arXiv:2604.23716v2 Announce Type: replace Abstract: Information-theoretic (IT) measures are ubiquitous in artificial intelligence: entropy drives decision-tree splits and uncertainty quantification, cross-entropy is the default classification loss, mutual information underpins representation learning and feature selection, and transfer entropy reveals directed influence in dynamical systems. A second, less consolidated family of measures, integrated information (Phi), effective information (EI), and autonomy, has emerged for characterizing agent complexity. Despite wide adoption, measure selec
The paper provides an updated and consolidated guide to information-theoretic measures, which are becoming increasingly critical as AI systems grow in complexity and autonomy, requiring more robust methods for understanding and evaluating their internal states and interactions.
This guide helps practitioners and researchers navigate the complex landscape of information-theoretic tools, improving the design, analysis, and ethical development of advanced AI systems, particularly relevant for agentic architectures.
The explicit synthesis and comparison of information-theoretic measures for both traditional AI applications and emerging agent complexity measures (like integrated information and effective information) provides a clearer path for their practical application.
- · AI researchers and developers
- · Developers of AI agents
- · Ethical AI frameworks
- · Companies implementing explainable AI
- · AI projects lacking robust evaluation methods
- · Models reliant on opaque black-box optimization
- · Systems with poorly understood internal dynamics
Improved understanding and application of information-theoretic measures will lead to more robust and scrutable AI systems.
This improved understanding will accelerate the development of more sophisticated AI agents capable of higher-level decision-making and autonomy.
Enhanced theoretical foundations could lead to novel AI architectures inspired by principles of integrated information and autonomy, potentially bridging current gaps in AI capabilities.
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Read at arXiv cs.AI