NN5: Driving: Feed-Forward Dynamics for Goal-Directed Learning — Anticipatory Control, Lookahead Generation, and Adaptive Guidance
Abstract
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…
Read the paper
Related studies
- Learning from First-Generation Qualitative Approaches in the IS Discipline: An Evolutionary View and Some Implications for Authors and Evaluators (PART 1/2)
- Commentary on the clinical and preclinical dosage limits of interstitially administered magnetic fluids for therapeutic hyperthermia based on current practice and efficacy models
- Prosthesis: New Technological Opportunities and Innovative Biomedical Devices
- The history and future of neural modeling for cochlear implants
- Ryanodine receptors: waking up from refractoriness
- Novel Transgenic Rabbit Model Sheds Light on the Puzzling Role of Matrix Metalloproteinase-12 in Atherosclerosis
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.