Audit batch 5: I-9, I-12, I-14, N-1..N-12, single-task wire single-transaction

cloud/audit_loop.py

@@ -224,6 +224,28 @@ def _validate_gate_results(gate_results):
             raise ValueError("gate_results must map gate name to bool")
 
 
+def _escalate(task_id, prev_round, passed_gates, criteria, reason,
+              regression=(), missing_gates=(), weakened=()):
+    """升级决策 dict（审计批 5，N-8：纯结构整理，行为不变）。
+
+    evaluate() 内四处 escalate 返回原来各写一遍同样的 dict
+    骨架（new_state + decision/round/packet/reason + 三个清单），
+    只有 passed_gates / criteria / reason / 清单内容不同；抽成
+    助手后各处只传不同项。new_state 的 round 恒为 prev_round
+    （升级不推进轮次）、last_decision 恒为 escalate。
+    """
+    new_state = LoopState(
+        task_id, round=prev_round, passed_gates=sorted(passed_gates),
+        criteria=criteria, last_decision="escalate")
+    return {
+        "decision": "escalate", "round": prev_round, "packet": None,
+        "new_state": new_state, "reason": reason,
+        "regression": list(regression),
+        "missing_gates": list(missing_gates),
+        "weakened": list(weakened),
+    }
+
+
 def evaluate(state, task_id, gate_results, findings=None, fixed=None):
     """纯函数：一轮审计的决策。
 
@@ -269,17 +291,10 @@ def evaluate(state, task_id, gate_results, findings=None, fixed=None):
     # 全量回归：上一轮通过的门本轮必须重跑，缺跑即升级。
     missing_gates = sorted(g for g in prev_passed if g not in gate_results)
     if missing_gates:
-        new_state = LoopState(
-            task_id, round=prev_round,
-            passed_gates=sorted(
-                g for g, v in gate_results.items() if v),
-            criteria=prev_criteria, last_decision="escalate")
-        return {
-            "decision": "escalate", "round": prev_round, "packet": None,
-            "new_state": new_state, "reason": "gate_missing",
-            "regression": [], "missing_gates": missing_gates,
-            "weakened": [],
-        }
+        return _escalate(
+            task_id, prev_round,
+            (g for g, v in gate_results.items() if v),
+            prev_criteria, "gate_missing", missing_gates=missing_gates)
 
     passed_now = {g for g, v in gate_results.items() if v}
     failed_now = {g for g, v in gate_results.items() if not v}
@@ -310,15 +325,11 @@ def evaluate(state, task_id, gate_results, findings=None, fixed=None):
         else:
             weakened.append(fid)
     if weakened:
-        new_state = LoopState(
-            task_id, round=prev_round, passed_gates=sorted(passed_now),
-            criteria=prev_criteria, last_decision="escalate")
-        return {
-            "decision": "escalate", "round": prev_round, "packet": None,
-            "new_state": new_state, "reason": "standards_weakened",
-            "regression": [f.to_dict() for f in regression],
-            "missing_gates": [], "weakened": sorted(weakened),
-        }
+        return _escalate(
+            task_id, prev_round, passed_now, prev_criteria,
+            "standards_weakened",
+            regression=[f.to_dict() for f in regression],
+            weakened=sorted(weakened))
 
     # 新标准 = 未解决的旧标准 + 本轮新 finding（含回归）。
     for finding in all_findings:
@@ -348,26 +359,16 @@ def evaluate(state, task_id, gate_results, findings=None, fixed=None):
     # 转 escalate），新行为后看到的是 gate_failed_without_
     # finding 升级——原因更精确，不是行为倒退。
     if failed_now and not all_findings:
-        new_state = LoopState(
-            task_id, round=prev_round, passed_gates=sorted(passed_now),
-            criteria=new_criteria, last_decision="escalate")
-        return {
-            "decision": "escalate", "round": prev_round, "packet": None,
-            "new_state": new_state, "reason": "gate_failed_without_finding",
-            "regression": [f.to_dict() for f in regression],
-            "missing_gates": [], "weakened": [],
-        }
+        return _escalate(
+            task_id, prev_round, passed_now, new_criteria,
+            "gate_failed_without_finding",
+            regression=[f.to_dict() for f in regression])
 
     if prev_round >= MAX_PUSHBACKS:
-        new_state = LoopState(
-            task_id, round=prev_round, passed_gates=sorted(passed_now),
-            criteria=new_criteria, last_decision="escalate")
-        return {
-            "decision": "escalate", "round": prev_round, "packet": None,
-            "new_state": new_state, "reason": "max_rounds_exceeded",
-            "regression": [f.to_dict() for f in regression],
-            "missing_gates": [], "weakened": [],
-        }
+        return _escalate(
+            task_id, prev_round, passed_now, new_criteria,
+            "max_rounds_exceeded",
+            regression=[f.to_dict() for f in regression])
 
     new_round = prev_round + 1
     packet = PushbackPacket(task_id, new_round, all_findings, new_criteria)