Toward a Physical Theory of Intelligence

• URL: http://arxiv.org/abs/2601.00021v1
• Date: Mon, 22 Dec 2025 20:40:27 GMT
• Title: Toward a Physical Theory of Intelligence
• Authors: Peter David Fagan,
• Abstract summary: We present a theory of intelligence grounded in irreversible information processing in systems constrained by conservation laws.<n>An intelligent system is modelled as a coupled agent-environment process whose evolution transforms information into goal-directed work.
• Score: 0.016144088896423884
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a physical theory of intelligence grounded in irreversible information processing in systems constrained by conservation laws. An intelligent system is modelled as a coupled agent-environment process whose evolution transforms information into goal-directed work. To connect information to physical state, we introduce the Conservation-Congruent Encoding (CCE) framework, in which encodings correspond to metastable basins of attraction whose separability is enforced by conservation laws. Within this framework, intelligence is defined as the amount of goal-directed work produced per nat of irreversibly processed information. From this definition we derive a hierarchy of physical constraints governing information intake, irreversible computation, and work extraction in open systems. The framework reveals how long-horizon efficiency requires the preservation of internal informational structure, giving rise to self-modelling, and it establishes that physically embodied intelligent systems possess intrinsic epistemic limits analogous to incompleteness phenomena. Applying the theory to biological systems, we analyse how oscillatory and near-critical dynamics optimise the trade-off between information preservation, dissipation, and useful work, placing the brain near an efficient operating regime predicted by the framework. At the architectural level, we develop a theory of continuous dynamical circuits in which classical Boolean logic emerges as a special case of attractor selection, while more general invariant geometries support computational modes beyond fixed-point logic. Finally, we propose a physically grounded perspective on artificial intelligence safety based on irreversible information flow and structural homeostasis. Together, these results provide a unified, substrate-neutral account of intelligence as a physical phenomenon.

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