Latest research
April 23, 2026
Introducing OlmoEarth embeddings: Custom embedding exports from OlmoEarth Studio for downstream analysis
OlmoEarth Studio now lets users export custom Earth-observation embeddings from our OlmoEarth foundation models and use them for tasks like similarity search, few-shot mapping, change detection, and unsupervised exploration.April 20, 2026
Train separately, merge together: Modular post-training with mixture-of-experts
BAR is a recipe for post-training language models one capability at a time—train domain experts independently, merge them into a single mixture-of-experts model, and upgrade any expert without impacting the others.April 13, 2026
Evaluating agents for scientific discovery
Two benchmarks developed at Ai2 – ScienceWorld and DiscoveryWorld – reveal that even incredibly strong AI science agents struggle with problems human scientists solve routinely.April 7, 2026
Introducing WildDet3D: Open-world 3D detection from a single image
WildDet3D is an open model that predicts 3D bounding boxes from a single image. It generalizes across cameras and object categories, and folds in depth signals when available—alongside a new dataset of verified 3D annotations.March 24, 2026
MolmoWeb: An open agent for automating web tasks
Introducing MolmoWeb, an open visual web agent that navigates and completes tasks in a browser using screenshots alone, along with MolmoWebMix, the largest public dataset for training web agents.March 18, 2026
MolmoPoint: Better pointing architecture for vision-language models
MolmoPoint is a new vision-language model architecture that replaces text-based coordinate outputs with a more natural, token-based pointing mechanism that directly selects regions from visual features.March 11, 2026
MolmoBot: Training robot manipulation entirely in simulation
MolmoBot is an open robotic manipulation model suite trained entirely in simulation—demonstrating zero-shot transfer to real-world robots without any real-world data collection or fine-tuning.March 5, 2026
Introducing Olmo Hybrid: Combining transformers and linear RNNs for superior scaling
Olmo Hybrid is a fully open 7B language model that combines transformer attention with linear RNN layers to achieve greater expressivity and significantly improved data and compute efficiency compared to pure transformer models.February 27, 2026
How do researchers actually use AI-powered science tools? Lessons from 250,000+ queries
The Asta Interaction Dataset (AID) contains real researcher queries revealing how scientists actually use AI-powered research tools, and where their habits diverge from what tool builders expect.1-9Next