RomanAILabs “White Hole Steering” is a lightweight, per-token logit-shaping controller for llama.cpp/LLM generation that uses a stable scalar from the White Hole formula to nudge word choice without retraining: at each token step we compute a steering strength s = F(x,y) where F(x,y) = H(φ(∑_{i=1}^{N} a1·f(i)·G(f(i)·K(i)·T(x,i)·T(y,0)·e^{−i·T(x,i)·T(y,0)}))) with H(z)=tanh(z), φ(z)=sign(z)·log(1+|z|), G(u)=1/(1+e^{−u}), f(i)=(i+1)^{−α}, K(i)=(i+1)^{−β}, and T(·) a simple transform; we then apply that s inside the sampler to gently reshape logits (and optionally temperature) so the model’s next-token probabilities shift toward different wording/styles while keeping overhead small, giving you a tunable “math knob” for output direction, diversity, and repetition control on local GGUF models.