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

docs/logic-trace-subnet.md

@@ -44,64 +44,35 @@
 
 ### 推理路径张量格式
 
-```json
-{
-  "header": {
-    "source": "logic_trace",
-    "target": "core",
-    "tensor_type": "reasoning_path",
-    "trace_id": "trace-uuid",
-    "timestamp": 1717200000
-  },
-  "payload": {
-    "reasoning_steps": [
-      {
-        "step_id": 1,
-        "type": "deduction",
-        "input": "用户想要查询天气",
-        "operation": "提取地点和时间信息",
-        "output": "地点: 北京, 时间: 今天",
-        "confidence": 0.95,
-        "source_evidence": ["memory:偏好_北京天气"]
-      },
-      {
-        "step_id": 2,
-        "type": "retrieval",
-        "input": "需要天气数据",
-        "operation": "查询记忆子网获取北京天气偏好",
-        "output": "用户关注空气质量>温度",
-        "confidence": 0.88,
-        "source_evidence": ["graph:用户_北京_HAS_PERSONA_关注空气质量"]
-      },
-      {
-        "step_id": 3,
-        "type": "verification",
-        "input": "步骤1-2结果",
-        "operation": "交叉验证",
-        "output": "结论一致",
-        "confidence": 0.92,
-        "source_evidence": ["cross_validation"]
-      }
-    ],
-    "final_confidence": 0.92,
-    "alternative_paths": [
-      {
-        "path_id": "alt_1",
-        "conclusion": "用户可能不在北京（上次对话提到出差）",
-        "confidence": 0.45,
-        "rejection_reason": "信息过时（3天前的记录）"
-      }
-    ],
-    "verified": true,
-    "error_log": []
-  },
-  "metadata": {
-    "step_count": 3,
-    "mcts_iterations": 50,
-    "search_depth": 5,
-    "verification_status": "passed"
-  }
-}
+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=4, parser_id=4]
+  payload:
+    [n_steps]                              # 推理步数
+    [step_embeddings...]                    # 每步的语义嵌入 [n_steps, 768]
+    [step_confidences...]                   # 每步置信度 [n_steps]
+    [search_depth, mcts_iters, verified]    # 元信息 [3]
+```
+
+**编码示例（内部推理路径 → 统一张量）**：
+
+```python
+def encode_reasoning_path(steps: list[Step],
+                          depth: int, iters: int) -> Tensor:
+    """内部推理路径 → 统一通信张量"""
+    n = len(steps)
+    step_vecs = [encode_step_io(s) for s in steps]     # 每步编码
+    confs = [s.confidence for s in steps]
+    
+    payload = concat(
+        tensor([n]),                               # 步数
+        stack(step_vecs).flatten(),                # 步嵌入
+        tensor(confs),                             # 置信度
+        tensor([depth, iters, 1.0 if verified])    # 元信息
+    )
+    return codec.encode(fmt_id=4, parser_id=4, data=payload)
 ```
 
 ## 子网内部架构