Ai2 at COLM 2026: Open research, from models to agents for science

Summary

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…