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How I Fixed LLM Hallucinations on a 512MB Server with Pure Math

DEV Community·ryujinchoi·5 months ago
#GzriuWAI
#dev#mathcal#boundary#heavy#axiomatic
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Hi Everyone,

While multi-billion dollar RAG pipelines and heavy neural guardrail frameworks dominate current LLM alignment security, I wanted to open-source a radically different, zero-overhead paradigm.

I have deployed a real-time validation engine that runs flawlessly on a 512MB RAM Render server using only the Python standard library. It maps text/embedding entropy onto a unit sphere in a separable infinite-dimensional Hilbert space ($\mathcal{H}$) to deterministically lock hallucination and vector drift.

Core Innovations

Axiomatic Closure : Models the core validator as a compact self-adjoint operator $T: \mathcal{H} \to \mathcal{H}$ with eigenvalues $\lambda_n = \frac{1}{n}$, enforcing a strict security corridor with an $\mathcal{O}(N^{-1})$ tail error bound derived via continuous Riemann upper sum integration criteria.

Hardware Epsilon Guard (0% Torch Overhead) : Operates entirely without heavy computational frameworks, utilizing a dynamic IEEE 754 machine epsilon guard ($N \times…

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