Stay updated with the latest developments in Artificial Intelligence
How we built the OlmoEarth Platform to fine-tune geospatial models and run continent-scale satellite inference while managing massive data pipelines, distributed compute, and automatically recovering from failures at scale.
Why fully open models and research artifacts are essential to independent scrutiny, broader participation, and continued U.S. scientific leadership in AI.
Building Shippy taught us that reliable agents depend less on the model itself than on deterministic tools, explicit guardrails, isolated infrastructure, and evaluations grounded in real-world workflows and live data.
Robotics engineer Binh Pham used MolmoAct 2 to build a voice-controlled robot that won South Park Commons’ embodied AI hackathon.
Danish Foundation Models is using FlexOlmo as the basis for FlexMoRE, a more efficient modular LLM architecture that lets institutions contribute specialized experts trained on sensitive or proprietary data without sharing that data—and run the resulting models on highly accessible hardware.
DiScoFormer is a transformer-based density and score estimator that can infer both quantities from a finite sample in one forward pass, generalizing classical KDE while staying accurate in high-dimensional and out-of-distribution settings without retraining for each new distribution.
New token-level analyses of Olmo 3 and Olmo Hybrid show that hybrid models predict meaning-bearing, context-dependent tokens better than transformers, while transformers retain an edge on verbatim copying.
Domyn and AISquared show how Ai2’s open releases are helping AI labs build models for regulated industries, where transparency, provenance, licensing, and control are essential for customer trust and compliance.
MolmoMotion is an open, language-guided 3D motion forecasting model that predicts how object points will move in the future, enabling stronger motion prediction for robotics, video generation, and other systems that need to reason about what happens next.
olmo-eval is an open evaluation workbench that helps model developers add, run, and analyze benchmarks across changing LLM checkpoints, extending OLMES from final-score reproducibility into the day-to-day model development loop.
PointCheck, an independent project, uses Molmo, MolmoWeb, and Olmo 3 to test web accessibility the way a keyboard user would—by navigating real pages and inspecting what's actually on screen.
OlmoEarth v1.1 is a more efficient family of remote-sensing models that cuts compute costs by up to 3x while maintaining similar performance, making large-scale satellite mapping faster and cheaper to run.
AIMIP is a new open benchmark and dataset for evaluating AI climate models, showing they can match or beat conventional models on some historical climate metrics while still struggling to generalize reliably to long-term warming trends and unseen climate scenarios.
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