3 papers
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.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)…