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

Theoretical framework proposes native meta-architecture to replace application-layer LLM cognitive simulation

A preprint from arXiv proposes a theoretical framework for embedding cognitive architecture natively into LLMs rather than simulating it via prompt engineering and context management. The framework introduces three mechanisms: Structural Tension (an endogenous loss function from information-manifold conflict), an Offline Recurrent Loop (sandboxed self-processing without external input), and Inference-time Plasticity (topology reconfiguration without weight modification). The authors argue these mechanisms could produce heterogeneous model instances with distinct topological structures through path-dependent evolution, while remaining within governance rails. The paper is primarily theoretical, offering operational definitions, reconfiguration operators, and falsification criteria rather than empirical results.