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A certified, differentiable estimator is used to bring the calculus into learning dynamics: grokking, where a complexity order parameter is known but has not been made to act, and the algorithmic estimator's distinct contribution is timing (when to fire and when to release), not attribution.
We connect two questions in Algorithmic Information Theory (AIT), Machine Learning (ML) and Artificial General Intelligence (AGI): whether LLMs estimate Solomonoff induction, and whether they can self-improve towards an AI Singularity. We provide theoretical, methodological and empirical answers in the negative but sho...
F-ICL is an open benchmark and toolkit that exhaustively enumerates the 86 million valid programs of length on a Turing-complete machine F, complement-symmetrised to remove output-polarity bias, and compute the exact posterior under a declared bounded Levin--Solomonoff prior.
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