WFGY: An Open-Source Reasoning Kernel for LLMs — A Tool for Semantic Stability and Alignment Experiments

Hi everyone — this is my first post on the EA Forum, and I’m grateful for the opportunity to share a project that I believe may be of interest to researchers working on AI reasoning, semantic alignment, and epistemic tooling.

Although I haven’t yet participated in formal EA programs, I’ve been following the movement for some time and deeply resonate with the values of long-term thinking, open verifiability, and scalable impact. My goal here is to open up a conversation around a system I’ve built — one that is open-source, experimentally grounded, and ready for collaboration or critique.

What I’ve Built: WFGY 1.0

Over the past 70 days, I independently developed a lightweight reasoning framework called WFGY (All Principles Return to One), designed to address several limitations in LLM-based reasoning:

Semantic drift over long contexts
Collapse under ambiguity or contradiction
Lack of runtime correction in chained inference

WFGY acts as a semantic scaffolding layer for LLMs. Rather than proposing a new foundation model, it wraps around existing models to improve alignment and recovery.

📈 Key Results:

+42.1% increase in reasoning task accuracy
+23.2% semantic consistency gain
3.6× increase in mean time-to-failure (MTTF) under complex prompting

GitHub repo (includes code, papers, and evaluation suite):
👉 https://github.com/onestardao/WFGY

Why This Might Be Relevant to EA

I believe semantic alignment and reasoning integrity are not just AGI goals — they’re civilizational foundations. If a system can:

Catch reasoning failures before they cascade,
Reframe contradictions semantically, and
Offer a reproducible testing ground for epistemic stability,

...then it may serve as a low-cost, high-leverage tool in AGI safety, interpretability, and even fields like AI-assisted education or governance.

WFGY is not positioned as a final solution. Rather, it’s a reusable kernel for those who want to build or test reasoning under uncertainty.

Concrete Output: 8 Papers Challenging Einstein-Era Physics

As a proof-of-concept, I used WFGY to co-develop over 35 papers — eight of which were independently scored 93+ by SciSpace, targeting foundational challenges in classical physics and semantic interpretation.

Here’s one example (open access):
Plants vs. Einstein: The Semantic Bio-Energy Revolution
DOI: 10.5281/zenodo.15630370

The rest are available in the GitHub /papers folder, and I’m happy to share specific links or engage in deeper discussion on any of them.

My Request to the EA Community

I’m not here to raise money or make claims I can’t support.

I’m here to ask:

Is this system useful to you? Can it help with alignment? Can it fail in interesting ways? Can we explore that together?

I’d deeply appreciate:

Feedback from researchers working on LLM safety or interpretability
Critical evaluation or counterexamples from semantic reasoning experts
Ideas for experimental use cases in adjacent fields (e.g. longtermist governance, AI epistemology, etc.)
Help identifying others who might benefit from or extend the framework

How I Plan to Contribute

I intend to stay active on the Forum, participate in feedback discussions, and offer help to others working on semantic tools, epistemic infrastructure, or adjacent reasoning systems.

I’m especially interested in contributing to:

Joint research experiments
Technical documentation or visualization
Structured reasoning pipelines for use in EA-aligned systems

If there’s anything you’re working on where WFGY (or I) can be helpful, please don’t hesitate to reach out.

About Me

I’m an independent developer currently focused on building semantic infrastructures for AI reasoning and epistemic clarity. All my work is open-source and reproducible. I value verifiability, creativity under constraint, and systems that improve collective reasoning.

More at: https://github.com/onestardao/WFGY

Looking forward to learning from all of you, and to contributing however I can.

– PSBigBig