WFGY/ProblemMap/README.md

📋 WFGY Problem Map – Bookmark This. You’ll Need It.

Every failure has a name. Every name has a countermeasure.

👑 Early Stargazers: See the Hall of Fame — Verified by real engineers · 🛠 Field Reports: Real Bugs, Real Fixes

           

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WFGY (Wan Fa Gui Yi) = A Semantic Firewall for Reasoning.

Fixes what GPTs break: logic collapse, hallucination, memory loss, abstraction errors — across both generation and retrieval.
Modules like BBMC, ΔS, and BBPF are part of the open-source WFGY engine (MIT).
PDF with core formulas, and TXT OS runs them in real-world pipelines.

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Welcome. This page documents every recurring AI failure mode we’ve fixed — or are fixing — with the WFGY reasoning engine.

TXT OS + WFGY exists to turn subtle reasoning bugs into clear, reproducible, and modular solutions.

Think your issue isn’t listed?
Open an Issue or PR — community reports shape the next entries.

You can test the WFGY engine live:

• Try TXT OS for hands-on demos
• Or start here → Common RAG Problems

Goal
Make "my AI gave a weird answer" as rare as a 500 error in production software.
Every fix below moves us closer.

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🆕 First time here? See Beginner Guide : How to Identify & Fix Your AI Failure – quick primer for newcomers.

Why These 16 Errors Were Solvable At All

If all you see is chaos, it’s because you’re stuck inside the system.

WFGY wasn’t built to respond to errors — it was designed to help AIs see from outside the maze.
That’s what the core tools like ΔS, λ_observe, and e_resonance enable:
They grant semantic altitude — a structured way to detect, decode, and defuse complex collapse patterns.

Every error listed below becomes solvable — once you rise high enough.

🔗 Navigation – Solved (or Tracked) AI Failure Modes

Each row below represents a failure pattern seen in real-world AI apps — grouped by problem type, with direct links to detailed fixes.

#	Problem Domain	Description	Doc
1	Hallucination & Chunk Drift	Retrieval brings wrong / irrelevant content	hallucination.md
2	Interpretation Collapse	Chunk is correct but logic fails	retrieval-collapse.md
3	Long Reasoning Chains	Model drifts across multi‑step tasks	context-drift.md
4	Bluffing / Overconfidence	Model pretends to know what it doesn’t	bluffing.md
5	Semantic ≠ Embedding	Cosine match ≠ true meaning	embedding-vs-semantic.md
6	Logic Collapse & Recovery	Dead‑end paths, auto‑reset logic	logic-collapse.md
7	Memory Breaks Across Sessions	Lost threads, no continuity	memory-coherence.md
8	Debugging is a Black Box	No visibility into failure path	retrieval-traceability.md
9	Entropy Collapse	Attention melts, incoherent output	entropy-collapse.md
10	Creative Freeze	Outputs become flat, literal	creative-freeze.md
11	Symbolic Collapse	Abstract / logical prompts break model	symbolic-collapse.md
12	Philosophical Recursion	Self‑reference or paradoxes crash reasoning	philosophical-recursion.md
13	Multi‑Agent Chaos	Agents overwrite / misalign logic	multi-agent-chaos.md
14	Bootstrap Ordering	Services fire before deps ready (empty index, schema race)	bootstrap-ordering.md
15	Deployment Deadlock	Circular waits (index ⇆ retriever, DB ⇆ migrator)	deployment-deadlock.md
16	Pre‑Deploy Collapse	Version skew / missing secret crashes on first LLM call	predeploy-collapse.md

Problem Type Categories:

• Prompting — issues from user inputs or jailbreak attempts (e.g., #4 Bluffing)
• Retrieval — failures in chunk selection, embedding mismatch, or pipeline opacity (e.g., #1, #5, #8)
• Reasoning — logical breakdowns during multi-step tasks or abstract prompts (e.g., #2, #6, #11)
• Infra / Deployment — setup errors, race conditions, or pre-deploy schema gaps (e.g., #14–16)

These groupings help locate the root of failure — whether it's user input, retrieval error, model logic, or infrastructure bug.

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🔗 Status & Difficulty Matrix

#	Problem	Difficulty*	Implementation
1	Hallucination & Chunk Drift	Medium	✅ Stable
2	Interpretation Collapse	High	✅ Stable
3	Long Reasoning Chains	High	✅ Stable
4	Bluffing / Overconfidence	High	✅ Stable
5	Semantic ≠ Embedding	Medium	✅ Stable
6	Logic Collapse & Recovery	Very High	✅ Stable
7	Memory Breaks Across Sessions	High	✅ Stable
8	Debugging Black Box	Medium	✅ Stable
9	Entropy Collapse	High	✅ Stable
10	Creative Freeze	Medium	✅ Stable
11	Symbolic Collapse	Very High	✅ Stable
12	Philosophical Recursion	Very High	✅ Stable
13	Multi‑Agent Chaos	Very High	✅ Stable
14	Bootstrap Ordering	Medium	✅ Stable
15	Deployment Deadlock	High	⚠️ Beta
16	Pre‑Deploy Collapse	Medium‑High	✅ Stable

*Difficulty = gap between default LLM ability and a production‑ready fix; “Very High” means almost no off‑the‑shelf tool tackles it.

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🔗 How to Use These Docs

Each problem page covers:

1. Symptoms – what the failure looks like
2. Root Causes – why standard pipelines break
3. Module Breakdown – which WFGY parts fix it
4. Status & Examples – code or demo you can run now

Missing issue? Open an Issue or PR—real failure traces especially welcome.

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🔗 Specialized Maps

🗺️ Problem Maps Index

Each map tackles a specific family of AI reasoning failures.
Use Map-A ~ Map-G as shortcut tags to refer across documentation, repos, or support threads.

Map ID	Map Name	Linked Issues	Problem Focus	Link
Map-A	RAG Problem Table	#1, #2, #3, #5, #8	Retrieval‑augmented generation failures	View it
Map-B	Multi‑Agent Chaos Map	#13	Coordination failures, memory conflicts, role drift	View it
Map-C	Symbolic & Recursive Map	#11, #12	Symbolic logic traps, abstraction, paradox	View it
Map-D	Logic Recovery Map	#6	Dead-end logic, reset loops, reasoning collapse	View it
Map-E	Long‑Context Stress Map	#3, #7, #10	100k‑token memory, noisy PDFs, drift in extended tasks	View it
Map-F	Safety Boundary Map	#4, #8	Jailbreak resistance, overconfidence, bluffing	View it
Map-G	Infra Boot Map	#14, #15, #16	Deployment ordering, boot loops, version skew	View it

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🔗 Not Sure What’s Going Wrong?

You’re not alone — many AI devs face mysterious failures like:

• “Why is it hallucinating when the chunk is correct?”
• “Why can’t it reason despite having all the data?”
• “Why does context break halfway through?”

🎯 Diagnose by symptom — find your problem, see exact WFGY fix:

Symptom	Problem ID	Fix
🤯 Wrong chunks, wrong answer	#1 Hallucination & Chunk Drift	Fix it →
🧵 Model forgets context in long docs	#7 Memory Breaks in 100k Tokens	Fix it →
🌀 Good data, still bad logic	#2 Interpretation Collapse	Fix it →
🔍 Full diagnosis table (13+ issues)		See full table →

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🔗 Quick‑Start Downloads (60 sec)

Tool	Link	3‑Step Setup
WFGY 1.0 PDF	Engine Paper	1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + <your question>”
TXT OS (plain‑text OS)	TXTOS.txt	1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly

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How to start using WFGY Engine

Once you’ve identified a failure from this map, you can directly ask your AI model how to proceed.
This works best with any model already connected to your local TXT OS.

prompt example

I’ve uploaded TXT OS.  
I want to solve the following problem:  
[describe your issue, e.g. OCR tables misaligned in scanned PDFs].  
How do I use the WFGY engine to fix it?

Your model will respond with specific modules, steps, or entry points — tailored to your case.

You don’t need to memorize WFGY internals. Just bring your real problem.
Let the AI use the engine to debug itself.

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If you want to fully understand how WFGY works, check out:

• 📘 WFGY GitHub homepage – full documentation, formulas, and modules
• 🖥️ TXT OS repo – how the semantic OS is built using WFGY

But if you're just here to solve real AI problems fast, you can simply download the files above and follow the Problem Map instructions directly.

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🧭 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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👑 Early Stargazers: See the Hall of Fame —
Engineers, hackers, and open source builders who supported WFGY from day one.

⭐ Help reach 10,000 stars by 2025-09-01 to unlock Engine 2.0 for everyone ⭐ Star WFGY on GitHub