Feasible Direction Methods in Transformer Architectures: An Open-Source Invention Disclosure

This document introduces a novel architecture for Large Language Models (LLMs) and neural sequence generators. It bridges the gap between modern attention mechanisms and the rigorous, active-set constrained optimization techniques developed for aerospace synthesis programs in the 1970s (such as CONMIN and ACSYNT). By embedding feasible-direction operators directly into the forward pass of a Transformer and the routing logic of a Mixture-of-Experts (MoE) network, this architecture forces the model to respect strict logical, syntactical, or safety boundaries $G(h, \Delta h) \le 0$ during token generation, rather than relying solely on unconstrained probabilistic search.