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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 sett
Language models can already help researchers search the literature, synthesize evidence, and work through complex questions. But scientific work places particular demands on these models—answers need to stay grounded in evidence, the models need to preserve what the evidence actually supports rather than quietly broadening a study’s conclusions, and researchers need to be able to verify the final outputs. We see that
AI models need to do more than produce correct answers. How they respond matters too: whether they’re helpful, fair, safe, respectful, and responsive to the people using them. For model builders, the challenge is knowing whether those “prosocial” behaviors hold up in practice—and whether evaluations capture how a model behaves when people interact with it in unexpected ways. Northeastern University MS student Soham P
AutoDiscovery, our AI agent for scientific research, analyzes datasets, proposes hypotheses, runs experiments to test them, and ranks the results by Bayesian surprise—the gap between what the model predicted and what the data showed. In a University of Washington classroom this spring, it surfaced a possible flaw in how battery aging is measured, an unexpected pattern in silicon polymers, and a recurring feature in p
AI can already assist with a surprising amount of scientific work: search literature, analyze datasets, write code, and generate and test hypotheses. The harder problem is building systems scientists can steer as a research project unfolds, especially when new evidence changes which hypotheses to pursue, experiments to run, or questions to investigate next. That challenge was the focus of an August 27 event at Ai2 ma
Ai2 and Providence Swedish Cancer Institute partner to advance AI-assisted scientific discovery The collaboration includes clinically meaningful research findings on lobular breast cancer, and a local deployment to enable next-gen discovery while keeping clinical data secure. This partnership represents an important milestone for AutoDiscovery as it moves beyond public research datasets into active cancer research pr
When a model reads a drug's class from its name—not its knowledge August 18, 2026 Ai2 People increasingly bring health and medication questions to AI models, often naming specific drugs. But a model can sound knowledgeable without knowing much about the drug itself. Many drug names encode their class through affixes – often suffixes like -pril, -olol, and -azoline – giving a model a shortcut: infer the answer from th
In this crosspost from AI Matters, a publication of ACM SIGAI, Ella Scallan interviews Akari Asai, winner of the 2025 AAAI Doctoral Dissertation Award. Asai's doctoral research focused on retrieval-augmented language models (LMs), arguing for moving beyond monolithic LMs to augmented LMs that are designed, trained, and deployed with complementary modules. She pioneered retrieval-augmented LMs, which retrieve relevant
Today we're introducing a preview of TutorMoments, a framework to measure whether cutting-edge LLMs can balance one of the hardest trade-offs in education: when to step in and help a student and when to hold back and let the student do more of the work. TutorMoments is a replay-based evaluation built off real one-on-one math tutoring sessions. Experienced math teachers go through transcripts collected from a U.S. tut
Ai2 expands collaboration with Hugging Face to accelerate open science August 6, 2026 Ai2 Ai2 and Hugging Face are expanding their partnership to make fully open AI easier to access and deploy. Hugging Face serves as a central platform for the AI community, and the expanded collaboration gives us substantially more storage and bandwidth to support our growing portfolio of models, datasets, benchmarks, and application
The OlmoEarth Platform: Geospatial inference at planetary scale July 28, 2026 Ai2 The OlmoEarth models are our family of Earth observation foundation models, pretrained on roughly 10 terabytes of multimodal satellite data. Governments, NGOs, and other mission-driven organizations are already adapting OlmoEarth for applications including deforestation monitoring, food security, and wildfire risk. At Ai2, we know how t
As AI becomes critical infrastructure for science, medicine, industry, and government, one question matters more than ever: who gets to understand how these systems work? At Ai2, that question is central to our work, and it’s what motivates our commitment to open science and open models. For us, openness is more than a licensing choice—it shapes how we build, release, and evaluate our work. Open-weight models let peo
Many problems in machine learning and the sciences come down to the same task: you have a collection of data points and want to recover the distribution they came from—which values are common, and which are rare. Pinning down that distribution means estimating two quantities: the distribution’s density and, more useful as dimensionality grows, its score. The density is the smooth version of a histogram—high where poi
Machines have become remarkably good at perceiving motion. Given a video, modern models can track how objects and points move through a scene with exceptionally high confidence. But perception is inherently retrospective: it explains motion that has already happened. Many of the systems and applications we want to build need tolook forward instead. A robot reaching for a cup has to anticipate how the cup will move be