Hugging Face Trending Papers August 7, 2026

bioMoR: Biology-Guided Mixture-of-Recursions for Effective Genomic Learning

Read the original on Hugging Face Trending Papers →

Transformer models for high-dimensional omics analysis process thousands of genes or pathways, although only a subset requires deep computation. Mixture-of-Recursions (MoR) improves efficiency through adaptive token-choice or expert-choice routing.

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 Hugging Face Trending Papers.

arXiv AI

Aug 10

bioMoR: Biology-Guided Mixture-of-Recursions for Effective Genomic Learning

arXiv:2608. 06727v1 Announce Type: new Abstract: Transformer models for high-dimensional omics analysis process thousands of genes or pathways, although only a subset requires deep computation.

By Koushik Howlader, Tirtho Roy, Md Tauhidul Islam, Wei Le

llms rag benchmarks safety

arXiv AI

Jun 4

You Only Train Once: Differentiable Subset Selection for Omics Data

arXiv:2512. 17678v2 Announce Type: replace-cross Abstract: Selecting compact and informative gene subsets from single-cell transcriptomic data is essential for biomarker discovery, improving interpretability, and cost-effective profiling.

By Daphn\'e Chopard, Jorge da Silva Gon\c{c}alves, Irene Cannistraci, Thomas M. Sutter, Julia E. Vogt

efficiency benchmarks safety

arXiv AI

Aug 14

EGRL: Edge generation-guided relation-aware learning for RNA-protein interaction prediction

arXiv:2608. 12906v1 Announce Type: cross Abstract: RNA-Protein Interactions (RPIs) are critical for regulating cellular functions.

By Danyu Li, Ling Zhou, Rubing Huang, Xian Zhong, Bin Zou, Kui Jiang

efficiency benchmarks

arXiv Machine Learning

Sep 22

Tool-Augmented On-Policy Distillation for LLM Domain Adaptation in Sequence-Based Omics Tasks

The paper introduces OmicsBench, a new reasoning benchmark for multi‑omics sequences that includes 1,160 expert‑validated questions across DNA regulation, RNA processing, and protein function tasks, requiring traceable evidence chains. Evaluation of 17 large language models shows that scientific LLMs, while more accurate in classification, often lack valid evidence, suggesting shortcut learning. To address this, the authors propose tool‑augmented on‑policy distillation (TA‑OPD), a post‑training method that improves both evidence grounding and predictive performance across five Qwen3.5 models of varying sizes.

By Jie Ying, Zhefan Wang, Zihong Chen, Zhengqing Li, Jinzhe Li, Gang Li, Jian Liu, Fang Hu, Tao Luo, Zhonghang Yuan, Wanli Ouyang, Stan Z. Li, Fan Yang, Nanqing Dong

llms fine-tuning efficiency benchmarks

arXiv AI

Jul 3

MKGR: Multimodal Knowledge-Graph Representation Learning for Cold-Start Protein-Protein Interaction Prediction

arXiv:2607. 01627v1 Announce Type: cross Abstract: Accurate protein-protein interaction (PPI) prediction is central to functional genomics, disease mechanism discovery, and drug development.

By Wenbo Zhang

rag multimodal benchmarks

arXiv Machine Learning

2d ago

Prototype Guided Post-pretraining for Single-Cell Representation Learning

arXiv:2605.07938v2 Announce Type: replace Abstract: Single-cell representation learning (SCRL) from gene expression data offers a way to uncover the complex regulatory logic underlying cellular funct...

By Sachini Weerasekara, Natasha Darras, Sagar Kamarthi, Colles Price, Jacqueline Isaacs

llms rag fine-tuning

One email a morning, machine-written

Related stories

bioMoR: Biology-Guided Mixture-of-Recursions for Effective Genomic Learning

You Only Train Once: Differentiable Subset Selection for Omics Data

EGRL: Edge generation-guided relation-aware learning for RNA-protein interaction prediction

Tool-Augmented On-Policy Distillation for LLM Domain Adaptation in Sequence-Based Omics Tasks

MKGR: Multimodal Knowledge-Graph Representation Learning for Cold-Start Protein-Protein Interaction Prediction

Prototype Guided Post-pretraining for Single-Cell Representation Learning