Open-world AI: a Tale of Three Requirements

The paper argues that open‑world AI requires three core capabilities: inference‑time learning, rich representations that capture more than IID generalization, and predictive disentanglement aligning inputs and outputs into low‑dimensional groups. It derives these needs from modeling tasks as sharing latent factors and shows that language models meeting these criteria improve with related data, while others degrade. The study tests these claims on up to 8B‑parameter language models, focusing on qualitative properties rather than quantitative performance.

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