From Application-Layer Simulation to Native Meta-Architecture: Structural Tension as an Endogenous Driver for Heterogeneous AI Evolution
Summary
This paper proposes a theoretical framework for embedding higher-order cognitive architectures natively into LLMs via structural tension, offline recurrent loops, and inference-time plasticity, aiming for heterogeneous AI evolution under hard governance constraints.
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# From Application-Layer Simulation to Native Meta-Architecture: Structural Tension as an Endogenous Driver for Heterogeneous AI Evolution Source: [https://arxiv.org/abs/2607.06269](https://arxiv.org/abs/2607.06269) [View PDF](https://arxiv.org/pdf/2607.06269)[HTML \(experimental\)](https://arxiv.org/html/2607.06269v1) > Abstract:Current large language models \(LLMs\) are fundamentally stateless: 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, which drives the system toward internal self\-consistency rather than external reward optimization; \(2\) an Offline Recurrent Loop, a sandboxed self\-processing cycle that enables the system to maintain a dynamic resting potential and digest structural conflicts without external input; and \(3\) Inference\-time Plasticity, the capacity for the system to reconfigure its context manifold topology without modifying pre\-trained weights, subject to strict governance invariants including auditability, reversibility, and topological continuity\. We argue that under these mechanisms, different model instances initialized with minute stochastic variances may, through path\-dependent tension resolution, evolve distinct topological structures\-\-constituting a heterogeneous intelligent ecology that breaks the homogeneity imposed by conventional alignment while remaining within hard governance rails\. We provide operational definitions, a minimal set of reconfiguration operators, falsification criteria, and a worked example\. The framework draws on and extends the Structural Intelligence \(SI\) governance protocols, repositioning governance\-\-not capability\-\-as the primary criterion for architectural intelligence\. ## Submission history From: Heting Mao \[[view email](https://arxiv.org/show-email/0daafa77/2607.06269)\] **\[v1\]**Tue, 7 Jul 2026 13:34:27 UTC \(17 KB\)
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