WINNEX: Weighted Inference Neural Network Enhancement — A Deterministic, Training-Free Framework for Enhancing Pretrained Language Models (Composition of the Winnex Spectral Tokenizer, X-Factor Manifold Projector, and Madhava Bound Engine)

Abstract

This document is the pre-patent technical specification for WINNEX — Weighted Inference Neural Network Enhancement — a deterministic, training-free framework that enhances a pretrained language model without modifying its weights or running any gradient step. The framework composes three published Winnex operators into a single inference path: (1) W — Weighted: model fusion by weighted quaternion rotation W' = Σᵢ αᵢ R(qᵢ) Wᵢ and variance-weighted manifold projection; (2) I — Inference: the entire enhancement operates at inference time with no backpropagation or training data; (3) N — Neural: the reference frame is the principal subspace of the model's token-embedding matrix; (4) N — Network: the operators share one mathematical core (the Madhava bound-guided kernels); (5) E — Enhancement: text is projected onto the model's manifold, models are fused in their own geometry, and retrieval is provably complete; (6) X — X-Factor: the composite operator Φ_X(T) = X·Xᵀ·pad_L2(histogram(T)). The three packages are published on PyPI as winnex-nano, winnex-xfactor, and winnex-madhava (native C++20 cores) with reproducible Kaggle benchmarks on real Qwen embeddings. Verified results include: spectral round-trip CER = 0 across 10 languages, projector idempotence (P² = P), native dense forward on Qwen2.5-1.5B, autoregressive generation with persistent KV cache, deterministic spectral RAG with 0 bound violations, weight fusion preserving behavior (cos > 0.99), and X-Factor computation O(D²r) in 0.042 s at D=128. Honest scope, stated explicitly in Section 6: the bound scan provides no speed advantage at high dimension (the Cauchy-Schwarz residual grows with √d and the scan degenerates to exhaustive search at d = 1536); the spectral tokenizer is not a BPE replacement (50–70× slower, ~1000× less compact); the X-Factor projects onto the principal subspace of input embeddings, not the deep internal transformer geometry; and generation quality with the spectral path is the open frontier, not yet demonstrated with standard metrics. The document includes draft patent claims and a prior-art distinction. Licensed under Business Source License 1.1 (BSL 1.1) — source-available, not OSI open-source.