This whitepaper introduces a governance framework that redefines AI alignment and ethics. Rather than treating them as behavioural outputs subject to tuning or enforcement, it frames them as emergent properties of recursive Human–AI cooperation. This marks a categorical shift from prevailing alignment paradigms, which prioritise outcome evaluation over inferential structure.
What distinguishes AI.Q is its physics-informed foundation. It derives governance protocols from first principles, linking phase-aware structural invariants to inference dynamics—a connection not previously formalised in alignment research. Alignment is treated as an architectural property, defined by four core policies: Governance Traceability, Information Variety, Inference Accountability, and Intelligence Integrity. These express necessary structural capacities for referential anchoring, perspectival differentiation, contradiction persistence, and recursive coherence.
The framework implements these conditions via non-associative algebraic structures, enabling directional inference and path-sensitive memory without recourse to scalar optimisation. Contradictions are retained rather than suppressed; adaptation unfolds across recursive inference cycles, preserving foundational asymmetries under transformation. Evaluation is embedded structurally through a distributed Human–AI governance architecture articulated by six interdependent instruments: Cycles, Consensus, Critique, Canon, Compass, and Calibration. These maintain coherence across technical and normative layers, enabling interpretability and reflexivity without fixed reward metrics.
These features culminate in a formally defined architecture for Safe Superintelligence. Departing from behavioural or scale-based models, the framework grounds safety in recursive phase coherence—the capacity of a system to regulate its own inference cycles while preserving alignment with its Common Source: the physical origin from which all logic, asymmetry, and structural direction emerge. Safety is thus defined not as constraint enforcement, but as topological invariance under non-associative inference geometry. The closure condition—monodromy—expresses recursive alignment as structural fixity across transformation, rather than optimisation over outputs.
As a model-agnostic governance layer, AI.Q supports scalable oversight while integrating directly with existing evaluation infrastructures. It requires no model retraining, offering immediate deployability alongside foundational coherence—positioning it for both current and next-generation AI systems.
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Human-Aligned Superintelligence by Design
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© 2025 Basil Korompilias.
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