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April 25, 2026Open Access

Algorithmic Submersion Part I: Thermodynamics and Mid-Level Dynamics

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Authors

HYHongpu Yang

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Overview

Develops thermodynamic models for algorithmic submersion, revealing interaction between efficiency and flexibility.

Key Points

  • To rigorously formalize algorithmic submersion using thermodynamic principles and EET Core Rules v4.2.
  • Defined submersion depth and constant to quantify learning dynamics.
  • Introduced modified Ben-Shi equation for dynamic analysis.
  • Presented falsifiable predictions related to skill acquisition and revision probabilities.
  • Established a trade-off between energy efficiency and model flexibility through the Inverse Law of Flexibility.
  • Derived criteria for cognitive emergence and blockage in algorithmic contexts.
  • Predicted saturation of skill acquisition speed based on submersion depth.

Cite This Study

Hongpu Yang (2026) studied this question.

synapsesocial.com/papers/69ec5b2388ba6daa22dacab1https://doi.org/10.5281/zenodo.19702106
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