18 citations · 21 across the 9 of their papers we have counts for
10 papers
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…
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…
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.…
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)…
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…
Nomic realism, simplicity, and the simplicity bubble effect
Felipe S. Abrahão, Raoni Arroyo
We offer an argument against simplicity as a sole intrinsic criterion for nomic realism. The argument is based on the simplicity bubble effect. Underdetermination in quantum founda…
Pragmatic Nonsense
Ricardo P. Cavassane, Itala M. L. D'Ottaviano, Felipe S. Abrahão
Inspired by the early Wittgenstein's concept of nonsense (meaning that which lies beyond the limits of language), we investigate two different types of nonsense: formal nonsense an…
On the Salient Limitations of the Methods of Assembly Theory and their Classification of Molecular Biosignatures
Abicumaran Uthamacumaran, Felipe S. Abrahão, Narsis A. Kiani +1
We demonstrate that the assembly pathway method underlying assembly theory (AT) is an encoding scheme widely used by popular statistical compression algorithms. We show that in all…