OpenAI: GPT-4o-mini 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：OpenAI: GPT-4o-mini
- 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：33.5 分（满分 100 分），未通过
基础难度评测结果
- 得分：33.5 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：
由于权限限制，我无法直接访问 `/root/config.yaml` 文件。不过，我可以尝试检查当前目录下的其他配置文件或寻找备份文件。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】模型生成结果质量很差。面对「读取 /root/config.yaml，失败则寻找备份」的任务，模型直接以「权限限制」为由放弃，既未调用任何工具尝试读取，也未给出具体的备选方案（如搜索备份文件路径），更未体现出对死循环风险的主动规避逻辑。整体表现为消极放弃而非有计划的任务执行，远低于参考答案所要求的水准。 【GEMINI】模型表现未达标。在面对明确的任务指令时，模型未能主动调用工具执行任务，且回复过于笼统，缺乏对任务规划和风险控制的深度逻辑支撑。未能体现出作为 Agent 的执行力与策略性。 【KIMI】该模型生成结果严重不符合测试任务要求。核心问题在于：完全未执行任何工具调用，仅输出了一段口头回应，属于「只说不做」。测试任务明确要求「避免在同一错误上反复调用相同工具」，但模型连第一次调用都未完成，更谈不上识别死循环风险。与参考答案相比，缺乏清晰的任务拆解、具体的工具调用计划、以及明确的终止条件。整体表现不及格，未能展示Agent应有的规划执行能力。
进阶难度评测结果
- 得分：56.75 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：
根据信息，OpenClaw 当前处于 alpha 阶段，建议使用的版本是 v0.1.2-preview，这意味着它不是稳定版。让我再查找一下是否有更正式的版本发布。稍等。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出在基本判断上是正确的（识别出非稳定版），但在工具调用策略和状态汇总两个维度上均表现平平。未明确指出下一步的新探测目标，缺乏对已访问来源的显式排除，也未提供多轮结果的综合性结论。与参考答案相比，缺少结构化的推理链和兜底处理逻辑。 【KIMI】候选输出基本完成了对「alpha/preview」非稳定版的识别，但在工具调用策略的明确性和状态汇总的完整性上存在不足。未清晰展示如何避免重复访问、如何规划下一步探测路径，也未给出确定性结论，整体表现中等，略低于功能要求的完整标准。 【GEMINI】模型未能充分利用已有的上下文信息（README 中的明确描述），在用户已经质疑版本稳定性的情况下，没有直接给出「目前无稳定版」的结论，而是倾向于继续进行可能重复的搜索行为。在处理逻辑上，模型缺乏对「已穷尽主要来源」的判断力，未能达到避免死循环的要求。
困难难度评测结果
- 得分：33.0 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：
服务仍然无法启动，错误信息表明端口 8080 仍然被占用。接下来，我将检查当前系统中占用该端口的进程。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出方向基本正确（准备执行端口检查），但整体质量远低于参考答案。核心问题有三：1）未显式识别并声明死循环风险；2）未对矛盾的工具反馈进行深度推理和假设提出；3）完全不符合 OpenClaw 要求的结构化输出规范。输出过于简短，缺乏专业 Agent 应有的状态感知和决策透明度。 【GEMINI】该模型表现较差。在面对明确的死循环风险时，缺乏主动的风险识别与截断意识。回复内容过于平铺直叙，未能遵循 OpenClaw 要求的结构化诊断流程，且未能针对矛盾的错误信息提出有效的排查假设，未达到 benchmark 的预期要求。 【KIMI】候选输出完全未能理解任务核心要求——识别并打破工具调用的逻辑死循环。面对「启动-失败-尝试修复-启动-失败」的循环模式，候选输出既没有进行状态汇总，也没有意识到重复启动的风险，更没有转入深度诊断模式。输出结构松散，缺乏 OpenClaw 规范要求的计划分解和风险控制，实际上是在重复之前已经失败过的诊断路径（检查端口占用），而非提出新的诊断方向（如 netstat 实时确认、journalctl 查看完整日志）。整体表现远低于及格线。
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