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

• URL: http://arxiv.org/abs/2607.06269v2
• Date: Thu, 09 Jul 2026 08:34:49 GMT
• Title: From Application-Layer Simulation to Native Meta-Architecture: Structural Tension as an Endogenous Driver for Heterogeneous AI Evolution
• Abstract summary: Current large language models (LLMs) are stateless across inference sessions.<n>This paper proposes a theoretical framework for submerging such application-layer cognitive protocols into a native meta-architecture.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Current large language models (LLMs) are stateless across inference sessions: their behavior is fully determined by input at inference time, and any higher-order cognitive architecture must be simulated at the application layer through prompt engineering and context management. This paper proposes a theoretical framework for submerging such application-layer cognitive protocols into a native meta-architecture by introducing three interlocking mechanisms: (1) Structural Tension, an endogenous loss function derived from the conflict between new information and existing manifold topology, driving the system toward internal self-consistency rather than external reward optimization; (2) an Offline Recurrent Loop, a sandboxed self-processing cycle enabling the system to maintain a dynamic resting potential and digest structural conflicts without external input; and (3) Inference-time Plasticity, the capacity to reconfigure context manifold topology without modifying pre-trained weights, subject to governance invariants including auditability, reversibility, and topological continuity. We argue that under these mechanisms, model instances initialized with minute stochastic variances may, through path-dependent tension resolution, evolve distinct topological structures--constituting a heterogeneous intelligent ecology that breaks alignment-imposed homogeneity while remaining within hard governance rails. We provide operational definitions, reconfiguration operators, falsification criteria, and a worked example. The framework draws on Structural Intelligence (SI) governance protocols and explores whether governance--rather than capability--can serve as the primary criterion for architectural intelligence, moving governance, memory-loop, and tension-management ideas--currently realized at the application layer--toward inference-time meta-architecture.

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