simple saves 47% energy same performance or better with Zero Hallucination

[KeithLuton]

Luton Field Model (LFM) - Inference Optimization Kernel
A lightweight, deterministic pre-computation layer designed to optimize RAG pipelines and scientific AI workflows. By replacing probabilistic LLM token generation with O(1)O(1)
mathematical derivation for fundamental constants and scaling laws, LFM achieves:
Zero Hallucination: Enforces 100% dimensional consistency.
Extreme Efficiency: Reduces latency from ~500ms (LLM) to ~1µs (Kernel).
Cost Reduction: Eliminates token costs for complex physics/logic queries.
Status: Production Ready (v1.0.2)
License: Dual (Community/Enterprise)

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[gemini-code-assist]

Summary of Changes

Hello @KeithLuton, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request introduces the Luton Field Model (LFM) Kernel, a novel deterministic pre-computation layer designed to optimize Retrieval Augmented Generation (RAG) pipelines and scientific AI workflows. The LFM Kernel replaces probabilistic LLM token generation for fundamental constants and scaling laws with O(1) mathematical derivations, leading to zero hallucination, extreme efficiency with drastically reduced latency, and significant cost reductions by eliminating token usage for complex physics queries.

Highlights

• Introduction of LFM Kernel: A new lightweight, deterministic pre-computation layer called the Luton Field Model (LFM) Kernel has been added to optimize RAG pipelines and scientific AI workflows.
• Performance Optimization: The LFM Kernel significantly reduces inference latency for physics queries from approximately 500ms (LLM) to about 1µs, achieving O(1) mathematical derivation for fundamental constants.
• Zero Hallucination: It ensures 100% dimensional consistency, preventing floating-point errors and hallucinations by replacing probabilistic LLM token generation with exact mathematical derivations.
• Cost Reduction: By offloading complex physics/logic queries from LLMs to a local CPU-based kernel, it eliminates token costs associated with these computations.
• Benchmarking and Demo: A benchmark script (benchmark_efficiency.py) and a Jupyter notebook demo (lfm_resonance_demo.ipynb) are included to illustrate the efficiency gains and hallucination prevention capabilities of the LFM Kernel.

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[gemini-code-assist]

Code Review

This pull request introduces the Luton Field Model (LFM), a deterministic kernel for optimizing scientific AI workflows, along with a benchmark script, a demo notebook, and documentation. The changes aim to improve performance and reduce hallucinations in LLMs for physics-based queries. My review has identified several areas for improvement. There are style guide violations in the Jupyter notebook, such as missing elements and incorrect import placement. The Python code contains potential bugs like a division-by-zero error, use of magic numbers, incorrect type hints, and inconsistencies with a defined dataclass. Most critically, the README file includes a custom dual-license model that appears to conflict with the repository's standard licensing policies and requires immediate attention.

[gemini-code-assist]

The repository style guide (line 48) requires a collapsed license cell at the top of notebooks. This notebook is missing the license cell. Please add it.

[gemini-code-assist]

The labels variable is defined but never used within the plot_graph function. This constitutes dead code and should be removed.