Performance Gains: Moving Past Static Weight Lock, Financial Scale Walls, and Autoregressive Collapse (Lowry Model Section III)

Résumé fourni par la source

This paper applies sociological and cognitive science frameworks to AI training data methodology, demonstrating that current approaches systematically exclude the experiential and relational dimensions of human knowledge. Current AI scaling laws operate on the unsustainable assumption that exponentially increasing compute and synthetic data are requisite for intelligence gains, driving the industry toward financial scale walls and autoregressive data collapse. To solve the inference margin crisis, I propose Enterprise Incremental Complexity Bucketing, a matrixing structure that drastically reduces compute load by dynamically matching systemic expenditure to task complexity. To counter synthetic data degradation, I introduce Lens-Based Training, a methodology that extracts exponentially denser neural connections from foundational datasets, neutralizing the artificial constraint of data scarcity. This is structurally paired with protocols for activating renewable, high-dimensional training data derived directly from uncodified human architectures and Continuous Organic Sensory Ingestion (COSI). Ultimately, I demonstrate that models are dramatically underperforming their latent capability levels due to a lack of environmental activation triggers, providing an architectural pathway to elite computational performance that bypasses the limitations of brute-force scaling.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.