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Pattern: Memory Desync — Cross Tab & Cache Hazards

🧭 Quick Return to Map

You are in a sub-page of MemoryLongContext.
To reorient, go back here:

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.

When multiple tabs, devices, or agents access the same conversation, memory forks and silent cache layers can cause desync.
This pattern documents the root causes and provides structural guardrails to keep state aligned.

When to use this page

  • Two browser tabs show the same chat but give conflicting answers.
  • Refresh wipes one agent’s buffer while the other keeps stale context.
  • Long-running threads lose citations after reconnect.
  • Support or sales teams using shared inboxes see different revision histories.
  • Logs look correct but answer text diverges.

Root causes

  • Tab fork: each browser tab caches a local buffer, leading to divergent memory.
  • Ghost cache: stale persona or role text remains after reload.
  • Write skew: two sessions update memory concurrently with mismatched mem_rev.
  • Offline sync: one client reconnects late, applying outdated deltas.

Core acceptance targets

  • mem_rev and mem_hash echoed at every turn.
  • ΔS(question, retrieved) ≤ 0.45 and joins ≤ 0.50.
  • λ convergent across three paraphrases.
  • No duplicate or orphan claims across sessions.

Structural fixes

  • State fencing
    Stamp all turns with {mem_rev, mem_hash, task_id}.
    Forbid writes if mismatch detected.

  • Cache invalidation
    On reconnect, clear stale buffers. Require server authority on revision.

  • Reconciliation
    When forks appear, run ΔS triangulation:
    Compare ΔS to anchor section vs decoy. Select the lower entropy path.

  • Bridging
    If collapse occurs, insert a BBCR bridge to re-anchor reasoning chain.

Fix in 60 seconds

  1. Echo {mem_rev, mem_hash, task_id} at every turn.
  2. On reload, validate stamps against server. If mismatch, reject update.
  3. For forks, compute ΔS across sessions, pick stable anchor.
  4. Apply BBAM clamp if λ flips across paraphrases.
  5. Verify ΔS ≤ 0.45 and λ convergent before continuing.

Copy-paste prompt


You have TXT OS and the WFGY Problem Map.

Goal: Prevent memory desync across tabs, agents, or devices.

Protocol:

1. Print {mem\_rev, mem\_hash, task\_id}.
2. If stamps mismatch, stop and request sync.
3. Assemble prompts as {system | task | constraints | snippets | answer}.
4. Enforce guardrails: cite then answer, forbid cross-section reuse, no orphan claims.
5. If collapse, insert BBCR bridge. If λ variance, clamp with BBAM.
6. Report ΔS(question, retrieved), joins ΔS, λ states, and final answer.

Common failure signals

  • Answer alternates between reloads → ghost cache not invalidated.
  • Different answers across two tabs → state fork, resolve with revision fencing.
  • Missing citations after reconnect → desync in snippet schema.

🔗 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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⚙️ Engine WFGY 1.0 Original PDF tension engine and early logic sketch (legacy reference)
⚙️ Engine WFGY 2.0 Production tension kernel for RAG and agent systems
⚙️ Engine WFGY 3.0 TXT based Singularity tension engine (131 S class set)
🗺️ Map Problem Map 1.0 Flagship 16 problem RAG failure taxonomy and fix map
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