diff --git a/apps/server/services/detection_service.py b/apps/server/services/detection_service.py
index 4789081..cdfe91b 100644
--- a/apps/server/services/detection_service.py
+++ b/apps/server/services/detection_service.py
@@ -12,6 +12,15 @@ import torch
 from .loitering_service import get_loitering_service
 from .adapters import DetectionAdapter
 from .event import AlertRuleEngine, EventAggregator, EventDecisionEngine
+from .event.frame_accumulator import MultiFrameAccumulator
+from .event.llm_trigger import LLMTrigger
+from .llm_analysis_service import (
+    LLMAnalysisService,
+    LLMAnalysisRequest,
+    create_llm_service_from_settings,
+)
+from .llm_cost_tracker import LLMCostTracker, get_global_tracker
+from .result_fusion import ResultFusion
 from core.settings import get_settings
 from models.event_schemas import DetectionSource
 
@@ -41,6 +50,41 @@ class DetectionService:
             dedup_window_seconds=settings.event_engine.dedup_window_seconds,
             max_active_events=settings.event_engine.max_active_events,
         )
+
+        # LLM 二次判断管道 (MVP-3 / D26-D30)
+        self._llm_enabled = settings.llm.enabled
+        if self._llm_enabled:
+            self._frame_accumulator = MultiFrameAccumulator(
+                window_seconds=settings.llm_trigger.window_seconds,
+            )
+            self._llm_trigger = LLMTrigger(
+                accumulator=self._frame_accumulator,
+                min_consecutive_hits=settings.llm_trigger.min_consecutive_hits,
+                min_avg_confidence=settings.llm_trigger.min_avg_confidence,
+                cooldown_seconds=settings.llm_trigger.cooldown_seconds,
+                severity_bypass=settings.llm_trigger.severity_bypass,
+            )
+            self._llm_service = create_llm_service_from_settings(settings)
+            self._cost_tracker = get_global_tracker()
+            self._result_fusion = ResultFusion(
+                strategy=settings.fusion.strategy,
+                yolo_weight=settings.fusion.yolo_weight,
+                llm_weight=settings.fusion.llm_weight,
+                suppress_on_llm_negative=settings.fusion.suppress_on_llm_negative,
+                fallback_to_yolo=settings.fusion.fallback_to_yolo,
+            )
+            logger.info(
+                "LLM 管道已初始化: provider=%s model=%s strategy=%s",
+                settings.llm.provider,
+                settings.llm.model,
+                settings.fusion.strategy,
+            )
+        else:
+            self._frame_accumulator = None
+            self._llm_trigger = None
+            self._llm_service = None
+            self._cost_tracker = None
+            self._result_fusion = None
     
     async def detect_image(
         self,
@@ -94,6 +138,7 @@ class DetectionService:
                     }
                 }
                 result_data = self._apply_event_pipeline(result_data, model_id)
+                result_data = await self._apply_llm_pipeline(result_data)
                 return result_data
             
             results = model(image, conf=confidence, iou=iou, verbose=False)
@@ -185,6 +230,8 @@ class DetectionService:
 
             # 事件管道 (MVP-1): 决策 → 规则 → 聚合
             result_data = self._apply_event_pipeline(result_data, model_id)
+            # LLM 二次判断管道 (MVP-3)
+            result_data = await self._apply_llm_pipeline(result_data, frame=image)
 
             return result_data
         except Exception as e:
@@ -268,6 +315,7 @@ class DetectionService:
                     }
                 }
                 result_data = self._apply_event_pipeline(result_data, model_id)
+                result_data = await self._apply_llm_pipeline(result_data, frame=frame)
                 
                 if draw:
                     frame = self.draw_detections(frame, detections, fps)
@@ -388,6 +436,8 @@ class DetectionService:
 
             # 事件管道 (MVP-1): 决策 → 规则 → 聚合
             result_data = self._apply_event_pipeline(result_data, model_id=model_id)
+            # LLM 二次判断管道 (MVP-3)
+            result_data = await self._apply_llm_pipeline(result_data, frame=frame)
 
             return frame, result_data
         except Exception as e:
@@ -502,6 +552,8 @@ class DetectionService:
                 model_id='fire_composite',
                 source=DetectionSource.COMPOSITE,
             )
+            # LLM 二次判断管道 (MVP-3)
+            result_data = await self._apply_llm_pipeline(result_data, frame=image)
             return result_data
 
         except Exception as e:
@@ -579,13 +631,15 @@ class DetectionService:
         model_id: Optional[str] = None,
         source_id: Optional[str] = None,
         source: DetectionSource = DetectionSource.YOLO,
+        frame: Optional[np.ndarray] = None,
     ) -> Dict:
-        """对检测结果执行 决策 → 规则 → 聚合 三段管道。
+        """对检测结果执行 决策 → 规则 → 聚合 → LLM 二次判断 → 融合 管道。
 
-        在 ``result_data`` 中追加两个字段:
+        在 ``result_data`` 中追加以下字段:
 
         - ``candidate_events``: List[dict]  决策引擎产出的候选事件
         - ``alert_events``:    List[dict]  规则命中后经聚合的预警事件
+        - ``llm_results``:     List[dict]  LLM 分析结果 (仅 LLM 启用时)
         """
 
         if not result_data.get('success') or not result_data.get('detections'):
@@ -615,6 +669,185 @@ class DetectionService:
 
         return result_data
 
+    async def _apply_llm_pipeline(
+        self,
+        result_data: Dict,
+        frame: Optional[np.ndarray] = None,
+    ) -> Dict:
+        """LLM 二次判断管道 (MVP-3 / D35)。
+
+        在已有 ``alert_events`` 的基础上:
+        1. 将候选事件喂给 LLMTrigger 判断是否需要 LLM 复审
+        2. 对触发的决策调用 LLMAnalysisService
+        3. 用 ResultFusion 融合 YOLO + LLM 结果
+        4. 用 LLMCostTracker 记录成本
+
+        追加字段:
+        - ``llm_results``: List[dict]  LLM 分析结果列表
+        """
+
+        if not self._llm_enabled:
+            result_data['llm_results'] = []
+            return result_data
+
+        alert_events = result_data.get('alert_events', [])
+        if not alert_events:
+            result_data['llm_results'] = []
+            return result_data
+
+        logger.info(
+            "[LLM管道] 开始评估 | alert_events=%d | 有frame=%s",
+            len(alert_events),
+            frame is not None and frame.size > 0,
+        )
+
+        try:
+            # 1. 从 alert_events 重建 CandidateEvent 列表供 LLMTrigger 评估
+            from models.event_schemas import CandidateEvent, AlertEvent, EventType, SeverityLevel, UnifiedDetection
+            candidates_for_llm = []
+            for evt_dict in alert_events:
+                try:
+                    # AlertEvent 用 detections(列表), CandidateEvent 用 detection(单个) → 取首个
+                    dets = evt_dict.get('detections', [])
+                    det = dets[0] if dets else None
+
+                    candidate = CandidateEvent(
+                        event_type=EventType(evt_dict['event_type']),
+                        severity=SeverityLevel(evt_dict.get('severity', 'high')),
+                        confidence=evt_dict.get('confidence', 0.5),
+                        detection=det,
+                        source_id=evt_dict.get('source_id'),
+                        timestamp=evt_dict.get('timestamp', evt_dict.get('first_seen')),
+                        triggered_rules=[evt_dict.get('rule_name')] if evt_dict.get('rule_name') else [],
+                    )
+                    candidates_for_llm.append(candidate)
+                except Exception as exc:
+                    logger.debug("[LLM管道] 重建候选事件失败: %s | 原始数据: %s", exc, {k: v for k, v in evt_dict.items() if k != 'detections'})
+                    continue
+
+            if not candidates_for_llm:
+                logger.info("[LLM管道] 无有效候选事件，跳过")
+                result_data['llm_results'] = []
+                return result_data
+
+            # 2. LLMTrigger 评估
+            trigger_decisions = self._llm_trigger.evaluate(candidates_for_llm)
+
+            if not trigger_decisions:
+                logger.info("[LLM管道] 触发器评估: 未达到触发条件 (需连续%d帧或critical严重性)", self._llm_trigger.min_consecutive_hits)
+                result_data['llm_results'] = []
+                return result_data
+
+            logger.info(
+                "[LLM管道] 触发器命中 %d 个决策 | 详情: %s",
+                len(trigger_decisions),
+                [{d.candidate.event_type.value: d.reason} for d in trigger_decisions],
+            )
+
+            # 3. 逐个触发决策调用 LLM + 融合
+            llm_results = []
+            fused_alert_events = []
+            for decision in trigger_decisions:
+                llm_result = None
+
+                # 检查成本追踪器是否允许调用
+                if self._cost_tracker and not self._cost_tracker.can_call():
+                    logger.info(
+                        "[LLM管道] 调用被降级拦截: %s",
+                        self._cost_tracker.reason_for_block(),
+                    )
+                elif self._llm_service:
+                    # 构造 LLM 请求
+                    frames = [frame] if frame is not None and frame.size > 0 else []
+                    logger.info(
+                        "[LLM管道] 调用 LLM 分析 | 事件=%s | 置信度=%.2f | 图片=%d张",
+                        decision.candidate.event_type.value,
+                        decision.candidate.confidence,
+                        len(frames),
+                    )
+                    request = LLMAnalysisRequest(
+                        candidate=decision.candidate,
+                        frames=frames,
+                    )
+                    llm_result = await self._llm_service.analyze_with_fallback(request)
+
+                    if llm_result:
+                        logger.info(
+                            "[LLM管道] LLM 返回 | confirmed=%s | confidence=%.2f | reasoning=%.80s",
+                            llm_result.confirmed,
+                            llm_result.confidence,
+                            (llm_result.reasoning or ''),
+                        )
+                    else:
+                        logger.warning("[LLM管道] LLM 返回空结果 (可能降级到 mock)")
+
+                    # 记录成本
+                    if self._cost_tracker and llm_result:
+                        self._cost_tracker.record(
+                            llm_result,
+                            image_count=len(frames) or 1,
+                        )
+
+                # 4. 找到对应的 AlertEvent 进行融合
+                alert_dict = None
+                for evt_dict in alert_events:
+                    if (
+                        evt_dict.get('event_type') == decision.candidate.event_type.value
+                        and evt_dict.get('source_id') == decision.candidate.source_id
+                    ):
+                        alert_dict = evt_dict
+                        break
+
+                if alert_dict is not None and self._result_fusion:
+                    try:
+                        alert_event = AlertEvent(**alert_dict)
+                        outcome = self._result_fusion.fuse(alert_event, llm_result)
+                        if outcome.alert is not None and not outcome.suppressed:
+                            fused_alert_events.append(outcome.alert.model_dump(mode='json'))
+                        llm_results.append({
+                            'trigger_reason': decision.reason,
+                            'llm_confirmed': llm_result.confirmed if llm_result else None,
+                            'llm_confidence': llm_result.confidence if llm_result else None,
+                            'llm_reasoning': llm_result.reasoning if llm_result else None,
+                            'fusion_strategy': outcome.strategy,
+                            'fusion_reason': outcome.reason,
+                            'final_confidence': outcome.final_confidence,
+                            'suppressed': outcome.suppressed,
+                        })
+                        logger.info(
+                            "[LLM管道] 融合完成 | 策略=%s | 最终置信度=%.2f | 抑制=%s | 原因=%s",
+                            outcome.strategy,
+                            outcome.final_confidence,
+                            outcome.suppressed,
+                            outcome.reason,
+                        )
+                    except Exception as exc:
+                        logger.error("LLM 融合失败: %s", exc)
+                        llm_results.append({
+                            'trigger_reason': decision.reason,
+                            'llm_confirmed': None,
+                            'error': str(exc),
+                        })
+
+            # 用融合后的结果替换原始 alert_events
+            if fused_alert_events:
+                result_data['alert_events'] = fused_alert_events
+
+            result_data['llm_results'] = llm_results
+            logger.info(
+                "[LLM管道] 完成 | 总决策=%d | LLM结果=%d | 融合后事件=%d",
+                len(trigger_decisions),
+                len(llm_results),
+                len(fused_alert_events),
+            )
+
+        except Exception as e:  # noqa: BLE001
+            logger.error("LLM 管道执行失败: %s", e)
+            result_data['llm_results'] = []
+            result_data['llm_pipeline_error'] = str(e)
+
+        return result_data
+
 
     def draw_detections(
         self,