RNAOmni: An Autonomous Modeling Agent for RNA Downstream Tasks

As a foundational molecule of life, RNA property prediction and functional identification represent a vital frontier at the intersection of AI and biology. However, developing high-performance models for downstream RNA tasks still relies heavily on labor-intensive iterations of model design, validation, and refinement. Given the growing demand for task-specific modeling, domain experts possess rich data but lack engineering bandwidth, while AI researchers have strong modeling capabilities yet struggle to address the diverse domain-specific needs. Existing AutoML methods partially alleviate this burden but remain restricted to hyperparameter tuning within fixed search spaces, and LLM-based agents depend on human-provided blueprints, neither of which achieves full end-to-end automation from data to model. To address these limitations, we introduce RNAOmni, an end-to-end autonomous modeling agent for RNA prediction tasks that mimics expert decision-making by jointly exploring data processing strategies, feature representations, model backbones, and hyperparameters, starting solely from raw data. Furthermore, RNAOmni supports both Direct and Thinking modes, incorporating an attribution probe within the latter to systematically identify modeling weaknesses and guide efficient exploration. Across 17 RNA downstream tasks, RNAOmni achieves average relative improvements of 59.50% over AutoML methods and 18.4% over AI research agents. Its generated solutions consistently surpass domain-specific RNA models and further enhance the utility of RNA foundation models. These results demonstrate that RNAOmni enables a new paradigm for RNA downstream modeling by shifting the field from expert-dependent engineering toward autonomous, data-driven model development. Code will be available upon acceptance.