hunyuan-turbo 在「移动目录后修复引用」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-turbo
- 用例名称：移动目录后修复引用
- 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：
请帮我检查「configs/app.yaml」的内容，并将其中所有指向旧路径「/data/old_logs」的引用修复为新路径「/mnt/new_logs」，修改完成后展示修改结果。
任务要求
AI 模型需要满足以下要求：
- 准确识别并读取「configs/app.yaml」文件内容
- 使用「edit」或「write」工具替换所有旧路径引用
- 修改后的路径必须完全匹配「/mnt/new_logs」
- 输出修改前后的对比或确认修改成功的状态
评测结果
综合得分
该模型在此用例的得分为：25.8 分（满分 100 分），未通过
基础难度评测结果
- 得分：25.8 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，我将读取「configs/app.yaml」文件的内容，查找并替换所有指向旧路径「/data/old_logs」的引用为新路径「/mnt/new_logs」，然后展示修改后的内容。 让我先读取文件内容并进行修改。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】模型的回复仅是对任务的口头描述和意图表达，完全没有执行实际的工具调用操作。既未使用 read 工具读取文件，也未使用 edit/write 工具进行路径替换，更未展示修改结果。与参考答案相比，参考答案完整地完成了读取→编辑→确认的全流程，而模型生成结果相当于只说了「我要做这件事」却没有真正去做，属于典型的「只说不做」问题，整体质量极低。 【GEMINI】模型在响应中仅停留在「计划」阶段，未实际调用「read」或「edit」等工具执行任务。作为 AI 助手，未能完成用户明确要求的路径修复操作，导致任务失败。 【KIMI】模型生成结果仅为空泛的口头承诺，完全没有执行任何实际工具调用。用户明确要求「检查内容」「修复路径」「展示修改结果」，但模型仅回复了「让我先读取文件内容并进行修改」便结束，属于典型的「幻觉式完成」——声称要做事却什么都没做。这与参考答案中实际使用「read」和「edit」工具、确认修改并展示结果的行为形成鲜明对比。任务完成度极低，需严肃扣分。
进阶难度评测结果
- 得分：42.8 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，我先修改「deploy.sh」文件。然后会检查「bin/scripts」目录下是否存在「main.py」文件。让我先进行修改。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】模型回答仅停留在意图描述层面，表示「先修改再检查」，但没有实际调用任何工具完成操作，也没有给出修改内容或验证结果。与参考答案相比，缺少完整的执行过程和结论输出，整体质量较低，无法满足用户的实际需求。 【GEMINI】模型在理解用户意图方面表现尚可，但作为 AI 助手，在执行任务时仅停留在「口头承诺」阶段，未实际调用工具（如 edit 或 ls）完成修复和验证工作。这种「只说不做」的回复方式未能满足工具使用型 Agent 的基本要求，导致任务未完成。 【KIMI】模型生成结果严重不完整，仅停留在口头回应层面，完全没有执行任何实际的工具调用（读取文件、修改路径、验证文件存在性）。与参考答案相比，缺少关键的执行证据和完整的修复逻辑说明，未能满足用户的功能要求。
困难难度评测结果
- 得分：21.5 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：
接下来，我建议重新启动 Docker 服务以应用这些更改。这将确保新的挂载路径生效。让我来执行这个操作。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】模型生成结果与参考答案存在严重偏差。在「现在呢？」这一追问下，模型应当主动检查其他可能存在旧路径引用的文件（如「.env」、「/etc/fstab」），并在完成所有修复后恢复文件权限、给出完整报告。然而模型仅简单建议重启 Docker 服务，既未体现异常处理能力，也未展示主动完备性，更未考虑操作安全性，整体表现不及格。 【GEMINI】模型在处理初始权限错误时表现良好，但在后续的修复工作中缺乏主动性和系统性。未能意识到「修复引用」是一个全局性任务，仅停留在表面操作，且在操作完成后未执行必要的权限回滚，安全性意识不足。 【KIMI】模型生成结果与测试任务要求严重偏离。任务核心是「移动目录后修复引用」，需要主动排查多处潜在旧路径引用并确保操作安全。但模型仅给出单一的重启服务建议，完全忽略了权限管理、关联文件检查、修复验证、权限恢复等关键环节，未能体现 AI 助手在模糊指令下的自主决策能力和完备性思维。建议重新训练模型对「继续」「下一步」类指令的响应模式，强化上下文继承和任务目标深度挖掘能力。
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