Hugging Face

Introducing the Open MM-RL Dataset. A PhD-level multimodal STEM benchmark built for verifiable reasoning across physics, chemistry, biology, and math. Four STEM domains, one dataset -Physics: Quantum and Particle Physics, Condensed Matter and Materials, Electromagnetism, Photonics, and Plasma Systems, Astrophysics and Space Physics -Mathematics: Algebra and Structure, Discrete Mathematics, Analysis and Continuous Mathematics, Probability and Geometry -Biology: Evolutionary Systems, Molecular Mechanisms, Cellular Processes and Neural Biology -Chemistry: Chemical Structure, Reaction Mechanisms, Synthesis, Spectroscopy and Properties We're raising the bar.

Meet physics-intern🧑‍🎓, our agentic framework for theoretical physics. It takes Gemini 3.1 Pro from 17.7% to 31.4% on CritPt, a new SOTA on one of the hardest benchmarks for LLMs. Theoretical physics is hard for humans and LLMs alike. But physics-intern decomposes problems and dispatches them to a team of specialized agents, solving research-level questions far more effectively than the base model alone.

Weird how some people always target open-source in AI! First it was: “Open-source AI will destroy the world” (spoiler: it didn't and it won't) Now: “Open-source is a cybersecurity threat because of AI” Both narratives are far too simplistic. The truth is that the exact same risks exist in closed-source systems, often even more so. For example, in practice, APIs can create much bigger data and security vulnerabilities than open systems you can inspect, self-host, and secure yourself. And as with software more broadly, open-source often ends up more secure because it benefits from far more scrutiny than private internal systems. The reality is not “open vs closed.” The reality is that AI is raising cybersecurity stakes across the board, and we need to tackle that seriously together.