Medium Article: https://medium.com/@jsmith0475/we-solved-ais-reproducibility-crisis-by-treating-it-like-a-physics-problem-8936aed52923
The article "Cognitive Anchoring," by Dr. Jerry A. Smith, details a novel solution to the reproducibility crisis in large language models (LLMs) by treating the issue as a physics coordination problem. The core proposal, cognitive anchoring, uses principles from gauge theory to synchronize the attention heads within transformer models, which otherwise drift and produce inconsistent reasoning paths. The authors introduce four specific anchoring mechanisms—symbolic, temporal, spatial, and symmetry—to constrain representational degrees of freedom without sacrificing logical content, leading to a 38% improvement in symbolic consistency during complex tasks like discovering field equations. The framework is presented as a mechanistic alternative to prompt engineering and is demonstrated to generalize across scientific discovery and behavioral science applications, such as modeling complex cultural multipliers in athletic valuation. Ultimately, the paper establishes anchoring as a foundational protocol for achieving stable and reliable inference in AI reasoning systems.

We Solved AI's Reproducibility Crisis by Treating It Like a Physics Problem

Medium Article: https://medium.com/@jsmith0475/how-ai-just-cracked-pharmaceutical-method-development-in-6-weeks-instead-of-12-months-492efc9a23a2
This article from an article by Dr. Jerry A. Smith that introduces a novel solution for achieving deterministic AI outputs essential for drug development regulation. It explains that pharmaceutical method development, a process currently taking up to twelve months, is stalled by the FDA's requirement for identical, reproducible results from AI, which probabilistic Large Language Models (LLMs) cannot naturally provide. The core breakthrough involves applying a 160-year-old mathematical concept, Maxwell's electromagnetic gauge theory, to constrain the internal workings of transformer models. By implementing a framework called cognitive anchoring with four mechanisms—symbolic, temporal, spatial, and symmetry anchoring—the research successfully channels the model’s internal representational freedom without compromising its semantic reasoning, achieving a high degree of functional determinism and potentially reducing method development time significantly. This innovation promises to unlock massive efficiency gains, reduce drug development costs, and accelerate patient access to therapies by making AI outputs acceptable for GMP (Good Manufacturing Practices) compliance.

How AI Just Cracked Pharmaceutical Method Development - In 6 Weeks Instead of 12 Months

In-Depth Explorations of Neuroscience-Inspired Architectures Revolutionizing AI.

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