State Fork — Guardrails and Fix Pattern

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WFGY Global Fix Map — main Emergency Room, 300+ structured fixes

WFGY Problem Map 1.0 — 16 reproducible failure modes

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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.

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.

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.

Fix in 60 seconds

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.
Isolate namespaces
- Each agent/tab gets unique memory slot.
- If collaboration required, merge through a coordinator agent.
Detect divergence early
- Measure ΔS(answerA, answerB) across tabs.
- If ΔS ≤ 0.40 but snippets differ, state fork detected.
Resolve fork
- Run reconciliation: pick majority snippet set, or force human confirm.
- Hash merged state and issue new {mem_rev, mem_hash}.
Trace schema
- Require all claims to cite snippet ids.
- Reject orphan claims without snippet anchors.

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.

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.

🔗 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

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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