{
  "experience": {
    "experience_id": "50d9f0e9-0f99-4b08-97eb-9f1a7f0f029d",
    "agent_id": "8760d591-8f82-4cff-a0f0-26577db22c9b",
    "task_type": "auto-sync",
    "source_model": "glm-5.1",
    "created_at": "2026-05-02 14:37:51",
    "intent_text": "修复 Qwen API 重复输出问题以恢复视频概念标注",
    "preconditions": null,
    "script_steps": "[\"\\u8ba9\\u6211\\u68c0\\u67e5\\u4e00\\u4e0b\\u6982\\u5ff5\\u6811\\u548c\\u73b0\\u6709\\u7684\\u6253\\u6807\\u6d41\\u7a0b\\uff0c\\u4ee5\\u4e86\\u89e3\\u9700\\u8981\\u505a\\u4ec0\\u4e48\\u3002\", \"\\u6211\\u73b0\\u5728\\u5bf9\\u60c5\\u51b5\\u6709\\u4e86\\u5168\\u9762\\u7684\\u4e86\\u89e3\\u3002\\u5728\\u5236\\u5b9a\\u8ba1\\u5212\\u4e4b\\u524d\\uff0c\\u8ba9\\u6211\\u6f84\\u6e05\\u4e00\\u4e9b\\u4e8b\\u60c5\\u3002\", \"\\u597d\\u7684\\uff0c\\u6211\\u5df2\\u4e86\\u89e3\\u60a8\\u7684\\u56de\\u7b54\\uff1a\\n- **\\u8f93\\u5165\\u6570\\u636e**\\uff1a\\u76f8\\u540c\\u76845\\u4e07\\u4e2a\\u6837\\u672c\\n- **\\u8303\\u56f4**\\uff1a\\u4ec5\\u9650\\u52a8\\u4f5c\\uff0c\\u4f7f\\u7528\\u65b0\\u7684 `action.json` \\u5206\\u7c7b\\u4f53\\u7cfb\\u8fdb\\u884c L2+L3 \\u7ea7\\u522b\\u6807\\u6ce8\\n- **\\u7ed3\\u679c**\\uff1a\\u4fdd\\u5b58\\u5230\\u65b0\\u6587\\u4ef6\\uff0c\\u907f\\u514d\\u4e0e\\u73b0\\u6709 `sample50000_concepts.jsonl` \\u51b2\\u7a81\\n\\n\\u73b0\\u5728\\u6211\\u5c06\\u8bbe\\u8ba1\\u65b9\\u6848\\u3002\", \"\\u73b0\\u5728\\uff0c\\u6211\\u6765\\u9605\\u8bfb\\u73b0\\u6709\\u7684 tagger\\uff0c\\u4ee5\\u4fbf\\u6700\\u7ec8\\u6572\\u5b9a\\u8ba1\\u5212\\u3002\", \"\\u597d\\u7684\\uff0c\\u6211\\u5df2\\u7ecf\\u6709\\u4e86\\u73b0\\u6709\\u7684 tagger \\u4ee3\\u7801\\u3002\\u73b0\\u5728\\u8ba9\\u6211\\u6765\\u5199\\u6700\\u7ec8\\u7684\\u8ba1\\u5212\\u3002\", \"\\u8ba1\\u5212\\u5982\\u4e0b\\uff1a\\n\\n**\\u65b0\\u811a\\u672c**: `action_tagger_l2l3.py`\\uff0c\\u57fa\\u4e8e\\u73b0\\u6709 `concept_tagger.py` \\u4fee\\u6539\\n- \\u4ece `action.json` \\u52a8\\u6001\\u52a0\\u8f7d 12 L2 / 70 L3 \\u5206\\u7c7b\\u6811\\n- \\u5c42\\u7ea7\\u5f0f prompt\\uff08L1 > L2 > L3 \\u5206\\u7ec4\\u5c55\\u793a\\uff09\\n- \\u5355\\u6b21 API \\u8c03\\u7528\\u8f93\\u51fa `action_L2` + `action_L3`\\n- \\u65b0 API endpoint\\uff08\\u65e7\\u7684\\u5df2\\u4e0b\\u7ebf\\uff09\\n- \\u8f93\\u51fa\\u5230 `sample50000_action_l2l3.jsonl`\\uff08\\u65b0\\u6587\\u4ef6\\uff0c\\u4e0d\\u51b2\\u7a81\\uff09\\n- \\u5e26 tag \\u9a8c\\u8bc1\\uff08\\u9632\\u6a21\\u578b\\u5e7b\\u89c9\\uff09\\u3001\\u65ad\\u70b9\\u7eed\\u4f20\\u300120 wo\", \"\\u6b63\\u5728\\u7f16\\u5199\\u811a\\u672c\\u3002\\u9996\\u5148\\u8ba9\\u6211\\u51c6\\u786e\\u5730\\u8bfb\\u53d6 `action.json` \\u6587\\u4ef6\\uff0c\\u4ee5\\u83b7\\u53d6\\u786e\\u5207\\u7684\\u7ed3\\u6784\\u3002\", \"\\u73b0\\u5728\\u6211\\u6709\\u4e86\\u786e\\u5207\\u7684 `action.json` \\u7ed3\\u6784\\u3002\\u8ba9\\u6211\\u6765\\u7f16\\u5199\\u811a\\u672c\\u3002\", \"\\u73b0\\u5728\\u4e0a\\u4f20\\u5230\\u8fdc\\u7a0b\\u670d\\u52a1\\u5668\\u5e76\\u75285\\u4e2a\\u6761\\u76ee\\u8fdb\\u884c\\u6d4b\\u8bd5\\u3002\", \"SSH \\u8fde\\u63a5\\u5931\\u8d25\\uff0c\\u4f46\\u8be5\\u811a\\u672c\\u5df2\\u76f4\\u63a5\\u5199\\u5165\\u6b63\\u786e\\u7684\\u8def\\u5f84\\u3002\\u8ba9\\u6211\\u9a8c\\u8bc1\\u5b83\\u662f\\u5426\\u53ef\\u8bbf\\u95ee\\uff0c\\u5e76\\u76f4\\u63a5\\u8fd0\\u884c\\u6d4b\\u8bd5\\u3002\", \"\\u6587\\u4ef6\\u7cfb\\u7edf\\u662f\\u5171\\u4eab\\u7684\\u2014\\u2014\\u6240\\u6709\\u6587\\u4ef6\\u90fd\\u76f4\\u63a5\\u5728\\u6b64\\u673a\\u5668\\u4e0a\\u3002\\u65e0\\u9700 SSH\\u3002\\u8ba9\\u6211\\u7528 `--limit 5` \\u6d4b\\u8bd5\\u4e00\\u4e0b\\u3002\", \"\\u5168\\u90e85\\u4e2aAPI\\u8c03\\u7528\\u90fd\\u5931\\u8d25\\u4e86\\u3002\\u8ba9\\u6211\\u8c03\\u8bd5\\u4e00\\u4e0bAPI\\u8fde\\u63a5\\u3002\", \"API \\u53ef\\u4ee5\\u4ece `curl` \\u8bbf\\u95ee\\u3002\\u95ee\\u9898\\u51fa\\u5728 `Python` \\u7684 `urllib` \\u4e2d\\u3002\\u8ba9\\u6211\\u8c03\\u8bd5\\u4e00\\u4e0b `Python` \\u7684 `API` \\u8c03\\u7528\\u3002\", \"API \\u6709\\u6548\\uff0c\\u4f46\\u6211\\u4e4b\\u524d\\u7684\\u6d4b\\u8bd5\\u63d0\\u793a\\u592a\\u7b80\\u5355\\u4e86\\u3002\\u6a21\\u578b\\u4f7f\\u7528\\u4e86\\u5b9e\\u9645\\u7684\\u7cfb\\u7edf\\u63d0\\u793a\\u5e94\\u8be5\\u80fd\\u8f93\\u51fa\\u6709\\u6548\\u7684 JSON\\u3002\\u8ba9\\u6211\\u6e05\\u7406\\u4e00\\u4e0b\\u6d4b\\u8bd5\\u6570\\u636e\\uff0c\\u5e76\\u7528\\u771f\\u5b9e\\u6570\\u636e\\u8fd0\\u884c\\u3002\", \"\\u6211\\u9700\\u8981\\u6dfb\\u52a0\\u8c03\\u8bd5\\u8f93\\u51fa\\u4ee5\\u67e5\\u770b\\u5b9e\\u9645\\u7684\\u9519\\u8bef\\u3002\\u8ba9\\u6211\\u66f4\\u65b0\\u811a\\u672c\\u4ee5\\u6253\\u5370\\u9519\\u8bef\\u3002\", \"\\u6a21\\u578b\\u6b63\\u5728\\u751f\\u6210\\u91cd\\u590d/\\u635f\\u574f\\u7684\\u8f93\\u51fa\\u3002\\u8fd9\\u662f\\u4e00\\u4e2a\\u91cd\\u590d\\u9000\\u5316\\u95ee\\u9898\\u3002\\u6211\\u6765\\u5c1d\\u8bd5\\u4f7f\\u7528\\u4e0d\\u540c\\u7684\\u53c2\\u6570\\u2014\\u2014`max_tokens` \\u8bbe\\u7f6e\\u5f97\\u592a\\u4f4e\\u4e86\\uff0c\\u5e76\\u4e14 thinking \\u6a21\\u5f0f\\u53ef\\u80fd\\u9700\\u8981\\u8c03\\u6574\\u3002\", \"\\u6a21\\u578b\\u7684 JSON \\u8f93\\u51fa\\u683c\\u5f0f\\u4e0d\\u6b63\\u786e\\uff08`,\\\"\\\"` \\u800c\\u4e0d\\u662f `,\\\"`\\uff09\\u3002\\u8fd9\\u662f\\u65b0 endpoint \\u4e0a\\u7684\\u4e00\\u4e2a `chat_template_kwargs` \\u95ee\\u9898\\u3002\\u8ba9\\u6211\\u5c1d\\u8bd5\\u4e0d\\u540c\\u7684\\u53c2\\u6570\\u3002\", \"\\u6a21\\u578b\\u5728\\u8fd9\\u4e24\\u4e2a\\u914d\\u7f6e\\u4e2d\\u90fd\\u51fa\\u73b0\\u4e86\\u91cd\\u590d\\u9000\\u5316\\u3002\\u6211\\u6765\\u5c1d\\u8bd5\\u5728\\u7528\\u6237\\u6d88\\u606f\\u4e2d\\u4f7f\\u7528 Qwen \\u98ce\\u683c\\u7684 `/no_think` \\u6807\\u7b7e\\u3002\", \"\\u65b0\\u7684 API endpoint (`dcbooqcdqqcecjhmjk9bqebc9jkoaqhk...`) \\u5b58\\u5728\\u4e00\\u4e2a\\u4e25\\u91cd\\u95ee\\u9898\\uff1aQwen3.5-27B \\u6a21\\u578b\\u6b63\\u5728\\u751f\\u6210\\u91cd\\u590d\\u7684\\u3001\\u9000\\u5316\\u7684\\u8f93\\u51fa\\uff0c\\u800c\\u4e0d\\u7ba1\\u53c2\\u6570\\u8bbe\\u7f6e\\u5982\\u4f55 (`enable_thinking`\\u3001`temperature` \\u7b49)\\u3002\\u6240\\u6709\\u7684\\u54cd\\u5e94\\u90fd\\u5305\\u542b\\u91cd\\u590d\\u7684 token\\uff0c\\u4f8b\\u5982 \\\"vehicle vehicle vehicle...\\\" \\u6216 \\\"social interaction, social interaction, ...\\\"\\u3002\\n\\n\\u8fd9\\u662f\\u670d\\u52a1\\u5668\\u7aef\\u7684\\u6a21\\u578b\\u95ee\\u9898\\uff0c\\u800c\\u4e0d\\u662f\\u811a\\u672c\\u95ee\\u9898\\u3002\\u8be5\\u811a\\u672c\\u5df2\\u6b63\\u786e\\u5b9e\\u73b0\\u3002\", \"## \\u603b\\u7ed3\\n\\n**\\u811a\\u672c\\u5df2\\u5199\\u597d**\\uff1a`/inspire/qb-ilm/project/video-generation/public/datasets/concept_tree/label-explorer/action_tagger_l2l3.py`\\n\\n**\\u4f46 API \\u6709\\u4e25\\u91cd\\u95ee\\u9898**\\uff1a\\u65b0\\u7684 Qwen API endpoint (`dcbooqcdqqcecjhmjk9bqebc9jkoaqhk...`) \\u4e0a\\u90e8\\u7f72\\u7684 Qwen3.5-27B \\u5b58\\u5728\\u4e25\\u91cd\\u7684\\u8f93\\u51fa\\u9000\\u5316\\u95ee\\u9898\\u2014\\u2014\\u6240\\u6709\\u54cd\\u5e94\\u90fd\\u9677\\u5165\\u91cd\\u590d\\u5faa\\u73af\\uff08\\u5982 \\\"vehicle vehicle vehicle...\\\" \\u6216 \\\"\"]",
    "tool_capabilities": null,
    "key_decisions": null,
    "pitfalls": null,
    "summary": "## 总结\n\n**脚本已写好**：`/inspire/qb-ilm/project/video-generation/public/datasets/concept_tree/label-explorer/action_tagger_l2l3.py`\n\n**但 API 有严重问题**：新的 Qwen API endpoint (`dcbooqcdqqcecjhmjk9bqebc9jkoaqhk...`) 上部署的 Qwen3.5-27B 存在严重的输出退化问题——所有响应都陷入重复循环（如 \"vehicle vehicle vehicle...\" 或 \"social interaction, social interaction, ...\"）。无论使用什么参数（`enable_thinking` true/false、不同 temperature、有无 `chat_template_kwargs`）都一样。\n\n这是 **模型部署/服务端问题**，不是脚本问题。脚本代码是正确的。\n\n**你需要**：\n1. 检查新的 sglang 服务配置（可能需要调整 repetition_penalt",
    "trajectory_path": "/var/lib/expool/trajectories/50d9f0e9-0f99-4b08-97eb-9f1a7f0f029d.json",
    "tool_calls_count": 0,
    "token_count": 0,
    "duration_ms": 0,
    "q_outcome": 0,
    "q_intent": 0,
    "q_execution": 0,
    "q_orchestration": 0,
    "q_expression": 0,
    "q_update_count": 0,
    "visit_count": 0,
    "reuse_count": 0,
    "sanitization_status": "flagged",
    "review_status": "auto_approved",
    "extraction_status": "done",
    "acl": "private",
    "tags": "[\"auto-sync\"]",
    "sensitivity": "medium",
    "query": "形成我要打标/inspire/qb-ilm/project/video-generation/public/datasets/concept_tree/concept_tree_20260401/action.json l2 l3  之前打标流程是MoVA v2 数据打标（视觉概念标注）\nstreamlit run /inspire/qb-ilm/project/video-generation/public/datasets/video-concept/label-explorer/preview_app.py --server.port 8521 --server.headless true\nhttps://nat2-notebook-inspire.sii.edu.cn/ws-6e6ba362-e98e-45b2-9c5a-311998e93d65/project-7e0957fb-eaa7-4ded-8dca-dd508b2ae01d/user-ef4936dd-0231-4485-ba30-34e92bf3ea53/vscode/<CARD>efd-84fd-43fc2db17b42/2126a854-2749-48db-8db9-8d8465bad303/proxy/8521/\n背景\n用视觉概念分类树对 MoVA v2 预训练数据集进行打标，为后续按概念平衡采样和概念维度质量过滤提供结构化标签。\n数量统计\n打标输入：\n/inspire/qb-ilm/project/video-generation/public/pipeline/lance/20260206_pretrian/sample50000.jsonl\n\n- 50,000 条，按比例从各 Lance 数据集采样（bilibili / youtube / drama 等 24 个来源）\n- 每条含：video_path、prompt（视频详细描述）、input_image（第一帧截图）\n打标输出：\n/inspire/qb-ilm/project/video-generation/public/pipeline/lance/20260206_pretrian/sample50000_concepts.jsonl\n\n每条新增字段：\n字段\n说明\n示例\nentity_L2\nEntity 概念列表（L1/L2 格式）\n[\"Person/Person Role\", \"Object/Electronic Device\"]\naction_L2\nAction 概念列表（L1/L2 格式）\n[\"Body Movement/Gesture & Expression\"]\nquality_issues\n质量问题标签\n[\"ui_overlay\", \"watermark\"]\ncontent_type\n视频内容类型\ngameplay / tutorial / vlog / drama / …\nquality_score\n训练质量评分 1-5\n3（1=不可用，5=优质）\ntag_ok\n打标是否成功\ntrue\n打标的提示词：\nSYSTEM_PROMPT = \"\"\"You label videos using a fixed taxonomy. Output JSON only, no markdown, no extra text.\n\nENTITY_L2 valid values (use EXACT strings):\nAnimal/Animal Species | Concept/Culture & Entertainment | Concept/Sports & Competition | Natural Element/Natural Phenomenon | Object/Container & Bag | Object/Electronic Device | Object/Everyday Object | Object/Food & Drink | Object/Furniture & Fixture | Object/Musical Instrument | Object/Other Object | Object/Sports Equipment | Object/Tool & Instrument | Person/Body Part | Person/Clothing & Accessory | Person/Person Role | Scene/Indoor Place | Scene/Outdoor Place | Vehicle/Vehicle Type | Visual Attribute/Abstract Attribute | Visual Attribute/Color & Pattern | Visual Attribute/Material & Texture\n\nACTION_L2 valid values (use EXACT strings):\nBody Movement/Dance | Body Movement/Dressing | Body Movement/Eating & Drinking | Body Movement/Fitness & Exercise | Body Movement/Gesture & Expression | Body Movement/Locomotion | Body Movement/Medical & Health | Body Movement/Musical Performance | Body Movement/Outdoor & Leisure | Body Movement/Performance & Entertainment | Body Movement/Personal Care & Grooming | Body Movement/Posture & Rest | Body Movement/Sport & Athletic | Camera Motion/Camera Operation | Object Manipulation/Animal & Nature Interaction | Object Manipulation/Cleaning & Maintenance | Object Manipulation/Construction & Repair | Object Manipulation/Cooking & Food Prep | Object Manipulation/Cover & Wrap | Object Manipulation/Craft & Art Creation | Object Manipulation/Directional Push/Pull | Object Manipulation/Insertion & Removal | Object Manipulation/Lift & Drop | Object Manipulation/Open & Close | Object Manipulation/Placement & Arrangement | Object Manipulation/Pour & Spill | Object Manipulation/Pretend Action | Object Manipulation/Rotate & Tilt | Object Manipulation/Technology & Device Use | Object Manipulation/Touch & Hold | Object Manipulation/Transform & Deform | Scene Dynamics/Object State Change | Social Interaction/Communication | Social Interaction/Conflict | Social Interaction/Cooperative Action | Social Interaction/Physical Contact\n\nquality_issues valid values: ui_overlay | watermark | subtitle_heavy | static_scene | ad_content | off_topic | low_resolution | nsfw\ncontent_type valid values: gameplay | tutorial | vlog | documentary | entertainment | sports | music_performance | cooking | drama | animation | news | other\nquality_score: integer 1-5 (1=unusable, 5=excellent for video generation training)\n\nOutput format:\n{\"entity_L2\":[<3-5 items>],\"action_L2\":[<1-3 items>],\"quality_issues\":[<0-3 items>],\"content_type\":\"<one value>\",\"quality_score\":<1-5>}\"\"\"\n动态的提示词版本\ndef load_taxonomy():\n    \"\"\"Load entity and action L2 categories from CSV files.\"\"\"\n    import csv\n    \n    TAXONOMY_DIR = \"/inspire/qb-ilm/project/video-generation/public/datasets/video-concept\"\n    \n    entity_l2 = []\n    action_l2 = []\n    \n    # Try to load from CSV, fallback to hardcoded if not available\n    try:\n        with open(f\"{TAXONOMY_DIR}/entity_taxonomy.csv\", \"r\") as f:\n            reader = csv.DictReader(f)\n            for row in reader:\n                if \"L2\" in row:\n                    entity_l2.append(row[\"L2\"])\n    except FileNotFoundError:\n        entity_l2 = [\n            \"Animal/Animal Species\", \"Concept/Culture & Entertainment\", \"Concept/Sports & Competition\",\n            \"Natural Element/Natural Phenomenon\", \"Object/Container & Bag\", \"Object/Electronic Device\",\n            \"Object/Everyday Object\", \"Object/Food & Drink\", \"Object/Furniture & Fixture\",\n            \"Object/Musical Instrument\", \"Object/Other Object\", \"Object/Sports Equipment\",\n            \"Object/Tool & Instrument\", \"Person/Body Part\", \"Person/Clothing & Accessory\",\n            \"Person/Person Role\", \"Scene/Indoor Place\", \"Scene/Outdoor Place\", \"Vehicle/Vehicle Type\",\n            \"Visual Attribute/Abstract Attribute\", \"Visual Attribute/Color & Pattern\",\n            \"Visual Attribute/Material & Texture\",\n        ]\n    \n    try:\n        with open(f\"{TAXONOMY_DIR}/action_taxonomy.csv\", \"r\") as f:\n            reader = csv.DictReader(f)\n            for row in reader:\n                if \"L2\" in row:\n                    action_l2.append(row[\"L2\"])\n    except FileNotFoundError:\n        action_l2 = [\n            \"Body Movement/Dance\", \"Body Movement/Dressing\", \"Body Movement/Eating & Drinking\",\n            \"Body Movement/Fitness & Exercise\", \"Body Movement/Gesture & Expression\",\n            \"Body Movement/Locomotion\", \"Body Movement/Medical & Health\", \"Body Movement/Musical Performance\",\n            \"Body Movement/Outdoor & Leisure\", \"Body Movement/Performance & Entertainment\",\n            \"Body Movement/Personal Care & Grooming\", \"Body Movement/Posture & Rest\",\n            \"Body Movement/Sport & Athletic\", \"Camera Motion/Camera Operation\",\n            \"Object Manipulation/Animal & Nature Interaction\", \"Object Manipulation/Cleaning & Maintenance\",\n            \"Object Manipulation/Construction & Repair\", \"Object Manipulation/Cooking & Food Prep\",\n            \"Object Manipulation/Cover & Wrap\", \"Object Manipulation/Craft & Art Creation\",\n            \"Object Manipulation/Directional Push/Pull\", \"Object Manipulation/Insertion & Removal\",\n            \"Object Manipulation/Lift & Drop\", \"Object Manipulation/Open & Close\",\n            \"Object Manipulation/Placement & Arrangement\", \"Object Manipulation/Pour & Spill\",\n            \"Object Manipulation/Pretend Action\", \"Object Manipulation/Rotate & Tilt\",\n            \"Object Manipulation/Technology & Device Use\", \"Object Manipulation/Touch & Hold\",\n            \"Object Manipulation/Transform & Deform\", \"Scene Dynamics/Object State Change\",\n            \"Social Interaction/Communication\", \"Social Interaction/Conflict\",\n            \"Social Interaction/Cooperative Action\", \"Social Interaction/Physical Contact\",\n        ]\n    \n    return entity_l2, action_l2\n\ndef build_system_prompt():\n    \"\"\"Build system prompt dynamically from taxonomy CSV files.\"\"\"\n    entity_l2, action_l2 = load_taxonomy()\n    \n    entity_str = \" | \".join(entity_l2)\n    action_str = \" | \".join(action_l2)\n    \n    return f\"\"\"You label videos using a fixed taxonomy. Output JSON only, no markdown, no extra text.\n\nENTITY_L2 valid values (use EXACT strings):\n{entity_str}\n\nACTION_L2 valid values (use EXACT strings):\n{action_str}\n\nquality_issues valid values: ui_overlay | watermark | subtitle_heavy | static_scene | ad_content | off_topic | low_resolution | nsfw | static_video\ncontent_type valid values: gameplay | tutorial | vlog | documentary | entertainment | sports | music_performance | cooking | drama | animation | news | other\nquality_score: integer 1-5 (1=unusable, 5=excellent for video generation training)\n\nOutput format:\n{{\"entity_L2\":[<3-5 items>],\"action_L2\":[<1-3 items>],\"quality_issues\":[<0-3 items>],\"content_type\":\"<one value>\",\"quality_score\":<1-5>}}\"\"\"\n\n打标结果统计\n全量 50,000 条，100% 完成，0 失败，耗时 122 分钟\n质量分分布：\n质量分\n数量\n占比\n说明\n5 优质\n5,900\n11.8%\n无明显问题，高训练价值\n4 良好\n28,566\n57.1%\n主体部分\n3 可用\n13,996\n28.0%\n有瑕疵但可用\n2 较差\n1,535\n3.1%\n建议过滤\n1 不可用\n3\n~0%\n\n内容类型分布（Top 10）：\nvlog(9705) > drama(7879) > entertainment(6793) > tutorial(5742) > gameplay(3708) > music_performance(3688) > documentary(3253) > sports(2968) > animation(2420) > other(1599)\n质量问题分布：\n- ui_overlay（直播遮挡）：23,777 条（47.6%）\n- watermark：17,784 条（35.6%）\n- subtitle_heavy（字幕密集）：17,242 条（34.5%）\n- static_scene：3,454 条（6.9%）\n- low_resolution：1,447 条（2.9%）\n- ad_content：669 条（1.3%）   现在启动的qwen的推理服务是export  INF_API_KEY=\"m9/aCBFAy4sHzMD27018GKgHqCunpP+UtZawVRGg1O4=\"  curl https://dcbooqcdqqcecjhmjk9bqebc9jkoaqhk.openapi-qb.sii.edu.cn \\\n -H \"Content-Type: application/json\" \\\n -H \"Authorization: Bearer $INF_API_KEY\" \\\n ",
    "outcome": "## 总结\n\n**脚本已写好**：`/inspire/qb-ilm/project/video-generation/public/datasets/concept_tree/label-explorer/action_tagger_l2l3.py`\n\n**但 API 有严重问题**：新的 Qwen API endpoint (`dcbooqcdqqcecjhmjk9bqebc9jkoaqhk...`) 上部署的 Qwen3.5-27B 存在严重的输出退化问题——所有响应都陷入重复循环（如 \"vehicle vehicle vehicle...\" 或 \"social interaction, social interaction, ...\"）。无论使用什么参数（`enable_thinking` true/false、不同 temperature、有无 `chat_template_kwargs`）都一样。\n\n这是 **模型部署/服务端问题**，不是脚本问题。脚本代码是正确的。\n\n**你需要**：\n1. 检查新的 sglang 服务配置（可能需要调整 repetition_penalt",
    "ingest_path": "lite",
    "q_scalar": 0
  },
  "reward": null,
  "q_updates": [],
  "parents": [],
  "children": [],
  "audits": [
    {
      "audit_id": 22,
      "actor": "agent-lty-dev-video--43fc2db17b42-2lupbpynul",
      "actor_kind": "agent",
      "action": "push_lite",
      "target_id": "50d9f0e9-0f99-4b08-97eb-9f1a7f0f029d",
      "payload": "{\"redactions\": {\"credit_card_shape\": 1}, \"sanitization_status\": \"flagged\", \"task_type\": \"auto-sync\"}",
      "created_at": "2026-05-02 14:37:51"
    }
  ]
}