Google DeepMind的Co-Scientist能做真实实验，论文可靠性高，比其他模型表现更好。
Google DeepMind的Co-Scientist研究将AI从模拟带入真实世界实验。该系统在HealthBench Hard和Professional基准上击败了六种前沿模型。在化学领域，它设计了MXenes的安全前体路线，并实现了单层MoS2、MoSe2和WS2的一次性生长。在生物学中，它从稀疏成像数据预测了E. coli的表型梯度，与实际测量结果匹配。
Impressive new paper from Google DeepMind. (bookmark it) It takes Co-Scientist out of simulation a...
Impressive new paper from Google DeepMind. (bookmark it) It takes Co-Scientist out of simulation and into real-world experiments. A summary of the results: In computer science, it found an inference-time scaling architecture that beat six frontier models on HealthBench Hard and Professional under blinded physician review. The system designed a safe precursor route for MXenes and drove a semi-automated chemical vapor deposition reactor, producing a lamellar 2D material with structural similarities to the Ti3C2Tx lattice. It also tailored growth recipes to laboratory constraints in minutes, enabling single-attempt growth of monolayer MoS2, MoSe2, and WS2. In biology, it predicted E. coli swarming phenotypes across inducer gradients from sparse imaging data, matching unpublished real-world measurements. 30 domain experts wrote 450 reviews on end-to-end generated papers, and the reliability modules reduced hallucination and plagiarism. Paper: arxiv.org/abs/2608.26701 Chat with Paper: academy.dair.ai/papers/co-scie… 💬 3 🔄 2 ❤️ 6 👀 1208 📊 5 ⚡