From Application-Layer Simulation to Native Meta-Architecture: Structural Tension as an Endogenous Driver for Heterogeneous AI Evolution

Current large language models (LLMs) are stateless functions that rely on application-layer simulations—such as prompt engineering and retrieval-augmented generation—to manage memory and cognitive architecture. This paper introduces a theoretical framework to move these cognitive protocols into a native meta-architecture. By treating governance as the primary criterion for intelligence, the author proposes a system that evolves through internal self-consistency rather than external reward signals.

The framework relies on three interlocking mechanisms:

1. Structural Tension: An endogenous loss function that measures the conflict between new information and the system's existing manifold topology. This drives the system to reconfigure its internal structure to maintain self-consistency.
2. Offline Recurrent Loop: A sandboxed self-processing cycle that allows the model to digest structural conflicts and maintain a dynamic resting potential when external input is absent.
3. Inference-time Plasticity: The ability to reconfigure the context manifold (the geometric distribution of hidden states) without modifying the model's pre-trained weights, ensuring that all changes remain auditable and reversible.

A key hypothesis of this work is that these mechanisms allow for a heterogeneous intelligent ecology. Because inference-phase computation is probabilistic and sensitive to initial stochastic variances, different model instances can resolve structural tensions through path-dependent topological changes. This allows individual instances to develop distinct cognitive organizations while remaining within strict governance rails, such as kernel immutability and causal traceability.

This research challenges the dominant paradigm of homogeneous alignment. By prioritizing governance—specifically auditability, reversibility, and causal traceability—the framework suggests that we can achieve robust, self-organizing AI systems that maintain safety without sacrificing cognitive diversity. It shifts the focus from capability-based scaling to architectural intelligence, providing a rigorous set of falsification criteria to test whether such systems can truly evolve without collapsing into chaos or mediocrity.