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This research aims to explore the architectural and energetic limitations of weight-based artificial intelligence systems.

April 23, 2026Open Access

The Stochastic Mirror: Reflecting on the Architectural and Energetic Limits of Weight-Based Artificial Intelligence

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This research aims to explore the architectural and energetic limitations of weight-based artificial intelligence systems.
Synthesized concepts from transfinite mathematics, thermodynamics, and computational theory.
Investigated scaling effects in large language models and their logical inconsistencies.
Proposed an Attention-Driven Firmware Architecture to enhance energy efficiency and reliability.
Identified the stochastic mirror effect, showing that increased scaling lacks logical consistency.
Demonstrated the physical unsustainability of weight-based scaling via the Landauer limit.
Critiqued industry fixes like retrieval-augmented generation for not addressing core logical issues.

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Technical note investigates limitations of current AI models, proposing efficient architecture changes.

汤云贵 (2026) studied this question.

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