EVA

EVA, short for Evolutionary Versatile Architect, is a generative RNA foundation model built for full-length RNA modeling and controllable RNA design. It learns from OpenRNA v1, a curated atlas of 114 million full-length RNA sequences across domains of life, and uses a 1.4B-parameter decoder-only Transformer with a Mixture-of-Experts backbone and an 8,192-token context window.

The model unifies RNA sequence scoring and generation in one framework. Causal language modeling supports de novo design and continuation, while general language modeling supports span infilling and domain redesign. EVA can condition generation on RNA type and taxonomic lineage, making it useful for designing mRNA, tRNA, rRNA, lncRNA, miRNA, circRNA, viral RNA, and other RNA classes.

What It Enables

• Full-length RNA sequence modeling without aggressive truncation.
• Zero-shot RNA fitness prediction and log-likelihood scoring.
• Controllable generation by RNA type, species, or lineage.
• De novo RNA design, domain redesign, and in-silico directed evolution.
• Sequence-to-structure-aware optimization for practical RNA engineering tasks.

My Role

• Training evaluation. Evaluated model training outcomes to diagnose performance limitations and identify priorities for further optimization.
• Benchmark design. Developed evaluation metrics and benchmark protocols to address limitations in existing benchmarks, capture previously underrepresented model strengths, and enable fairer assessment of core capabilities.
• Comparative evaluation at scale. Conducted systematic comparisons with prior state-of-the-art RNA foundation models, drawing on our corpus of 114 million full-length RNA sequences to evaluate their performance at scale.