diff --git a/monitor.py b/monitor.py
index 56b2e4d..c7a3b40 100644
--- a/monitor.py
+++ b/monitor.py
@@ -4748,26 +4748,33 @@ class MessageMonitor:
             # 策略2：反向包含剥离——逗号分段后逐段判断是否属于 bot 回复
             _has_comma = '，' in ts or (',' in ts and re.search(r'[\u4e00-\u9fff],[\u4e00-\u9fff]', ts))
             if not stripped_bot and _has_comma:
-                parts = re.split(r'[，,]', ts)
-                parts = [p.strip() for p in parts if p.strip()]
-                if len(parts) >= 2:
-                    user_parts = []
-                    for part in parts:
-                        if len(part) < 2:
-                            continue
-                        is_bot_frag = False
-                        npart = self._norm_frag(part)
-                        if npart:
-                            for r in replied:
-                                nr = self._norm_frag(r)
-                                if len(nr) >= 2 and npart in nr:
-                                    is_bot_frag = True
-                                    break
-                        if not is_bot_frag:
-                            user_parts.append(part)
-                    if len(user_parts) < len(parts):
-                        ts = '，'.join(user_parts).strip('，').strip()
-                        stripped_bot = True
+                # ⭐ 2026-08-17 问句豁免：用户提问即使含与旧回复共享的片段，也不应被剥离
+                # 例："那领地系统有哪两种操作模式啊？游戏专" — "操作"与旧回复表头匹配
+                # 但这是用户新问句，不是旧回复的回声
+                is_question_mark = bool(re.search(r'[?？]', ts))
+                is_question_word = any(qw in ts for qw in ('吗', '呢', '多少', '怎么', '如何', '哪', '什么', '几'))
+                is_question = is_question_mark or is_question_word
+                if not is_question:
+                    parts = re.split(r'[，,]', ts)
+                    parts = [p.strip() for p in parts if p.strip()]
+                    if len(parts) >= 2:
+                        user_parts = []
+                        for part in parts:
+                            if len(part) < 2:
+                                continue
+                            is_bot_frag = False
+                            npart = self._norm_frag(part)
+                            if npart:
+                                for r in replied:
+                                    nr = self._norm_frag(r)
+                                    if len(nr) >= 2 and npart in nr:
+                                        is_bot_frag = True
+                                        break
+                            if not is_bot_frag:
+                                user_parts.append(part)
+                        if len(user_parts) < len(parts):
+                            ts = '，'.join(user_parts).strip('，').strip()
+                            stripped_bot = True
             # 策略3：尾部匹配——OCR把bot回复直接拼在用户消息末尾
             # 使用纯中文相似度匹配，抵抗OCR噪声和英文/数字干扰
             if not stripped_bot:
@@ -5265,7 +5272,7 @@ class MessageMonitor:
         self._semantic_fingerprint_cache = kept
 
         result = []
-        SIM_THRESHOLD = 0.3  # Jaccard相似度阈值：>=0.3视为语义重复
+        SIM_THRESHOLD = 0.75  # Jaccard相似度阈值：>=0.75视为语义重复（提高防误杀）
         for text in texts:
             is_dup = False
             for kept_text in result:
@@ -5305,7 +5312,7 @@ class MessageMonitor:
         ]
 
         # 与最近查询比较
-        SIM_THRESHOLD = 0.3
+        SIM_THRESHOLD = 0.75  # Jaccard相似度阈值：>=0.75视为语义重复（提高防误杀）
         for recent_text, recent_ts in self._recent_user_queries:
             sim = self._semantic_similarity(text, recent_text)
             if sim >= SIM_THRESHOLD: