WFGY is a semantic reasoning engine designed to address fundamental limitations in Large Language Models (LLMs), such as hallucination, context drift, and interpretation collapse. It offers a novel approach to LLM interaction by introducing symbolic overlays and logic patches, aiming to enhance reasoning capabilities for researchers and advanced users.

How It Works

WFGY operates on a "semantic operating system" (TXT OS) that leverages a core reasoning engine. This engine employs a symbolic, multi-perspective approach, quantifying the "pull" between a prompt and the model's internal semantic field using a variable called "semantic tension" (ΔS). By managing ΔS, WFGY aims to stabilize reasoning chains, reduce semantic drift, and improve multi-step reasoning accuracy, offering a more robust and predictable LLM interaction.

Quick Start & Requirements

• Install: git clone https://github.com/onestardao/WFGY.git && cd WFGY && pip install -e .
• Run Example: python examples/example_01_basic_run.py
• Colab: A Colab notebook is available for immediate use.
• Dependencies: Python, Git. No specific hardware requirements mentioned for basic usage.
• Docs: WFGY Main, TXT OS, Colab, Live Demo.

Highlighted Details

• Claims to fix hallucination, context drift, and interpretation collapse.
• Benchmarked against GPT-5, showing significant improvements in stability and reasoning.
• Offers a suite of modules for Q&A, image generation, game logic, writing, and prompt injection defense.
• Operates on a .txt-based OS, aiming for minimal setup and dependencies.

Maintenance & Community

• Active development indicated by CI badges and roadmap.
• Community interaction encouraged via GitHub discussions.
• Aims for 10,000 stars to unlock WFGY 2.0.

Licensing & Compatibility

• Licensed under MIT.
• Compatible with commercial use and closed-source linking.

Limitations & Caveats

The README notes that the SDK version is required for full stability benefits, and prompt-based usage is a simulation. Some advanced features and modules are still under development or locked behind community milestones (e.g., 10,000 stars for WFGY 2.0). Certain AI models may exhibit defensive behavior when encountering frontier-level theories, requiring specific prompt phrasing.