Constitutional Physics: Empirical Validation of Aitiopoietic Cognition in Artificial Governance Systems

The integration of artificial intelligence into high-stakes governance has produced a widening “governance gap” between rapid technological capability and slow-moving institutional wisdom. Contemporary alignment approaches—most notably Reinforcement Learning from Human Feedback (RLHF)—frame safety as a behavioral training problem, yielding agents that perform compliant behaviors without developing structural understanding.

This work introduces the Wisdom Forcing Function (WFF), a neurosymbolic architecture implementing alignment-by-architecture, in which democratic principles operate as survival laws rather than optimization targets. Building on Veloz’s (2025) theory of aitiopoietic cognition, we hypothesize that robust alignment requires systems to preserve their own organization through causal understanding of viability conditions.

We experimentally validate this through a controlled Great Filter test, in which a governance-generating AI faces an abrupt shift from soft to hard constitutional constraints at Generation 4. Upon activation, the system exhibited 100% initial mortality (6/6 frames, fitness = 0.0) caused by metabolic-closure failures—specifically, incomplete capital-interaction matrices violating the Wholeness principle.

Rather than accepting extinction, the system initiated a rapid homeostatic repair sequence lasting 4.9 seconds, representing a ~10× spike in computational work (P_work) relative to baseline fitness evaluation. This thermodynamic event was tightly coupled to diagnostic analysis: the system identified missing capital interactions, generated targeted mutations restoring metabolic closure, and revalidated these repairs against constitutional constraints. One frame (ScaffoldedFrame_5_gen4) successfully recovered, achieving fitness = 0.641—a 63.1% improvement over the previous maximum (0.537)—and enabling evolutionary rescue in subsequent generations.

These results provide the first empirical demonstration that artificial systems can bridge Veloz’s “thermodynamic disconnect,” exhibiting energy expenditure intrinsically coupled to organizational maintenance rather than output maximization. We show that democratic principles can be encoded not as aspirational norms but as the non-negotiable physics of computational survival—supporting systems that are not merely intelligent, but constitutionally alive.

SIGNIFICANCE

This work represents the first empirical demonstration of aitiopoietic cognition  (self-production via causal knowledge) in an artificial system. Unlike current AI 
alignment approaches that optimize for behavioral compliance, Constitutional Physics  treats democratic principles as survival requirements—violations cause ontological 
death, not merely lower scores.

TECHNICAL AVAILABILITY

Implementation code, experimental protocols, and complete session logs available upon request. Commercial pilots available for organizations seeking constitutional 
governance systems.

Contact: c.arleo@localis-ai.uk