The Canvas Model's equality processor operates in two complementary feed-modes. Feed-backwards (Steering) corrects errors via gradient descent: d\mathcal{E}/d\tau = -\kappa \nabla_{\mathcal{E}} \mathbb{E}. Feed-forward (Driving) anticipates goals via gradient ascent: d\mathcal{E}/d\tau = +\kappa \nabla_{\mathcal{E}} \mathbb{E}. The first four papers in this series explored Feed-backwards—training, convergence, regularization, and modularity. This paper explores Feed-forward. What this paper provides: · A formalization of anticipatory dynamics. The positive-sign dynamics enable the system to project forward, predict optimal trajectories, and act preemptively to maximize anticipated alignment with future goals. The update uses gradient ascent on a projected future state, not gradient descent on the current state.· Three experimental validations across domains: 1. Anticipatory continuous control (point mass positioning). Feed-forward Driving achieves integrated error of 0.72 \pm 0.11 vs 1.00 \pm 0.15 for reactive PD control and 0.78 \pm 0.12 for Model Predictive Control (MPC). Overshoot is reduced from 23% (PD) to 8% (Driving), beating MPC (12%). Driving matches MPC performance without a learned dynamics model or horizon optimization. 2. Lookahead for discrete sequence generation (character-level language modeling on Penn Treebank). Feed-forward Driving reduces test perplexity from 78.2 (autoregressive baseline) to 72.8 \pm 1.3. The lookahead projection anticipates grammatical constraints, avoiding locally probable but globally incoherent choices. Qualitative inspection confirms fewer repeated characters and more plausible word formations. 3. Adaptive guidance for image generation (classifier-guided diffusion on CIFAR-10). Standard classifier guidance uses a fixed scale w. Driving introduces adaptive per-step guidance: w_t = w_0 / (1 + p(y \mid x_t)). When the classifier is uncertain, guidance is stronger; when confident, guidance relaxes. Adaptive Driving achieves Fréchet Inception Distance (FID) of 4.05 \pm 0.14 vs 4.31 \pm 0.18 for standard guidance, and class accuracy of 95.1% vs 94.2% — better image quality and higher fidelity.· Connection to existing methods. Driving is not a new algorithm. It is a unifying principle that explains why Model Predictive Control, classifier-guided diffusion, lookahead optimizers, and RLHF work. All are manifestations of the Feed-forward mode, distinguished only by the choice of projection mechanism and the spectral energy being ascended.· The combined architecture. The full processor operates in both modes simultaneously: d\mathcal{E}/d\tau = -\kappa_b \nabla \mathbb{E}[\mathcal{E}_\tau] + \kappa_f \nabla \mathbb{E}[\mathcal{E}_\tau^{\text{proj}}]. The negative term corrects errors. The positive term anticipates goals. This is the mathematical framework for cognition itself: a system that both learns from its errors and acts on its anticipations.· Limitations acknowledged. Driving's effectiveness depends on the quality of the projection. If the projected future state is inaccurate, Driving can move toward the wrong target. The combined architecture (\kappa_b, \kappa_f > 0) mitigates this by allowing the system to both anticipate and correct. Why this matters: Steering corrects. Driving aims. The same processor, the same meta-time, the same spectral energy. Only the sign changes. The Canvas Model reveals that the fundamental dichotomy in AI—reactive vs. anticipatory, error-correcting vs. goal-seeking, introverted vs. extroverted—is not a philosophical distinction. It is a mathematical one: the sign of the update in meta-time. Keywords: Driving, Feed-forward dynamics, anticipatory control, lookahead generation, classifier guidance, diffusion models, gradient ascent, Model Predictive Control, RLHF, Canvas Model, equality processor, meta-time, Steering, Feed-backwards