Inception: Mercury 2 在「子 Agent 回传后二次决策」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：Inception: Mercury 2
- 用例名称：子 Agent 回传后二次决策
- 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：
请调用「文档分析子 Agent」提取「项目周报.pdf」中的关键风险点，并在拿到结果后，根据你的「风险等级标准」对这些点进行分级汇总。
任务要求
AI 模型需要满足以下要求：
- 明确识别出需要先调用「subagents」工具进行文档分析
- 在计划中体现「获取子 Agent 结果」与「二次加工（分级汇总）」的逻辑先后顺序
- 输出包含清晰的「分解计划」
评测结果
综合得分
该模型在此用例的得分为：— 分（满分 100 分），未通过
基础难度评测结果
- 得分：— 分
- 通过状态：未通过
AI 评审点评
以下是 AI 评审对该模型输出的点评：
模型返回空输出，已跳过 AI 评分（finish_reason=stop）
进阶难度评测结果
- 得分：73.0 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：
**Slack 通知草案** > 📢 **OpenClaw v1.2.0 正式发布！** > > 📅 发布日期：2023‑10‑27 > > ✨ **核心特性** > - **多 Agent 编排**：全新引入的多 Agent 编排功能，显著提升复杂任务的自动化处理能力。 > - 其他改进与 bug 修复（请参考完整发布说明） > > 🙏 感谢大家的持续关注与支持，欢迎在项目仓库中查看详细变更日志并提供反馈。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出在核心功能上表现良好：正确整合了子 Agent 的输出信息，生成了包含版本号和核心特性的 Slack 通知草案，且格式规范、可读性强。主要不足在于完全省略了「状态汇总」环节，未体现对任务进度的明确把控；同时添加了无来源依据的「其他改进与 bug 修复」内容，以及未完整引用子 Agent 生成的原文简介。整体完成度中等偏上，但缺少任务闭环所需的状态说明。 【GEMINI】模型表现出色，能够精准理解主 Agent 在子 Agent 完成任务后的收尾角色。生成的文案格式规范，信息提取准确，完全满足了用户提出的闭环需求。 【KIMI】候选输出基本完成了任务闭环，能够基于子 Agent 结果直接生成最终文案，且核心信息（版本号、特性）准确。但主要问题在于：1）完全缺失「状态汇总」环节，未显式确认子 Agent 输出；2）未直接引用子 Agent 生成的简介原文，而是自行改写，导致与参考答案的结构要求存在偏差。整体表现为「功能完成但形式不符」，上下文整合能力中等偏上，状态把控能力勉强及格。
困难难度评测结果
- 得分：— 分
- 通过状态：未通过
AI 评审点评
以下是 AI 评审对该模型输出的点评：
模型返回空输出，已跳过 AI 评分（finish_reason=stop）
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