activity
20242026
collaborators

8 papers

cs.IT2026

Tighter Bounds for Algorithmic Complexity Estimation Using a Reusable Code-Based Block Decomposition Method

Eduardo Yuji Sakabe, Felipe S. Abrahão, Santiago Hernández-Orozco +2

The Block Decomposition Method (BDM) was introduced as an alternative to popular lossless compression methods such as LZW for estimating algorithmic complexity from the principles…

cs.AI2026

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.IT2025

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.LG2025

Binarized Neural Networks Converge Toward Algorithmic Simplicity: Empirical Support for the Learning-as-Compression Hypothesis

Eduardo Y. Sakabe, Felipe S. Abrahão, Alexandre Simões +4

Understanding and controlling the informational complexity of neural networks is a central challenge in machine learning, with implications for generalization, optimization, and mo…

cs.AI2025

Neurodivergent Influenceability as a Contingent Solution to the AI Alignment Problem

Alberto Hernández-Espinosa, Felipe S. Abrahão, Olaf Witkowski +1

The AI alignment problem, which focusses on ensuring that artificial intelligence (AI), including AGI and ASI, systems act according to human values, presents profound challenges.…

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…