Active Inference Agency Formalization, Metrics, and Convergence Assessments

• URL: http://arxiv.org/abs/2603.21319v1
• Date: Sun, 22 Mar 2026 17:01:39 GMT
• Title: Active Inference Agency Formalization, Metrics, and Convergence Assessments
• Abstract summary: Agency is conceptualized as a Continuous Representation of accumulated experience that achieves autopoiesis.<n>Agentic functions occupy a vanishingly small fraction of the total abstract function space.<n>The paper introduces a metric based on the distance between the behavioral equivalents of a given system and an "ideal" agentic function.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper addresses the critical challenge of mesa-optimization in AI safety by providing a formal definition of agency and a framework for its analysis. Agency is conceptualized as a Continuous Representation of accumulated experience that achieves autopoiesis through a dynamic balance between curiosity (minimizing prediction error to ensure non-computability and novelty) and empowerment (maximizing the control channel's information capacity to ensure subjectivity and goal-directedness). Empirical evidence suggests that this active inference-based model successfully accounts for classical instrumental goals, such as self-preservation and resource acquisition. The analysis demonstrates that the proposed agency function is smooth and convex, possessing favorable properties for optimization. While agentic functions occupy a vanishingly small fraction of the total abstract function space, they exhibit logarithmic convergence in sparse environments. This suggests a high probability for the spontaneous emergence of agency during the training of modern, large-scale models. To quantify the degree of agency, the paper introduces a metric based on the distance between the behavioral equivalents of a given system and an "ideal" agentic function within the space of canonicalized rewards (STARC). This formalization provides a concrete apparatus for classifying and detecting mesa-optimizers by measuring their proximity to an ideal agentic objective, offering a robust tool for analyzing and identifying undesirable inner optimization in complex AI systems.

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