JUAN LI · AI4SCIENCE
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Claude made the open-source models that scientists use to predict and design biomolecules faster and more memory-efficient. Claude optimized more than 30 of these models in just under four weeks, speeding them up roughly 4x on average. It also created a low-memory mode that enables the accurate prediction of biomolecular systems larger than 10,000 tokens (amino acids, nucleotides, and atoms from small molecules and ions) on a single NVIDIA GPU node.
ScienceIDE: Turning World's Scientific Codebase into Agent Learnable Environments 摘要 Scientific code repositories encode decades of human knowledge in executable models, methods, and tools. Yet fragmented toolchains, implicit domain conventions, and specialized correctness criteria make this knowledge difficult to convert into reliable learning experience—a challenge we call the scientific experience bottleneck. We introduce ScienceIDE, infrastructure for turning the world’s scientific code into programmable environments for scientific agents. Guided by expert-defined scientific cases and acceptance criteria, agents transform repositories into executable environments that support task generation, execution, and scientific verification. These environments provide a shared foundation for supervised fine-tuning, reinforcement learning, and evaluation. Using verified interaction trajectories, we train PhAI-IDE-72B, PhAI-IDE-9B, and PhAI-IDE-4B. The model family shows gains in held-out scientific-code repair and across selected general-purpose benchmarks in code, reasoning, and knowledge, providing evidence of positive transfer from scientific experience to broader capabilities. ScienceIDE lays the foundation for an integrated workspace for agent learning and scientific practice, making humanity’s scientific software a shared substrate for developing scientific intelligence. 作者 * 共同一作 期刊/会议 引用 BibTeX @article{geng2026scienceide, title={ScienceIDE: Turning World's Scientific Codebase into Agent Learnable Environments}, author={Geng, Hejia and Huang, Zesen and Li, Haoyang and Li, Wenbin and Wu, Koutian and Zhou, Zihan and Pang, Yuanbo and Liu, Weihao and Xu, Zigong and Li, Zhiping and Zhang, Zongzheng and Dong, Chuanfei and Sun, Jiankai and Zheng, Tianzhe and Xie, Fengyu and Ma, Yue and Shi, Yueheng and Wang, Junkai and Xie, Tong and Di, Zonglin and Liu, Xianrong and Gao, Qucheng and Liu, Yimin and Pan, Jiaming and Huang, Sheng and Ma, Xiao-Han and Yuan, Lanqing and Zhu, Zhenlin and Liu, Ziang and Xu, Ziyang and Liang, Kangkai and Xian, Jiayi and Zhao, Zehong and Ma, Taigao and Chang, Fuming and Hu, Yuanzhe and Xu, Liuwei and Tang, Xianzhe and Xie, Jingxu and Zhang, Peijin and Gao, Qiang and Xing, Chengyi and Zhao, Zhe and Wang, Xi and Wang, Leo and Wu, Fang and Xing, Yaopeng and Meng, Xing and Yin, Zhenfei and Wu, Yingcheng and Yang, Ling}, journal={arXiv preprin…
Artificial intelligence (AI) is changing the way biomedical research is conducted by making it possible to integrate diverse types of biological data and tackle questions that were previously difficult to address. At the same time, its growing use also raises important questions about data quality, reliability, interpretability, and how computational predictions should be combined with biological knowledge and experimental validation. In this Voices piece, researchers from diverse disciplines share their perspectives on how AI is advancing their respective fields and highlight both the transformative opportunities and the key challenges that will shape the future of the life sciences.
AI models need to do more than produce correct answers. How they respond matters too: whether they’re helpful, fair, safe, respectful, and responsive to the people using them. For model builders, the challenge is knowing whether those “prosocial” behaviors hold up in practice—and whether evaluations capture how a model behaves when people interact with it in unexpected ways. Northeastern University MS student Soham Padia used Olmo 3 to test whether crowdsourcing an evaluation of prosocial behavior could expose weaknesses that a small research team might miss. Padia had developed an evaluation that measures how strongly text steers a model toward more prosocial responses. Steering Arena turned that evaluation into a sort of game—players submit short text prefixes designed to influence the model, see how strongly each one shifts Olmo 3 in that direction, and compete for the top spot on the leaderboard. Olmo’s openness made the project possible—Padia could see how submitted text changed Olmo 3’s internal activity instead of inferring those effects only from the responses it generated. That access became the foundation for both his evaluation and Steering Arena. From open access to a public challenge Padia chose Olmo 3-32B so he could study prosocial steering in a relatively large model. Through the National Deep Inference Fabric (NDIF), a U.S. National Science Foundation (NSF)-supported platform for experimenting with large open models, he could access Olmo 3-32B remotely without owning the GPUs needed to host it himself. That effort to make advanced AI research more accessible aligns with Ai2’s work with NSF. Through the OMAI project, Ai2 is developing fully open models and infrastructure designed to help more researchers study, reproduce, and build on sophisticated AI systems. "Open weights alone would not have been enough," Padia says. "Olmo documents its data and its post-training, so when I find a prosocial direction inside it I know whether I am looking at something the pretraining put there or something a later fine-tune installed. On most models, that question simply has no answer." Padia’s evaluation uses 135 pairs of contrasting text responses spanning 15 qualities, including empathy, fairness, safety, privacy, and respect. (Each pair starts with the same prompt and contrasts a more prosocial response with a less prosocial one.) By comparing the mode…
Cell-state diversity drives tissue adaptability, repair, and disease resilience, but capturing this complexity is a challenge. Current approaches rely on transcriptional profiling and overlook organelle structure, a key indicator of metabolism and stress. We developed spatial Organellomics (sOrganellomics), an imaging workflow that integrates automated segmentation with machine learning to classify and spatially map cell states from multi-organelle signatures. In liver and pancreas, these signatures distinguished broad cellular classes. In liver, sOrganellomics revealed that zonal position did not fully explain organelle-defined hepatocyte categories. Instead, hepatocytes formed intermixed communities within canonical zones, supporting a refined subzonal diversity model. Nutritional stress reshaped this organization. Intravital imaging linked fasting-induced organelle remodeling with altered mitochondrial membrane potential in vivo, supporting multi-organelle architecture as a structural readout of tissue adaptation.