Memory Coherence — Multi-Session and State Alignment

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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.

Keep multi-turn and multi-session dialogs stable by fencing memory state.
This page shows how to prevent forks, desync, and ghost buffers when conversations span long contexts or multiple agents.

When to use this page

Long support chats (~days) forget earlier task context.
Model switches or tab refreshes flip prior facts.
Two agents on the same ticket give inconsistent answers.
OCR transcripts look fine but later steps rewrite history.
Persona or role change contaminates state with old context.

Core acceptance targets

Each turn stamped with mem_rev and mem_hash.
No forks across sessions for the same task_id.
ΔS(question, retrieved) ≤ 0.45 with joins ≤ 0.50.
λ remains convergent across three paraphrases.
All claims cite snippet_id, no orphans.

Structural fixes

Stamp and fence
Require mem_rev, mem_hash, and task_id at every turn.
Forbid writes if stamps mismatch.
Shard state
Partition prompts as {system | task | constraints | snippets | answer}.
Forbid snippet reuse across sections.
Normalize consistently
Enforce Unicode NFC, strip zero width marks, unify full/half width.
Block OCR lines below confidence threshold.
Recover forks
If two agents diverge, reconcile by ΔS triangulation and pick the lower-entropy path.
Bridge collapse
Apply BBCR if attention melt or desync detected mid-chain.

Fix in 60 seconds

At turn start, echo {mem_rev, mem_hash, task_id}.
If stamps mismatch, reject write and request sync.
Split snippets by section, forbid cross-reuse.
Normalize all inputs.
Apply BBAM/BBCR if λ drifts or collapse appears.
Verify ΔS(question, retrieved) ≤ 0.45 and joins ≤ 0.50.

Copy-paste prompt


You have TXT OS and the WFGY Problem Map.

Goal: Keep memory coherent across multi-session dialogs.

Protocol:

1. Print {mem\_rev, mem\_hash, task\_id}.
2. Assemble prompt as {system | task | constraints | snippets | answer}.
3. Enforce guardrails:

   * cite then answer
   * forbid cross-section reuse
   * reject orphan claims without snippet\_id
4. If λ flips, apply BBAM. If collapse, insert BBCR bridge.
5. Report ΔS(question, retrieved), ΔS across joins, λ states, and final answer.

Common failure patterns

State fork: two parallel tabs rewrite history differently.
Ghost buffer: old role text leaks into new session.
Desync: memory IDs mismatch after refresh.
OCR drift: spacing or casing breaks snippet alignment.

🔗 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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