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tauansloboda
semantic-thermodynamics
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DOCUMENTO FUNDACIONAL_ Termodinâmica Semântica v1.docx
DOCUMENTO FUNDACIONAL_ Termodinâmica Semântica v1.docx
README.md
README.md
Semantic_Thermodynamics_Whitepaper.pdf
Semantic_Thermodynamics_Whitepaper.pdf
experimento_um.py
experimento_um.py
experimento_um_resultados.csv
experimento_um_resultados.csv
experimento_zero.py
experimento_zero.py
experimento_zero_resultados.csv
experimento_zero_resultados.csv
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Semantic Thermodynamics: LLM Entropy Minimization
This repository contains the foundational whitepaper, execution scripts, and raw empirical data for the Semantic Thermodynamics framework.
The Premise
Large Language Models operate as Bayesian inference engines. In environments with high semantic entropy, they expend excess compute mapping infinite phase spaces. By applying "Narrative Gravity"—a precise formula of persona, teleological vectors, and destructive pruning—we can force the model into a deterministic geodesic, drastically reducing cost and latency.
Empirical Results (Experiment Zero)
By restructuring a standard data-extraction prompt using the Formula v2.0 , we observed:
79.29% reduction in completion tokens.
60.73% reduction in system latency (from 3.4s to 1.3s).
Repository Contents
Semantic_Thermodynamics_Whitepaper.pdf: The complete theoretical framework and the Law of Entropic Proportionality ($\Lambda$).
experimento_zero.py: The async Python script used to benchmark the token and latency collapse.
experimento_um.py: The script mapping the "Structural Friction" and the optimal semantic gradient.
*.csv: Raw telemetry data from the OpenAI API runs.
How to Test the Physics
Clone this repo.
pip install openai
Export your API key: export OPENAI_API_KEY="sk-..."
Run python experimento_zero.py and watch the latency drop.
Author: Tauan Vinicius Guahyba Sloboda
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