Scaling RNA Foundation Models through Iterative Refinement
cs.LG
Submitted: 2026-10-09
Updated: 2026-10-09
Comments: Technical Report. 20 pages, 5 figures, 9 tables
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Recursive Inference Scaling: A Winning Path to Scalable Inference in Language and Multimodal Systems
- Mixture-of-Recursions: Learning Dynamic Recursive Depths for Adaptive Token-Level Computation
- Large Language Monkeys: Scaling Inference Compute with Repeated Sampling
- Interpretable RNA Foundation Model from Unannotated Data for Highly Accurate RNA Structure and Function Predictions
- Scalable Deep Learning for RNA Secondary Structure Prediction
- Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach
- DeepLoop: Depth Scaling for Looped Transformers
- Parcae: Scaling Laws For Stable Looped Language Models
- ncRNA Classification with Graph Convolutional Networks
- GLU Variants Improve Transformer
- Stabilizing Recurrent Dynamics for Test-Time Scalable Latent Reasoning in Looped Language Models
- SpiralFormer: Looped Transformers Can Learn Hierarchical Dependencies via Multi-Resolution Recursion
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