WFGY/ProblemMap/GlobalFixMap/MemoryLongContext/state-fork.md

State Fork — Guardrails and Fix Pattern

When the same task_id is held in multiple tabs, agents, or sessions, their memories may diverge.
This creates two or more competing state branches, producing unstable or contradictory answers.

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Symptoms

• Two browser tabs answer the same task differently.
• Multi-agent orchestration produces conflicting citations.
• Session resumes but history appears rewritten or selectively dropped.
• Answers alternate between two incompatible interpretations.
• Logs show inconsistent mem_rev or mem_hash values for the same task_id.

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Root causes

• Concurrent writes to the same memory namespace.
• Missing checks for mem_rev version control.
• Agents refreshing at different times and overwriting buffers.
• Weak schema: task identity not fenced by {task_id, mem_rev, mem_hash}.
• No conflict resolution logic when branches emerge.

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Fix in 60 seconds

1. Version control memory

   • Every write stamped with {task_id, mem_rev, mem_hash}.
   • Reject writes if mem_rev < server mem_rev.
   • Require conflict resolution if two branches exist.

2. Isolate namespaces

   • Each agent/tab gets unique memory slot.
   • If collaboration required, merge through a coordinator agent.

3. Detect divergence early

   • Measure ΔS(answerA, answerB) across tabs.
   • If ΔS ≤ 0.40 but snippets differ, state fork detected.

4. Resolve fork

   • Run reconciliation: pick majority snippet set, or force human confirm.
   • Hash merged state and issue new {mem_rev, mem_hash}.

5. Trace schema

   • Require all claims to cite snippet ids.
   • Reject orphan claims without snippet anchors.

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Copy-paste diagnostic prompt

You have TXTOS and the WFGY Problem Map.

Task: Detect and repair state forks across tabs or agents.

Protocol:
1. Print {task_id, mem_rev, mem_hash}.
2. If two active branches share task_id with different mem_rev → flag fork.
3. Compare ΔS(answerA, answerB).
   - If ΔS ≤ 0.40 but snippets differ → fork confirmed.
4. Apply resolution:
   - Choose majority snippet set or request human input.
   - Issue new {mem_rev, mem_hash}.
5. Report ΔS, λ states, and resolution path.

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Acceptance targets

• No conflicting branches for the same task_id.
• All writes validated against server-side mem_rev.
• ΔS(answerA, answerB) ≥ 0.60 after resolution.
• λ remains convergent across three paraphrases.
• Audit log records merge or reject actions explicitly.

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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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