Lyra’s subquadratic O(N log N) complexity dramatically reduces memory (125x–2600x less than Evo and ESM-1b) and accelerates inference—up to 239x faster than ESM-1b, processing sequences up to 1M length.

Lyra achieves SOTA results in 6 out of 7 intrinsically disordered protein region tasks, with an average AUC of 0.89, outperforming a ProtT5-based model (avg AUC 0.86). Lyra accomplishes this using only 55K parameters, compared to ProtT5’s 3 billion parameters—a >50,000-fold reduction in model size.

Lyra’s consistently strong performance across different tasks using orders of magnitude fewer parameters allows researchers to spend less time optimizing models and more time generating biological insights.

To unify biological sequence modeling across DNA, RNA, and proteins into a single computational framework, we revisited epistasis—the phenomenon where mutations influence each other—which can be characterized by multilinear polynomials.

🧬 Meet Lyra, a new paradigm for accessible, powerful modeling of biological sequences. Lyra is a lightweight SSM achieving SOTA performance across DNA, RNA, and protein tasks—yet up to 120,000x smaller than foundation models (ESM, Evo). Bonus: you can train it on your Mac.
arxiv.org/abs/2503.16351