[Paper] WFGY 1.0: A Universal Semantic Kernel for Self-Healing LLMs

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Note: English isn’t my native language, so I use AI to help present my work clearly. Thanks for understanding!

Hi everyone,

This is my very first post on Hugging Face, and I’m excited (and a little nervous) to share something special.
I’m an independent developer and this is a research paper I wrote myself. Inside, I introduce four original concepts that directly repair common LLM issues.

I personally believe this is a big breakthrough — not only because of the results, but because all improvements are achieved purely through prompt engineering. As far as I know, this is something quite new in the field.

So, here it is — I’d love to share my work and get your feedback!

[Paper] WFGY 1.0: Semantic Kernel for Self-Healing LLMs

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:bar_chart: Semantic Accuracy ↑ 22.4% | Reasoning Success Rate ↑ 42.1% | Stability ↑ 3.6×
(Benchmarked on MMLU, GSM8K, VQAv2, OK-VQA, etc.)

Paper Here: WFGY 1.0: A Universal Unification Framework for Large-Scale Self-Healing LLMs

Overview:
WFGY 1.0 (“All Principles Return to One”) is a mathematical framework designed to instantly upgrade the semantic reasoning and stability of any LLM — no installation, no retraining, no fine-tuning required.

This paper is the product.
Simply upload the PDF to any modern LLM that supports document reading, and the model will immediately gain access to WFGY’s semantic kernel and methods.
All benchmarks show significant gains in accuracy, reasoning, and stability, across a wide range of models and domains.

Zenodo is a CERN-backed international open science archive — all files are virus-free, citable, and safe for direct download.

:test_tube: Try It Yourself: Prompt Set

For best results, I’ve published a suite of prompts you can copy-paste directly into your favorite LLM or AI platform, find it in my GitHub

The prompt pack includes evaluation templates, semantic stress tests, and some hidden surprises for curious testers.

If you’re interested in semantic reasoning, self-healing AI, or making your LLMs more robust without retraining, I’d love your feedback!
Full mathematical proofs, benchmarks, and results are in the paper.

Developed and independently evaluated by PSBigBig.

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Hey there it’s exciting to see more researchers exploring semantics, symbolics, prompting evolutions, and agentic healing. I believe we may be exploring similar directions by measuring meaning/information density under constraint with the concept of Symbolic Residue, similar to your Semantic Residue. The convergence in concepts even though we live across the world from each other is quite interesting to me.

I’m a psychology researcher and developer. I’m part of a team of researchers and engineers exploring Evolutionary AI, with a focus on bridging recursive reasoning, symbolics, adaptive context and frontier machine learning architectures.

Here’s one of our projects on symbolics, as well the preprints we submitted to NeurIPS. Let me know if youd to like to discuss any topics further:

Symbolic Residue Diagnostic Suite: Tracks and diagnoses transformer model failure modes: silent inconsistencies or “residues” in reasoning paths. The structural data vectors behind why advanced reasoning fails.

Self-Tracing: Building on Anthropic’s Circuit Tracer, Neuronpedia, and Circuit Tracing (Lindsey et al., 2025), we attempt to extend the paradigm with novel schemas to enable recursive self-interpretation, where models continuously monitor, trace, and explain their own decision processes, presented as interactive artifacts hosted on each frontier AI’s system.

Langton’s Emergence (personal passion):

Recently I have been researching emergent complexities through first principles reductionism of Langton’s Ant and related cellular automata in the hopes that they could potentially offer insights into the emergent intricacies of frontier large language models.

Preprints of NeurIPS 2025 Position Papers:

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