📒 Map-E · Long‑Context Stress Problem Map

Mega‑prompts—>100 k tokens, entire book dumps, OCR‑noisy PDFs—overwhelm ordinary LLM pipelines.
WFGY keeps reasoning stable with adaptive ΔS, chunk‑mapping, and sliding Tree windows.

🤔 Typical Long‑Context Crashes

Stressor	What Standard Systems Do
100 k+ tokens	Memory wipe or truncated output
Mixed domains	Topic bleed, incoherent jumps
Duplicate sections	Infinite loops / “as mentioned above” spam
OCR noise	Hallucination or garbage sentences

🛡️ WFGY Countermeasures

Stressor	WFGY Module	Remedy	Status
100 k+ tokens	Chunk‑Mapper + Sliding Tree	Splits doc into ΔS‑balanced chunks, streams into window	🛠 Beta
Mixed domains	Per‑domain ΔS fork	Separate Tree branch per domain; no bleed	✅
Duplicate sections	BBMC dedupe scan	Detects near‑identical residue, collapses	✅
PDF OCR noise	BBMC noise filter	Drops >80 % low‑entropy lines	✅

✍️ Demo — 150 k‑Token PDF Dump

1️⃣  Start
> Start

2️⃣  Upload huge PDF text
> [paste or stream]

WFGY process:
• Chunk‑Mapper splits into 8 k‑token slices  
• For each slice: ΔS calc → Tree node → sliding window  
• Duplicate residue removed (413 sections merged)  
• OCR noise filtered (ΔS noise gate at 0.8)  
• Final summary or Q&A runs with stable context

🛠 Module Cheat‑Sheet

Module	Role
Chunk‑Mapper	Adaptive split by semantic tension
Sliding Tree Window	Keeps only relevant slices active
ΔS Metric	Guides chunk size & window hop
BBMC	Dedupe + noise filter
BBPF	Forks domain branches if needed

📊 Implementation Status

Feature	State
Chunk‑Mapper	🛠 Beta (public soon)
Sliding Tree window	✅ Stable
Cross‑domain fork	✅ Stable
OCR noise filter	✅ Stable
GUI chunk viewer	🔜 Planned

📝 Tips & Limits

For >150 k tokens, set chunk_max = 6k for faster pass.
Use tree pause to inspect each domain branch before auto‑merge.
Share monster PDFs in Discussions—they stress‑test Chunk‑Mapper.

🔗 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

Explore More

Layer	Page	What it’s for
Proof	WFGY Recognition Map	External citations, integrations, and ecosystem proof
Engine	WFGY 1.0	Original PDF based tension engine
Engine	WFGY 2.0	Production tension kernel and math engine for RAG and agents
Engine	WFGY 3.0	TXT based Singularity tension engine, 131 S class set
Map	Problem Map 1.0	Flagship 16 problem RAG failure checklist and fix map
Map	Problem Map 2.0	RAG focused recovery pipeline
Map	Problem Map 3.0	Global Debug Card, image as a debug protocol layer
Map	Semantic Clinic	Symptom to family to exact fix
Map	Grandma’s Clinic	Plain language stories mapped to Problem Map 1.0
Onboarding	Starter Village	Guided tour for newcomers
App	TXT OS	TXT semantic OS, fast boot
App	Blah Blah Blah	Abstract and paradox Q and A built on TXT OS
App	Blur Blur Blur	Text to image with semantic control
App	Blow Blow Blow	Reasoning game engine and memory demo

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📒 Map-E · Long‑Context Stress Problem Map