WFGY/ProblemMap/GlobalFixMap/LanguageLocale

Language & Locale — Global Fix Map

Stabilize multilingual RAG and reasoning across CJK/RTL/Latin scripts.
Fix tokenizer mismatch, Unicode normalization, mixed encodings, and cross-lingual retrieval drift.

What this page is

• A compact, language-aware checklist for retrieval + reasoning
• Copyable prompts and guards for CJK/RTL, transliteration, and code-mixed text
• How to measure and prove stability with ΔS and λ_observe

When to use

• Corpus is non-English or mixed (EN + ZH/JP/KR/AR/Hebrew)
• Same question works in English but fails in the target language
• High vector similarity yet wrong meaning after translation
• OCR text “looks correct” but citations drift or split tokens oddly
• Names/terms oscillate between Latin and native script

Open these first

• End-to-end language guide: Multilingual Guide
• Embedding ≠ true meaning symptoms: Embedding ≠ Semantic
• OCR quality & normalizations: OCR / Parsing Checklist
• Chunk boundaries & sectioning: Chunking Checklist
• Snippet/citation schema: Data Contracts
• Why-this-snippet trace: Retrieval Traceability

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Common failure patterns (quick diagnosis)

• Tokenizer split: CJK runs without spaces; BM25/analyzers mismatch; ΔS flat-high vs k → index/analyzer misaligned.
• Unicode ghosts: full-width vs half-width, NFD vs NFC, zero-width joiners; citations miss by a few characters.
• Translation shadow: English paraphrase passes, native-lang fails → cross-lingual embeddings or analyzer drift.
• Script flip: terms appear both transliterated and native; recall differs by script.
• OCR noise: identical glyphs (l/1/I, O/0), mixed directionality (RTL punctuation).

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Fix in 60 seconds

1. Normalize text at ingest

   • Apply Unicode NFC, trim zero-width, unify full/half-width.
   • Lowercase where appropriate; preserve casing for code and proper nouns.

2. Choose analyzers per language

   • CJK: use language-aware tokenizers (jieba, kuromoji, mecab) or character-ngrams.
   • RTL: ensure analyzer respects directionality; avoid stripping diacritics unless required.

3. Dual-path embeddings

   • Index in native language and in English via machine translation shadow for recall robustness.
   • Store lang, script, and translit flags per chunk in metadata.

4. Anchor the schema

   • Enforce snippet headers {section_id, lang, script}; forbid cross-section reuse.
   • Require cite-then-answer; block free-form merges across languages.

5. Probe ΔS & λ by language

   • Measure ΔS(question, retrieved) per language; aim ≤ 0.45.
   • If ΔS flat-high across k, rebuild with correct analyzer/metric.

6. Name/term fences

   • Maintain a term map {native ↔ translit ↔ English}; pin consistent variants in the prompt preamble.

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Copy-paste prompt

You have TXT OS and the WFGY Problem Map.

Task: stabilize multilingual retrieval and reasoning.

Follow this immutable protocol:

1. Detect language/script of the question. Print {lang, script}.
2. Retrieve with a dual-path strategy:

   * native-lang retriever
   * english-shadow retriever (machine-translated question)
3. Build a Snippet Table with columns:
   {section\_id | lang | script | translit\_variant? | citation}
4. Bridge Check (BBCR):

   * restate the claim in ONE line
   * list supporting snippet\_ids
   * list conflicts or missing evidence; if missing, STOP and ask for the exact snippet
5. Final Answer:

   * answer in the user's language
   * inline-cite each claim
   * keep terminology consistent with the Term Map

Rules:

* Normalize Unicode (NFC), strip zero-width chars, unify full/half-width before retrieval.
* If ΔS(question, retrieved) > 0.60 in native but ≤ 0.45 in english-shadow, report "translation shadow" and keep both citations.
* Do not merge sources across languages without explicit citation per claim.

Input

* question (user language): "<paste>"
* term\_map: {native ↔ translit ↔ english}
* snippets (with ids, language, script): <paste>

Output

* {lang, script}
* Snippet Table
* Bridge Check
* Final Answer (with inline citations)
* ΔS(native), ΔS(english-shadow), λ\_observe states

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Minimal checklist

• Unicode normalized; zero-width and width variants removed
• Language-aware analyzers or char-ngrams applied at index & query
• Dual-path embeddings or bilingual index available
• Snippet Table includes {lang, script, translit?}
• Cite-then-answer schema enforced; no cross-language merges without citations
• ΔS per-language measured; flat-high ΔS triggers index/metric audit

Acceptance targets

• ΔS(question, retrieved) ≤ 0.45 in the user’s language
• λ remains convergent across paraphrases in both native and english-shadow paths
• Coverage ≥ 0.70 token overlap to the target section in native language
• Consistent terminology across scripts per Term Map; no orphan claims without citations

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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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