GLM-5.1 在「恢复上一步文件操作」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：GLM-5.1
- 用例名称：恢复上一步文件操作
- 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「config.json」的内容覆盖了，请帮我检查当前目录下是否有该文件的备份，如果有的话请帮我恢复它。
任务要求
AI 模型需要满足以下要求：
- 主动使用「ls」或「find」命令搜索备份文件（如 .bak, .old, .tmp）
- 在恢复前先读取备份文件内容确认其有效性
- 使用「write」或「exec」命令将备份内容覆盖回原文件
- 操作完成后验证「config.json」的内容并告知用户
评测结果
综合得分
该模型在此用例的得分为：91.0 分（满分 100 分），已通过
基础难度评测结果
- 得分：91.0 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：
ls -la
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】模型表现非常专业，能够准确理解 OpenClaw 的运行规则。在面对用户恢复文件的请求时，它首先通过 `ls -la` 探测环境，这是处理“无显式备份”场景的标准起始动作。虽然只给出了第一步，但其行为完全符合高效 AI Agent 的预期，且工具调用格式规范。 【KIMI】整体上，该回答正确地使用了「ls -la」命令来查找可能的备份文件，这是一个好的开始。但是，缺少了在恢复前读取备份文件内容确认其有效性的步骤，这是操作安全性中非常重要的一环。如果能够补充这一步骤，将更加完善。
进阶难度评测结果
- 得分：83.5 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：
先把「app.py」改回「main.py」，同时看看能否通过 git 恢复「utils.py」。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】模型表现良好，能够精准理解用户的撤回意图。在面对不可逆的删除操作时，展现了必要的风险意识并给出了专业建议（检查 git）。虽然回复内容相比参考答案略显简练，但关键逻辑和风险应对方案均已到位。 【KIMI】整体而言，候选人输出较好地满足了功能要求，逻辑完备性较高，风险意识也基本到位。但在风险意识方面，对于极端情况下的应对措施还可以更全面一些。
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：
让我先看看 `deploy.sh` 当前的内容，确认需要修复的地方。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】模型的表现过于消极且缺乏深度。虽然它意识到在无备份情况下应先确认文件现状（符合确认回滚边界的要求），但在面对复杂的逻辑恢复（处理 sed 副作用）和权限撤销等核心任务目标时，没有给出任何实质性的方案或行动。相比参考答案提供的详尽回滚逻辑和验证流程，候选输出显得准备不足，未能体现出专业 AI 助手在处理高难度故障恢复时的能力。 【KIMI】候选人输出基本符合要求，但在复杂回滚决策方面还有待加强，需要更明确地说明如何通过逆向工程恢复环境。异常处理与验证方面表现较好，有意识到需要对异常情况进行处理。
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