feat(v1): T-R3 采纳 llmrouter 词边界与失败安全——难度英文标记整词命中 + HF 分类器两级回落

- difficulty：标记表编译拆分——英文标记改词边界正则（修真实误判：'int' 子串
  命中 'print'/'point'、'log' 命中 'logic'、'list' 命中 'listen'），中文标记
  保持子串语义；命中行为对合法用例不变（整词出现照常计数）
- classifier：HuggingFaceClassifier 推理期异常回落内置 RuleClassifier（单次
  推理异常不打垮路由）；build_classifier 的 hf 分支构造失败（ML 依赖缺失/
  模型加载失败）打印提示并回落规则分类器（外置规则照常合并）
- 新增 tests/test_failsafe.py 5 项；全量 43 passed（38+5）

router_system/classifier.py

@@ -214,6 +214,8 @@ class HuggingFaceClassifier(BaseClassifier):
     """可选：基于 transformers 的序列分类模型。
 
     仅当安装 torch+transformers 且模型可加载时可用；否则抛错提示。
+    失败安全（T-R3，采纳 llmrouter 分类失败静默降级思想）：模型加载成功但
+    推理期异常时，自动回落内置规则分类器，不让单次推理异常打垮路由。
     """
 
     def __init__(self, model_name: str, num_labels: int = 5, confidence_floor: float = 0.55):
@@ -229,23 +231,27 @@ class HuggingFaceClassifier(BaseClassifier):
         )
         self.labels = ["code", "math", "legal", "medical", "general"]
         self.confidence_floor = confidence_floor
+        self._rule_fallback = RuleClassifier(confidence_floor=confidence_floor)
 
     def classify(self, query: str) -> Classification:
-        import torch  # type: ignore
+        try:
+            import torch  # type: ignore
 
-        inputs = self.tokenizer(query, return_tensors="pt", truncation=True, max_length=256)
-        with torch.no_grad():
-            logits = self.model(**inputs).logits
-        probs = torch.softmax(logits, dim=-1)[0]
-        idx = int(probs.argmax())
-        diff, ds = estimate_difficulty(query)
-        return Classification(
-            domain=self.labels[idx],
-            confidence=round(float(probs[idx]), 4),
-            difficulty=diff,
-            difficulty_score=ds,
-            raw_scores={self.labels[i]: round(float(probs[i]), 3) for i in range(len(self.labels))},
-        )
+            inputs = self.tokenizer(query, return_tensors="pt", truncation=True, max_length=256)
+            with torch.no_grad():
+                logits = self.model(**inputs).logits
+            probs = torch.softmax(logits, dim=-1)[0]
+            idx = int(probs.argmax())
+            diff, ds = estimate_difficulty(query)
+            return Classification(
+                domain=self.labels[idx],
+                confidence=round(float(probs[idx]), 4),
+                difficulty=diff,
+                difficulty_score=ds,
+                raw_scores={self.labels[i]: round(float(probs[i]), 3) for i in range(len(self.labels))},
+            )
+        except Exception:  # noqa: BLE001  推理失败回落规则分类器（失败安全）
+            return self._rule_fallback.classify(query)
 
 
 def build_classifier(cfg: Dict) -> BaseClassifier:
@@ -265,6 +271,18 @@ def build_classifier(cfg: Dict) -> BaseClassifier:
             clf.rules = {**DOMAIN_RULES, **external}
         return clf
     if ctype == "hf":
-        return HuggingFaceClassifier(cfg.get("model", "Qwen/Qwen3-0.6B"), confidence_floor=floor)
+        # 失败安全（T-R3）：ML 依赖缺失 / 模型加载失败时回落规则分类器
+        try:
+            return HuggingFaceClassifier(
+                cfg.get("model", "Qwen/Qwen3-0.6B"), confidence_floor=floor)
+        except Exception as e:  # noqa: BLE001
+            print(f"[classifier] HF 分类器不可用（{type(e).__name__}），回落规则分类器")
+            clf = RuleClassifier(confidence_floor=floor)
+            rules_file = cfg.get("rules_file")
+            external = load_domain_rules(rules_file) if rules_file \
+                else (load_domain_rules() if DEFAULT_RULES_FILE.exists() else None)
+            if external:
+                clf.rules = {**DOMAIN_RULES, **external}
+            return clf
     raise ValueError(f"未知分类器类型: {ctype}（支持 rule | hf）")