fix: compliance optimizer now requires score improvement, retries LLM on failure

Bug: 'passed=True' check for soft quality issues (AI套话, 缺互动引导)
always exits immediately even when LLM failed to make changes.
Score 85 stays 85, content unchanged.

Fix:
1. polish_with_llm: retry once on exception (was: single attempt)
2. main loop: only accept fix when score actually improves AND
   LLM produced output (opt_logs non-empty). If LLM returns same html
   unchanged, treat as failed attempt and retry.

scripts/compliance_optimizer.py

@@ -159,36 +159,40 @@ def fix_tags(html: str, platform: str) -> str:
 def polish_with_llm(html: str, platform: str, remaining_issues: Optional[List[Dict]] = None) -> Tuple[str, Optional[str]]:
     """用 LLM 优化文章内容，返回 (html, log_message_or_None)
     如果指定 remaining_issues，则针对性修复合规问题
+    LLM 失败时自动重试一次
     """
     if not HAVE_LLM:
         return html, None
     llm_cfg = get_llm_config()
-    try:
-        model = llm_cfg.get('model') if llm_cfg else None
-        temperature = llm_cfg.get('temperature', 0.5) if llm_cfg else 0.5
-        max_tokens = llm_cfg.get('max_tokens', 4000) if llm_cfg else 4000
-        system_prompt = llm_cfg.get('system_prompt') if llm_cfg else "你是一个专业的内容合规与优化助手，擅长在保持文章质量和可读性的前提下修复合规问题。"
+    for attempt in range(2):
+        try:
+            model = llm_cfg.get('model') if llm_cfg else None
+            temperature = llm_cfg.get('temperature', 0.5) if llm_cfg else 0.5
+            max_tokens = llm_cfg.get('max_tokens', 4000) if llm_cfg else 4000
+            system_prompt = llm_cfg.get('system_prompt') if llm_cfg else "你是一个专业的内容合规与优化助手，擅长在保持文章质量和可读性的前提下修复合规问题。"
 
-        if remaining_issues:
-            issues_desc = "\n".join(
-                f"- [{i['type']}] {i.get('category','')}: {i.get('detail','')}"
-                for i in remaining_issues
-            )
-            prompt = get_prompt("compliance_fix", issues_desc=issues_desc, html=html)
-        else:
-            prompt = get_prompt("compliance_polish", html=html)
-        polished = call_llm(prompt, model=model, temperature=temperature, max_tokens=max_tokens, system_prompt=system_prompt)
-        polished = clean_html_content(polished)
-        polished = strip_ai_preface(polished)
-        polished = strip_thinking_html(polished)
-        if '<h2' in polished or '<p>' in polished:
-            if len(polished) > len(html) * 0.3 and len(polished) > 100:
-                if not any(kw in polished for kw in ['保留', '建议', '可以', '应该', '推荐', '改为', '替换为']):
-                    tag = "针对性修复" if remaining_issues else "常规润色"
-                    return polished, f"LLM {tag}"
-            logger.warning(f"LLM 优化输出异常（过短或含建议性文字），保留原文 (len={len(polished)})")
-    except Exception as e:
-        logger.warning(f"LLM 优化失败: {e}")
+            if remaining_issues:
+                issues_desc = "\n".join(
+                    f"- [{i['type']}] {i.get('category','')}: {i.get('detail','')}"
+                    for i in remaining_issues
+                )
+                prompt = get_prompt("compliance_fix", issues_desc=issues_desc, html=html)
+            else:
+                prompt = get_prompt("compliance_polish", html=html)
+            polished = call_llm(prompt, model=model, temperature=temperature, max_tokens=max_tokens, system_prompt=system_prompt)
+            polished = clean_html_content(polished)
+            polished = strip_ai_preface(polished)
+            polished = strip_thinking_html(polished)
+            if '<h2' in polished or '<p>' in polished:
+                if len(polished) > len(html) * 0.3 and len(polished) > 100:
+                    if not any(kw in polished for kw in ['保留', '建议', '可以', '应该', '推荐', '改为', '替换为']):
+                        tag = "针对性修复" if remaining_issues else "常规润色"
+                        return polished, f"LLM {tag}"
+                logger.warning(f"LLM 优化输出异常（过短或含建议性文字），保留原文 (len={len(polished)})")
+        except Exception as e:
+            logger.warning(f"LLM 优化失败 (尝试 {attempt+1}/2): {e}")
+        if attempt == 0:
+            logger.info(f"重试 LLM 调用...")
     return html, None
 
 def optimize_article(html: str, platform: str, topic_data: Dict, remaining_issues: Optional[List[Dict]] = None) -> Tuple[str, List[str]]:
@@ -263,12 +267,13 @@ def main(topic_ids: List[str] = None):
         if issues:
             current_html = html
             current_issues = list(issues)
+            current_score = score
             for attempt in range(3):
                 optimized_html, opt_logs = optimize_article(current_html, platform_dir, topic_data, current_issues)
                 recheck = check_article(optimized_html, platform_dir, topic_data)
-                if recheck['passed']:
+                if recheck['passed'] and opt_logs and recheck['score'] > current_score:
                     save_article(topic_id, platform_dir, optimized_html)
-                    logger.info(f"✅ {label} 已修复并通过审查 (第{attempt+1}次修复)")
+                    logger.info(f"✅ {label} 已修复并通过审查 (第{attempt+1}次修复) 分数: {current_score}→{recheck['score']}")
                     results.append(OptimizationResult(
                         file=f"db:{platform_dir}_{topic_id}",
                         platform=platform_dir,
@@ -280,11 +285,15 @@ def main(topic_ids: List[str] = None):
                         status="passed"
                     ))
                     break
+                if optimized_html == current_html and not opt_logs:
+                    logger.warning(f"{label} LLM 未输出有效修改（第{attempt+1}次），继续重试...")
+                    continue
                 current_issues = recheck['issues']
                 current_html = optimized_html
+                current_score = recheck['score']
             else:
                 save_article(topic_id, platform_dir, current_html)
-                logger.warning(f"⚠️ {label} 仍有 {len(current_issues)} 个问题未修复，已强制通过")
+                logger.warning(f"⚠️ {label} 仍有 {len(current_issues)} 个问题未修复，已强制通过（当前分 {current_score}）")
                 results.append(OptimizationResult(
                     file=f"db:{platform_dir}_{topic_id}",
                     platform=platform_dir,
@@ -292,7 +301,7 @@ def main(topic_ids: List[str] = None):
                     title=topic_data.get('title',''),
                     original_issues=len(issues),
                     fixed_issues=len(issues) - len(current_issues),
-                    final_score=recheck['score'],
+                    final_score=current_score,
                     status="passed"
                 ))
         else: