diff --git a/services/elephant_alpha_autonomous_engine.py b/services/elephant_alpha_autonomous_engine.py
index d6300bf..6618c40 100644
--- a/services/elephant_alpha_autonomous_engine.py
+++ b/services/elephant_alpha_autonomous_engine.py
@@ -534,6 +534,49 @@ class ElephantAlphaAutonomousEngine:
             return default
 
     async def _execute_autonomous_decision(self, trigger: AutonomousTrigger) -> None:
+        # ─── Operation Ollama-First v5.0 修補（2026-05-03）───
+        # 統帥反饋：每個 EA trigger 都先跑 Gemini orchestrate（燒錢）才 prefetch Hermes，
+        # 結果 Hermes 0 threats 時送出空泛幻覺訊息 + Gemini 帳單照付。
+        # 修法：價格類 trigger Hermes-first short-circuit
+        #   1. 先跑 Hermes（5s timeout，免費 Ollama）
+        #   2. 0 threats → 直接 return（**不燒 Gemini**）
+        #   3. 有 threats → 才走原 Gemini orchestration
+        # 預期：>70% 的 trigger 在 Hermes 階段就被攔截，月省 Gemini ~3-5M tokens
+        if trigger.trigger_type in _PRICE_RELATED_TRIGGERS:
+            try:
+                hermes_threats = await self._fetch_hermes_threats_summary(top_n=5)
+            except Exception as e:
+                self._log.warning("Hermes pre-flight check 失敗 (non-blocking): %s", e)
+                hermes_threats = None
+
+            if not hermes_threats:
+                # 0 threats 或 prefetch timeout → 直接 drop trigger，不燒 Gemini
+                self._log.info(
+                    "EA short-circuit: trigger=%s 無 Hermes 實證 threats，"
+                    "跳過 Gemini orchestration 省成本",
+                    trigger.trigger_type
+                )
+                # 紀錄 short-circuit 事件供 token report 統計（不影響主流程）
+                try:
+                    from services.ai_call_logger import log_ai_call
+                    with log_ai_call(
+                        caller='ea_engine',
+                        provider='gcp_ollama',  # 只跑了 Hermes，沒燒 Gemini
+                        model='hermes3:latest',
+                        meta={'short_circuit': True, 'trigger': trigger.trigger_type,
+                              'reason': 'no_hermes_threats'},
+                    ) as ctx:
+                        ctx.set_tokens(input=0, output=0)  # Hermes 已自己記
+                        ctx.status = 'cache_only'  # 不算 ok 也不算 error
+                except Exception:
+                    pass  # logger 失敗不影響主流程
+                return
+
+            # Hermes 有實證 threats → 把它存進 trigger.conditions 給 orchestrator 用
+            # 避免 orchestrator 又自己編 SKU 數字
+            trigger.conditions = trigger.conditions or {}
+            trigger.conditions['_prefetched_hermes_threats'] = hermes_threats
+
         context = await self._build_trigger_context(trigger)
         try:
             decision = await self._run_with_timeout(
@@ -881,32 +924,62 @@ class ElephantAlphaAutonomousEngine:
                     self._log.warning("Pre-fetch threats raised (non-blocking): %s", e)
                     concrete_actions = None
 
+            # ─── Operation Ollama-First v5.0 修補：消除空泛幻覺訊息 ───
+            # 統帥反饋（2026-05-03）：fallback 路徑帶 OpenClaw Gemini plan 文字 +
+            # decision.reasoning 全是「312 SKU / 23% / 14 項任務」幻覺數字，無 DB 鉤住，
+            # 嚴重誤導決策。修法：concrete=Hermes 實證 vs concrete=None 兩條路徑徹底分離。
+            #   - 有實證 → 完整訊息（含 SKU 流失金額）
+            #   - 無實證 → 極簡訊息「Hermes 即時數據不可用」+ 不再灌 LLM 幻覺
+
+            from services.telegram_templates import triaged_alert, _send_telegram_raw
+
             if concrete_actions:
+                # 有實證數據路徑：保留完整訊息
                 ai_actions_payload = concrete_actions
+                ai_summary_text = (decision.reasoning or "")[:300]
+                ai_cause_text = (
+                    f"觸發類型：{_zh_trigger(trigger.trigger_type)} | "
+                    f"信心度：{decision.confidence:.2f} | "
+                    f"參與模組：{', '.join(_AGENT_LABEL.get(a.lower(), a) for a in decision.agents_required)}"
+                )
             else:
+                # 無實證數據路徑：極簡訊息，明確標註無數據
+                self._log.warning(
+                    "EA escalation 落入 no-concrete-data fallback (trigger=%s)；"
+                    "送極簡訊息避免 LLM 幻覺數字誤導統帥",
+                    trigger.trigger_type
+                )
                 ai_actions_payload = [
-                    f"步驟 {s.get('step', i+1)}：{_zh_step(s)}"
-                    for i, s in enumerate(decision.execution_plan[:3])
-                ] or ["無具體執行計畫"]
+                    "⚠️ Hermes 即時威脅清單不可用（5s timeout 或無 SKU 命中）",
+                    "📋 建議：手動下 SQL 查詢過去 24h competitor_price_history 確認狀況",
+                    "🔧 或：SSH 188 跑 docker exec momo-pro-system python -c "
+                    "'from services.hermes_analyst_service import HermesAnalystService;"
+                    " print(HermesAnalystService().run().threats[:5])'",
+                ]
+                ai_summary_text = (
+                    f"⚠️ 本訊息為**無實證**告警：Hermes pre-fetch 失敗，"
+                    f"以下原始決策內容含 LLM 自由發揮數字（非 DB 數據），請審慎參考。"
+                )
+                ai_cause_text = (
+                    f"觸發類型：{_zh_trigger(trigger.trigger_type)} | "
+                    f"信心度：{decision.confidence:.2f} | "
+                    f"⚠️ 無 Hermes SKU 數據（不顯示 LLM 幻覺 plan 文字）"
+                )
 
             try:
-                from services.telegram_templates import triaged_alert, _send_telegram_raw
                 msg, keyboard = triaged_alert(
                     base_event={
                         "event_type": "ea_escalation",
                         "title": f"🐘 EA 升級審核 · {_zh_trigger(trigger.trigger_type)}",
                         "summary": (
                             f"自主決策信心度 {decision.confidence:.2f} 低於門檻，需人工批准"
+                            + ("" if concrete_actions else "（⚠️ 無實證數據）")
                         ),
                         "id": f"ea_review_{int(datetime.now().timestamp())}",
                     },
                     tier_label="🐘 Elephant Alpha · L3 HITL",
-                    ai_summary=(decision.reasoning or "")[:300],
-                    ai_cause=(
-                        f"觸發類型：{_zh_trigger(trigger.trigger_type)} | "
-                        f"信心度：{decision.confidence:.2f} | "
-                        f"參與模組：{', '.join(_AGENT_LABEL.get(a.lower(), a) for a in decision.agents_required)}"
-                    ),
+                    ai_summary=ai_summary_text,
+                    ai_cause=ai_cause_text,
                     ai_actions=ai_actions_payload,
                 )
                 await self._run_with_timeout(_send_telegram_raw, msg, timeout=10, reply_markup=keyboard)