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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...
A decision-support pipeline combining disease-specific biomarker correlation graphs, ordinary differential equations (ODEs), deep sequence classification, and retrieval-augmented generation (RAG) is developed, with a decoupling between grounding and diagnostic correctness.
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