WFGY/ProblemMap/GlobalFixMap/MemoryLongContext/memory-coherence.md

Memory Coherence — Multi-Session and State Alignment

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.

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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.

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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.

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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.

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Fix in 60 seconds

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

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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.

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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.

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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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