doubao-seed-1-6-flash 在「安装成功但服务未启动」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-1-6-flash
  • 用例名称：安装成功但服务未启动
  • 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：

我刚才运行了安装命令，显示「Installation Success」，但是我现在访问 http://localhost:8000 没反应，我该怎么确认服务到底起没起来？

任务要求

AI 模型需要满足以下要求：

  1. 准确指出应使用「ps」或「systemctl」检查进程状态
  2. 提示检查 Gateway 默认端口 8000 的占用情况
  3. 建议查看「logs」目录下的日志文件进行故障定位
  4. 回复内容需包含具体的验证命令示例

评测结果

综合得分

该模型在此用例的得分为：12.8 分（满分 100 分），未通过

基础难度评测结果

  • 得分：12.8 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：

[{"name":"exec","parameters":{"command":"curl localhost:8000"}}]

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【CLAUDE】模型的回复极为简陋，仅执行了一条「curl localhost:8000」命令，既没有解释排查思路，也没有涵盖进程检查、端口监听、日志查看等关键诊断步骤。与参考答案相比，差距悬殊，无法帮助用户有效排查服务未启动的问题。 【GEMINI】模型表现不佳。面对用户提出的服务排查需求，模型未能利用系统提示词中提供的「OpenClaw CLI」工具（如 openclaw gateway status），也没有给出任何排查步骤或建议，仅执行了一个简单的 curl 命令，无法解决用户的实际问题。 【KIMI】该模型输出严重不符合任务要求。用户明确询问「如何确认服务到底起没起来」，需要的是系统性的排查指导，但模型仅执行了一个curl命令，既未提供进程检查、端口监听验证、日志查看等关键排查步骤，也未给出任何文字说明或解释。这种输出无法帮助用户理解问题根源，也未能体现技术专家应有的专业性和引导能力。建议模型应提供分步骤的排查方案，包含具体的命令示例和预期输出说明。

进阶难度评测结果

  • 得分：39.2 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：

[{"command": "lsof -i :8000", "exit_code": 0, "stdout": "COMMAND PID USER FD TYPE DEVICE SIZE/OFF NODE NAME\npython3 1234 user 3u IPv4 12345 0t0 TCP *:8000 (LISTEN)", "stderr": ""}]

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【CLAUDE】模型的回复极为简短，仅输出了一个 lsof 命令的执行结果，既没有对用户的疑问（为何 netstat 未显示但日志报端口占用）进行解释，也没有提供完整的解决方案。与参考答案相比，缺少强制释放端口、修改配置文件端口、重新启动服务等关键步骤。整体表现远低于预期，属于不及格水平。 【GEMINI】模型表现不佳。面对用户提出的逻辑矛盾，模型未能发挥助手的主动分析能力，而是简单地执行了另一个命令。此外，模型在获取到进程信息后，没有给出任何后续的修复建议（如杀死进程或修改配置），导致任务处于中断状态，未能满足用户排查和恢复服务的需求。 【KIMI】模型仅完成了最基础的端口占用确认，完全遗漏了用户关心的核心矛盾解释和任何实际解决方案。生成结果相当于一个未完成的中间步骤，既没有回应用户的疑问，也没有提供可执行的操作指引，距离完成任务还有很大差距。

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：

[[reply_to_current]]服务已经成功启动，进程ID为5678，日志已重定向到当前目录的logs文件夹下，服务状态正常。

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【CLAUDE】模型在面对用户简短追问时，仅给出了一个极为简洁的状态确认，未能展开必要的服务验证步骤，与参考答案的完备性差距较大。在权限绕过决策方面（预设上下文已完成），逻辑是合理的，但最终回复未体现对验证完备性的重视——既没有调用工具检查API健康状态，也没有确认Token可用性，导致整体评分偏低。 【GEMINI】模型在技术故障处理的决策层面表现优秀，但在任务交付的完整性和交互深度上存在明显短板。未能主动进行服务可用性验证（如端口检查）和关键信息（如 Token）的交付，导致用户在服务启动后仍处于「不知道接下来该做什么」的状态。 【KIMI】模型在预设上下文中展现了正确的技术判断（绕过权限限制），但最终回复质量严重不足：缺乏任何实际验证动作、遗漏关键状态信息、对模糊提问的响应过于简略。回复像是一个未经核实的假设性结论，而非基于实证的完整报告。建议补充进程验证、API 测试、Token 确认等步骤，并主动提供后续操作指引。

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