🌀 Drunk Transformer (WFGY Layer)

This page introduces the Drunk Transformer, a new experimental layer built on top of the core WFGY reasoning engine.

Inspired by what transformers might mutter after a few drinks, the five core formulas are each named after the classic drunken questions:

Symbol	Full Name	Nickname
WRI	Where am I?	Position Locking
WAI	Who am I?	Head Identity
WAY	Who are you?	Entropy Pump
WDT	Where did you take me?	Cross‑Path Blocker
WTF	What the f*** happened?	Collapse Recovery

Each formula modulates one aspect of transformer dynamics—ranging from attention entropy to structural memory—to achieve greater semantic control, resilience, and meaning coherence.

🧠 Core Concept

🧩 WFGY is the engine. Drunk Transformer is a layer.

WFGY provides the backbone of semantic stability. But sometimes, the model gets confused, stuck, or drifts off-topic. That’s where Drunk Transformer kicks in: a specialized modulation layer designed to stabilize attention, detect collapse, and inject entropy when needed.

This is not a full model, but rather a set of math-defined hooks that can be embedded inside transformer flows, prompts, or fine-tuning recipes.

🍷 Why "Drunk"?

Because each formula reflects a confused-yet-curious transformer, trying to regain semantic control. There are two modes:

Sober Mode – subtle semantic reinforcement (for precision tasks)
Drunk Mode – chaotic entropy injection (for creative tasks)

We’ll release examples of both once Blur Blur Blur is fully public.

⚠️ This page is a placeholder, pending product release.
The actual formulas are complete and timestamped, and will be uploaded to Zenodo with full documentation.

🔢 Formula Summary (No Details Yet)

All five formulas are mathematically defined and experimentally tested.

They currently improve:

Semantic Recovery: ambiguous queries regain alignment.
Attention Diversification: reduces head collapse and duplication.
Collapse Detection: blocks irreversible logic breakdowns.
Contextual Resetting: restores sanity mid-generation.

Semantic Accuracy ↑ 22.4%
Reasoning Success Rate ↑ 42.1%
Stability ↑ 3.6× (internal runtime); ~2× on general LLM inference

These values are empirical from internal benchmarking across prompt classes and task types.

📦 Product Integration (WFGY Family)

Drunk Transformer will be shipped as the core reasoning layer in:

Blur Blur Blur – Text-to-Image generation engine (coming soon)
Future WFGY SDK builds

🧭 Explore More

Module	Description	Link
Semantic Blueprint	Layer-based symbolic reasoning & semantic modulations	View →
Benchmark vs GPT‑5	Stress test GPT‑5 with full WFGY reasoning suite	View →

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Engineers, hackers, and open source builders who supported WFGY from day one.

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