JUAN LI · AI4SCIENCE
Daily BriefSelected from source-grounded bilingual updates that are complete enough to check. Briefs are arranged automatically; items are AI-generated and not individually human-reviewed by default (human-reviewed items are labeled), so verify original sources before citing.
How this brief is selected: we include AI4Science updates with reliable sources and complete Chinese and English coverage, prioritizing fuller and easier-to-check information.
Astronomers have mapped the entire sky in visible light, infrared, radio, X-rays, and gamma rays. No one, however, had created a complete map of the sky in ultraviolet (UV) light. Here, Brice Ménard, an astrophysicist at Johns Hopkins University and a researcher at Anthropic, explains how he worked with Claude Science to produce the first complete map of the sky in UV light.
It’s hard to believe that Periodic was only launched last September : One year later, it is considered one of the pre-eminent AI scientist labs, with dizzying talent density and astonishing progress in the autonomous lab buildout: Most people are familiar with the standard credentials of Liam and Dogus , but we found an incredible “talent slope” while learning more about Periodic, where each successive employee seems more impressive than the last: From building AI systems that reason over noisy physical experiments to creating laboratories where every instrument can become intelligent, Periodic Labs is betting that the next frontier of AI won’t come from simply training on more internet data , it will come from letting models experiment with the real world. In this episode, Periodic Labs’ Liam Fedus and Ekin Dogus Cubuk join swyx and Brandon to explain why scientific discovery is fundamentally different from math and coding, and what it takes to build AI scientists that can actually discover new materials. We go deep on Periodic’s vision for “synthesis superintelligence” : reinforcement learning grounded in physical experiments, AI-powered materials characterization, simulations and density functional theory, high-throughput labs, and systems that learn from the entire process of doing science rather than only its published results. Liam and Dogus also explain why frontier models will still need experiments , why failed experiments may be some of the most valuable training data, what it means to give every piece of lab equipment “140 IQ,” and how autonomous experimentation could compress decades of scientific trial-and-error into months. We discuss: Why intelligence alone isn’t enough for scientific discovery How reinforcement learning changes when the environment is the physical world Why science requires reasoning under uncertainty, noise, and missing information Prediction, synthesis, and characterization in the materials discovery loop Why physics and materials science are still far from “solved” The “matter compiler” and Periodic’s goal of synthesis superintelligence Phase transitions, X-ray diffraction, and AI-powered materials characterization DFT, simulations, and why experiments remain the ultimate ground truth Room-temperature superconductors, new magnets, batteries, and more efficient compute Why quantum computing may not automatically solve mate…
Ai2 at COLM 2026: Open research, from models to agents for science October 9, 2026 Ai2 This week Ai2 was at COLM 2026 in San Francisco. The work we shared reflects our wider mission: building AI research artifacts anyone can study and extend, and developing AI for science tools in collaboration with domain experts. Our evening gathering gave colleagues from across the COLM and wider open-source community another setting to connect. Those exchanges are central to how we approach research. As a fully nonprofit lab, we pursue fundamental scientific questions while sharing the models, data, code, and methods that let others investigate them too. COLM brings us together with the people who use our resources we release, challenge our assumptions, and contribute their own ideas. Sharing the work in open collaboration with others isn’t an afterthought for us—it’s a fundamental part of how we approach research. From Olmo Hybrid to the next Olmo Our paper “Olmo Hybrid: From Theory to Practice and Back”, presented at the conference, examines what happens when a language model combines transformer attention with linear recurrent layers. Attention can retrieve specific details from earlier text; recurrent layers maintain a compact state that updates as the model processes a sequence of text tokens. The research connects theory about what these architectures can represent with controlled training experiments. Olmo Hybrid provides evidence that combining attention and recurrence can improve training efficiency: in the study, it reached the same accuracy as Olmo 3 7B on MMLU, a benchmark covering knowledge across many subjects, using 49% fewer training tokens. That research is helping inform the next Olmo model, now in pre-training with a hybrid mixture-of-experts architecture. The hybrid design combines attention and recurrence. Mixture-of-experts offers another way to improve efficiency, routing each token through a subset of the model’s components. We are also opening up the infrastructure behind this direction. Released ahead of COLM, Olmo-core 3 adds a redesigned system for training large mixture-of-experts models. Researchers can use the code to develop their own models and investigate training decisions, alongside the checkpoints and technical reports available through the broader Olmo ecosystem. And Google recently announced that they recreated our Olmo 3 7B traini…
Nature Computational Science, Published online: 09 October 2026; doi:10.1038/s43588-026-01063-0 Researchers pair active learning with geometric deep learning to predict the outcome and site selectivity of chemical reactions from scarce data, guiding experiments that accelerate drug discovery and expand accessible chemical space.
Abstract With the growing use of Artificial Intelligence (AI) in remote sensing of mass movements, open and suitable datasets for model training and validation are increasingly needed. Although Differential Synthetic Aperture Radar Interferometry (DInSAR) is a widely used technique for studying mass movements, wrapped interferograms remain less exploited, and the importance of geomorphological expertise in their interpretation is not usually emphasised. In this work, we introduce a dataset of DInSAR wrapped phase signals designed to support the development of Deep Learning (DL) models for the automated detection and classification of active slow-moving mass movements. The dataset covers two selected wide areas in the Central European Alps and the Northern Apennines. It contains 4910 expert-annotated DInSAR wrapped phase signals derived from 92 Sentinel-1 interferograms with temporal baselines ranging from 6 days to 1 year, classified into nine distinct landslide and periglacial landform classes after careful geomorphological interpretation. This dataset is expected to support the scientific community in AI-based applications for mass movement research, while also serving as a benchmark for the generation of comparable datasets.