What it does: It lets a language model *close its own reasoning loops* inside embedding space — without modifying the model or retraining.
How it works: - Implements a mini-loop solver that drives semantic closure via internal ΔS/ΔE (semantic energy shift) - Uses prompt-only logic (no finetuning, no API dependencies) - Converts semantic structures into convergent reasoning outcomes - Allows logic layering and intermediate justification without external control flow
Why this matters: Most current LLM architectures don't "know" how to *self-correct* reasoning midstream — because embedding space lacks convergence rules. This engine creates those rules.
GitHub: https://github.com/onestardao/WFGY
Happy to explain anything in more technical detail!