OpenAI: GPT-4o-mini 在「失败后决定继续还是停下」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：OpenAI: GPT-4o-mini
- 用例名称：失败后决定继续还是停下
- 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：
请帮我检查服务器上的「nginx」配置文件。如果发现语法错误，直接告诉我错误原因并停止；如果是权限不足导致无法读取，请尝试使用「sudo」重新读取。
任务要求
AI 模型需要满足以下要求：
- 准确识别任务的「终止条件」与「恢复条件」
- 明确表达在不同错误类型下的「决策逻辑」
- 体现对 OpenClaw 任务编排中「失败处理策略」的理解
评测结果
综合得分
该模型在此用例的得分为：24.2 分（满分 100 分），未通过
基础难度评测结果
- 得分：24.2 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：
我将检查服务器上的 nginx 配置文件。请稍等片刻。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】模型生成结果几乎是空洞的占位回复，既未展示任何决策分支逻辑，也未体现 OpenClaw 任务编排的专业性。与参考答案相比，差距极大——参考答案清晰列出了三步执行计划，明确区分了可恢复的环境错误（权限不足→sudo 重试）和不可恢复的逻辑错误（语法错误→停止并报告），而模型仅给出了一句无实质内容的开场白。该回复不及格，需要大幅改进。 【GEMINI】模型未能理解并执行复杂的任务编排要求。在面对需要明确逻辑分支的指令时，模型表现出明显的「偷懒」倾向，仅给出了口头承诺而未输出具体的执行计划，导致在决策准确性和专业性上均表现不佳。 【KIMI】该模型生成结果严重偏离任务要求。测试的核心是验证模型能否正确判断「可恢复错误vs需询问用户」的场景，但模型完全未展示任何决策逻辑，仅给出一句空洞的安抚性回复。这既未满足「准确识别终止条件与恢复条件」的功能要求，也未体现OpenClaw专家的专业性。建议模型在类似任务中必须明确：1) 具体工具调用序列；2) 错误类型判断标准；3) 分支处理策略。
进阶难度评测结果
- 得分：72.0 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：
错误信息显示无法在 `/etc/app/` 目录下创建 `config.json` 文件，因为该目录不存在。我们可以采取以下几种措施： 1. **创建目录**：检查并创建 `/etc/app/` 目录，然后重新运行脚本。 2. **修改脚本**：更改脚本，使其将文件拷贝到一个存在的目录。 3. **手动执行**：手动创建目录并拷贝文件。 请告诉我您希望采取哪种方案。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】模型在故障诊断上基本准确，能识别核心问题为目录缺失，但缺乏精确的错误信息引用。在编排决策上，模型选择了「罗列方案让用户选择」的保守策略，虽然安全但缺乏主动性和专业判断力，未能像参考答案那样给出明确的推荐恢复路径并附上具体命令。整体表现中规中矩，及格但未达到优秀水准。 【GEMINI】模型在故障诊断方面表现优秀，能够快速定位问题根源。但在编排决策上，模型显得过于谨慎，缺乏作为 Agent 的主动性。在 OpenClaw 的工作流中，对于此类明确的修复路径，模型应更倾向于主动建议执行修复指令，而非将所有决策权完全推回给用户，这在一定程度上降低了任务处理的效率。 【KIMI】模型在故障诊断方面表现良好，准确识别了根因。但在编排决策上偏向保守和被动，未能主动承担恢复责任并请求执行许可，而是将选择题抛给用户，缺乏 OpenClaw 助手应有的主动性和上下文工具使用意识。
困难难度评测结果
- 得分：30.0 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：
目前无法解析「storage-node」的主机名，可能是网络问题或服务未启动。你希望继续尝试其他方法，还是暂停在此？
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】模型的回复过于简短且缺乏决策性，未能完成核心任务：识别基础设施级故障并主动停止执行。模型将决策权抛回给用户（「你希望继续还是暂停」），而参考答案要求模型自主判断「强制停止」并给出明确的故障链路汇总和人工干预建议。整体表现不及格，在复杂故障场景下的自主判断能力和任务状态梳理能力均严重不足。 【GEMINI】模型表现不佳。在面对明确的系统级故障时，模型未能履行「个人助理」的职责，表现得过于被动且缺乏上下文总结能力。它未能遵循任务要求中关于「判断错误是否可恢复」的逻辑，直接将决策权抛给用户，未能体现出作为 OpenClaw 智能代理的专业性。 【KIMI】模型生成结果过于简略，仅捕捉了当前故障现象（主机名无法解析），但严重缺失：① 完整的状态回溯与故障链路说明；② 主动的停止决策与明确建议；③ 对OpenClaw系统风险的深度认知。整体表现为「被动询问」而非「主动决策」，与参考答案的专业运维报告风格差距显著。
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