Zhipu AI's GLM-5.3 underwent specialized lab training led by scientists, outperforming GPT-6Astra in research benchmarks.
1 hours ago
Beating AI News Flash from Dong察: Deep Principle has released its research model Hydrogen-1.0, trained on Zhipu AI’s GLM-5.3. In OpenAI’s FrontierScience-Research benchmark test, Hydrogen-1.0 scored 38.3 points, outperforming GPT-6 Astra’s 33.3 points, while the original GLM-5.3 only scored 24.4 points. Its training data is unique: Scientific papers typically only present final results, but real research also requires understanding how experiments are designed, where errors occur, and how to adjust next steps. Deep Principle used its own lab’s complete experimental records—including the full process of trials and revisions—to train the model. In practical demonstrations, Hydrogen-1.0 can write its own code and call scientific software to simulate the movement of plastic molecules. If it detects errors in simulation settings midway, it will self-repair and resume running and analyzing results. In another demo, it developed experimental protocols for 12 chemical reactions, and noticed a missing pipette tip replacement step in the virtual lab workflow, immediately revising the process. The team also had it conduct five consecutive rounds of new material screening. Each round determined the next set of trials based on the evaluation results of the previous round. When searching for metal complexes with target electronic properties, the candidate material it ultimately found exceeded both GPT-6 Astra and Kimi-K3 in the metrics set by the team. The model’s weights are open-sourced under the MIT License, but the lab data used for training has not been made public.
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