2 citations · 4 across the 13 of their papers we have counts for
4 papers · 1 filter
Large Language Models As Shannon Lossy Compressors Not Solomonoff Induction Estimators: The Singularity Is Not Near Without Symbolic Model Synthesis in Program Space
Hector Zenil, Abicumaran Uthamacumaran, Luan Ozelim
On the one hand, the question of whether Large Language Models (LLMs) are Solomonoff induction estimators has become an explicit question at the intersection of Algorithmic Informa…
Assembly Theory Reduced to Shannon Entropy and Rendered Redundant by Naive Statistical Algorithms
Luan Ozelim, Abicumaran Uthamacumaran, Felipe S. Abrahão +4
Assembly Theory (AT) and its central measure, the assembly index (Ai), represent an invaluable opportunity to address some of the most persistent and widespread conflations and mis…
Non-Random Data Encodes its Geometric and Topological Dimensions
Hector Zenil, Felipe S. Abrahão, Luan C. S. M. Ozelim
Based on the principles of information theory, measure theory, and theoretical computer science, we introduce a signal deconvolution method with a wide range of applications to cod…
Assembly Theory is an approximation to algorithmic complexity based on LZ compression that does not explain selection or evolution
Felipe S. Abrahão, Santiago Hernández-Orozco, Narsis A. Kiani +2
We prove the full equivalence between Assembly Theory (AT) and Shannon Entropy via a method based upon the principles of statistical compression renamed `assembly index' that belon…