feat(sense): T-G4 线性头（离线 softmax 回归 + 纯 Python 推理）

- classifier.py：LinearHead.load（JSON 工件，缺失/损坏 -> ArtifactMissing）+
  predict（纯 Python 点积 + 稳定 softmax，D-G5 serving 零依赖）；LoraRemote 预留
- scripts/train_tier_head.py：numpy softmax 回归（类别权重均衡/L2），
  70/15/15 切分，head.json + metrics.json（accuracy/macro-F1/逐档 F1）+ 工件登记；
  --synthetic 合成自测链路；CLI 出口 UTF-8 reconfigure
- requirements-ml.txt 登记 numpy（离线训练用，D-P6/D-G5 说明理由）
- 测试 +5（黄金向量 softmax 一致/argmax/缺失/损坏/合成训练-推理链路），全量 400 passed

gateway/sense/classifier.py

@@ -0,0 +1,72 @@
+"""分级头（T-G4）：LinearHead 纯 Python 推理 + LoraRemote 预留（T-G8）。
+
+D-G5：serving 路径零新依赖——线性头推理是纯 Python 点积 + softmax（768 维
+≈0.1ms）。工件为 JSON（weights 3×dim / bias 3 / dim / version），由
+scripts/train_tier_head.py（numpy 离线训练）产出。
+缺失/损坏 -> ArtifactMissing（D-G4 降级信号）。
+"""
+from __future__ import annotations
+
+import json
+import math
+from pathlib import Path
+from typing import Any, Dict, List, Optional
+
+from gateway.sense.errors import ArtifactMissing
+
+TIERS = ("t1", "t2", "t3")
+
+
+class LinearHead:
+    """线性有序三分类头（softmax；类别序 t1<t2<t3 与升级阶梯一致）。"""
+
+    def __init__(self, weights: List[List[float]], bias: List[float],
+                 version: str = "dev", labels: Optional[List[str]] = None):
+        self.weights = weights
+        self.bias = bias
+        self.version = version
+        self.labels = labels or ["t1", "t2", "t3"]
+        if len(self.weights) != len(self.bias):
+            raise ArtifactMissing("线性头权重与偏置维度不一致")
+
+    @classmethod
+    def load(cls, path: str | Path, version: str = "") -> "LinearHead":
+        p = Path(path)
+        if not p.exists():
+            raise ArtifactMissing(f"线性头工件不存在: {p}")
+        try:
+            data = json.loads(p.read_text(encoding="utf-8"))
+            weights = data["weights"]
+            bias = data["bias"]
+            if not weights or not bias:
+                raise ValueError("空权重")
+            return cls(weights=weights, bias=bias,
+                       version=str(data.get("version") or version or p.parent.name),
+                       labels=data.get("labels"))
+        except ArtifactMissing:
+            raise
+        except Exception as e:  # noqa: BLE001
+            raise ArtifactMissing(f"线性头工件损坏: {type(e).__name__}: {e}") from e
+
+    def predict(self, vec: List[float]) -> Dict[str, float]:
+        """点积 + softmax -> {t1, t2, t3} 概率（和为 1）。"""
+        logits = []
+        for w, b in zip(self.weights, self.bias):
+            n = min(len(w), len(vec))
+            logits.append(sum(wi * vi for wi, vi in zip(w[:n], vec[:n])) + b)
+        m = max(logits)
+        exps = [math.exp(z - m) for z in logits]
+        total = sum(exps)
+        return {label: e / total for label, e in zip(self.labels, exps)}
+
+
+class LoraRemote:
+    """LoRA 远程分类（vLLM /v1/classify）——T-G8 可选实验，本版预留。"""
+
+    def __init__(self, base_url: str, model: str, api_key: Optional[str] = None):
+        self.base_url = base_url.rstrip("/")
+        self.model = model
+        self.api_key = api_key
+
+    def predict(self, vec: List[float]) -> Dict[str, float]:
+        raise NotImplementedError("T-G8 可选实验（LoRA/vLLM 分类服务）")