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.
arXiv:2608.22849v2 Announce Type: replace Abstract: Full-length RNAs, particularly messenger RNAs, often exceed the context lengths used to pretrain existing RNA foundation models, limiting complete-...
arXiv:2603. 19636v2 Announce Type: replace Abstract: Accurate RNA structure modeling remains difficult because RNA backbones are highly flexible, non-canonical interactions are prevalent, and experimentally determined 3D structures are comparatively scarce.
arXiv:2609.36885v1 Announce Type: cross Abstract: RNA design aims to identify sequences that fold into specified secondary structures. Existing methods formulate the task as target-specific search or...
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.
arXiv:2609.39644v2 Announce Type: new Abstract: Ribosome profiling (Ribo-seq) measures ribosome distributions along mRNAs, but observed occupancy profiles also contain experiment-specific distortions...
arXiv:2608. 00697v1 Announce Type: cross Abstract: Variational autoencoders (VAEs) trained on multiple sequence alignments (MSAs) have emerged as powerful generative models for biological sequences, with applications ranging from disease variant prediction to functional RNA design.
MORAD is an online reinforcement learning framework that trains a single shared policy for backbone‑conditioned RNA diffusion models, enabling structure‑conditioned RNA inverse design across diverse target backbones. It integrates backbone‑conditioned group sampling, group‑relative reward normalization, and a forward‑process diffusion objective, jointly optimizing six measurements—including tertiary geometry, base pairing, ensemble behavior, and sequence composition—by mapping each to a bounded desirability score and combining them with a weighted geometric mean. Compared to a tertiary‑structure‑only reward, MORAD improves 10 of 11 evaluation metrics, achieving significant gains in GDT‑TS, pairing MCC, and normalized ensemble defect, and surpasses existing methods such as RiboDiffusion, gRNAde, and RDesign on tertiary structural accuracy, secondary‑structure pairing, and thermodynamic quality.
La‑Ribo is a generative framework that jointly designs RNA nucleotide sequences and their three‑dimensional structures using geometry‑latent flow matching. It preserves a sparse phosphate‑sugar‑base scaffold while encoding nucleotide identity and local conformation in residue‑wise latent variables, which a shared flow network generates before an RNA‑specific decoder reconstructs all heavy atoms. The authors also built a large, quality‑controlled corpus of 168,561 RNA structures—combining experimental data with predictions from three folding models—to expand supervision, and demonstrate that La‑Ribo outperforms baselines in designability and co‑designability across sampling budgets and refolding models, while also enabling scaffold‑conditioned inverse folding without extra training.
arXiv:2608. 15062v1 Announce Type: cross Abstract: Scaling transformer language models creates an inherent tension between expressivity and memory efficiency.
arXiv:2605.01513v2 Announce Type: replace Abstract: Designing therapeutic messenger RNA (mRNA) requires generating full-length transcripts that optimally balance intracellular stability and translati...
arXiv:2602. 15253v2 Announce Type: replace Abstract: Neural scaling laws -- power-law relationships between loss, model size, and data -- have been extensively documented for language and vision transformers, yet their existence in single-cell genomics remains largely unexplored.
The paper introduces Gated Recurrent Transformers, a depth‑sharing architecture that brackets a single shared core with fixed prelude and coda blocks and uses a lightweight projection and element‑wise update gate to modulate recurrent updates. This design allows functional specialization across recurrences while reducing memory footprint. Experiments show that, under equal FLOPs or parameter budgets, the recurrent model matches or surpasses deeper GPT‑2 Small baselines, achieving similar or better accuracy with fewer parameters and lower peak decoding memory.