WFGY/ProblemMap/GlobalFixMap/LocalDeploy_Inference/jan.md

Jan: Guardrails and Fix Patterns

🧭 Quick Return to Map

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

• LocalDeploy_Inference — on-prem deployment and model inference
• 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.

Jan is a desktop-native inference environment that allows you to run local LLMs with a polished UI, cross-platform support, and tight integration with quantized model formats. While easier to use than CLI runtimes, Jan inherits common problems: unstable context handling, schema drift, citation loss, and device-specific crashes. This page gives WFGY-based fixes to stabilize Jan deployments.

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Open these first

• Architecture recovery: RAG Architecture & Recovery
• Retrieval knobs: retrieval-playbook.md
• Embedding sanity: embedding-vs-semantic.md
• Context stability: context-drift.md, entropy-collapse.md
• Safety and schema: prompt-injection.md, logic-collapse.md
• Boot/deploy: bootstrap-ordering.md, predeploy-collapse.md

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Core acceptance

• ΔS(question, retrieved) ≤ 0.45
• Coverage ≥ 0.70 for the target section
• λ convergent across 3 paraphrases × 2 seeds
• JSON schema locked for tool calls
• Observability of ΔS and λ logged per run

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Common Jan breakpoints

Symptom	Likely Cause	Fix
First run fails on GPU device	CUDA/Metal init order	bootstrap-ordering.md
Correct snippets but drifting answers	Schema mismatch in local context buffer	retrieval-traceability.md, data-contracts.md
Answers alternate between runs	λ flip, unstable headers	context-drift.md
JSON parse breaks	Inconsistent serialization in UI layer	logic-collapse.md
Safety refusal hides citations	Missing citation-first prompting	retrieval-traceability.md

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Fix in 60 seconds

1. Run warm-up: issue a small dummy query to stabilize device kernels.
2. Schema enforce: lock JSON outputs for tools and citations.
3. Trace citations: enforce cite-then-explain.
4. Measure ΔS and λ: if ΔS ≥ 0.60, rebuild index with proper embedding metric.
5. Watch entropy: reset conversation memory after 4k–8k tokens or entropy rise.

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Diagnostic prompt (copy-paste)

I am using Jan to run a local GGUF/GGML model.
Question: "{user_question}"

Return:
- ΔS(question, retrieved)
- λ across paraphrases and seeds
- JSON schema compliance
- Which WFGY fix page to open if ΔS ≥ 0.60

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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

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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