After each model encodes the complex with its own trunk, a single sampler evolves a population of candidate structures: at every step, all models denoise the same noisy structure, the sampler follows their normalized product of experts, and Feynman–Kac weights penalize disagreement between models. A common all-atom coordinate space, per-step rigid alignment of each prediction, native noise ranges for each model, and a correction adapted to the shared AlphaFold 3-style sampler make this composition well posed. FKCFold requires no training. Across multiple benchmarks, FKCFold combining Protenix-v2, OpenDDE and ESMFold2 consistently matches or outperforms each of the models it combines, with the largest gains on antibody–antigen complexes.