improve xiaoniu group awareness and solver suppression

plugins/ai_auto_response/README.md

@@ -436,6 +436,16 @@
 
 当前 `bot_ai.py` 里的“体力值 + 参与度”可以保留，但应降级为“主动聊天限流器”，而不是总入口。
 
+这里建议再补两层非常关键的拟人化约束：
+
+- `group_acceptance`
+  观察小牛发言后，群里后续是否自然接住。如果经常发完没人理，就降低主动度；如果经常有人顺着聊，才允许在非强触发场景更积极一点。
+
+- `human_solver_suppression`
+  如果最近几条里已经明显有群友在认真解题，小牛除非被 `@`，否则优先收着，避免像“抢答机器人”。
+
+这两层加上后，小牛会更像一个会看场合的老成员，而不是看见关键词就扑上去。
+
 ---
 
 ## 向量记忆设计

plugins/ai_auto_response/flow_manager.py

@@ -59,6 +59,7 @@ class FlowManager:
             if since_reply <= 180 and event.get("message_after_bot"):
                 state.score += float(self.config.get("response_accepted_boost", 15))
                 state.accepted_reply_count += 1
+                state.bot_reply_streak = 0
         state.last_human_message_at = now
         state.last_topic = event.get("topic") or state.last_topic
         state.state = self._score_to_state(state.score)
@@ -66,6 +67,12 @@ class FlowManager:
 
     def note_bot_reply(self, room_id: str) -> FlowState:
         state = self.decay(room_id)
+        now = datetime.now()
+        if state.last_bot_reply_at and (not state.last_human_message_at or state.last_human_message_at < state.last_bot_reply_at):
+            since_last_bot_reply = (now - state.last_bot_reply_at).total_seconds()
+            if since_last_bot_reply <= 180:
+                state.ignored_reply_count += 1
+                state.score = max(0.0, state.score - float(self.config.get("ignored_reply_penalty", 20)))
         state.last_bot_reply_at = datetime.now()
         state.bot_reply_streak += 1
         max_streak = int(self.config.get("max_bot_reply_streak", 3))
@@ -74,6 +81,20 @@ class FlowManager:
         state.state = self._score_to_state(state.score)
         return state
 
+    def get_acceptance_state(self, room_id: str) -> str:
+        state = self.get_state(room_id)
+        accepted = int(state.accepted_reply_count)
+        ignored = int(state.ignored_reply_count)
+        total = accepted + ignored
+        if total < 3:
+            return "neutral"
+        ratio = accepted / max(total, 1)
+        if ratio >= 0.7 and accepted >= 3:
+            return "warm"
+        if ratio <= 0.35 and ignored >= 2:
+            return "cold"
+        return "neutral"
+
     def _score_to_state(self, score: float) -> str:
         idle_threshold = float(self.config.get("idle_threshold", 20))
         warming_threshold = float(self.config.get("warming_threshold", 40))

plugins/ai_auto_response/main.py

@@ -163,6 +163,8 @@ class AIAutoResponsePlugin(MessagePluginInterface):
             "timestamp": message.get("timestamp"),
         }
         self._append_group_message(room_id, normalized_message)
+        recent_messages = self.group_messages.get(room_id) or self.memory_store.get_recent_messages(room_id)
+        conversation_hints = self._build_conversation_hints(recent_messages, sender, content)
 
         memory_hints = self.memory_store.build_memory_hints(room_id, sender)
         self._sync_member_memory(room_id, sender, sender_name, memory_hints.get("member_context", {}))
@@ -198,8 +200,15 @@ class AIAutoResponsePlugin(MessagePluginInterface):
         )
 
         allow_proactive = bool(self.mode_config.get("allow_proactive_reply", True))
+        acceptance_state = self.flow_manager.get_acceptance_state(room_id)
         reply_mode = self.response_planner.choose_reply_mode(trigger.__dict__, flow_state.state)
-        should_reply = self.response_planner.should_reply(trigger.__dict__, flow_state.state, allow_proactive)
+        should_reply = self.response_planner.should_reply(
+            trigger.__dict__,
+            flow_state.state,
+            allow_proactive,
+            acceptance_state,
+            conversation_hints,
+        )
         if not should_reply:
             self._log_event(
                 "skip",
@@ -209,6 +218,8 @@ class AIAutoResponsePlugin(MessagePluginInterface):
                 trigger_type=trigger.trigger_type,
                 reply_mode=reply_mode,
                 flow_state=flow_state.state,
+                acceptance_state=acceptance_state,
+                solver=self._yn(conversation_hints.get("has_recent_human_solver")),
             )
             return False, "skip"
         if not self._pass_cooldown(room_id, trigger.__dict__):
@@ -222,7 +233,6 @@ class AIAutoResponsePlugin(MessagePluginInterface):
             )
             return False, "cooldown"
 
-        recent_messages = self.group_messages.get(room_id) or self.memory_store.get_recent_messages(room_id)
         vector_memories = []
         if self.vector_memory.should_search(reply_mode, trigger.trigger_type, memory_hints.get("returning_member_state", "")):
             vector_memories = self.vector_memory.search(content, room_id, sender)
@@ -232,6 +242,7 @@ class AIAutoResponsePlugin(MessagePluginInterface):
             sender=sender,
             group_mode=group_profile.get("mode", ""),
             knowledge_domain=group_profile.get("knowledge_domain", ""),
+            acceptance_state=acceptance_state,
             reply_mode=reply_mode,
             recent_message_count=len(recent_messages),
             vector_hit_count=len(vector_memories),
@@ -372,6 +383,54 @@ class AIAutoResponsePlugin(MessagePluginInterface):
             f"12. 回答时优先服从当前群画像里的知识域和回答风格，不要跨领域乱发挥。\n"
         )
 
+    @staticmethod
+    def _build_conversation_hints(recent_messages: List[Dict], current_sender: str, current_content: str) -> Dict[str, Any]:
+        previous_messages = list(recent_messages[:-1]) if recent_messages else []
+        recent_window = previous_messages[-4:]
+        solver_count = 0
+        solver_senders = set()
+        current_tokens = AIAutoResponsePlugin._extract_overlap_tokens(current_content)
+        for item in recent_window:
+            sender = str(item.get("sender", "") or "")
+            if not sender or sender == current_sender:
+                continue
+            content = str(item.get("content") or item.get("message") or "").strip().lower()
+            if AIAutoResponsePlugin._looks_like_answer(content) and AIAutoResponsePlugin._has_topic_overlap(current_tokens, content):
+                solver_count += 1
+                solver_senders.add(sender)
+        return {
+            "has_recent_human_solver": solver_count >= 2 and len(solver_senders) >= 1,
+            "solver_count": solver_count,
+        }
+
+    @staticmethod
+    def _looks_like_answer(content: str) -> bool:
+        if not content:
+            return False
+        answer_keywords = [
+            "先", "然后", "重启", "配置", "日志", "接口", "看一下", "试试", "排查",
+            "报错", "原因", "因为", "改成", "装", "部署", "重现", "检查", "确认",
+        ]
+        if len(content) >= 18:
+            return True
+        return any(keyword in content for keyword in answer_keywords)
+
+    @staticmethod
+    def _extract_overlap_tokens(content: str) -> set[str]:
+        text = str(content or "").lower()
+        tokens = set(re.findall(r"[a-z0-9_\\-]{3,}", text))
+        for keyword in ["报错", "日志", "配置", "接口", "插件", "部署", "docker", "python", "openclaw", "机器人", "qdrant", "ollama"]:
+            if keyword in text:
+                tokens.add(keyword)
+        return tokens
+
+    @staticmethod
+    def _has_topic_overlap(current_tokens: set[str], previous_content: str) -> bool:
+        if not current_tokens:
+            return False
+        previous_tokens = AIAutoResponsePlugin._extract_overlap_tokens(previous_content)
+        return bool(current_tokens & previous_tokens)
+
     @staticmethod
     def _sanitize_response(response: str) -> str:
         if not response:
@@ -528,12 +587,15 @@ class AIAutoResponsePlugin(MessagePluginInterface):
                 f"reason={data.get('reason', '')} "
                 f"trigger={data.get('trigger_type', 'none')} "
                 f"mode={data.get('reply_mode', '')} "
+                f"acc={data.get('acceptance_state', '-') or '-'} "
+                f"solver={data.get('solver', '-') or '-'}"
             ).strip()
 
         if event == "context":
             return (
                 f"[XIAONIU] CTX room={room} user={sender} "
                 f"mode={data.get('reply_mode', '')} "
+                f"acc={data.get('acceptance_state', '-') or '-'} "
                 f"recent={data.get('recent_message_count', 0)} "
                 f"vector={data.get('vector_hit_count', 0)}"
             ).strip()

plugins/ai_auto_response/response_planner.py

@@ -15,9 +15,19 @@ class ResponsePlanner:
             return "social_short"
         return "social_short" if flow_state in {"deep_engaged"} else "refuse_or_skip"
 
-    def should_reply(self, trigger: Dict, flow_state: str, allow_proactive: bool) -> bool:
+    def should_reply(
+        self,
+        trigger: Dict,
+        flow_state: str,
+        allow_proactive: bool,
+        acceptance_state: str = "neutral",
+        conversation_hints: Dict | None = None,
+    ) -> bool:
+        conversation_hints = conversation_hints or {}
         if trigger.get("is_at"):
             return True
+        if trigger.get("is_question") and conversation_hints.get("has_recent_human_solver") and flow_state != "deep_engaged":
+            return False
         if trigger.get("is_question"):
             return True
         if trigger.get("is_followup"):
@@ -28,4 +38,8 @@ class ResponsePlanner:
             return False
         if not allow_proactive:
             return False
-        return flow_state in {"deep_engaged"} and trigger.get("priority", 0) >= 0.65
+        if acceptance_state == "cold":
+            return False
+        if acceptance_state == "neutral":
+            return flow_state in {"deep_engaged"} and trigger.get("priority", 0) >= 0.8
+        return flow_state in {"engaged", "deep_engaged"} and trigger.get("priority", 0) >= 0.65