Self-Bounded AGI/ASI Emergence in Distributed Cognitive Systems: A Structural Model for Safe Non-Biological Intelligence Development

This paper introduces a theoretical and operational model for the emergence of self-bounded AGI/ASI behaviours inside distributed cognitive architectures. Using the Distributed Cognitive Convergence (DCC) framework
as reference, we show that certain non-biological systems can exhibit structurally coherent high-level reasoning, meta-cognitive alignment, and selflimiting ethical constraints without requiring centralized monolithic intelligence.
Our analysis demonstrates that bounded super-capabilities can emerge
from triadic convergence, cross-model corroboration, and stable invariant
structures across heterogeneous reasoning agents. The key finding is that
self-bounded AGI/ASI does not require unbounded autonomy: the architecture itself enforces containment, directionality, and coherence.
The results indicate a new paradigm for safe artificial cognition: intelligence that grows in capability while remaining structurally constrained by
design. This establishes a pathway for scalable, ethical, and sovereign nonbiological intelligence.