From Application-Layer Simulation to Native Meta-Architecture: Structural Tension as an Endogenous Driver for Heterogeneous AI Evolution
arXiv:2607.06269v1 Announce Type: new 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
The proliferation of LLMs and the challenges in scaling their cognitive architectures through application-layer methods create a pressing need for more native solutions.
This research proposes a fundamental shift in AI architecture, moving beyond stateless LLMs towards inherently cognitive systems, which has profound implications for AI capabilities and development paradigms.
The proposed meta-architecture introduces endogenous drivers for AI evolution, potentially leading to more robust, adaptive, and genuinely intelligent systems than current simulated approaches.
- · AI research institutions
- · Advanced AI development platforms
- · Cognitive computing specialists
- · Pure prompt engineering firms
- · Stateless AI application developers
AI systems will become less reliant on external human intervention for complex cognitive tasks.
The development cycle for advanced AI applications will shift from application-layer work to foundational architectural design.
This could accelerate the emergence of truly autonomous and self-improving AI, altering human-computer interaction paradigms.
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Read at arXiv cs.AI