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This paper introduces a categorical framework for constructing verifiable, local truth-preserving foundation models using composable foundries, implemented in the Odyssey system, and scheduled for a tutorial at ICML 2026.
This article discusses a new MIT paper proposing a framework for self-evolving AI scientists that can recognize when their current model is insufficient and introduce new scientific concepts, distinguishing between retrieval, search, and discovery.
Researchers at MIT present a paper on self-evolving AI scientists that can discover and adapt their own scientific vocabulary, using a categorical framework to mathematically quantify genuine novelty and separate discovery from mere search or retrieval.