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ProblemMap/GlobalFixMap/Language/query_language_detection.md
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ProblemMap/GlobalFixMap/Language/query_language_detection.md
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# Query Language Detection · Global Fix Map
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Detect the query language and script correctly, route it to the right analyzer and tokenizer, and keep λ stable across paraphrases. This page gives a small contract, deterministic fallbacks, and tests so short queries, code-switched inputs, and romanized forms do not break retrieval.
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---
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## Open these first
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* Visual map and recovery → [rag-architecture-and-recovery.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md)
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* End to end retrieval knobs → [retrieval-playbook.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-playbook.md)
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* Why this snippet → [retrieval-traceability.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md)
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* Contract the payload → [data-contracts.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/data-contracts.md)
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* Embedding vs meaning → [embedding-vs-semantic.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md)
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* Tokenizer variance → [tokenizer\_mismatch.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Language/tokenizer_mismatch.md)
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* Mixed scripts in one query → [script\_mixing.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Language/script_mixing.md)
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* Locale normalization and width/diacritics → [locale\_drift.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Language/locale_drift.md)
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* Proper noun aliases → [proper\_noun\_aliases.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Language/proper_noun_aliases.md)
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* Romanization and transliteration → [romanization\_transliteration.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Language/romanization_transliteration.md)
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* Multilingual overview → [multilingual\_guide.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Language/multilingual_guide.md)
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---
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## Core acceptance targets
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* ΔS(question, retrieved) ≤ 0.45 across three paraphrases and two seeds
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* Coverage of target section ≥ 0.70
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* λ remains convergent when detector confidence is low or when code-switching is present
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* Detector outputs BCP-47 `lang` and ISO 15924 `script` with an explicit confidence and rationale
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* No false collapse when romanized forms are used instead of native script
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---
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## Minimal contract
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**Inputs**
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```
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q_text # user query raw
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hints.lang_pref # optional ui/user preference e.g. "ja"
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hints.romanizer # optional, e.g. "hepburn"
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context.domain # optional product/domain which biases vocabulary
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```
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**Detector output**
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```
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lang # BCP-47 primary tag, null if unknown (e.g., "zh", "ja", "en")
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script # ISO 15924, e.g., "Hans", "Hant", "Latn", "Cyrl", "Arab"
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confidence # 0..1
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rationale # short note, e.g., "CJK bigram ratio 0.82"
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variants # list of plausible alternates, sorted by confidence
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romanized_suspect # bool, true if looks like transliteration of non-Latin
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```
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**Router decision**
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```
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analyzer_id # store-specific analyzer to call
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tokenizer_id # LLM or retriever tokenizer profile
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alias_view # whether to search romanized alias field(s)
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```
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All five fields must be logged with the retrieval response so you can audit flips.
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---
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## Typical failure → exact fix
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| Symptom | Likely cause | Open this |
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| ------------------------------------------------------------------------- | --------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
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| Short query mis-detected as English, CJK missed | length bias without script probe | [script\_mixing.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Language/script_mixing.md), [locale\_drift.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Language/locale_drift.md) |
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| Romanized Japanese finds wrong page or no hit | detector returns `en+Latn` but romanized\_suspect not set | [romanization\_transliteration.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Language/romanization_transliteration.md) |
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| Arabic mixed digits and ASCII flips direction and rank | RTL controls and width not normalized | [locale\_drift.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Language/locale_drift.md) |
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| Brand or person whose alias equals a common word routes to wrong language | alias collision without scope fence | [proper\_noun\_aliases.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Language/proper_noun_aliases.md), [retrieval-traceability.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md) |
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| High similarity yet wrong meaning across languages | analyzer or metric mismatch | [embedding-vs-semantic.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/embedding-vs-semantic.md), [tokenizer\_mismatch.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Language/tokenizer_mismatch.md) |
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---
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## 60-second fix checklist
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1. **Two-stage detection**
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Script-first using Unicode ranges, then language model on normalized text. Never rely on language-only detectors for queries shorter than 6 tokens.
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2. **Confidence bands**
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If `confidence < 0.65`, run mixed routing: search native analyzer for all `variants.script` plus the romanized alias view.
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3. **Romanized suspect path**
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If `romanized_suspect=true`, search native-script alias view and bias reranker to prefer canonical snippets.
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4. **Width and diacritics**
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Fold width and diacritics only for the detection step and alias view, not for canonical matching. See [locale\_drift.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Language/locale_drift.md).
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5. **Log ΔS and λ**
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Keep per-variant logs so you can see which analyzer produced stable evidence.
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---
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## Copy snippets
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**A. Script-first detector skeleton**
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```python
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import unicodedata as ud
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from collections import Counter
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def guess_script(s: str) -> tuple[str, float]:
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buckets = Counter()
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total = 0
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for ch in s:
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if ch.isspace() or ch.isdigit():
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continue
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total += 1
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name = ud.name(ch, "")
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# very light bins, expand as needed
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if "CJK" in name or "HIRAGANA" in name or "KATAKANA" in name or "HANGUL" in name:
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buckets["CJK"] += 1
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elif "CYRILLIC" in name:
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buckets["CYRL"] += 1
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elif "ARABIC" in name or "HEBREW" in name:
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buckets["RTL"] += 1
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else:
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buckets["LATN"] += 1
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if total == 0:
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return "UNK", 0.0
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script, cnt = max(buckets.items(), key=lambda x: x[1])
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conf = cnt / total
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# map to ISO 15924 class
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iso = {"CJK":"Han", "CYRL":"Cyrl", "RTL":"Arab", "LATN":"Latn"}.get(script, "Zyyy")
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return iso, conf
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```
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**B. Romanized suspect heuristic**
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```python
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def is_romanized_suspect(q: str, script_iso: str) -> bool:
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# e.g., looks like "Tōkyō", "Toukyou", "Zhongguo", "Rossiya"
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if script_iso != "Latn":
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return False
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vowels = sum(ch.lower() in "aeiou" for ch in q)
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tone_marks = any(ch in "āáǎàēéěèīíǐìōóǒòūúǔùǖǘǚǜ" for ch in q)
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hyphen = "-" in q
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long_vowel = any(seq in q.lower() for seq in ["ou","aa","ee","oo","uu"])
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return tone_marks or hyphen or long_vowel or vowels >= max(4, len(q)//3)
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```
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**C. Router decision**
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```python
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def route(q_text, hints):
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script, s_conf = guess_script(q_text)
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roman_sus = is_romanized_suspect(q_text, script)
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low_conf = s_conf < 0.65 or len(q_text.split()) < 6
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routes = []
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if script in ["Han", "Hira", "Kana", "Hang"]:
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routes.append(("analyzer:cjk", "tokenizer:cjk", False))
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elif script == "Cyrl":
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routes.append(("analyzer:cyrl", "tokenizer:default", False))
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elif script == "Arab":
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routes.append(("analyzer:rtl", "tokenizer:default", False))
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else:
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routes.append(("analyzer:latn", "tokenizer:default", roman_sus))
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if low_conf:
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# add alternates and alias view
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routes.append(("analyzer:latn", "tokenizer:default", True))
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routes.append(("analyzer:cjk", "tokenizer:cjk", True))
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return {
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"script": script,
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"confidence": round(s_conf, 2),
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"romanized_suspect": roman_sus,
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"routes": routes
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}
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```
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**D. Prompt fence for detectors**
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```
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You have TXTOS and the WFGY Problem Map.
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When a query is short or mixed:
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1) Detect script first. If confidence is low, search both native script and romanized alias views.
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2) Cite the snippet in the canonical script if available. Use cite-then-explain.
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3) Report {lang, script, detector_confidence, romanized_suspect} in the trace.
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```
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---
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## Eval plan
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Use the set from [code\_switching\_eval.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/GlobalFixMap/Language/code_switching_eval.md). Add 3 extra buckets:
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* short queries with 1 to 3 tokens
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* romanized vs native for the same entity
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* mixed ASCII and RTL digits
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Targets
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* detector accuracy on script ≥ 0.97 for length ≥ 6 tokens, ≥ 0.90 for length 1–5
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* ΔS(question, retrieved) ≤ 0.45 and λ convergent across two seeds
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* no rank flip between native and romanized when evidence matches
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If recall is fine but ranking flips, clamp reranker and verify with [retrieval-traceability.md](https://github.com/onestardao/WFGY/blob/main/ProblemMap/retrieval-traceability.md).
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---
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### 🧭 Explore More
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| Module | Description | Link |
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|-----------------------|----------------------------------------------------------|----------|
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| WFGY Core | WFGY 2.0 engine is live: full symbolic reasoning architecture and math stack | [View →](https://github.com/onestardao/WFGY/tree/main/core/README.md) |
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| Problem Map 1.0 | Initial 16-mode diagnostic and symbolic fix framework | [View →](https://github.com/onestardao/WFGY/tree/main/ProblemMap/README.md) |
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| Problem Map 2.0 | RAG-focused failure tree, modular fixes, and pipelines | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/rag-architecture-and-recovery.md) |
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| Semantic Clinic Index | Expanded failure catalog: prompt injection, memory bugs, logic drift | [View →](https://github.com/onestardao/WFGY/blob/main/ProblemMap/SemanticClinicIndex.md) |
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| Semantic Blueprint | Layer-based symbolic reasoning & semantic modulations | [View →](https://github.com/onestardao/WFGY/tree/main/SemanticBlueprint/README.md) |
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| Benchmark vs GPT-5 | Stress test GPT-5 with full WFGY reasoning suite | [View →](https://github.com/onestardao/WFGY/tree/main/benchmarks/benchmark-vs-gpt5/README.md) |
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---
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> 👑 **Early Stargazers: [See the Hall of Fame](https://github.com/onestardao/WFGY/tree/main/stargazers)** —
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> Engineers, hackers, and open source builders who supported WFGY from day one.
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> <img src="https://img.shields.io/github/stars/onestardao/WFGY?style=social" alt="GitHub stars"> ⭐ [WFGY Engine 2.0](https://github.com/onestardao/WFGY/blob/main/core/README.md) is already unlocked. ⭐ Star the repo to help others discover it and unlock more on the [Unlock Board](https://github.com/onestardao/WFGY/blob/main/STAR_UNLOCKS.md).
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<div align="center">
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[](https://github.com/onestardao/WFGY)
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[](https://github.com/onestardao/WFGY/tree/main/OS)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlahBlahBlah)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlotBlotBlot)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlocBlocBloc)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlurBlurBlur)
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[](https://github.com/onestardao/WFGY/tree/main/OS/BlowBlowBlow)
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</div>
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