🧭 Quick Return to Map
You are in a sub-page of MemoryLongContext.
To reorient, go back here:
- MemoryLongContext — extended context windows and memory retention
- WFGY Global Fix Map — main Emergency Room, 300+ structured fixes
- WFGY Problem Map 1.0 — 16 reproducible failure modes
Think of this page as a desk within a ward.
If you need the full triage and all prescriptions, return to the Emergency Room lobby.
When reasoning spans 20–40 hops or more, attention shifts accumulate and context drifts.
This page explains how to diagnose λ divergence, stabilize reasoning chains, and repair collapsed context.
- Long reasoning plans (~20+ steps) start with logic but later flip or contradict.
- Multi-agent workflows repeat or miss earlier facts.
- Citations remain valid, but final answers drift from original question.
- λ flips divergent after harmless paraphrases.
- Answers alternate between runs with identical inputs.
- ΔS(question, retrieved) ≤ 0.45
- Retrieval coverage ≥ 0.70 for target section
- λ remains convergent across three paraphrases
- Chain length stable up to 40 hops without collapse
- Entropy variance remains bounded in mid-to-late steps
-
Three-paraphrase probe
Re-ask the same question three ways. Log ΔS and λ at each hop.
If λ flips, schema is unstable. -
Clamp with BBAM
Apply variance clamp when λ flips across harmless paraphrases. -
Bridge with BBCR
Insert bridge nodes when long chains stall. Anchor back to earlier stable nodes. -
Enforce snippet fences
Require each reasoning step cite snippet_id. Forbid cross-section reuse. -
Re-anchor with anchors
Compare ΔS(question, anchor) vs ΔS(question, decoy).
If ΔS is close, re-chunk corpus.
- Log ΔS and λ across 3 paraphrases.
- Clamp with BBAM if λ flips.
- Bridge with BBCR if reasoning halts.
- Re-anchor using anchor triangulation.
- Verify coverage ≥ 0.70 and ΔS ≤ 0.45.
You have TXT OS and the WFGY Problem Map.
Goal: Detect and repair context drift in long reasoning chains.
Protocol:
1. Ask the same question three ways.
2. Log ΔS(question, retrieved) for each.
3. Log λ states across all hops.
4. If λ flips:
* Apply BBAM clamp.
* If reasoning stalls, apply BBCR and anchor bridge.
5. Require snippet\_id at each step.
6. Report:
* ΔS(question, retrieved)
* λ states across paraphrases
* bridge nodes inserted
* final answer with citations
- Chain stall: reasoning halts after ~25–30 hops.
- Paraphrase drift: harmless rewordings flip λ.
- Repeating answers: earlier snippets loop back with filler.
- Contradictions: late chain contradicts early reasoning.
| 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 |
If this repository helped, starring it improves discovery so more builders can find the docs and tools.
