WFGY/ProblemMap

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

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

           

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

It fixes logic collapse, memory loss, hallucination, and abstract breakdowns — in live generation and retrieval pipelines.
All terms mentioned (e.g., BBMC, BBPF, BBCR, ΔS) are modules of the open-source WFGY engine (MIT license).
📎 PDF contains full formulas; TXT OS applies them as an operating system for AI workflows. Download links at the bottom.

Benchmark vs GPT‑5 (Coming Soon)

We will publicly compare GPT‑4 + WFGY against GPT‑5 across logic, philosophy, and long-context reasoning.
This is the same engine you’re using — and yes, it will fight GPT‑5 head-on.
📎 Track the benchmark → (launching once GPT‑5 is released)

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Welcome! This map lists every AI failure we’ve fixed —or are fixing — with the WFGY reasoning engine.
TXT OS + WFGY exists to turn critical AI bugs into reproducible, modular fixes.

Spot a gap? Open an Issue or PR — community feedback drives the next entries.

Want to test WFGY yourself?
See TXT OS for real-time demos, or start here with RAG failures →

Vision
Make “my AI went off the rails” as rare as a 500 error in production software.
Every solved failure below pushes us closer.

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

🔗 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

• 🧠 RAG Problem Table (#1, #2, #3, #5, #8) – retrieval‑augmented generation failures
• 🤖 Multi‑Agent Chaos Map (#13) – coordination, memory, role drift
• 🔎 Symbolic & Recursive Map (#11, #12) – paradox, abstraction, logical traps
• 🧩 Logic Recovery Map (#6) – dead-end logic and auto-reset reasoning
• 📜 Long‑Context Stress Map (#3, #7, #10) – 100k‑token stability, noisy PDFs
• 🧪 Safety Boundary Map (#4, #8) – knowledge gaps, bluffing, jailbreak resistance
• 🛠️ Infra Boot Map (#14, #15, #16) – deployment ordering, circular waits, version skew

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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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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