I'm the original author of this open-source reasoning engine.
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.
I'm the original author of this open-source reasoning engine.
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!
If you can't even do the prompt engineering to adapt the AI to HN's style, it's hard to believe that you're doing this work in any meaning
Actually I am really new to here. I will check the rules
welcome to leave any message here