Jan: Guardrails and Fix Patterns

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.

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

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

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

Fix in 60 seconds

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

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

🔗 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 →

👑 Early Stargazers: See the Hall of Fame ⭐ WFGY Engine 2.0 is already unlocked. ⭐ Star the repo to help others discover it and unlock more on the Unlock Board.

8.9 KiB Raw Blame History Unescape Escape