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Allen Institute for AI

AI2 · North America

Open science AI institute.

Signals · 6

Allen Institute for AI
blog · 1d ago

When a model reads a drug's class from its name—not its knowledge — Researchers used Olmo 3 and its open training data to show that models can infer a drug’s class from its name instead of knowing the specific medication, and traced that shortcut to how often drugs appeared in training.

Researchers used OLMo 3 to show that language models often infer a drug's class from its name rather than actual knowledge.

interpretabilitytraining dataevaluationsSource
Allen Institute for AI
blog · 12d ago

TutorMoments: Do AI tutors know when to help and when to hold back? — TutorMoments is an open, replay-based evaluation framework that tests whether AI tutors can recognize when to support a student and when to hold back and encourage deeper reasoning.

Allen Institute for AI
blog · 13d ago

Ai2 expands collaboration with Hugging Face to accelerate open science — Ai2 is expanding its partnership with Hugging Face to give its growing portfolio of fully open models, datasets, benchmarks, and applications the storage, bandwidth, and integrations needed to reach more researchers and developers.

Allen Institute for AI
blog · 19d ago

Tracing distinctive language in AI-written text — Stony Brook researchers used our infini-gram engine to trace distinctive phrases in AI-generated writing back to existing sources, finding that top-selling self-published books on Amazon with substantial detected AI text overlap more heavily with rare language from previously published works.

Allen Institute for AI
blog · 22d ago

The OlmoEarth Platform: Geospatial inference at planetary scale — 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.

Allen Institute for AI
blog · 26d ago

Who gets to understand AI? — Why fully open models and research artifacts are essential to independent scrutiny, broader participation, and continued U.S. scientific leadership in AI.