I went through the structure and found the semantic correction idea pretty intriguing.

Can you explain a bit more about how WFGY actually achieves such improvements in reasoning and stability? Specifically, what makes it different from just engineering better prompts or using more advanced LLMs?

Great question—and I totally get the skepticism. WFGY isn’t just another prompt hack, and it’s definitely not about making the prompts longer or more “creative.” Here’s the real trick:

    It’s a logic protocol, not just words: The core of WFGY is a semantic “kernel” (documented as a PDF protocol) that inserts logic checks into the model’s reasoning process. Every major step—like inference, contradiction detection, or “projection collapse”—is made explicit and then evaluated by the LLM itself.

    Why not just use a bigger model? Even top-tier models like GPT-4 or Llama-3 are surprisingly easy to derail with ambiguity, loops, or context drift—especially on complex reasoning. WFGY gives you a portable, model-agnostic way to stabilize any model’s outputs, by structuring the logic path directly in the prompt.

    Empirical results, not just vibes: On standard tasks, we saw over 40% improvement in multi-hop reasoning and a big drop in contradiction or instability—even when running on smaller models. All evaluation code and sample runs are included, so you can check or replicate the claims.