2 papers
cs.CL2026
Logit-KL Flow Matching: Non-Autoregressive Text Generation via Sampling-Hybrid Inference
Egor Sevriugov, Nikita Dragunov, Anton Razzhigaev +2
Non-autoregressive (NAR) language models offer notable efficiency in text generation by circumventing the sequential bottleneck of autoregressive decoding. However, accurately mode…
cs.LG2026
Matcha: Multi-Stage Riemannian Flow Matching for Accurate and Physically Valid Molecular Docking
Daria Frolova, Talgat Daulbaev, Egor Sevriugov +4
Accurate prediction of protein-ligand binding poses is crucial for structure-based drug design, yet existing methods struggle to balance speed, accuracy, and physical plausibility.…