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ryujinchoi
ryujinchoi

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

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 ($N \times \epsilon_{\text{mach}}$) with a <15MB memory footprint and nanosecond execution latency.

Any structural hallucinatory perturbation or boundary breach breaks this topological corridor, causing the continuous tail energy to diverge towards infinity, instantly triggering a strict, deterministic boundary flag.

I officially invite the community, ML infrastructure architects, and system engineers to review the codebase, audit the mathematical derivations, and attempt to break the boundary metrics of this closed system.

[Axiomatic Infrastructure Assets]

You can instantly test the strict boundary flag directly from your terminal using a single curl instance:
curl -X POST onrender.com -H "Content-Type: application/json" -d '{"text": "Axiomatic Telemetry Closed."}'

Would love to hear your thoughts on substituting massive compute-heavy guardrails with rigid infinite-dimensional functional analysis.

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