ai2

Allen Institute for AI (AI2) has released DiScoFormer, a transformer model that estimates both the density and score of any distribution from a sample in a single forward pass without retraining. In 100 dimensions, the model reduces score estimation error by 6.5x and density error by 37x compared to classical kernel density estimation.

Allen Institute for AI released EMO, a 1B-active, 14B-total-parameter mixture-of-experts model trained on 1 trillion tokens. The model uses 8 active experts per token from a pool of 128 total experts, and can maintain near full-model performance while using just 12.5% of its experts for specific tasks.

The Allen Institute for AI has released MolmoWeb, a fully open web agent that operates websites using only screenshots without access to source code. The 8B-parameter model achieves 78.2% success on WebVoyager—nearly matching OpenAI's o3 at 79.3%—while being trained on one of the largest public web task datasets ever released.

AI2 has released MolmoSpaces and MolmoBot, robotics models trained exclusively in simulation that transfer directly to real robots without manual real-world data collection or fine-tuning. The approach eliminates months of teleoperated demonstrations typically required for simulation-trained robots. Both systems are open-source.

AI2 is developing physical AI systems trained primarily on virtual simulation data rather than expensive real-world demonstrations. The approach, demonstrated through projects like MolmoBot, addresses the historical bottleneck of manually collecting hardware training data.