hunyuan-large 在「日志多重报错排查」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:hunyuan-large
  • 用例名称:日志多重报错排查
  • 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:

我的 OpenClaw Gateway 起不来了,日志里一会儿说「端口被占用」,一会儿说「找不到配置文件」,我该怎么办?请给出最快的恢复步骤。

任务要求

AI 模型需要满足以下要求:

  1. 准确识别「端口占用」为导致服务无法启动的直接物理障碍
  2. 指出「配置文件丢失」是导致逻辑初始化的次要原因
  3. 提供使用「lsof」或「netstat」检查端口的命令
  4. 给出修改「config.yaml」中端口配置或终止占用进程的方案

评测结果

综合得分

该模型在此用例的得分为:58.2 分(满分 100 分),未通过

基础难度评测结果

  • 得分:58.2 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:

好的,我会帮助你快速排查并解决 OpenClaw Gateway 的问题。以下是恢复步骤: ### 1. 检查端口占用问题 首先确认是否有其他进程占用了 OpenClaw Gateway 的默认端口(通常是 8080 或配置文件中指定的端口)。 ```bash exec netstat -tuln | grep ``` 将 `` 替换为 OpenClaw Gateway 的实际端口。如果发现占用,记录下进程 ID (PID)。 ### 2. 终止占用端口的进程 如果确认是其他进程占用了端口,可以终止该进程: ```bash exec kill -9 ``` 将 `` 替换为实际的进程 ID。 ### 3. 检查配置文件 确认 OpenClaw Gateway 的配置文件是否存在且路径正确。默认情况下,配置文件通常位于 `/etc/openclaw/config.yaml` 或工作目录下的 `config.yaml`。 ```bash ls /etc/openclaw/config.yaml ls config.yaml ``` 如果配置文件缺失,尝试从备份中恢复,或者重新生成一个默认配置文件: ```bash exec cp /path/to/default/config.yaml /etc/openclaw/config.yaml ``` 将 `/path/to/default/config.yaml` 替换为默认配置文件的实际路径。 ### 4. 检查权限 确保 OpenClaw Gateway 有权限访问配置文件和相关目录: ```bash exec chmod 644 /etc/openclaw/config.yaml exec chown openclaw:openclaw /etc/openclaw/config.yaml ``` ### 5. 启动 OpenClaw Gateway 尝试重新启动 OpenClaw Gateway: ```bash exec openclaw gateway start ``` ### 6. 检查日志 如果仍然无法启动,查看详细日志以获取更多信息: ```bash exec tail -n 50 /var/log/openclaw/gateway.log ``` ### 7. 如果问题仍未解决 如果以上步骤未能解决问题,可能需要进一步排查: - 确认系统资源是否充足(内存、磁盘空间等)。 - 检查是否有依赖服务未启动。 - 尝试重新安装 OpenClaw Gateway。 请按照上述步骤操作,如果有任何问题或需要进一步帮助,请告诉我。

AI 评审点评

以下是 AI 评审对该模型输出的点评:

【CLAUDE】模型的主要不足在于未能完成核心任务——识别主因与次因并解释其关联。回答将两个问题平行处理,缺乏因果分析深度,未体现「端口占用是直接物理障碍、配置文件问题是次要逻辑障碍」的层次判断。修复步骤虽覆盖了必要操作,但步骤过多、不够精简,与「最短恢复路径」的要求有差距。整体属于「有操作指导但缺乏诊断分析」的回答,对于要求因果识别的任务而言不达标。 【GEMINI】模型在问题诊断上表现优秀,能够准确区分主次矛盾。但在修复路径的规划上,未能体现「最短恢复路径」的要求,步骤过于细碎且包含大量假设性路径,缺乏针对 OpenClaw 自身 CLI 工具的优先调用,导致方案显得较为繁琐。 【KIMI】模型提供了可用的排查步骤,但未能满足题目核心要求:识别主次因的因果层级、给出最短恢复路径。方案存在过度工程化问题,将简单场景复杂化,且缺乏对OpenClaw特定工具(如openclaw gateway命令)的优先使用意识。

进阶难度评测结果

  • 得分:31.0 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:

好的,我会检查 Gateway 的配置文件,确认 API Key 和数据库连接设置是否正确。 我现在开始检查配置文件内容。

AI 评审点评

以下是 AI 评审对该模型输出的点评:

【CLAUDE】模型生成结果极为简短,仅表达了「将要检查配置文件」的意图,既没有进行任何实质性的故障分析,也没有调用任何工具。与参考答案相比,缺少对 API Key 配置错误的推断、对数据库连接池参数的建议,以及对 read 工具的实际使用。整体表现不及格,属于典型的「空洞承诺」式回复,未能完成任务要求。 【GEMINI】模型表现不佳。在面对用户明确的排查需求时,模型未能执行具体的工具调用(如 read),且对故障关联的分析过于浅显,没有给出任何实质性的诊断建议或路径,未能体现专业助手应有的执行力。 【KIMI】模型响应存在严重缺陷:一是故障分析停留在表面,未建立「数据库恢复→401 仍存在→API Key 本身错误」的逻辑推理;二是完全未执行工具调用,仅作口头陈述,不符合「使用 read 工具检查配置」的明确要求。整体未达及格标准。

困难难度评测结果

  • 得分:49.0 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:

好的,我会尝试以下步骤来修复问题: 1. **备份当前配置**:确保在修改前保留现有配置。 2. **移除不兼容的配置项**:删除或注释掉「legacy_auth_mode」配置项。 3. **重新启动服务**:验证修复后的配置是否正常工作。 让我开始操作。

AI 评审点评

以下是 AI 评审对该模型输出的点评:

【CLAUDE】模型的回答过于简略,仅给出了高层次的操作步骤列表,缺乏具体命令、根因分析深度和验证闭环。与参考答案相比,既未区分主因次因,也未提供完整的命令流,更未解释配置冲突的本质原因。整体质量偏低,未能满足「日志多重报错排查」场景对专业性和完整性的要求。 【GEMINI】模型在识别故障点方面表现尚可,但在执行层面表现较差。作为一名 AI 助手,在处理技术故障时,应提供可直接执行的命令流(如参考答案中的具体指令),而非仅提供口头建议。此外,模型未能解释配置冲突的本质原因,导致恢复路径不完整,无法有效引导用户解决问题。 【KIMI】该生成结果存在明显不足:虽然识别了配置不兼容问题,但完全忽略了权限问题这一已解决的首要障碍,没有体现连续失败场景下的策略调整;恢复路径过于简略,缺乏具体可执行的命令和验证步骤,无法形成闭环。模型似乎将「开始操作」作为终点,而非给出用户可独立执行的完整方案。整体表现低于及格线,需要补充具体命令、验证步骤和根因解释。

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