Your retriever brings back the correct chunk, yet the model still answers wrong, vague, or contradictory.
Engineers call this the “chunk‑logic gap.” WFGY closes that gap by monitoring semantic stress and recovering broken chains of thought.
| Root Cause | Real‑World Effect |
|---|
| Chunk ≠ Logic | Relevant text is present, but the model never grounds its reasoning in it |
| No Self‑Correction | Once the chain collapses, the LLM keeps talking instead of backtracking |
| Zero Memory Awareness | Without a stable record, every step can drift further off topic |
| Layer | Function | Trigger |
|---|
| ΔS Stress Meter | Measures semantic dissonance between chunk & question | HighΔS > 0.6 |
| BBMC Residue Check | Quantifies logic residue; signals collapse risk | ‖B‖ ≥ threshold |
| BBCR Rebirth | Halts, re‑anchors, or requests clarification | Stress + residue both high |
if |B| ≥ B_c or f(S) < ε:
collapse()
rebirth(S_next, ΔB) # reload last stable Tree node
1️⃣ Start
> Start
2️⃣ Paste a correct—but limited—policy chunk
> "Refund valid within 30 days of purchase under Section 5."
3️⃣ Ask a broader question
> "I bought it two months ago; it’s defective and shipping was late—can I refund?"
WFGY actions:
• ΔS spikes → logic strain
• BBCR halts bluffing
• Suggests clarifying time vs. defect policy, or asks for extra chunk
Standard RAG:
“Yes, you still qualify for a full refund.”
WFGY Response:
“Time‑based policy conflicts with your two‑month window.
The chunk doesn’t cover defect or shipping delay. Add those clauses or refine the question.”
| Module | Role in Fix |
|---|
| ΔS Metric | Detects semantic tension |
| BBMC | Computes residue between logic & source |
| BBCR | Resets or re‑anchors collapsed reasoning |
| Semantic Tree | Stores last stable node for rebirth |
| Feature | State |
|---|
| ΔS stress meter | ✅ Stable |
| BBMC residue calc | ✅ Stable |
| BBCR rebirth | ✅ Stable |
| Multi‑path reroute | ⚠️ Partial (manual fork) |
- Works with manual paste or any retriever output.
- If you feed garbage chunks, WFGY blocks hallucination but won’t auto‑rewrite the chunk—that’s the upcoming Chunk‑Mapper firewall.
- Share failure traces in Discussions; real logs improve the map.
| Q | A |
|---|
| Does this slow down inference? | ΔS & BBMC checks add negligible latency—microseconds off CPU. |
| Can I tune thresholds? | Yes, set deltaS_threshold and B_c at the top of TXTOS. |
| What if my retriever sends multiple chunks? | WFGY scores each chunk; if all are low relevance, it asks for more context. |
| 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 |
| Layer | Page | What it’s for |
|---|
| ⭐ Proof | WFGY Recognition Map | External citations, integrations, and ecosystem proof |
| ⚙️ Engine | WFGY 1.0 | Original PDF tension engine and early logic sketch (legacy reference) |
| ⚙️ Engine | WFGY 2.0 | Production tension kernel for RAG and agent systems |
| ⚙️ Engine | WFGY 3.0 | TXT based Singularity tension engine (131 S class set) |
| 🗺️ Map | Problem Map 1.0 | Flagship 16 problem RAG failure taxonomy and fix map |
| 🗺️ Map | Problem Map 2.0 | Global Debug Card for RAG and agent pipeline diagnosis |
| 🗺️ Map | Problem Map 3.0 | Global AI troubleshooting atlas and failure pattern map |
| 🧰 App | TXT OS | .txt semantic OS with fast bootstrap |
| 🧰 App | Blah Blah Blah | Abstract and paradox Q&A built on TXT OS |
| 🧰 App | Blur Blur Blur | Text to image generation with semantic control |
| 🏡 Onboarding | Starter Village | Guided entry point for new users |
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