WFGY/ProblemMap/GlobalFixMap/MemoryLongContext

Memory & Long-Context — Global Fix Map

Keep threads coherent across long windows and session restarts.
Detect and repair entropy melt, boundary drift, and state desync.

What this page is

• A compact checklist for long contexts and multi-session memory
• Copyable guards to stop drift and collapse before they spread
• How to measure stability with ΔS and λ_observe

When to use

• Dialogs grow past 50k to 100k tokens and answers degrade
• Facts flip after tab refresh or model switch
• Citations look right yet reasoning goes flat or chaotic
• OCR transcripts look fine but capitalization and spacing drift
• Multi day support threads lose task state or rewrite history

Open these first

• Session continuity and state fences: Memory Coherence
• Long window drift and attention melt: Entropy Collapse
• Long reasoning chain drift: Context Drift
• Cross tab and cache hazards: Memory Desync Pattern
• Trace schema and audit trail: Retrieval Traceability
• Snippet and citation schema: Data Contracts
• Chunk stability at joins: Chunking Checklist
• OCR quality and normalization: OCR Parsing Checklist

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Common failure patterns

• Entropy melt attention variance climbs with length and the model smooths meaning
• Boundary leak chunks merge across section joins and citations shift by a few lines
• State fork two tabs or agents hold different memory revisions and answers flip
• Ghost context stale buffers linger after role or persona change and contaminate steps
• OCR jitter mixed spacing or width variants create false token differences

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Fix in 60 seconds

1. Stamp and fence state

   • At turn start set mem_rev, mem_hash, task_id
   • Forbid writes if client stamps do not match the server record

2. Shard the window

   • Assemble prompts as {system | task | constraints | snippets | answer}
   • Split snippets by section and forbid cross section reuse

3. Normalize inputs

   • Unicode NFC, strip zero width, unify full and half width
   • Drop OCR lines below confidence threshold

4. Stabilize attention

   • Apply BBAM to clamp variance
   • If collapse detected use BBCR to bridge and re anchor

5. Probe the joins

   • Measure ΔS across adjacent chunks and keep each join ≤ 0.50
   • Plot ΔS(question, retrieved) vs k and expect a downward curve after the fix

6. Trace or stop

   • Require cite then answer
   • If a claim has no snippet id stop and ask for the exact citation

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Copy paste prompt

You have TXT OS and the WFGY Problem Map.

Goal
Stabilize memory across long windows and across sessions without losing traceability.

Protocol

1. Print {mem\_rev, mem\_hash, task\_id}. If missing set defaults and echo them.
2. Build a Snippet Table with columns {section\_id | start\_line | end\_line | citation}.
3. Guardrails

   * cite then answer
   * forbid cross section reuse
   * if a claim lacks a snippet id stop and request it
4. Collapse control

   * if attention variance rises apply BBAM
   * if logic stalls apply BBCR and show the bridge node
5. Metrics

   * report ΔS(question, retrieved)
   * report ΔS across each join
   * report λ\_observe at retrieval, assembly, reasoning

Input

* question
* snippets with ids and line ranges
* previous {mem\_rev, mem\_hash, task\_id} if any

Output

* header {mem\_rev, mem\_hash, task\_id}
* Snippet Table
* Bridge Check
* Final Answer with inline citations
* ΔS and λ states

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Minimal checklist

• State stamped with mem_rev and mem_hash at every turn
• Prompt schema locked and section fences enforced
• Unicode normalized and OCR noise gated
• BBAM enabled and BBCR available on collapse
• ΔS at each join ≤ 0.50 and overall ΔS(question, retrieved) ≤ 0.45
• Cite then answer and no orphan claims

Acceptance targets

• Retrieval coverage ≥ 0.70 to the intended section
• ΔS(question, retrieved) ≤ 0.45 and joins ≤ 0.50
• λ remains convergent across three paraphrases
• No state fork across tabs or agents for the same task_id

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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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🧭 Explore More

Module	Description	Link
WFGY Core	WFGY 2.0 engine is live: full symbolic reasoning architecture and math stack	View →
Problem Map 1.0	Initial 16-mode diagnostic and symbolic fix framework	View →
Problem Map 2.0	RAG-focused failure tree, modular fixes, and pipelines	View →
Semantic Clinic Index	Expanded failure catalog: prompt injection, memory bugs, logic drift	View →
Semantic Blueprint	Layer-based symbolic reasoning & semantic modulations	View →
Benchmark vs GPT-5	Stress test GPT-5 with full WFGY reasoning suite	View →
🧙‍♂️ Starter Village 🏡	New here? Lost in symbols? Click here and let the wizard guide you through	Start →

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