What it is

For any model, the X-Factor is the orthonormal basis of the manifold where its token embeddings live:

Ē = E − mean(E)                  (center)
C = ĒᵀĒ / V                       (D×D covariance)
C = U Λ Uᵀ                         (eigh, Λ descending)
r = min{r : cumsum(Λ₁..r)/ΣΛ ≥ τ}  (effective rank)
X = U[:, :r]                       (the X factor: D×r manifold basis)
P = X·Xᵀ                           (orthogonal projector)

The projector is idempotent (P²=P) and symmetric (Pᵀ=P) — the orthogonal projection onto the model's embedding manifold.

Why it is fast

The eigendecomposition uses power iteration with deflation:

O(D²·r·iters)  vs  O(D³) for a full Jacobi/eigh

~2000× faster at D=2048, with the same deterministic guarantee (the Madhava philosophy: bound-guided selection, provable operators, no black boxes).

Install

pip install winnex-xfactor

Requires Python ≥ 3.8 and NumPy. The C++ core ships pre-built in the wheel.

Quick start

import numpy as np
import winnex_xfactor

# 1. Compute the X-Factor from a model's embed_tokens (a sample is enough).
E = np.random.randn(5000, 2048).astype(np.float32)
P, rank, variance = winnex_xfactor.compute_xfactor(E, 5000, 2048, tau=0.95)
print(f"rank={rank}, variance captured={variance:.3f}")

# 2. Embed a spectral token into the model's manifold.
psi = np.ones(64, dtype=np.float32)
h = winnex_xfactor.embed_spectral(psi, P)   # length 2048, on the manifold

API

• compute_xfactor(embed_tokens, vocab, dim, tau=0.95) -> (P, rank, variance)
• XFactor(embed_tokens, vocab, dim, tau) — object with .project, .project_batch, .projector, .basis, .rank, .variance_captured, .dim
• expand_spectral(psi, d, D) — zero-pad + L2 normalize
• embed_spectral(psi, P) — full text→manifold projection

License

BSL 1.1 | pay@winnex.ai | (c) Winnex Brasil Soluções Empresariais LTDA-ME