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20242026
most citedAssembly Theory Reduced to Shannon Entropy and Rendered Redundant by Naive Statistical Algorithms

2 citations · 2 across the 4 of their papers we have counts for

collaborators

6 papers

cs.LG2026

What Can Language Models Inherit from Solomonoff Induction? Limits, Attainable Properties, and What Served Conditionals Reveal

Luan Ozelim, Abicumaran Uthamacumaran, Hector Zenil

A familiar line of reasoning runs as follows: language models are flexible approximators trained under a strictly proper loss, so scale brings them closer to Solomonoff induction,…

cs.LG2026

Measuring in-context algorithmic reasoning in language models against an exact Bayes-optimal reference

Luan Ozelim, Hector Zenil

Whether large language models perform algorithmic inference or pattern completion is hard to test, because most benchmarks supply answers but no distributional reference for what t…

cs.LG2026

Algorithmic Information Dynamics of Learning: A Certified, Differentiable Complexity Controller for Grokking

Luan Ozelim, Hector Zenil, Abicumaran Uthamacumaran

Algorithmic Information Dynamics (AID) studies systems by perturbing them and measuring changes in algorithmic complexity, but its usual estimator, the Block Decomposition Method,…

cs.AI2025

Can Complexity and Uncomputability Explain Intelligence? SuperARC: A Test for Artificial Super Intelligence Based on Recursive Compression

Alberto Hernández-Espinosa, Luan Ozelim, Felipe S. Abrahão +1

We introduce an increasing-complexity, open-ended, and human-agnostic metric to evaluate foundational and frontier AI models in the context of Artificial General Intelligence (AGI)…

cs.IT2024★ 2 cited

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…

cs.IT2024

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…