Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Multimodal Models
Today's most advanced multimodal models remain proprietary. The strongest open-weight models rely heavily on synthetic data from proprietary VLMs to achieve good performance, effectively distilling…
Application of the AI2 Climate Emulator to E3SMv2's global atmosphere model, with a focus on precipitation fidelity
Can the current successes of global machine learning-based weather simulators be generalized beyond 2-week forecasts to stable and accurate multiyear runs? The recently developed AI2 Climate…
OLMoE: Open Mixture-of-Experts Language Models
We introduce OLMoE, a fully open, state-of-the-art language model leveraging sparse Mixture-of-Experts (MoE). OLMoE-1B-7B has 7 billion (B) parameters but uses only 1B per input token. We pretrain…
Pushing the frontiers in climate modelling and analysis with machine learning
Climate modelling and analysis are facing new demands to enhance projections and climate information. Here we argue that now is the time to push the frontiers of machine learning beyond…
Weather and climate predicted accurately — without using a supercomputer
A cutting-edge global model of the atmosphere combines machine learning with a numerical model based on the laws of physics. This ‘hybrid’ system accurately predicts the weather — and even shows…
AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents
Autonomous agents that address day-to-day digital tasks (e.g., ordering groceries for a household), must not only operate multiple apps (e.g., notes, messaging, shopping app) via APIs, but also…
Can Language Models Serve as Text-Based World Simulators?
Virtual environments play a key role in benchmarking advances in complex planning and decision-making tasks but are expensive and complicated to build by hand. Can current language models themselves…
CHIME: LLM-Assisted Hierarchical Organization of Scientific Studies for Literature Review Support
Literature review requires researchers to synthesize a large amount of information and is increasingly challenging as the scientific literature expands. In this work, we investigate the potential of…
Few-shot Dialogue Strategy Learning for Motivational Interviewing via Inductive Reasoning
We consider the task of building a dialogue system that can motivate users to adopt positive lifestyle changes: Motivational Interviewing. Addressing such a task requires a system that can infer…
Overview of the Context24 Shared Task on Contextualizing Scientific Claims
To appropriately interpret and use scientific claims for sensemaking and decision-making, it is critical to contextualize them, not just with textual evidence that the claim was in fact asserted,…