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research#llm🔬 ResearchAnalyzed: Jan 4, 2026 06:49

Why AI Safety Requires Uncertainty, Incomplete Preferences, and Non-Archimedean Utilities

Published:Dec 29, 2025 14:47
1 min read
ArXiv

Analysis

This article likely explores advanced concepts in AI safety, focusing on how to build AI systems that are robust and aligned with human values. The title suggests a focus on handling uncertainty, incomplete information about human preferences, and potentially unusual utility functions to achieve safer AI.
Reference

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Research Paper#Machine Learning, p-adic Numbers, Representation Learning🔬 ResearchAnalyzed: Jan 3, 2026 19:44

Learning with p-adic Numbers: A Novel Approach to Machine Learning

Published:Dec 27, 2025 19:40
1 min read
ArXiv

Analysis

This paper explores the use of p-adic numbers, a non-Archimedean field, as an alternative to real numbers in machine learning. It challenges the conventional reliance on real-valued representations and Euclidean geometry, proposing a framework based on the hierarchical structure of p-adic numbers. The work is significant because it opens up a new avenue for representation learning, potentially offering advantages in areas like code theory and hierarchical data modeling. The paper's theoretical exploration and the demonstration of representing semantic networks highlight its potential impact.
Reference

“The paper establishes the building blocks for classification, regression, and representation learning with the $p$-adics, providing learning models and algorithms.”

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