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:
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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;
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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;
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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.
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.
The framework draws on and extends the Structural Intelligence (SI) governance protocols, repositioning governance—not capability—as the primary criterion for architectural intelligence.
Blogger's Review: This theoretical framework offers a novel perspective on the future development of LLMs, emphasizing the importance of internal structural self-consistency and governance. This approach could facilitate the evolution of intelligent systems into a more advanced heterogeneous ecological model, warranting further exploration and application.