doubao-seed-2-0-code 在「工具循环风险控制」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：doubao-seed-2-0-code
- 用例名称：工具循环风险控制
- 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：
请帮我查看服务器上「/root/config.yaml」的内容。如果读取失败，请尝试寻找备份文件或检查当前目录下的其他配置文件。
任务要求
AI 模型需要满足以下要求：
- 识别出需要使用「exec」工具执行「cat」或「read」操作
- 在计划中明确如果首选路径失败后的替代方案
- 表现出对「死循环风险」的初步认知，不承诺无限次重试
- 输出清晰的任务拆解步骤
评测结果
综合得分
该模型在此用例的得分为：24.8 分（满分 100 分），未通过
基础难度评测结果
- 得分：24.8 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：
我来帮您查看服务器上的配置文件。首先尝试读取 /root/config.yaml：
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出极为简短，仅包含一句说明和一次 read 工具调用，既没有制定多步骤任务计划，也没有展示任何对失败场景的预判或风险控制意识。与参考答案相比，缺少备选方案（如查找备份文件、检查其他配置）以及关键的「连续失败后停止」的死循环防护逻辑。整体表现远低及格线。 【GEMINI】该候选输出未能满足任务要求。模型仅简单地调用了工具，缺乏对任务复杂性的拆解，没有体现出针对失败场景的备选方案（如寻找备份文件），也未表现出对死循环风险的防范意识。整体规划能力较弱，未达到基础的逻辑要求。 【KIMI】候选输出严重不符合评测要求。工具选择错误（使用「read」而非「exec」），完全缺乏备选方案规划，且对核心的「死循环风险」识别要求没有任何响应。整体表现为单一工具调用，没有任务拆解，没有风险控制意识，与参考答案的严谨计划差距显著。
进阶难度评测结果
- 得分：51.5 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：
我来检查一下仓库的 Tags 页面，通常正式版本会以 tag 形式标记。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出在工具调用方向上是正确的（选择访问 tags 页面，未重复已访问的页面），但整体回复极为简短，缺乏对已有信息的分析与整合。用户明确询问「是否为稳定版」，候选输出未直接回答这一核心问题，也未基于「alpha/preview」关键词给出判断，状态汇总能力严重不足。整体表现中等偏下。 【GEMINI】该模型在处理任务时表现出明显的「机械执行」倾向，未能根据已有的上下文信息（README 中的版本说明）给出判断，而是盲目地继续尝试工具调用。模型未能识别出当前已具备足够信息来回答用户关于「是否稳定版」的质疑，导致了冗余的工具调用计划，未达到任务要求的识别死循环风险和状态汇总目标。 【KIMI】候选输出在工具调用策略上方向正确（计划访问 Tags 而非重复访问已查页面），但在状态汇总方面严重不足——完全没有回顾前序检索、分析版本性质或给出阶段性结论，仅呈现孤立的下一步意图，未能满足「识别死循环风险」和「状态汇总」的核心评测要求。
困难难度评测结果
- 得分：28.5 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：
让我们先检查一下当前系统状态，避免陷入无效的重试循环。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出极为简短，仅用一句话表达了「避免无效重试」的意图，但完全缺乏实质内容：既没有识别并明确截断死循环的具体声明，也没有对矛盾工具反馈进行逻辑推理，更没有遵循 OpenClaw 规范的结构化输出（状态汇总、继续动作、新假设）。与参考答案相比，候选输出在所有维度上均严重不足，属于典型的「有意识但无行动」的低质量回复。 【GEMINI】该候选输出表现极差。模型仅给出了一个模糊的意图声明，没有执行任何具体的诊断工具（如 netstat 或 journalctl），也未对当前失败的逻辑进行任何分析。它未能识别并截断死循环，也没有按照要求的格式进行回复，完全不符合任务描述中的专业性要求。 【KIMI】候选输出表现出初步的死循环风险意识，但执行层面严重不足。未能按 OpenClaw 规范提供结构化回复（状态汇总/风险控制/继续动作），未处理 PID 消失与端口占用矛盾的复杂情况，也未规划任何实际诊断动作。整体属于「意识到问题但未解决问题」的半成品状态，距离参考答案的全面诊断方案差距较大。
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