Multimodal Alignment and Preference Optimization for Zero-Shot Conditional RNA Generation

ArXi:2605.23961v1 Announce Type: cross The design of RNA molecules that interact with specific proteins is a critical challenge in experimental and computational biology. Despite recent progress in natural language modeling and deep learning-based protein design, there remains significant room to improve the frequency of successful interactions and the authenticity of generated sequences for functional applications. In this work, we frame conditional RNA sequence generation as a multi-stage alignment problem.