← Back to all news
arXiv Machine Learning By Pengan Chen, Liang Hong, Xinyuan Liu, Han Huang, Ziqian Lin, Jiayang Chen, Yimin Fan, Jiyue Jiang, Yu Li

Scaling RNA Foundation Models through Iterative Refinement

Read the original on arXiv Machine Learning →

Eco‑RNA is an 80‑million‑parameter RNA foundation model that introduces adjustable inference depth via learned iterative refinement. The shared, input‑conditioned Transformer core repeatedly refines sequence representations, with the number of iterations chosen after training to control effective depth. On the RNAGym fitness benchmark, increasing iterations from one to four improves Spearman correlation from 0.242 to 0.286, and at four iterations Eco‑RNA outperforms the 40‑billion‑parameter Evo 2 model across all aggregate fitness metrics, while also excelling in non‑coding RNA classification, secondary‑structure prediction, and chemical probing profile prediction.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
Oct 5

RNADyn: A Benchmark for Generating and Understanding RNA Dynamics

RNADynBench is a new standardized benchmark for RNA molecular dynamics, comprising 2,585 high‑quality 100‑nanosecond all‑atom trajectories with carefully controlled data splits. RNADynNet is a unified model that simultaneously generates RNA trajectories and extracts dynamics fingerprints from a single conformer, leveraging coordinate denoising, alignment, and physical grounding. The model achieves strong agreement with MD data, achieving RMSF correlations of 0.875 and 0.766 on test sets, including a high‑flexibility challenge set.

By Yiming Huang, Lennart Bastian, Hanqun Cao, Luis Vollmers, Tolga Birdal
benchmarkssafety
More like this →