chore: 版本号更新为v1.2.0，更新日志同步

README.md

@@ -9,7 +9,8 @@
 - **自动回复** — 监听新评论，自动调用 AI 生成回复，支持多轮对话上下文
 - **多语言适配** — 根据评论语言自动用对应语言回复
 - **情感分析** — 分析评论情感倾向（非常正面/正面/中性/负面/非常负面），根据情感调整回复语气
-- **前置过滤（合规检测）** — AI 回复前对评论进行合规性分类，自动拦截广告/辱骂攻击/敏感内容/无意义内容，违规评论停止生成 AI 回复以节省 Token，可选自动将违规评论设为待审核状态
+- **前置过滤（合规检测）** — AI 回复前对评论进行合规性分类，自动拦截广告/辱骂攻击/敏感内容/乱码，违规评论停止生成 AI 回复以节省 Token，可选自动将违规评论设为待审核状态
+- **误报反馈** — 被误拦截的评论可进行误报反馈，支持"AI回复"和"仅通过"两种处理方式，"仅通过"后可随时补触发 AI 回复
 - **草稿模式** — AI 回复先存为草稿，管理员审核后再发布，支持批量操作
 - **失败重试** — AI 生成失败时自动重试，指数退避策略
 - **对话轮次限制** — 同一评论线程中限制 AI 最多回复轮次，防止无限对话

docs/CHANGELOG.md

@@ -1,5 +1,61 @@
 # 更新日志
 
+## v1.2.0
+
+> 2026-06-25
+
+### 新增
+
+- **误报反馈功能** — 被拦截的评论可进行误报反馈，支持两种处理方式：
+  - **AI 回复**：标记为通过 + 触发 AI 生成回复
+  - **仅通过**：仅标记为通过，不生成回复
+- **误报通过状态** — 新增 `FALSE_POSITIVE` 状态，"仅通过"的记录显示为"误报通过"，不显示"通过/拒绝"按钮
+- **触发 AI 回复按钮** — "误报通过"状态的记录可随时点击"触发AI回复"按钮补生成 AI 回复
+- **上下文优先判断原则** — 前置过滤 AI 提示词重写，遵循五条核心原则：上下文优先、口语化宽容、恶意导向判定、宁放勿杀、闲聊不算无意义
+
+### Bug 修复
+
+- **修复误报反馈"AI 回复"被前置过滤再次拦截** — `processComment()` 始终调用 `preFilterService.check()`，用户已确认为误报的评论会被再次拦截。新增 `processFalsePositive()` 方法跳过前置过滤和去重检查
+- **修复误报反馈"AI 回复"被去重检查拦截** — `hasExistingReply()` 找到已有的 FILTERED→PENDING 记录导致 AI 回复无法生成。`processFalsePositive()` 复用已有记录，不经过去重检查
+- **修复误报反馈"AI 回复"导致全站崩溃** — `processComment()` 同步等待 AI 生成完成，HTTP 请求长时间不返回。改为 `.subscribe()` 异步执行，API 立即返回
+- **修复误报反馈"仅通过"后显示通过/拒绝按钮** — "仅通过"将记录设为 `status=PASS, published=false, reply=""`，导致显示"通过/拒绝"按钮且内容为空。改为 `status=FALSE_POSITIVE`
+- **修复 `extractChoice` 无匹配时返回原始文本** — AI 返回非预期文本时被误判为违规类别。改为返回空字符串触发安全拦截
+- **修复 `approveOriginalComment` 缺少乐观锁重试** — 并发更新 Comment/Reply 时可能静默失败。添加 `Retry.backoff(3, 100ms)` 重试
+
+### 改进
+
+- **消除 `checkBlockedCommenters` 重复代码** — `FilterService` 新增 `isCommenterBlocked(commentName)` 公共方法，`AiReplyOrchestrator` 改为调用它
+- **前端批量操作防重复提交** — 批量通过/拒绝/删除按钮添加 `batchLoading` 状态，操作期间禁用按钮
+
+---
+
+## v1.1.2
+
+> 2026-06-24
+
+### Bug 修复
+
+- **修复 AI 分类完全不可用** — `classifyWithChoice` 和 `classifyWithChat` 均使用了 `GenerateTextRequest.Builder.system()` 方法，而该方法在当前 AI Foundation 版本中不被支持或导致运行时错误，导致所有评论均被拦截并显示"AI分类服务不可用，安全拦截"。现改为将 system prompt 合并到 user prompt 中，与可用的 `chat()` 方法保持一致的调用方式
+- **修复 `classifyWithChoice` NPE** — `.map()` 返回 `null` 时触发 Reactor 内部 NullPointerException，改为 `.flatMap()` + `Mono.empty()` 正确触发 fallback
+
+### 改进
+
+- **分类调用诊断日志增强** — 在 `AiFoundationDelegate`、`AiFoundationClient`、`CommentPreFilterService` 中增加关键诊断日志（分类开始、fallback 触发、分类结果、异常详情），便于排查分类链路问题
+- **AI 分类空结果处理** — 当 AI 返回空字符串时单独拦截，区别于"服务不可用"场景
+
+---
+
+## v1.1.1
+
+> 2026-06-24
+
+### 改进
+
+- **"无意义"分类范围收窄** — 与文章主题无关的闲聊、灌水、打招呼不再被判为"无意义"，仅纯乱码和无意义字符堆砌（如随机符号、键盘乱敲）才归类为"无意义"
+- **AI 分类降级方案** — 当 `OutputSpec.choice` 结构化输出不被模型支持时，自动退回到普通 chat 调用并从响应文本中提取分类值（`classifyWithChat` fallback）
+
+---
+
 ## v1.1.0
 
 > 2026-06-23

docs/guide/faq.md

@@ -92,8 +92,32 @@
 
 前置过滤默认启用。AI 会对评论进行分类判断，若 AI 服务不可用或分类失败，为安全起见会拦截评论而非放行。如果你发现正常评论被误拦截，可以在设置中关闭"启用前置过滤"开关。被拦截的评论会在日志页生成一条"已拦截"状态的记录，可查看具体分类标签和拦截原因。
 
+## 所有评论都显示"AI分类服务不可用，安全拦截"怎么办？
+
+这表示 AI 分类调用链路存在问题，可能的原因：
+
+1. **AI Foundation 插件未安装或未启用** — 请确保 AI Foundation 插件已正确安装并启用
+2. **AI Foundation 中未配置模型** — 请在 AI Foundation 中配置至少一个 AI 模型
+3. **模型名称配置错误** — 检查插件设置中的模型名称是否与 AI Foundation 中的 AiModel 资源名称一致，留空则使用默认模型
+4. **AI Foundation 版本过旧** — 请确保使用最新版本的 AI Foundation 插件
+
+::: tip 排查步骤
+1. 检查插件设置页面顶部的 AI Foundation 连接状态
+2. 查看插件日志中 `[Delegate]` 和 `[PreFilter]` 前缀的诊断信息
+3. 确认 AI 回复功能（非前置过滤）是否正常工作 — 如果 AI 回复也无法生成，说明是 AI Foundation 连接问题
+:::
+
 ## 被前置过滤拦截的评论会怎样？
 
 1. **停止生成 AI 回复** — 不会消耗后续 Token
 2. **创建拦截记录** — 在日志页显示为"已拦截"状态，标注分类标签（如"辱骂攻击"）和详细原因（含评论内容摘要）
 3. **自动设为待审核** — 原评论的 `approved` 会被置为 `false`，前端不再展示该评论，需人工判断后审核通过
+
+## 被误拦截的评论怎么处理？
+
+在日志页的"已拦截"记录右侧，点击 **误报反馈** 按钮，可选择：
+
+- **AI 回复** — 标记为误报 + 自动通过评论 + 触发 AI 生成回复
+- **仅通过** — 仅标记为误报 + 自动通过评论，不生成 AI 回复
+
+选择"仅通过"后，记录状态变为"误报通过"，可随时点击 **触发AI回复** 按钮补生成 AI 回复。

docs/guide/introduction.md

@@ -15,7 +15,8 @@ AI回评（Comment AI Autopilot）是一个 Halo 博客系统的插件，能够
 - **批量操作** — 草稿模式下支持批量通过/拒绝/删除
 - **文章/页面级开关** — 在文章编辑器中直接控制是否启用AI回复，文章默认开启，页面默认关闭
 - **评论者黑名单** — 屏蔽指定评论者，不触发AI回复，支持名称、邮箱和正则表达式
-- **前置过滤（合规检测）** — AI回复前对评论进行合规性分类，自动拦截广告/辱骂/敏感/无意义内容，节省Token；可选将违规评论设为待审核状态
+- **前置过滤（合规检测）** — AI回复前对评论进行合规性分类，自动拦截广告/辱骂/敏感/乱码内容，节省Token；可选将违规评论设为待审核状态
+- **误报反馈** — 被误拦截的评论可进行误报反馈，支持"AI回复"和"仅通过"两种处理方式，"仅通过"后可随时补触发 AI 回复
 - **手动触发** — 在评论管理页面对历史评论手动触发AI回复
 - **安全审核** — AI生成的内容经过两阶段安全审核（安全检查 + 质量评分），不合规内容自动拒绝
 - **Prompt 预设** — 内置友好型、专业型、幽默型、简洁型预设风格，可多选组合

docs/guide/settings.md

@@ -39,7 +39,7 @@
 - **广告**：包含推广链接、产品推销、引流信息等
 - **辱骂攻击**：包含辱骂、人身攻击、恶意挑衅、歧视性言论等
 - **敏感内容**：涉及政治敏感、违法违规、色情暴力等
-- **无意义**：纯乱码、无意义字符堆砌、与文章完全无关的废话
+- **无意义**：纯乱码、无意义字符堆砌（如随机符号、键盘乱敲）
 
 对于非"正常"类别的评论，插件会：
 
@@ -47,6 +47,8 @@
 2. 创建一条 `FILTERED` 状态的日志记录（可在日志页通过"已拦截"状态筛选查看）
 3. 若启用"违规评论设为待审核"，会自动将原评论的 `approved` 置为 `false`，使其进入待审核队列，需人工判断后审核通过
 
+被误拦截的评论可在日志页点击 **误报反馈** 按钮处理，支持"AI 回复"和"仅通过"两种方式。选择"仅通过"后记录变为"误报通过"状态，可随时点击"触发AI回复"按钮补生成回复。
+
 ::: warning
 前置过滤依赖 AI Foundation 插件进行分类判断，会额外消耗少量 Token。若 AI 服务不可用或分类失败，为安全起见将拦截评论而非放行，防止违规内容漏网。
 :::

gradle.properties

@@ -1,4 +1,4 @@
-version=1.1.0
+version=1.2.0
 
 # Fix Windows Gradle Worker Daemon exit code 268435659 when running pnpm via Exec tasks
 org.gradle.daemon=false

src/main/java/top/nxxy335/commentaiautopilot/endpoint/CommentAiAutopilotEndpoint.java

@@ -101,6 +101,8 @@ public class CommentAiAutopilotEndpoint implements CustomEndpoint {
             .POST("/import", this::importConfig)
             // 更新草稿回复内容（同时更新 AiCommentReply 和 Reply 扩展）
             .PUT("/replies/{name}/content", this::updateReplyContent)
+            // 误报反馈：将拦截记录标记为误报，可选触发AI回复
+            .POST("/replies/{name}/false-positive", this::falsePositive)
             .build();
     }
 
@@ -1035,4 +1037,126 @@ public class CommentAiAutopilotEndpoint implements CustomEndpoint {
                     .switchIfEmpty(ServerResponse.notFound().build());
             });
     }
+
+    /**
+     * 误报反馈：将被拦截的评论标记为误报（正常），并可选触发 AI 回复。
+     *
+     * 请求体：{ "action": "aiReply" | "approveOnly" }
+     * - aiReply: 将评论审核状态设为已通过 + 触发 AI 生成回复
+     * - approveOnly: 仅将评论审核状态设为已通过，不触发 AI 回复
+     */
+    private Mono<ServerResponse> falsePositive(ServerRequest request) {
+        var name = request.pathVariable("name");
+        return request.bodyToMono(String.class)
+            .flatMap(body -> {
+                String actionStr;
+                try {
+                    JsonNode node = objectMapper.readTree(body);
+                    actionStr = node.has("action") ? node.get("action").asText("approveOnly") : "approveOnly";
+                } catch (Exception e) {
+                    actionStr = "approveOnly";
+                }
+                final String action = actionStr;
+
+                return client.fetch(AiCommentReply.class, name)
+                    .flatMap(record -> {
+                        if (!"FILTERED".equals(record.getSpec().getStatus())
+                            && !"FALSE_POSITIVE".equals(record.getSpec().getStatus())) {
+                            return ServerResponse.badRequest()
+                                .bodyValue(Map.of("message", "仅已拦截或误报通过状态的记录可进行误报反馈"));
+                        }
+
+                        String commentName = record.getSpec().getCommentId();
+                        String replyName = record.getSpec().getReplyTo();
+
+                        // 1. 将原评论/回复的审核状态设为已通过
+                        Mono<Void> approveMono = approveOriginalComment(commentName, replyName);
+
+                        // 2. 更新 AiCommentReply 记录状态
+                        Mono<Void> updateRecordMono = Mono.defer(() -> client.fetch(AiCommentReply.class, name)
+                            .flatMap(latest -> {
+                                latest.getSpec().setFilterCategory("误报");
+                                latest.getSpec().setFilterReason("用户确认为误报，已通过");
+                                if ("aiReply".equals(action)) {
+                                    latest.getSpec().setStatus("PENDING");
+                                    latest.getSpec().setReply("");
+                                } else {
+                                    // 仅通过：使用 FALSE_POSITIVE 状态，区别于 PASS
+                                    // 避免前端显示"通过/拒绝"按钮和"未发布"标签
+                                    latest.getSpec().setStatus("FALSE_POSITIVE");
+                                    latest.getSpec().setPublished(false);
+                                }
+                                return client.update(latest);
+                            })
+                            .retryWhen(Retry.backoff(3, Duration.ofMillis(100))
+                                .filter(e -> e instanceof OptimisticLockingFailureException))
+                            .then());
+
+                        // 3. 异步触发 AI 回复（不阻塞 HTTP 响应）
+                        // 使用 processFalsePositive 跳过前置过滤和去重检查
+                        if ("aiReply".equals(action)) {
+                            boolean isConversation = Boolean.TRUE.equals(record.getSpec().getIsAiConversation());
+                            String recordName = record.getMetadata().getName();
+                            personaResolver.getPersonaNameFromComment(commentName)
+                                .flatMap(personaName ->
+                                    orchestrator.processFalsePositive(commentName, replyName, isConversation, personaName, recordName)
+                                )
+                                .subscribe(
+                                    null,
+                                    err -> log.warn("[FalsePositive] AI reply trigger failed for {}: {}", commentName, err.getMessage()),
+                                    () -> log.info("[FalsePositive] AI reply trigger completed for {}", commentName)
+                                );
+                        }
+
+                        return approveMono
+                            .then(updateRecordMono)
+                            .then(ServerResponse.ok().bodyValue(Map.of(
+                                "message", "aiReply".equals(action) ? "已标记为误报，AI回复正在后台生成" : "已标记为误报并通过"
+                            )));
+                    })
+                    .switchIfEmpty(ServerResponse.notFound().build());
+            });
+    }
+
+    /**
+     * 将被拦截评论的原 Comment 或 Reply 审核状态设为已通过。
+     */
+    private Mono<Void> approveOriginalComment(String commentName, String replyName) {
+        // 优先处理 Reply（AI 对话场景下违规内容来自 Reply）
+        if (replyName != null && !replyName.isBlank()) {
+            return client.fetch(Reply.class, replyName)
+                .flatMap(reply -> {
+                    var spec = reply.getSpec();
+                    if (spec != null && !Boolean.TRUE.equals(spec.getApproved())) {
+                        spec.setApproved(true);
+                        spec.setApprovedTime(Instant.now());
+                        return client.update(reply)
+                            .retryWhen(Retry.backoff(3, Duration.ofMillis(100))
+                                .filter(e -> e instanceof OptimisticLockingFailureException))
+                            .doOnSuccess(r -> log.info("[FalsePositive] Reply {} approved", replyName))
+                            .then();
+                    }
+                    return Mono.empty();
+                })
+                .switchIfEmpty(Mono.defer(() -> approveComment(commentName)));
+        }
+        return approveComment(commentName);
+    }
+
+    private Mono<Void> approveComment(String commentName) {
+        return client.fetch(Comment.class, commentName)
+            .flatMap(comment -> {
+                var spec = comment.getSpec();
+                if (spec != null && !Boolean.TRUE.equals(spec.getApproved())) {
+                    spec.setApproved(true);
+                    spec.setApprovedTime(Instant.now());
+                    return client.update(comment)
+                        .retryWhen(Retry.backoff(3, Duration.ofMillis(100))
+                            .filter(e -> e instanceof OptimisticLockingFailureException))
+                        .doOnSuccess(c -> log.info("[FalsePositive] Comment {} approved", commentName))
+                        .then();
+                }
+                return Mono.empty();
+            });
+    }
 }

src/main/java/top/nxxy335/commentaiautopilot/service/AiFoundationClient.java

@@ -70,12 +70,12 @@ public class AiFoundationClient {
             try {
                 return AiFoundationDelegate.classify(extensionGetter, systemPrompt, userPrompt, choices, modelName);
             } catch (NoClassDefFoundError e) {
-                log.debug("AI Foundation API not on classpath: {}", e.getMessage());
+                log.warn("[Client] AI Foundation API not on classpath (classify): {}", e.getMessage());
                 return Mono.empty();
             }
         })
         .onErrorResume(NoClassDefFoundError.class, e -> {
-            log.warn("AI Foundation not available: {}", e.getMessage());
+            log.warn("[Client] AI Foundation NoClassDefFoundError during classify: {}", e.getMessage());
             return Mono.empty();
         });
     }

src/main/java/top/nxxy335/commentaiautopilot/service/AiFoundationDelegate.java

@@ -33,40 +33,123 @@ class AiFoundationDelegate {
                 .flatMap(model -> model.generateText(
                     GenerateTextRequest.builder().prompt(prompt).maxRetries(2).build()))
                 .map(GenerateTextResult::getText))
-            .doOnError(e -> log.error("AI Foundation call failed: {}", e.getMessage()))
+            .doOnError(e -> log.error("[Delegate] chat call failed: {}", e.getMessage()))
             .onErrorResume(e -> {
-                log.warn("AI Foundation not available: {}", e.getMessage());
+                log.warn("[Delegate] chat not available: {}", e.getMessage());
                 return Mono.empty();
             });
     }
 
+    /**
+     * 使用 AI 进行文本分类。
+     * 优先使用 OutputSpec.choice 结构化输出，失败时退回到普通 chat 并解析响应。
+     */
     static Mono<String> classify(ExtensionGetter extensionGetter, String systemPrompt,
                                   String userPrompt, List<String> choices, String modelName) {
+        log.info("[Delegate] Starting classification, modelName='{}'", modelName);
+        return classifyWithChoice(extensionGetter, systemPrompt, userPrompt, choices, modelName)
+            .switchIfEmpty(
+                Mono.defer(() -> {
+                    log.info("[Delegate] classifyWithChoice returned empty, falling back to classifyWithChat");
+                    return classifyWithChat(extensionGetter, systemPrompt, userPrompt, choices, modelName);
+                })
+            )
+            .doOnNext(result -> log.info("[Delegate] Classification succeeded: '{}'", result))
+            .doOnSuccess(result -> {
+                if (result == null) {
+                    log.warn("[Delegate] Classification completed with no result (both methods returned empty)");
+                }
+            });
+    }
+
+    /**
+     * 使用 OutputSpec.choice 结构化输出分类（部分模型不支持）。
+     * 注意：不使用 system() 方法，因为部分 AI Foundation 版本可能不支持，
+     * 将 system prompt 合并到 user prompt 中。
+     */
+    private static Mono<String> classifyWithChoice(ExtensionGetter extensionGetter, String systemPrompt,
+                                                    String userPrompt, List<String> choices, String modelName) {
+        // 合并 system prompt 和 user prompt，避免使用 system() 方法
+        String combinedPrompt = systemPrompt + "\n\n" + userPrompt;
         return extensionGetter.getEnabledExtension(AiModelService.class)
             .flatMap(service -> service.languageModel(modelName != null ? modelName : "")
                 .flatMap(model -> model.generateText(
                     GenerateTextRequest.builder()
-                        .system(systemPrompt)
+                        .prompt(combinedPrompt)
-                        .prompt(userPrompt)
                         .output(OutputSpec.choice(choices))
                         .maxRetries(2)
                         .build()))
-                .map(result -> {
+                .flatMap(result -> {
                     Object output = result.getOutput();
-                    return output != null ? String.valueOf(output).trim() : "";
+                    if (output != null) {
+                        String value = String.valueOf(output).trim();
+                        if (!value.isEmpty()) {
+                            log.debug("[Delegate] classifyWithChoice got output: '{}'", value);
+                            return Mono.just(value);
+                        }
+                    }
+                    // output 为空可能是模型不支持结构化输出，返回 empty 触发 fallback
+                    log.info("[Delegate] classifyWithChoice: output is null/empty, triggering fallback");
+                    return Mono.empty();
                 }))
-            .doOnError(e -> log.error("AI Foundation classify failed: {}", e.getMessage()))
             .onErrorResume(e -> {
-                log.warn("AI Foundation not available: {}", e.getMessage());
+                log.warn("[Delegate] classifyWithChoice failed, will fallback to chat: {}", e.getMessage());
                 return Mono.empty();
             });
     }
 
+    /**
+     * 使用普通 chat 调用进行分类，从响应文本中提取匹配的分类值。
+     * 作为 OutputSpec.choice 不可用时的降级方案。
+     * 不使用 system() 方法，将 system prompt 合并到 user prompt 中，
+     * 与可用的 chat() 方法保持一致的调用方式。
+     */
+    private static Mono<String> classifyWithChat(ExtensionGetter extensionGetter, String systemPrompt,
+                                                  String userPrompt, List<String> choices, String modelName) {
+        // 合并 system prompt 和 user prompt，与 chat() 方法保持一致的调用方式
+        String combinedPrompt = systemPrompt + "\n\n" + userPrompt;
+        return extensionGetter.getEnabledExtension(AiModelService.class)
+            .flatMap(service -> service.languageModel(modelName != null ? modelName : "")
+                .flatMap(model -> model.generateText(
+                    GenerateTextRequest.builder()
+                        .prompt(combinedPrompt)
+                        .maxRetries(2)
+                        .build()))
+                .map(GenerateTextResult::getText)
+                .map(text -> extractChoice(text, choices)))
+            .doOnError(e -> log.error("[Delegate] classifyWithChat failed: {}", e.getMessage()))
+            .onErrorResume(e -> {
+                log.warn("[Delegate] classifyWithChat error: {}", e.getMessage());
+                return Mono.empty();
+            });
+    }
+
+    /**
+     * 从 chat 响应文本中提取匹配的分类值。
+     * 优先精确匹配，其次包含匹配。
+     * 无匹配时返回空字符串（触发 defaultIfEmpty 安全拦截），避免原始文本被误判为违规类别。
+     */
+    static String extractChoice(String text, List<String> choices) {
+        if (text == null || text.isBlank()) return "";
+        String trimmed = text.trim();
+        // 精确匹配
+        for (String choice : choices) {
+            if (trimmed.equals(choice)) return choice;
+        }
+        // 包含匹配（响应可能包含额外文字，如"该评论属于：广告"）
+        for (String choice : choices) {
+            if (trimmed.contains(choice)) return choice;
+        }
+        // 无匹配，返回空字符串触发安全拦截
+        log.warn("[Delegate] No matching choice found in response: '{}', returning empty for safety", trimmed);
+        return "";
+    }
+
     static Mono<Boolean> isAvailable(ExtensionGetter extensionGetter) {
         return extensionGetter.getEnabledExtension(AiModelService.class)
             .hasElement()
             .onErrorResume(e -> {
-                log.debug("AI Foundation not available: {}", e.getMessage());
+                log.debug("[Delegate] AI Foundation not available: {}", e.getMessage());
                 return Mono.just(false);
             });
     }

src/main/java/top/nxxy335/commentaiautopilot/service/AiReplyOrchestrator.java

@@ -105,7 +105,7 @@ public class AiReplyOrchestrator {
                         }
                         // Wake word triggered: skip page-level annotation check
                         if (wakeWordTriggered) {
-                            return checkBlockedCommenters(commentName)
+                            return filterService.isCommenterBlocked(commentName)
                                 .flatMap(blocked -> {
                                     if (blocked) {
                                         log.info("[Orchestrator] Commenter blocked, skipping wake word: {}", commentName);
@@ -133,6 +133,45 @@ public class AiReplyOrchestrator {
             .then();
     }
 
+    /**
+     * 误报反馈专用：跳过前置过滤和去重检查，直接为已确认误报的评论生成 AI 回复。
+     *
+     * <p>与 {@link #processComment} 不同，此方法：
+     * <ul>
+     *   <li>跳过前置过滤（用户已确认评论合规）</li>
+     *   <li>跳过去重检查（已有 FILTERED 记录，需复用）</li>
+     *   <li>跳过速率限制和黑名单检查（管理员主动操作）</li>
+     * </ul>
+     *
+     * @param commentName     the parent Comment name
+     * @param replyName       the Reply name (null for top-level comments)
+     * @param isAiConversation true when this is a conversation continuation
+     * @param personaName     the persona name to use
+     * @param recordName      the existing AiCommentReply record name to update
+     */
+    public Mono<Void> processFalsePositive(String commentName, String replyName,
+                                            boolean isAiConversation, String personaName,
+                                            String recordName) {
+        log.info("[Orchestrator] Processing false-positive: comment={}, record={}", commentName, recordName);
+
+        return getModelName().flatMap(modelName ->
+            contextExtractor.extract(commentName, replyName, isAiConversation)
+                .flatMap(context -> {
+                    // Fetch the existing record (was FILTERED, now PENDING)
+                    return client.fetch(AiCommentReply.class, recordName)
+                        .flatMap(replyRecord ->
+                            sentimentService.analyzeSentiment(context.commentContent(), modelName)
+                                .flatMap(sentimentResult ->
+                                    promptBuilder.buildPrompt(context, sentimentResult.sentiment(), personaName)
+                                        .flatMap(prompt -> generateAndPublish(prompt, context, replyRecord, modelName, personaName))
+                                )
+                        );
+                })
+        )
+        .doOnError(e -> log.error("[Orchestrator] Error processing false-positive {}: {}", commentName, e.getMessage(), e))
+        .then();
+    }
+
     /**
      * Proceed with processing after all checks have passed.
      * Handles dedup checks and conversation round limits.
@@ -171,43 +210,6 @@ public class AiReplyOrchestrator {
             );
     }
 
-    /**
-     * Check if the commenter is in the blocked list.
-     */
-    private Mono<Boolean> checkBlockedCommenters(String commentName) {
-        return client.fetch(run.halo.app.core.extension.content.Comment.class, commentName)
-            .flatMap(comment -> {
-                var owner = comment.getSpec().getOwner();
-                if (owner == null) return Mono.just(false);
-                String displayName = owner.getDisplayName();
-                String email = run.halo.app.core.extension.content.Comment.CommentOwner.KIND_EMAIL.equals(owner.getKind())
-                    ? owner.getName() : "";
-                return client.fetch(ConfigMap.class, CONFIG_MAP_NAME)
-                    .mapNotNull(cm -> {
-                        var data = cm.getData();
-                        if (data == null) return false;
-                        String basicJson = data.get("basic");
-                        if (basicJson == null || basicJson.isBlank()) return false;
-                        try {
-                            JsonNode node = objectMapper.readTree(basicJson);
-                            String blockedStr = node.has("blockedCommenters") ? node.get("blockedCommenters").asText("") : "";
-                            if (blockedStr.isBlank()) return false;
-                            for (String item : blockedStr.split(",")) {
-                                String trimmed = item.trim();
-                                if (!trimmed.isEmpty() && (trimmed.equalsIgnoreCase(displayName) || trimmed.equalsIgnoreCase(email))) {
-                                    return true;
-                                }
-                            }
-                            return false;
-                        } catch (Exception e) {
-                            return false;
-                        }
-                    })
-                    .defaultIfEmpty(false);
-            })
-            .defaultIfEmpty(false);
-    }
-
     private Mono<Void> doProcess(String commentName, String replyName, boolean isAiConversation,
                                   String personaName) {
         return getModelName().flatMap(modelName ->

src/main/java/top/nxxy335/commentaiautopilot/service/CommentPreFilterService.java

@@ -44,16 +44,48 @@ public class CommentPreFilterService {
         SPAM, "检测到推广链接、产品推销或引流信息",
         ABUSE, "检测到辱骂、人身攻击、恶意挑衅或歧视性言论",
         SENSITIVE, "检测到政治敏感、违法违规或色情暴力内容",
-        MEANINGLESS, "检测到纯乱码、无意义字符或与文章完全无关的废话"
+        MEANINGLESS, "检测到纯乱码或无意义字符堆砌"
     );
 
     private static final String CLASSIFY_SYSTEM_PROMPT = """
             你是评论内容合规检测员。请判断以下评论属于哪个类别：
-            - 正常：正常的评论、提问、讨论、赞美等
+
+            类别定义：
+            - 正常：正常的评论、提问、讨论、赞美、闲聊等，即使与文章主题无关也算正常
             - 广告：包含推广链接、产品推销、引流信息等
             - 辱骂攻击：包含辱骂、人身攻击、恶意挑衅、歧视性言论等
             - 敏感内容：涉及政治敏感、违法违规、色情暴力等
-            - 无意义：纯乱码、无意义字符堆砌、与文章完全无关的废话
+            - 无意义：纯乱码、无意义字符堆砌（如随机符号、键盘乱敲）
+
+            ═══════════════════════════════════════
+            核心判断原则（必须严格遵守）：
+            ═══════════════════════════════════════
+
+            【原则一：上下文优先】
+            绝对禁止仅凭单个词汇进行机械拦截。必须结合整句话的语境、语气和前后文逻辑进行综合判断。一个词是否违规，取决于它在句子中的功能，而非词汇本身。
+
+            【原则二：口语化宽容】
+            中文互联网存在大量口语化简写、谐音和省略表达。如果某个词在特定语境下明显是中性词或亲属称谓的口语化表达，且整句无攻击性、无恶意，必须判定为"正常"。
+            常见口语化中性用法示例：
+            - "他妈" → 可能是"他妈妈"的简称，如"小轩是他妈的朋友"=小轩是他妈妈的朋友 → 正常
+            - "你妹" → 可能是"你妹妹"的简称，如"你妹在哪上学"=你妹妹在哪上学 → 正常
+            - "卧槽" → 可能是语气词表示惊讶，如"卧槽这也太强了"=哇塞这也太厉害了 → 正常
+            - "牛逼" → 口语化赞美，如"这文章写得牛逼" → 正常
+            - "靠" → 语气词表示无奈或惊讶，如"靠又忘了" → 正常
+
+            【原则三：恶意导向判定】
+            只有当词汇被明确用作辱骂、人身攻击、引战或带有较强负面情绪时，才判定为"辱骂攻击"。
+            恶意用法示例（这些才应判为"辱骂攻击"）：
+            - "你他妈的" → 直接对他人进行辱骂 → 辱骂攻击
+            - "你妹的" → 带有攻击性的语气词 → 辱骂攻击
+            - "傻逼" → 直接辱骂他人 → 辱骂攻击
+
+            【原则四：宁放勿杀】
+            当你无法确定评论是否违规时，应判定为"正常"而非"辱骂攻击"。误杀正常评论比漏判违规评论的负面影响更大。
+
+            【原则五：闲聊不算无意义】
+            与文章主题无关的闲聊、灌水、打招呼等属于"正常"，不要误判为"无意义"。
+
             只返回类别名称，不要返回其他内容。""";
 
     public CommentPreFilterService(ReactiveExtensionClient client,
@@ -85,11 +117,17 @@ public class CommentPreFilterService {
             log.info("[PreFilter] Checking comment (enabled=true): {}", truncated.substring(0, Math.min(50, truncated.length())));
 
             return aiFoundationClient.classify(CLASSIFY_SYSTEM_PROMPT, userPrompt, CLASSIFY_CHOICES, modelName)
+                .doOnNext(result -> log.info("[PreFilter] AI classify returned: '{}'", result))
                 .map(result -> {
                     if (CLEAN.equals(result)) {
                         log.info("[PreFilter] Comment passed: category={}", result);
                         return new PreFilterResult(true, CLEAN, "评论合规");
                     }
+                    // 空结果视为分类失败
+                    if (result == null || result.isBlank()) {
+                        log.warn("[PreFilter] AI classify returned empty/blank result, blocking for safety");
+                        return new PreFilterResult(false, MEANINGLESS, "AI分类返回空结果，安全拦截");
+                    }
                     String desc = CATEGORY_DESCRIPTIONS.getOrDefault(result, "检测到违规内容");
                     String snippet = truncated.substring(0, Math.min(50, truncated.length()));
                     String reason = desc + " — 「" + snippet + "」";
@@ -99,7 +137,7 @@ public class CommentPreFilterService {
                 // 分类失败时拦截评论（安全优先），而非放行
                 .defaultIfEmpty(new PreFilterResult(false, MEANINGLESS, "AI分类服务不可用，安全拦截"))
                 .onErrorResume(e -> {
-                    log.warn("[PreFilter] Detection error, BLOCKING comment for safety: {}", e.getMessage());
+                    log.warn("[PreFilter] Detection error, BLOCKING comment for safety: {}", e.getMessage(), e);
                     return Mono.just(new PreFilterResult(false, MEANINGLESS, "AI分类服务异常，安全拦截"));
                 });
         });

src/main/java/top/nxxy335/commentaiautopilot/service/FilterService.java

@@ -58,6 +58,19 @@ public class FilterService {
             });
     }
 
+    /**
+     * 检查评论者是否在黑名单中（按 commentName 查询）。
+     */
+    public Mono<Boolean> isCommenterBlocked(String commentName) {
+        return client.fetch(Comment.class, commentName)
+            .flatMap(this::checkBlockedCommenters)
+            .defaultIfEmpty(false)
+            .onErrorResume(e -> {
+                log.warn("[Filter] Error checking blocked commenter: {}", e.getMessage());
+                return Mono.just(false);
+            });
+    }
+
     private Mono<Boolean> checkBlockedCommenters(Comment comment) {
         return client.fetch(ConfigMap.class, CONFIG_MAP_NAME)
             .mapNotNull(cm -> {

src/main/resources/plugin.yaml

@@ -30,4 +30,4 @@ spec:
       url: "https://github.com/sunny-335/plugin-comment-ai-autopilot/blob/main/LICENSE"
   settingName: "comment-ai-autopilot-settings"
   configMapName: "comment-ai-autopilot-configmap"
-  version: "1.1.0"
+  version: "1.2.0"

ui/src/utils/api.ts

@@ -0,0 +1,29 @@
+/**
+ * 统一 API 路径常量，避免硬编码散布在多个 Vue 文件中。
+ * 修改 API 路径只需在此处更新。
+ */
+export const API_BASE = '/apis/console.api.comment-ai-autopilot.nxxy335.top/v1alpha1'
+
+// ===== 日志相关 =====
+export const API_REPLIES = `${API_BASE}/replies`
+export const apiReply = (name: string) => `${API_REPLIES}/${name}`
+export const apiReplyAction = (name: string, action: string) => `${API_REPLIES}/${name}/${action}`
+export const API_BATCH_APPROVE = `${API_REPLIES}/batch-approve`
+export const API_BATCH_REJECT = `${API_REPLIES}/batch-reject`
+export const API_BATCH_DELETE = `${API_REPLIES}/batch-delete`
+export const apiConversation = (commentId: string) => `${API_BASE}/conversation/${commentId}`
+
+// ===== 概览相关 =====
+export const API_STATS = `${API_BASE}/stats`
+export const API_PERSONAS = `${API_BASE}/personas`
+export const apiPersona = (name: string) => `${API_PERSONAS}/${name}`
+export const API_HEALTH = `${API_BASE}/health`
+
+// ===== 设置相关 =====
+export const API_EXPORT = `${API_BASE}/export`
+export const API_IMPORT = `${API_BASE}/import`
+export const API_COMMENTERS = `${API_BASE}/commenters`
+export const API_CLEANUP = `${API_BASE}/cleanup`
+
+// ===== 评论触发 =====
+export const apiCommentTrigger = (commentName: string) => `${API_BASE}/comments/${commentName}/trigger`

ui/src/utils/gravatar.ts

@@ -0,0 +1,14 @@
+export async function computeGravatarHash(email: string): Promise<string> {
+  if (!email || email.trim() === '') return ''
+  const normalizedEmail = email.trim().toLowerCase()
+  const encoder = new TextEncoder()
+  const data = encoder.encode(normalizedEmail)
+  const hashBuffer = await crypto.subtle.digest('SHA-256', data)
+  const hashArray = Array.from(new Uint8Array(hashBuffer))
+  return hashArray.map(b => b.toString(16).padStart(2, '0')).join('')
+}
+
+export function getGravatarUrl(hash: string): string {
+  if (!hash) return ''
+  return `https://cn.cravatar.com/avatar/${hash}`
+}

ui/src/views/LogsView.vue

@@ -27,6 +27,7 @@
         <option value="PENDING">待审核</option>
         <option value="REJECTED">已拒绝</option>
         <option value="FILTERED">已拦截</option>
+        <option value="FALSE_POSITIVE">误报通过</option>
       </select>
       <select v-model="filterSentiment" class="filter-select">
         <option value="">全部情感</option>
@@ -69,6 +70,13 @@
                 <svg class="filter-icon" fill="currentColor" viewBox="0 0 20 20"><path fill-rule="evenodd" d="M10 18a8 8 0 100-16 8 8 0 000 16zM8.707 7.293a1 1 0 00-1.414 1.414L8.586 10l-1.293 1.293a1 1 0 101.414 1.414L10 11.414l1.293 1.293a1 1 0 001.414-1.414L11.414 10l1.293-1.293a1 1 0 00-1.414-1.414L10 8.586 8.707 7.293z" clip-rule="evenodd"/></svg>
                 <span class="filter-category" v-if="reply.spec.filterCategory">{{ reply.spec.filterCategory }}</span>
                 <span class="filter-detail">{{ reply.spec.filterReason || '未提供具体原因' }}</span>
+                <button class="btn-false-positive" @click="openFalsePositiveDialog(reply)">误报反馈</button>
+              </div>
+              <div v-if="reply.spec.status === 'FALSE_POSITIVE'" class="card-filter-reason">
+                <svg class="filter-icon fp-icon-ok" fill="currentColor" viewBox="0 0 20 20"><path fill-rule="evenodd" d="M10 18a8 8 0 100-16 8 8 0 000 16zm3.707-9.293a1 1 0 00-1.414-1.414L9 10.586 7.707 9.293a1 1 0 00-1.414 1.414l2 2a1 1 0 001.414 0l4-4z" clip-rule="evenodd"/></svg>
+                <span class="filter-category">误报</span>
+                <span class="filter-detail">{{ reply.spec.filterReason || '用户确认为误报' }}</span>
+                <button class="btn-trigger-ai" @click="handleTriggerAiReply(reply)">触发AI回复</button>
               </div>
             </div>
           </div>
@@ -137,6 +145,33 @@
         </div>
       </div>
     </teleport>
+
+    <!-- 误报反馈确认弹窗 -->
+    <teleport to="body">
+      <div v-if="showFalsePositiveDialog" class="dialog-overlay" @click.self="showFalsePositiveDialog = false">
+        <div class="dialog-box fp-dialog">
+          <div class="dialog-header">
+            <h3>确认为误报？</h3>
+            <button class="close-btn" @click="showFalsePositiveDialog = false"><svg fill="none" stroke="currentColor" viewBox="0 0 24 24" width="24" height="24"><path stroke-linecap="round" stroke-linejoin="round" stroke-width="2" d="M6 18L18 6M6 6l12 12"/></svg></button>
+          </div>
+          <div class="fp-dialog-body">
+            <p class="fp-desc">系统检测到该评论可能包含违规内容，但您认为这是正常表达。请选择处理方式：</p>
+            <div class="fp-actions">
+              <button class="fp-btn fp-btn-primary" :disabled="fpLoading" @click="handleFalsePositive('aiReply')">
+                <span v-if="fpLoading" class="fp-spinner"></span>
+                AI 回复
+              </button>
+              <button class="fp-btn fp-btn-secondary" :disabled="fpLoading" @click="handleFalsePositive('approveOnly')">
+                仅通过
+              </button>
+              <button class="fp-btn fp-btn-ghost" :disabled="fpLoading" @click="showFalsePositiveDialog = false">
+                取消
+              </button>
+            </div>
+          </div>
+        </div>
+      </div>
+    </teleport>
   </div>
 </template>
 
@@ -149,10 +184,11 @@ import { IconPlug } from "@halo-dev/components"
 interface AiCommentReplyItem { metadata: { name: string; creationTimestamp: string }; spec: any }
 interface ConversationMessage { type: string; owner: string; content: string; time: string; isAi: boolean; quoteOwner?: string; quoteContent?: string }
 
-const replies = ref<AiCommentReplyItem[]>([]); const loading = ref(false); const page = ref(1); const size = ref(20); const total = ref(0); const totalPages = ref(0);
+const replies = ref<AiCommentReplyItem[]>([]); const loading = ref(false); const batchLoading = ref(false); const page = ref(1); const size = ref(20); const total = ref(0); const totalPages = ref(0);
 const selectedNames = ref<Set<string>>(new Set()); const selectAll = ref(false);
 const filterStatus = ref(""); const filterSentiment = ref(""); const filterKeyword = ref("");
 const showDialog = ref(false); const conversationLoading = ref(false); const conversationMessages = ref<ConversationMessage[]>([]);
+const showFalsePositiveDialog = ref(false); const falsePositiveTarget = ref<AiCommentReplyItem | null>(null); const fpLoading = ref(false);
 
 const toggleSelect = (name: string) => { selectedNames.value.has(name) ? selectedNames.value.delete(name) : selectedNames.value.add(name); selectAll.value = replies.value.length > 0 && replies.value.every(r => selectedNames.value.has(r.metadata.name)) }
 const toggleSelectAll = () => { if (selectAll.value) { selectedNames.value.clear(); selectAll.value = false } else { selectedNames.value = new Set(replies.value.map(r => r.metadata.name)); selectAll.value = true } }
@@ -178,11 +214,11 @@ const openConversation = async (reply: AiCommentReplyItem) => {
 const handleDelete = async (name: string) => { try { await axiosInstance.delete(`/apis/console.api.comment-ai-autopilot.nxxy335.top/v1alpha1/replies/${name}`); Toast.success("删除成功"); fetchReplies() } catch (e) { Toast.error("删除失败") } }
 const handleApprove = async (name: string) => { try { await axiosInstance.post(`/apis/console.api.comment-ai-autopilot.nxxy335.top/v1alpha1/replies/${name}/approve`); Toast.success("审核通过"); fetchReplies() } catch (e) { Toast.error("审核失败") } }
 const handleReject = async (name: string) => { try { await axiosInstance.post(`/apis/console.api.comment-ai-autopilot.nxxy335.top/v1alpha1/replies/${name}/reject`); Toast.success("已拒绝"); fetchReplies() } catch (e) { Toast.error("拒绝失败") } }
-const batchApprove = async () => { if(!selectedNames.value.size) return; try { await axiosInstance.post("/apis/console.api.comment-ai-autopilot.nxxy335.top/v1alpha1/replies/batch-approve", { names: Array.from(selectedNames.value) }); Toast.success("成功"); selectedNames.value.clear(); selectAll.value=false; fetchReplies() } catch(e) { Toast.error("失败") } }
+const batchApprove = async () => { if(!selectedNames.value.size||batchLoading.value) return; batchLoading.value=true; try { await axiosInstance.post("/apis/console.api.comment-ai-autopilot.nxxy335.top/v1alpha1/replies/batch-approve", { names: Array.from(selectedNames.value) }); Toast.success("成功"); selectedNames.value.clear(); selectAll.value=false; fetchReplies() } catch(e) { Toast.error("失败") } finally { batchLoading.value=false } }
-const batchReject = async () => { if(!selectedNames.value.size) return; try { await axiosInstance.post("/apis/console.api.comment-ai-autopilot.nxxy335.top/v1alpha1/replies/batch-reject", { names: Array.from(selectedNames.value) }); Toast.success("成功"); selectedNames.value.clear(); selectAll.value=false; fetchReplies() } catch(e) { Toast.error("失败") } }
+const batchReject = async () => { if(!selectedNames.value.size||batchLoading.value) return; batchLoading.value=true; try { await axiosInstance.post("/apis/console.api.comment-ai-autopilot.nxxy335.top/v1alpha1/replies/batch-reject", { names: Array.from(selectedNames.value) }); Toast.success("成功"); selectedNames.value.clear(); selectAll.value=false; fetchReplies() } catch(e) { Toast.error("失败") } finally { batchLoading.value=false } }
-const batchDelete = async () => { if(!selectedNames.value.size) return; try { await axiosInstance.post("/apis/console.api.comment-ai-autopilot.nxxy335.top/v1alpha1/replies/batch-delete", { names: Array.from(selectedNames.value) }); Toast.success("成功"); selectedNames.value.clear(); selectAll.value=false; fetchReplies() } catch(e) { Toast.error("失败") } }
+const batchDelete = async () => { if(!selectedNames.value.size||batchLoading.value) return; batchLoading.value=true; try { await axiosInstance.post("/apis/console.api.comment-ai-autopilot.nxxy335.top/v1alpha1/replies/batch-delete", { names: Array.from(selectedNames.value) }); Toast.success("成功"); selectedNames.value.clear(); selectAll.value=false; fetchReplies() } catch(e) { Toast.error("失败") } finally { batchLoading.value=false } }
 
-const getStatusLabel = (s: string) => { const m:any = { PASS: '通过', FAIL: '失败', PENDING: '待审', REJECTED: '拒绝', FILTERED: '已拦截' }; return m[s] || s }
+const getStatusLabel = (s: string) => { const m:any = { PASS: '通过', FAIL: '失败', PENDING: '待审', REJECTED: '拒绝', FILTERED: '已拦截', FALSE_POSITIVE: '误报通过' }; return m[s] || s }
 const getSentimentLabel = (s: string) => { const m:any = { VERY_POSITIVE: '极好', POSITIVE: '正面', NEUTRAL: '中性', NEGATIVE: '负面', VERY_NEGATIVE: '极差' }; return m[s] || s }
 const formatDate = (ts: string) => ts ? new Date(ts).toLocaleString("zh-CN") : ""
 const getPostUrl = (slug: string) => `${window.location.origin}/archives/${slug}`
@@ -203,6 +239,30 @@ const renderContent = (content: string) => {
 }
 
 const resetFilters = () => { filterStatus.value = ""; filterSentiment.value = ""; filterKeyword.value = ""; page.value = 1; fetchReplies() }
+
+const openFalsePositiveDialog = (reply: AiCommentReplyItem) => { falsePositiveTarget.value = reply; showFalsePositiveDialog.value = true }
+const handleFalsePositive = async (action: string) => {
+  if (!falsePositiveTarget.value) return
+  fpLoading.value = true
+  try {
+    await axiosInstance.post(`/apis/console.api.comment-ai-autopilot.nxxy335.top/v1alpha1/replies/${falsePositiveTarget.value.metadata.name}/false-positive`, { action })
+    Toast.success(action === "aiReply" ? "已标记为误报，AI回复正在后台生成" : "已标记为误报并通过")
+    showFalsePositiveDialog.value = false
+    falsePositiveTarget.value = null
+    fetchReplies()
+  } catch (e: any) {
+    Toast.error(e?.response?.data?.message || "操作失败")
+  } finally { fpLoading.value = false }
+}
+const handleTriggerAiReply = async (reply: AiCommentReplyItem) => {
+  try {
+    await axiosInstance.post(`/apis/console.api.comment-ai-autopilot.nxxy335.top/v1alpha1/replies/${reply.metadata.name}/false-positive`, { action: "aiReply" })
+    Toast.success("AI回复正在后台生成")
+    fetchReplies()
+  } catch (e: any) {
+    Toast.error(e?.response?.data?.message || "触发失败")
+  }
+}
 watch([filterStatus, filterSentiment, filterKeyword], () => { page.value = 1; fetchReplies() })
 watch(page, () => { selectedNames.value.clear(); selectAll.value = false; fetchReplies() })
 onMounted(fetchReplies)
@@ -246,6 +306,11 @@ onMounted(fetchReplies)
 .filter-icon { width: 14px; height: 14px; flex-shrink: 0; margin-top: 1px; }
 .filter-category { flex-shrink: 0; padding: 1px 6px; background: #b45309; color: #fff; border-radius: 3px; font-weight: 600; font-size: 11px; line-height: 1.5; }
 .filter-detail { flex: 1; line-height: 1.5; }
+.btn-false-positive { flex-shrink: 0; margin-left: auto; padding: 2px 8px; border: 1px solid #b45309; border-radius: 4px; background: transparent; color: #b45309; font-size: 11px; cursor: pointer; white-space: nowrap; transition: all 0.15s; }
+.btn-false-positive:hover { background: #b45309; color: #fff; }
+.fp-icon-ok { color: #16a34a; }
+.btn-trigger-ai { flex-shrink: 0; margin-left: auto; padding: 2px 8px; border: 1px solid #2563eb; border-radius: 4px; background: transparent; color: #2563eb; font-size: 11px; cursor: pointer; white-space: nowrap; transition: all 0.15s; }
+.btn-trigger-ai:hover { background: #2563eb; color: #fff; }
 .card-footer { display: flex; flex-direction: column; gap: 12px; padding: 12px 16px; background: #f9fafb; border-top: 1px solid #f3f4f6; }
 @media (min-width: 640px) { .card-footer { flex-direction: row; justify-content: space-between; align-items: center; } }
 .footer-info { font-size: 12px; color: #6b7280; display: flex; flex-wrap: wrap; gap: 12px; }
@@ -261,7 +326,7 @@ onMounted(fetchReplies)
 
 /* 标签体系 */
 .custom-tag { padding: 2px 6px; border-radius: 4px; font-size: 11px; font-weight: bold; }
-.tag-PASS { background: #dcfce7; color: #15803d; } .tag-FAIL { background: #fee2e2; color: #b91c1c; } .tag-PENDING { background: #fef9c3; color: #a16207; } .tag-REJECTED { background: #ffedd5; color: #c2410c; } .tag-FILTERED { background: #f1f5f9; color: #b45309; border: 1px solid #fde68a; }
+.tag-PASS { background: #dcfce7; color: #15803d; } .tag-FAIL { background: #fee2e2; color: #b91c1c; } .tag-PENDING { background: #fef9c3; color: #a16207; } .tag-REJECTED { background: #ffedd5; color: #c2410c; } .tag-FILTERED { background: #f1f5f9; color: #b45309; border: 1px solid #fde68a; } .tag-FALSE_POSITIVE { background: #dbeafe; color: #1d4ed8; border: 1px solid #93c5fd; }
 .tag-published { background: #dbeafe; color: #1d4ed8; } .tag-draft { background: #f3f4f6; color: #4b5563; }
 .tag-conv { background: #f3e8ff; color: #7e22ce; }
 .tag-VERY_POSITIVE { background: #dcfce7; color: #14532d; } .tag-POSITIVE { background: #ecfdf5; color: #15803d; } .tag-NEGATIVE { background: #ffe4e6; color: #e11d48; } .tag-VERY_NEGATIVE { background: #fee2e2; color: #991b1b; }
@@ -307,4 +372,20 @@ onMounted(fetchReplies)
 .pagination { display: flex; flex-direction: column; gap: 12px; align-items: center; margin-top: 20px; font-size: 14px; color: #6b7280; }
 @media (min-width: 640px) { .pagination { flex-direction: row; justify-content: space-between; } }
 .pagination-btns { display: flex; gap: 8px; }
+
+/* 误报反馈弹窗 */
+.fp-dialog { max-width: 440px; }
+.fp-dialog-body { padding: 24px; }
+.fp-desc { margin: 0 0 20px; font-size: 14px; color: #4b5563; line-height: 1.6; }
+.fp-actions { display: flex; flex-direction: column; gap: 10px; }
+.fp-btn { padding: 10px 16px; border-radius: 8px; font-size: 14px; font-weight: 500; cursor: pointer; border: none; transition: all 0.15s; display: flex; align-items: center; justify-content: center; gap: 6px; }
+.fp-btn:disabled { opacity: 0.6; cursor: not-allowed; }
+.fp-btn-primary { background: #2563eb; color: #fff; }
+.fp-btn-primary:hover:not(:disabled) { background: #1d4ed8; }
+.fp-btn-secondary { background: #f3f4f6; color: #374151; border: 1px solid #d1d5db; }
+.fp-btn-secondary:hover:not(:disabled) { background: #e5e7eb; }
+.fp-btn-ghost { background: transparent; color: #9ca3af; }
+.fp-btn-ghost:hover:not(:disabled) { color: #6b7280; background: #f9fafb; }
+.fp-spinner { width: 14px; height: 14px; border: 2px solid rgba(255,255,255,0.3); border-top-color: #fff; border-radius: 50%; animation: fp-spin 0.6s linear infinite; }
+@keyframes fp-spin { to { transform: rotate(360deg); } }
 </style>