feat: protocol base lib + rule-based parser MVP + 31 tests

docs/llm-subnet.md

@@ -75,42 +75,52 @@ NextAgent LLM 子网：
 | **输入** | 结构化推理路径张量（来自 Core，经过张量解码层转换） |
 | **输出1** | 自然语言文本（直接发送给用户） |
 | **输出2** | 表达验证张量（发送给 Core） |
-| **输出2格式** | 标准张量协议，tensor_type=`expression_verify` |
+| **输出2格式** | 统一通信张量，fmt_id=5 (expression_verify) |
 
 ### 表达验证张量格式
 
-```json
-{
-  "header": {
-    "source": "llm",
-    "target": "core",
-    "tensor_type": "expression_verify",
-    "trace_id": "trace-uuid",
-    "timestamp": 1717200000
-  },
-  "metadata": {
-    "confidence": 0.94,
-    "semantic_encoding_dim": 768,
-    "expression_structure": "sentence+paragraph",
-    "sentiment": "positive",
-    "language": "zh-CN",
-    "token_count": 128,
-    "verification_status": "verified"
-  },
-  "payload": {
-    "semantic_encoding": [0.23, -0.56, ...],   // 输出文本的语义向量
-    "intention_alignment": 0.96,                // 与输入推理路径的意图对齐度
-    "factual_consistency": 0.92,                 // 与输入事实的一致性
-    "sentence_embeddings": [                     // 每句的编码（可选）
-      {"index": 0, "embedding": [...]},
-      {"index": 1, "embedding": [...]}
-    ],
-    "emotion_vector": [0.1, 0.8, 0.3, ...],     // 情感多维度向量
-    "key_entities_preserved": ["天气", "北京", "今天"],  // 关键实体保留
-    "omission_detected": false                  // 是否有遗漏信息
-  },
-  "signature": "sha256-checksum"
-}
+遵循统一通信张量协议，**无 JSON 结构**，全部为纯数值张量：
+
+```
+统一通信张量 (纯数值)
+  prefix: [fmt_id=5, parser_id=5]
+  payload:
+    [semantic_encoding (768)]              # 输出文本的语义向量
+    [intention_alignment (1)]              # 与输入推理路径的意图对齐度 (0~1)
+    [factual_consistency (1)]              # 与输入事实的一致性 (0~1)
+    [emotion_vector (n_dims)]              # 情感向量
+    [entity_preserved_mask (n_entities)]   # 关键实体保留掩码 [0/1]
+    [overall_score (1)]                    # 综合验证得分 (0~1)
+```
+
+**编码示例**：
+
+```python
+def encode_expression_verify(output_text: str, 
+                             reasoning_path: Tensor) -> Tensor:
+    """LLM 自然语言输出 → 表达验证张量 (通道2)
+    
+    通道1: 自然语言文本直接输出给用户
+    通道2: 此函数编码验证特征为纯张量返回给 Core
+    """
+    sem_enc = sentence_encoder(output_text)                  # [768]
+    alignment = cosine_sim(sem_enc, path_embedding)          # [1]
+    consistency = compute_factual_consistency(
+        output_text, reasoning_path)                         # [1]
+    emotion = emotion_analyzer(output_text)                  # [n]
+    entity_mask = check_entities_preserved(output_text)      # [n_entities]
+    overall = compute_overall_score([alignment, consistency, 
+                                     entity_mask.mean()])    # [1]
+    
+    payload = concat(sem_enc, alignment, consistency,
+                     emotion, entity_mask, overall)
+    return codec.encode(fmt_id=5, parser_id=5, data=payload)
+
+# Core 收到后解析
+payload = codec.decode(comm_tensor)  # 剥离 fmt_id=5
+overall = payload[-1]
+if overall > THRESHOLD:  # 验证通过
+    user_output = channel_1_text  # 允许输出
 ```
 
 ## 子网内部架构