Deep Principle releases Hydrogen-1.0, an open-weight science model post-trained from GLM-5.3
omarsar0 · x · 2026-11-03
Deep Principle introduced Hydrogen-1.0, a scientific discovery model built by post-training GLM-5.3 on domain data from chemistry, materials science, and life sciences, plus lab records and agent trajectories from its own labs.
- The blog cites recent AI-for-science milestones—OpenAI's agent-produced Navier–Stokes proof with Lean formalization and Anthropic's discovery of an enzyme system with CRISPR-like repeats—noting these capabilities remain proprietary with no open weights or public API.
- Hydrogen-1.0 positions itself as the open-weight alternative: researchers can inspect, adapt, and reproduce scientific discovery, building on shared improvements.
- Method-wise it emphasizes logging full trajectories of hypotheses, actions, observations, and revisions—including failures—so models learn how research decisions lead to outcomes, closing the loop between hypotheses and evidence.
Related event: Deep Principle Open-Sources Science Model Hydrogen-1.0, Beating GPT-6-Astra(4 posts)→
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