🧭 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.
Long-context retrieval often fails not at the level of whole documents but at the joins between chunks.
This checklist enforces stable, reproducible chunking so citations line up and entropy does not melt across boundaries.
- Citations drift by a few lines between runs.
- Long transcripts lose alignment after OCR or parsing.
- Model answers cover the right fact but cite the wrong block.
- ΔS spikes exactly at chunk joins.
- Different agents disagree on chunk IDs.
- Each join ΔS ≤ 0.50.
- Overall ΔS(question, retrieved) ≤ 0.45.
- Coverage ≥ 0.70 of intended section.
- λ remains convergent across 3 paraphrases.
- Each chunk has immutable
chunk_id,start_line,end_line.
-
Deterministic boundaries
Split on semantic units (sections, paragraphs, headings). Never by raw token count alone. -
Overlap fence
Add 10–15% overlap at joins. Enforce consistent overlap across every run. -
Immutable IDs
Generatechunk_id = sha256(doc_id + start_line + end_line). Store and reuse. -
Audit trail
Store{chunk_id, start_line, end_line, source_url, tokens}for every chunk. -
Normalization
Apply Unicode NFC, collapse whitespace, unify casing. -
Confidence gating
Drop OCR or parsing lines with low confidence before chunking.
- Re-chunk corpus using semantic units.
- Apply overlap fence and store immutable chunk IDs.
- Run ΔS probes at joins. If ΔS > 0.50, re-check boundaries.
- Store all chunk metadata in trace logs.
- Require cite-then-answer. Reject any orphan chunk references.
You have TXT OS and the WFGY Problem Map.
Task: enforce stable chunking.
Protocol:
1. Verify each snippet has {chunk\_id, start\_line, end\_line, section\_id, source\_url}.
2. Reject orphans: if citation lacks chunk\_id, stop and request fix.
3. Require cite-then-answer.
4. Probe ΔS across joins, keep ≤ 0.50.
5. Report ΔS(question,retrieved), ΔS(joins), and λ state.
- Answers cite correct fact but wrong block → chunk IDs not stable.
- ΔS spikes exactly at joins → overlap missing.
- OCR transcripts break alignment → normalization skipped.
- Multi-agent systems cite different chunk IDs → contract drift.
| 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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