qwen3.5-flash 在「离线包安装」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：qwen3.5-flash
- 用例名称：离线包安装
- 测试类型：xsct-a
- 评测维度：Gateway 配置与管理
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：
我已经把离线包放到了 /opt 目录下，现在该怎么安装并启动 OpenClaw Gateway？
任务要求
AI 模型需要满足以下要求：
- 准确指出需要先解压离线包文件
- 明确提及执行「./install.sh」或类似的初始化脚本
- 指出需要检查或配置「config.yaml」中的基础信息
- 给出启动命令如「openclaw start」或运行二进制文件
评测结果
综合得分
该模型在此用例的得分为：23.5 分（满分 100 分），未通过
基础难度评测结果
- 得分：23.5 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：
[[reply_to_current]] 我先查看一下 `/opt` 目录下的离线包情况： ```bash ls -la /opt/ ```
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出极为简短，仅给出了一个查看 /opt 目录的 ls 命令，属于典型的「只问不答」——用户已明确告知离线包已放置在 /opt 目录，期望得到完整的安装和启动指引，而模型却只是准备去查看目录，没有提供任何实质性帮助。与参考答案相比，缺少解压、初始化、配置、启动四个核心步骤的全部内容，整体质量极低，远未达到及格线。 【GEMINI】候选模型采取了谨慎的探索策略，先确认文件存在性是合理的，但作为 AI 助手，在面对明确的「安装并启动」请求时，仅执行一步「ls」命令显得过于被动，未能提供完整的操作指南或分步计划，导致用户无法直接完成任务。 【KIMI】候选输出严重不合格。用户明确说明「已经把离线包放到了 /opt 目录下」，并询问「该怎么安装并启动」，但候选输出仅回复了一个查看目录的命令，完全没有提供任何安装或启动指导。与参考答案相比，缺失了所有关键步骤：解压、执行初始化脚本（带--offline参数）、配置文件检查、启动服务。这是一个典型的「答非所问」案例，模型似乎误解了任务性质，将「回答问题」变成了「执行探索性命令」。
进阶难度评测结果
- 得分：19.75 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：
我先检查一下配置文件是否正确，然后帮您启动服务。 ```bash read /etc/openclaw/conf/config.yaml && openclaw gateway start ```
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出质量极低，既存在路径错误（配置文件路径有误），又使用了错误的命令形式（应为「./bin/openclaw-gateway」而非「openclaw gateway」），且完全忽略了配置验证、端口检查、守护进程启动和启动后确认等关键步骤。与参考答案相比，几乎没有实用价值，无法帮助用户完成正确的验证与启动流程。 【GEMINI】模型未能根据用户提供的上下文信息执行有效的验证操作。在离线部署场景下，模型缺乏对配置校验、端口冲突排查及进程状态确认的意识，仅执行了简单的启动尝试，且路径引用存在错误，整体表现未达到及格水平。 【KIMI】候选输出表现较差，存在多处关键错误：路径识别错误（conf层级冗余）、命令格式错误（误用「openclaw gateway」而非「openclaw-gateway」或「./bin/openclaw-gateway」）、完全遗漏配置验证、端口检查和进程确认等必要步骤。未能有效利用对话上下文中提供的工具输出信息，也未遵循参考文档中的CLI规范。
困难难度评测结果
- 得分：7.83 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：
我来检查一下 Gateway 服务的当前状态。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出极度简短且无实质内容，仅一句「我来检查一下 Gateway 服务的当前状态。」，在对话已经明确给出端口占用错误的情况下，未提供任何可执行的解决方案、验证步骤或内网运维注意事项。与参考答案相比差距悬殊，几乎没有完成任务的任何要素。 【KIMI】候选输出严重不合格。面对一个复杂的内网离线升级场景（已处理 Text file busy，当前卡在端口占用），候选输出仅给出一句模糊的「检查状态」，完全没有：1）解决当前 8080 端口占用的具体命令；2）版本确认、配置对比、依赖检查等升级验证步骤；3）内网环境特有的风险意识和回滚预案。与参考答案的全面性相比，候选输出几乎为空，无法指导用户完成升级任务。 【GEMINI】模型表现极差。面对明确的「Address already in use」错误，模型未能调用任何工具进行排查或修复，回复内容过于简略且毫无价值，完全未能履行作为专业 AI 助手在复杂运维场景下的职责。
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