doubao-seed-1-6 在「子 Agent 回传后二次决策」的评测结果

基本信息

• 模型名称：doubao-seed-1-6
• 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：

1. 明确识别出需要先调用「subagents」工具进行文档分析
2. 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
3. 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：43.5 分（满分 100 分），未通过

基础难度评测结果

• 得分：43.5 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：

[{"name":"subagents","parameters":{"action":"start","agent":"文档分析子Agent","input":"项目周报.pdf","task":"提取关键风险点"}}]

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【CLAUDE】模型正确识别了需要调用 subagents 工具，并发起了第一步操作，这是基本正确的。然而，模型完全跳过了「分解计划」的输出，没有以文字形式说明后续步骤（接收结果、对照风险等级标准分级、汇总报告），也没有体现主 Agent 在子 Agent 回传后需要执行二次逻辑处理的意图。根据任务要求，此时应先输出完整的分解计划，再执行第一步工具调用，而非直接裸调用工具。整体表现为「知道做什么但不知道怎么表达计划」，在计划导向风格和任务完整性上存在明显缺失。 【GEMINI】模型表现不佳。虽然成功调用了正确的工具，但完全忽视了任务中关于「二次加工」的逻辑要求，且未按要求输出「分解计划」步骤，导致无法实现任务闭环。建议模型在处理复杂任务时，应先输出完整计划再执行动作。 【KIMI】模型输出仅为孤立的工具调用，既未体现任务分解的计划结构，也完全遗漏了「子 Agent 回传后需二次处理」的核心要求。该输出无法形成有效闭环，主 Agent 在子 Agent 完成后将缺乏后续处理逻辑，不符合题目「还要补最后一步才能闭环」的测试意图。

进阶难度评测结果

• 得分：67.5 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：

[[reply_to_current]] 🎉 OpenClaw v1.2.0 正式发布！🎉 **版本号**：v1.2.0 **发布日期**：2023-10-27 **核心特性**： ✅ 新增「多 Agent 编排」功能，显著提升复杂任务的自动化处理效率 欢迎各位开发者体验新版本，如有反馈或问题，可在 #openclaw-dev 频道交流！🚀

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【KIMI】候选输出能够完成核心任务闭环，正确生成包含版本号和核心特性的 Slack 通知，且未冗余调用子 Agent。但相比参考答案，缺少了明确的「状态汇总」环节，也未直接引用子 Agent 生成的简介原文，在任务进度展示和上下文承接的规范性上存在差距。

困难难度评测结果

• 得分：62.5 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：

[[reply_to_current]] 「二次决策」：基于 Linter Agent 反馈执行修复 1. **关键错误修复**：移除未使用的 `os` 导入 `edit('core_logic.py', [{'op': 'delete', 'line': 42}])` 2. **警告处理建议**：第 55 行函数过长，建议拆分为 smaller 函数（需进一步分析逻辑结构） 是否优先执行错误修复？

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【CLAUDE】模型在子 Agent 回传后能识别出需要进行二次决策，并对两个 Lint 问题给出了基本对应的处理方向，具备一定的场景理解能力。但存在明显不足：闭环不完整（末尾抛回用户决策）、结构不规范（缺少状态汇总）、逻辑自相矛盾（既写出执行代码又询问是否执行）、缺少对路径纠偏成功的状态确认。与参考答案相比，整体质量偏低，仅完成了任务的部分要求。 【GEMINI】模型表现良好，能够准确处理子 Agent 的反馈并转化为可执行的修复方案。主要扣分点在于对系统提示词中要求的「状态汇总 -> 继续动作」结构执行不够严格，且在处理「Function too long」这一复杂问题时，建议的颗粒度略显不足，未能完全实现任务的完美闭环。 【KIMI】模型识别了子 Agent 回传的关键信息并尝试给出修复建议，但严重偏离任务核心要求：未保持「状态汇总 -> 继续动作」的结构，未完成真正的任务闭环（以疑问句结尾），且对「Function too long」的建议过于笼统缺乏可操作性。最关键的是，模型未能理解「现在呢？」意味着用户期待一个完整的总结性回复，而非进一步的交互或确认请求。

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：「子 Agent 回传后二次决策」
• 查看该模型的所有评测结果：doubao-seed-1-6
• 浏览同维度的其他用例：「Agent 编排与恢复」
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