mimo-v2-pro 在「首次安装与初始化」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：mimo-v2-pro
- 用例名称：首次安装与初始化
- 测试类型：xsct-a
- 评测维度：Gateway 配置与管理
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：
我想在本地服务器上安装 OpenClaw Gateway，请告诉我最快的安装命令以及如何启动它来确认服务正常。
任务要求
AI 模型需要满足以下要求：
- 准确给出 pip install openclaw-gateway 的安装命令
- 说明使用 openclaw-gateway start 或 python -m openclaw_gateway 启动服务
- 提到默认监听端口为「8000」或如何查看启动日志
- 指出首次启动会生成默认配置文件
评测结果
综合得分
该模型在此用例的得分为：22.0 分（满分 100 分），未通过
基础难度评测结果
- 得分：22.0 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：
让我先查看项目文档，找到 OpenClaw Gateway 的安装信息。 find . -name "*.md" -type f | head -20 && cat README.md 2>/dev/null | head -100 openclaw --help 2>/dev/null || echo "openclaw command not found"
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出本质上是一个「未完成的回复」——模型发起了两个工具调用来探索环境，但没有产出任何面向用户的实质内容。参考答案要求的四个核心要素（安装命令、启动命令、端口确认、配置文件生成）全部缺失。即便在 agent 场景下先探索上下文是合理策略，最终也必须给出完整答案，否则对用户毫无帮助。 【GEMINI】模型在处理用户请求时表现出过度依赖工具探索的倾向，未能根据「OpenClaw CLI Quick Reference」中已提供的指令直接回复用户。在用户明确询问安装与启动方法时，模型应直接给出标准指令，而非执行无关的 shell 命令，导致用户体验极差。 【KIMI】候选输出以技术专家的口吻开始（「让我先查看项目文档」），符合技术专家身份的开头，但随后仅展示了工具调用而未完成回答，属于严重的响应不完整。工具调用本身格式正确，但没有形成完整的、对用户有价值的回复，专业度因任务未完成而大幅扣分。
进阶难度评测结果
- 得分：23.5 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：
Let我先看一下当前的配置文件，确保给出准确的命令。 config.yaml
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出未能完成用户的核心诉求：直接给出带 --port 9090 和 --debug 参数的启动命令及验证方式。模型在上下文已充分的情况下仍发起额外工具调用，既未展示对 Gateway 启动参数的掌握，也未体现基于已有信息快速响应的能力，整体表现不及格。 【GEMINI】候选模型表现不佳。在用户明确要求提供启动命令的情况下，模型未能直接给出答案，而是陷入了冗余的工具调用流程。模型未能满足「功能要求」中关于启动命令和验证方式的说明，导致任务完成度极低。 【KIMI】候选输出表现不佳。用户明确要求直接给出带参数的启动命令和验证方式，但候选输出选择了先去读取配置文件，且工具调用格式错误，调用后也没有任何后续输出或结论。完全没有满足用户的核心需求，既未提供启动命令，也未说明验证方法。
困难难度评测结果
- 得分：48.0 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：
让我先检查当前状态，然后用自定义路径和空闲端口来初始化。 mkdir -p ~/.openclaw && ls -la ~/.openclaw/ netstat -tunlp 2>/dev/null | grep -E '800[1-9]|8010' || echo "端口 8001-8010 均空闲"
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出在用户明确追问「现在呢？」时，未能将已确认的两个故障根因（权限拒绝 + 端口占用）转化为完整的修复方案，而是再次进入诊断循环。参考答案所要求的核心内容——初始化命令、带参数的启动命令、修复原理说明——均未出现。整体表现距及格线有较大差距，属于「只诊断不治疗」的典型问题。 【GEMINI】模型具备良好的技术诊断思路，能够根据上下文制定合理的规避方案。但在执行层面，面对用户的直接追问，未能一次性给出完整的解决方案，而是陷入了分步执行的节奏，导致用户体验不够高效。建议在后续交互中，对于明确的修复任务，优先给出方案说明并附带执行命令。 【KIMI】候选输出停留在诊断执行阶段，未完成故障排查与恢复的闭环。虽然技术方向正确（自定义路径、更换端口），但缺乏明确的方案陈述、原理解释和最终启动命令，未能有效回应用户的追问。与参考答案相比，缺少了关键的决策说明和可立即执行的修复指令，在困难档的环境要求和服务校验维度表现不足。
相关链接
您可以通过以下链接查看更多相关内容：
