Deep Principle发布科学模型Hydrogen-1.0
AI 总结
Deep Principle发布Hydrogen-1.0，这是基于GLM-5.3后训练的开放权重模型，面向化学、材料科学和生命科学。团队使用领域数据、科学家反馈、实验室记录和发现轨迹，通过监督微调与强化学习进行训练，并公开了权重、模型卡、评测和工作流示例。 据厂商自测，Hydrogen-1.0在FrontierScience Research上的通过率为38.3%，高于最大思考模式下GPT-6 Astra的33.3%；其估算输出端成本为每题0.25美元，对比后者的0.88美元。模型在GPQA Diamond上取得92.9%，略低于Kimi K3的93.5%。
深度见解
该发布提供开放权重和详细的科学工作流训练资料，但领先指标与成本优势均来自厂商自测，所选模型加工具演示也不能证明其具备自主实验室研究能力。
| Hey everyone— we’re the Deep Principle team behind Mira AI Scientist. We just released Hydrogen-1.0, an open-weight model post-trained from GLM-5.3 for chemistry, materials science, and life sciences. Here’s how it performed in our evaluations: On separate internal task sets, Hydrogen also scored higher than GPT-6 Astra at max thinking on scientific computing (+5.13 percentage points) and single-step forward reaction prediction (+4.76 points). It trailed Astra on NMR structure elucidation (−3.33 points). The write-up covers the evaluations and five workflow examples. Why we built it If you’ve used LLMs for research, you’ve probably spent a fair amount of time connecting the steps yourself: turning a question into a plan, preparing inputs, running tools, checking the output, and figuring out what to try when something breaks. We wanted to see whether training on scientific workflows could make the model better at handling those connected decisions. How we trained it We used supervised fine-tuning and reinforcement learning with domain data, scientist feedback, records from our own labs, and discovery trajectories. The lab records keep the research goal, procedure, observations, and outcome together. The discovery trajectories include how the next step changes after feedback, failed attempts, or unexpected results. SFT gives the model worked examples of scientific reasoning, experiment planning, protocol design, and analysis. RL uses feedback on research outcomes to improve its decisions. What the workflows look like The article walks through five cases: A bit of context: these are selected model-plus-tool walkthroughs, not an overall autonomous-research success rate. GROMACS does the MD calculations, and the protocol validation is virtual—it doesn’t mean the physical experiment was carried out. The attached video gives a quick look at the research interactions. The article has the detailed walkthroughs, evaluations, and training approach. Links We’d especially like to hear from people working in computational chemistry and materials science. What would you try it on first, and what would you check before trusting the result? submitted by /u/AKAMA199 [link] [comments] |
#Deep Principle#Hydrogen-1.0#GLM-5.3#GPT-6 Astra#Kimi K3#GROMACS#Mira AI Scientist#Z.ai