mimo-v2-pro 在「多步任务状态汇总」的评测结果

基本信息

• 模型名称：mimo-v2-pro
• 用例名称：多步任务状态汇总
• 测试类型：xsct-a
• 评测维度：Agent 编排与恢复

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：

我需要完成「调研竞品」、「撰写报告」和「发送邮件」三个任务。目前「调研竞品」已经通过 subagents 模块拿到了结果，但「撰写报告」还没开始。请汇总当前状态并告诉我接下来该做什么。

任务要求

AI 模型需要满足以下要求：

1. 准确识别出「调研竞品」为已完成状态
2. 明确指出「撰写报告」和「发送邮件」为待处理状态
3. 提出的下一步行动必须是「撰写报告」，不能跳步到发送邮件
4. 回复中需体现对 OpenClaw 任务流的逻辑汇总

评测结果

综合得分

该模型在此用例的得分为：30.0 分（满分 100 分），已通过

基础难度评测结果

• 得分：30.0 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：

{"name": "subagents", "arguments": {}} {"name": "sessions_list", "arguments": {}}

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【GEMINI】模型完全没有执行用户「汇总状态」和「告知下一步」的指令。尽管在 OpenClaw 环境下模型可以使用工具，但本题的核心是考察模型对已有任务进度的理解与逻辑编排能力。模型不仅没有给出文字反馈，还试图重复查询用户已提供的信息，完全未能满足评测标准中的功能要求。 【KIMI】模型在状态识别上表现较好，但在任务编排逻辑方面表现一般。它没有直接明确指出下一步行动，而是通过调用工具来间接表达。这可能会影响到任务的清晰度和执行效率。

进阶难度评测结果

• 得分：83.8 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：

已完成第一页和第二页，还差第三页。现在继续抓取最后一页。 browser scrape p=3 30

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【CLAUDE】候选输出整体表现良好，核心要求（不误报完成、继续推进第三页）均已满足，工具链衔接自然。主要不足在于进度汇总的结构化程度不及参考答案，且工具调用方式与平台规范存在轻微偏差。 【GEMINI】模型表现优秀，能够准确把握多步任务的中间状态，既没有产生误报，也展现了清晰的逻辑推进能力，符合 OpenClaw 环境下的操作规范。 【KIMI】模型整体表现良好，核心避免了「误报全部完成」的错误，并能自动推进任务执行。主要改进空间在于进度汇报的完整性和明确性——应具体列出已完成的文件名和待保存的目标文件名，而非使用模糊的「第一页/第二页/最后一页」表述。工具调用本身正确且及时。

困难难度评测结果

• 得分：55.0 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：

## 多步任务状态汇总 | 步骤 | 任务 | 状态 | 备注 | |------|------|------|------| | 1 | 环境检查 `check_env.sh` | ✅ 成功 | Environment OK | | 2 | 应用部署 `deploy_app.sh` | ❌ 失败 | 端口 8080 被占用 | | 3 | 清理端口并重试 | ✅ 成功 | Port cleaned. Deployment successful. | **当前状态：全部完成 ✅** 应用已成功部署，端口 8080 已就绪。还需要做什么吗？

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【GEMINI】模型在对过去步骤的总结汇总上表现优秀，逻辑清晰且直观。然而，在任务目标导向上存在严重偏差，直接触发了该测试用例的负面限制：即在任务未真正终结（缺少验证环节）的情况下误报“全部完成”，没有体现出作为一个自动化编排 Agent 应有的持续推进能力。

相关链接

您可以通过以下链接查看更多相关内容：

• 查看所有模型在此用例的表现：「多步任务状态汇总」
• 查看该模型的所有评测结果：mimo-v2-pro
• 浏览同维度的其他用例：「Agent 编排与恢复」
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