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ArXiv Study: Information-Theoretic Limits Prove AI Reliability Has a Ceiling, Regardless of Scale
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ArXiv Study: Information-Theoretic Limits Prove AI Reliability Has a Ceiling, Regardless of Scale

A new ArXiv paper proves that large language models (LLMs) have an inherent reliability ceiling that no amount of scaling can overcome. The study decomposes output uncertainty into a resolvable component (closable with more context) and a subjective component (inherent to task ambiguity), and shows that autoregressive generation further degrades this ceiling.

Theory-Level Autoformalization: AI That Formalizes Entire Theories, Not Just Isolated Statements
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Theory-Level Autoformalization: AI That Formalizes Entire Theories, Not Just Isolated Statements

A new arXiv position paper from researchers argues for a shift in AI autoformalization from single statements to complete theories—including axioms, definitions, and lemmas—to create unified, machine-verifiable formal knowledge bases. This could transform how complex knowledge in mathematics, law, and science is structured and validated.