arXiv:2606. 01042v1 Announce Type: cross Abstract: Perturbation experiments are central to understanding cellular mechanisms, but remain costly and sparse, motivating prediction of gene expression responses for unobserved conditions.
By Xinyu Yuan, Xixian Liu, Jianan Zhao, Yashi Zhang, Hongyu Guo, Jian Tang
arXiv:2608. 16419v1 Announce Type: cross Abstract: Large language models can describe mechanisms, yet scalable post-training still depends on costly, manually curated biological reasoning traces.
By Zhenchao Tang, Xiaogang Xu, Tianxu Lv, Jiahui Guan, Jiale Zhou, Haohuai He, Zhi Song, Hanbo Huang, Jiehui Huang, Jiafei Wu, Zhe Liu
OmniVCBench is a figure‑centric, source‑traceable benchmark designed to evaluate the interpretation component of Artificial Intelligence Virtual Cells (AIVCs). It comprises 6,077 curated question–answer pairs drawn from scientific figures and experimental contexts, organized into three scientific reasoning tasks that mirror the AIVC Predict–Explain–Discover agenda. The benchmark also introduces AIVC‑Judge, a task‑conditioned MLLM‑as‑a‑judge framework with reference‑aware rubrics, and a Model‑Derived Hard‑Negative Mining strategy to generate multiple‑choice distractors for efficient evaluation.
By Manyu Li, Xunkai Li, Yongfu Xiong, Yi Liu, Rong-Hua Li, Guoren Wang
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
arXiv:2608. 12036v1 Announce Type: new Abstract: AI models have achieved remarkable success across diverse domains, yet the mechanisms underlying their capabilities and the risks they may pose remain poorly understood.
By Mengru Wang, Junfeng Fang, Shuofei Qiao, Zhenqian Xu, Haoming Xu, Haoxiong Wang, Shumin Deng, Linyi Yang, Zhixiang Cui, Xin Xu, Yunzhi Yao, Buqiang Xu, Fei Shen, Haozhe Luo, Yunxiang Wei, Ningyu Zhang, Julian McAuley, Tat Seng Chua, Huajun Chen
arXiv:2607. 16262v1 Announce Type: cross Abstract: The acceleration of automated scientific discovery has been fundamentally bottlenecked by the epistemic gap between the semantic reasoning of large language models (LLMs) and the deterministic physics of mammalian biology.
By Christopher Baker, Tianyu Ren, Karen Rafferty, Hui Wang, Simon McDade