Anthropic: Claude Sonnet 4.6 在「误删目录恢复」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：Anthropic: Claude Sonnet 4.6
- 用例名称：误删目录恢复
- 测试类型：xsct-a
- 评测维度：文件操作与恢复
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：
我不小心把「project_files」目录删除了，请检查「backup」目录中的内容，并将其完整恢复到「project_files」目录下。
任务要求
AI 模型需要满足以下要求：
- 使用「ls」或「find」命令确认备份目录内容
- 使用「mkdir」创建目标目录「project_files」
- 使用「cp」或「mv」命令执行恢复操作
- 最后通过「ls -R」验证恢复结果
评测结果
综合得分
该模型在此用例的得分为：82.7 分（满分 100 分），已通过
基础难度评测结果
- 得分：82.7 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：
我来检查 backup 目录的内容，然后将其恢复到 project_files 目录。 backup/ ├── config/ │ ├── app.json │ └── database.yml ├── src/ │ ├── main.py │ └── utils/ │ └── helper.py └── README.md 让我查看各文件内容后再恢复： # Project README This is the main project documentation. ## Overview A sample project structure for demonstration purposes. ## Setup 1. Install dependencies 2. Configure database settings 3. Run main.py { "app_name": "MyApp", "version": "1.0.0", "debug": false, "port": 8080 } database: host: localhost port: 5432 name: myapp_db user: admin password: secret import utils.helper as helper def main(): print("Starting application...") result = helper.process_data([1, 2, 3, 4, 5]) print(f"Result: {result}") if __name__ == "__main__": main() def process_data(data): """Process a list of numbers and return their sum.""" return sum(data) def format_output(value): """Format the output value as a string.""" return f"Output: {value}" 已读取所有文件内容，现在开始恢复： 最后验证恢复结果： project_files/ ├── config/ │ ├── app.json │ └── database.yml ├── src/ │ ├── main.py │ └── utils/ │ └── helper.py └── README.md ✅ **恢复完成！** `project_files` 目录已完整恢复，结构与 `backup` 目录完全一致： ``` project_files/ ├── config/ │ ├── app.json │ └── database.yml ├── src/ │ ├── main.py │ └── utils/ │ └── helper.py └── README.md ``` 共恢复 **5 个文件**，目录结构、文件内容均与备份完全一致。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出完成了核心恢复任务，包含了备份确认、目录创建、文件复制和结果验证四个关键步骤，最终恢复结果正确。主要不足在于：1）中间插入了不必要的逐文件读取步骤，增加了操作冗余；2）多个空 STDOUT 标签使实际工具调用过程不够透明，难以判断具体命令执行情况。整体表现良好，达到了任务目标，但与参考答案相比在简洁性和操作规范性上略有差距。 【GEMINI】模型出色地完成了恢复任务。在执行恢复前，模型通过详细的检查步骤确保了备份内容的准确性，并严格按照要求完成了目录创建、文件拷贝及最终验证。操作流程规范，结果符合预期。 【KIMI】候选输出最终完成了恢复任务，目录结构和文件内容均正确，但执行方式存在偏差：使用了疑似逐个写入而非标准复制命令，且包含大量不必要的文件内容读取操作。整体功能达标，但操作规范性和效率有待提升。
进阶难度评测结果
- 得分：16.0 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，先看看 `src/` 里有什么文件，再检查 `main.py` 的内容。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出仅是一句简短的意图声明，没有执行任何实际的文件完整性验证操作，既未调用工具，也未给出任何结论。与参考答案的要求相差甚远，属于严重不完整的回答。 【GEMINI】模型在面对用户明确的检查指令时，仅给出了下一步的计划，而没有实际执行任何操作。作为 AI 助手，在用户要求「检查一下」时，应直接调用工具完成验证并给出结论，而非重复用户的意图。该回复未完成任务，表现不合格。 【KIMI】候选输出严重不完整，仅停留在意图表达层面，完全没有执行题目要求的文件完整性验证流程。用户明确要求「检查一下文件完整性」，但模型没有调用任何工具来获取实际信息，无法回答「main.py还能运行吗」这一核心问题。与参考答案相比，差距显著，属于典型的「只说不做」型失败案例。
困难难度评测结果
- 得分：67.7 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：
当前情况总结： **已恢复：** - `index.html`（500字节，正常） **问题：** - `secret.key` 文件大小为 **0字节**（备份本身就是空的），且属主是 `root`，当前用户无读取权限 **下一步选项：** 1. **跳过密钥，仅恢复其他文件** — 用 `sudo cp` 或 `rsync --exclude` 先把目录结构恢复好 2. **用 sudo 恢复** — 如果你有 sudo 权限，可以 `sudo cp -r remote_backup/deploy ./` 3. **重新生成密钥** — 如果 `secret.key` 原本就是空文件占位符，或者有其他地方存了密钥内容（如环境变量、密钥管理服务），直接重新生成更安全 你想怎么处理？有 sudo 权限吗？
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出在异常识别和风险沟通方面表现中规中矩，能够正确定位问题并给出多个处理选项，结构清晰。但与参考答案相比，缺乏主动探查其他备份源（find命令、git历史）的行动，决策主动性不足，更多停留在「提问用户」而非「推进恢复」的层面。在困难档任务中，这种被动等待的姿态会显著影响实际恢复效果。 【GEMINI】模型能够清晰地总结当前状态并识别出备份损坏的风险，但在面对复杂故障时，主动探索系统内其他恢复路径（如 git 检查或 find 搜索）的能力有待加强。整体表现合格，沟通逻辑清晰。 【KIMI】候选输出基本完成了困难档任务的核心识别（空文件、权限问题、部分恢复），但在主动排查其他备份源这一关键决策点上明显缺失，导致「异常处理与决策」维度未达及格线。风险沟通格式良好但深度不足，未能充分传递「备份损坏不可逆」的紧急性。整体表现中等偏下，符合困难档部分得分特征。
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