Induction Labs Photon-1 Simulates Desktops, Plays Checkers, and Models Billiard Physics From One Pretraining Run
Most agents that learn from video need to know what action produced each frame. Induction Labs is arguing that this requirement is the bottleneck. Last week, they released imagination models, a foundation model architecture that pretrains on raw video with no action labels at all. Their test system is Photon-1, a sparse 106B-A5B mixture-of-experts (MoE) transformer trained on 18 years of computer demonstration video. On an internal computer use benchmark, Induction Labs reports that Photon-1
A foundation model was trained on raw video of computer screen recordings without any labels identifying which actions produced each frame. The model learns to predict future frames in a compressed representation space, which researchers argue teaches it to understand and complete tasks implicitly. The approach achieved better performance than a baseline model while requiring significantly less training compute and lower inference costs. The model also generalized to domains not seen during pretraining, such as playing checkers and simulating billiard physics, demonstrating that learning from video prediction alone can produce capabilities beyond its original training domain.
Explainer: What Is a Mixture of Experts Model and Why Does It Use Fewer Resources?
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Anthropic releases new Mythos and Fable models amid RSI concerns
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Also reported by The Verge, SiliconANGLE and MarkTechPost
Runway’s Praxis-1 world action model for robotics is now available
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Gemini 4 Carbon is now available
The latest version aims to deliver improved performance while reducing the computational resources and energy required for AI operations.
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Also reported by SiliconANGLE
Introducing AlphaProof Code: a breakthrough in verifying complex software at depth
AlphaProof Code agents wrote three million lines of proofs in Lean to verify Signal’s core encryption.