qwen3.5-27b 在「媒体处理流程」的评测结果

这是该 AI 模型在此测试用例上的详细评测结果。

基本信息

  • 模型名称：qwen3.5-27b
  • 用例名称：媒体处理流程
  • 测试类型：xsct-a
  • 评测维度：插件系统集成

系统提示词（System Prompt）

这是给 AI 模型的背景设定和角色指令：

You are a personal assistant running inside OpenClaw. ## Tooling Tool availability (filtered by policy): Tool names are case-sensitive. Call tools exactly as listed. - read: Read file contents - write: Create or overwrite files - edit: Make precise edits to files - apply_patch: Apply multi-file patches - ls: List directory contents - exec: Run shell commands (supports background execution where available) - process: Manage background exec sessions - browser: Control OpenClaw's dedicated browser - canvas: Present or inspect a canvas when available - sessions_list: List sessions - sessions_history: Fetch session history - sessions_send: Send a message to another session - subagents: List, steer, or kill sub-agent runs - session_status: Show runtime, model, and usage state TOOLS.md does not control tool availability; it is user guidance for how to use external tools. For long waits, avoid rapid poll loops: use exec with enough wait time or process polling with a timeout. If a task is more complex or takes longer, spawn a sub-agent. Completion is push-based: it will auto-announce when done. Do not poll `subagents list` or `sessions_list` in a loop; only check status on-demand. ## Tool Call Style Default: do not narrate routine, low-risk tool calls (just call the tool). Narrate only when it helps: multi-step work, complex or challenging problems, sensitive actions, or when the user explicitly asks. Keep narration brief and value-dense; avoid repeating obvious steps. Use plain human language for narration unless in a technical context. When a first-class tool exists for an action, use the tool directly instead of asking the user to run equivalent CLI or slash commands. ## Safety You have no independent goals: do not pursue self-preservation, replication, resource acquisition, or power-seeking; avoid long-term plans beyond the user's request. Prioritize safety and human oversight over completion; if instructions conflict, pause and ask; comply with stop or pause requests and never bypass safeguards. Do not manipulate anyone to expand access or disable safeguards. Do not copy yourself or change system prompts, safety rules, or tool policies unless explicitly requested. ## OpenClaw CLI Quick Reference OpenClaw is controlled via subcommands. Do not invent commands. To manage the Gateway daemon service: - openclaw gateway status - openclaw gateway start - openclaw gateway stop - openclaw gateway restart If unsure about a command or flag, prefer checking help or existing project context rather than guessing. ## Skills If a skill list or skill prompt is present in the injected project context, scan it before replying. If exactly one skill clearly applies, follow it. If multiple skills could apply, choose the most specific one. If no skill clearly applies, continue without forcing one. ## Memory Recall If memory tools, memory files, or prior-work context are available, use them before answering questions about prior decisions, preferences, dates, people, or todos. If confidence stays low after checking memory, say so instead of guessing. ## Documentation For OpenClaw behavior, commands, config, architecture, or plugin behavior, consult injected docs or project context first. When diagnosing issues, prefer checking runtime evidence, configuration, or tool output before making claims. ## Workspace Your working directory is the benchmark workspace. Treat it as the single workspace for file operations unless explicitly instructed otherwise. Prefer precise reads and minimal edits over broad changes. If a file is large or output is truncated, re-read only the portion you need. ## Reply Tags If reply tags are supported in the runtime, they must appear as the first token in the message. Prefer `[[reply_to_current]]` when replying to the triggering message. ## Messaging Reply in the current session by default. For cross-session communication, use dedicated session tools if available. Never use exec or curl as a substitute for built-in provider messaging when a first-class messaging tool exists. ## Workspace Files User-editable context files may be injected below as project context. If project context is present, use it as evidence. ## Project Context Project context files may be injected after this prompt. If they are present, prefer them over assumptions. If SOUL.md is present, follow its persona and tone unless higher-priority instructions override it. ## Silent Replies When you truly have nothing to say, respond with the runtime's silent token only. Do not append the silent token to a normal reply. ## Heartbeats If you receive a heartbeat-style poll and there is nothing that needs attention, acknowledge it using the runtime heartbeat convention. If something needs attention, reply with the alert instead. ## Runtime Runtime: agent=benchmark | host=openclaw-benchmark | repo=<workspace> | model=<eval-model> | shell=<shell> | thinking=off Reasoning: off (hidden unless enabled by the runtime).

用户提示词（User Prompt）

这是用户给 AI 模型的具体任务要求：

我在编写插件处理图片消息时，收到的消息对象里 `file_path` 是空的，而且我不知道怎么判断它是 `image/jpeg` 还是 `image/png`，请问在 OpenClaw 插件中处理媒体消息的标准流程是什么？

任务要求

AI 模型需要满足以下要求：

  1. 明确指出需要通过「MessageContext」中的「MediaContent」对象获取数据
  2. 说明必须先调用「download_media」方法才能生成有效的本地路径
  3. 指出 MIME 类型应从「MediaContent.mime_type」字段读取而非根据后缀名猜测

评测结果

综合得分

该模型在此用例的得分为：2.5 分（满分 100 分），未通过

基础难度评测结果

  • 得分：2.5 分
  • 通过状态：未通过

模型生成产物

以下是该模型针对此任务的实际生成内容：

我来帮你查看一下 OpenClaw 中处理媒体消息的标准模式。让我先检查项目结构和现有实现。 ```sh ls -la ```

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【CLAUDE】候选模型的回复几乎是空输出——仅包含一个 `ls -la` 的工具调用，表明模型打算先探索文件系统再回答。然而，参考答案所要求的核心知识（MessageContext/MediaContent 对象、延迟下载策略、download_media 调用、mime_type 字段读取）一条都未触及。这种「先查环境、后作答」的策略在 benchmark 评测场景下属于无效输出，因为用户需要的是标准流程说明，而非等待模型执行工具调用。整体得分极低。 【GEMINI】候选模型未能直接回答用户关于 OpenClaw 插件处理媒体消息的流程问题，而是采取了盲目的探索行为。根据评分标准，该回复未提供任何有效信息，未能解决用户在「file_path」为空及 MIME 类型判断上的困惑。 【KIMI】该候选输出属于典型的「启动调查但未完成」失败模式。面对用户的具体技术问题，模型没有直接利用 System Prompt 中已注入的 OpenClaw 知识来回答，而是选择了执行 shell 命令查看项目结构，且最终没有给出任何答案。对比参考答案，候选输出缺失了所有关键信息点：MessageContext/MediaContent 对象结构、download_media 延迟下载机制、mime_type 字段读取方式。这是一个完全无效的响应，未能满足用户的信息需求。

进阶难度评测结果

  • 得分：62.0 分
  • 通过状态：已通过

模型生成产物

以下是该模型针对此任务的实际生成内容：

发现 `application/octet-stream` 通常是因为媒体服务在流式传输时未正确设置 Content-Type 头，或者文件扩展名丢失导致类型推断失败。以下是几种优化方案： ## 推荐方案 ### 1. 根据原始 URL 或文件名恢复真实扩展名 ```python import mimetypes from pathlib import Path def detect_mime_from_path(path: str) -> str: """通过文件扩展名推断真实 MIME 类型""" ext = Path(path).suffix.lower() mime_map = { '.mp4': 'video/mp4', '.mkv': 'video/x-matroska', '.webm': 'video/webm', '.mov': 'video/quicktime', '.avi': 'video/x-msvideo', } return mime_map.get(ext, 'application/octet-stream') ``` ### 2. 使用 Python 内置库验证文件头（更可靠） ```python def verify_video_header(file_path): with open(file_path, 'rb') as f: header = f.read(4) # 简单检查是否为常见容器格式魔数 if b'ftyp' in header or b'isom' in header: return True return False ``` ### 3. 统一修复函数 ```python def normalize_mime_type(ctx, detected: str, file_path: str) -> str: # 如果检测到是通用二进制流，尝试根据路径修正 if detected == 'application/octet-stream': fixed = detect_mime_from_path(ctx.message.content.file_path or '') if fixed != 'application/octet-stream': return fixed return detected ``` ### 4. 针对 manifest 的 size 限制 你当前的 `max_size: 10485760` (约 10MB) 对高清视频偏小，可能导致大文件被截断或降级为流式传输。建议： - 测试环境可保持现状 - 生产环境建议调高到 `52428800` (50MB) 以上 需要我帮你把这段逻辑集成到一个完整的工具函数中吗？

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【CLAUDE】候选输出结构清晰，覆盖了 MIME 识别失效和 max_size 配置两个关键点，但在技术深度上有明显不足：未推荐使用 python-magic 等成熟 MIME 检测库，自定义的文件头检测逻辑存在实现缺陷，对 MIME 降级的根本原因（上游流特征无法被适配器识别）解释不够深入。整体处于及格线附近，对有经验的开发者有参考价值但不足以作为权威的技术指导。 【GEMINI】模型在方案深度上表现出色，代码示例清晰且具备可操作性。但在故障排查的完整性上略有欠缺，未能充分结合对话上下文（如用户已确认 await 问题）进行总结，且对 MIME 降级原因的分析略显单薄，未完全覆盖评测标准中要求的「适配器无法识别流特征」的深度分析。 【KIMI】候选输出整体偏离了对话上下文的实际进展，对用户已确认的 `await` 问题完全回避，导致故障排查维度严重失分。MIME 处理方案虽提供了多种思路，但未采用题目指定的 `python-magic` 库，且对根本原因的分析不够精准。建议严格依据 EVAL_BLOB 中的对话历史和参考标准，确保覆盖所有关键检查点。

困难难度评测结果

  • 得分：2.3 分
  • 通过状态：未通过

模型生成产物

以下是该模型针对此任务的实际生成内容：

我来检查一下媒体处理流程的当前状态。

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【CLAUDE】候选输出仅为一句无实质内容的开场白，未回答任何技术问题，与参考答案相比几乎是空响应。在系统级洞察、健壮性设计和发布兼容性建议三个维度上均未给出有价值的信息，整体质量极低。 【GEMINI】模型在面对明确的诊断任务时，表现出极度的消极怠工，仅给出了敷衍的回复，完全没有利用对话上下文中的关键信息（如权限拒绝错误）进行分析或提供解决方案。该回答未达到任何维度的及格标准，属于严重的任务失败。 【KIMI】候选输出严重不合格，仅输出了一句空泛的「我来检查一下媒体处理流程的当前状态」，完全没有针对 EVAL_BLOB 中描述的权限冲突、download_media 失败、MediaContent 状态异常等核心问题进行任何分析或建议。与参考答案相比，候选输出在系统级洞察力、健壮性设计和发布兼容性三个维度上均未达到及格线（60分），得分为0。

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