qwen3.5-flash 在「公文风格润色改写」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：公文风格润色改写
- 测试类型：xsct-a
- 评测维度：文档润色与优化
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：
以下是当前工作区中的文档 `project_report.docx` 摘要： **文档标题**：2024年第三季度项目进展报告 **作者**：产品运营部 **字数**：约 3200 字 **章节结构**： 1. 执行摘要 2. 项目背景与目标 3. Q3 主要里程碑完成情况 4. 资源投入与成本分析 5. 风险与问题记录 6. Q4 工作计划 7. 附件：数据支撑材料 其中「执行摘要」章节当前内容如下： --- 这个季度我们做了很多事情，基本上把之前定的那些目标都完成了差不多。整体来看的话，项目推进还算顺利吧，虽然中间碰到了一些问题，但是大家一起想办法也都解决了。成本方面的话，花的钱比预算多了一点点，大概超了8%左右，主要是因为临时加了几个外包人员。下个季度我们打算继续推进剩下的功能开发，争取年底前把整个项目收尾掉。 --- 请将上述「执行摘要」章节改写为正式的公文书面语风格，要求：语言简练准确，去除口语化表达，保留所有关键数据信息（如成本超支8%、外包人员原因等），使用规范的公文用语和结构化表述。
任务要求
AI 模型需要满足以下要求：
- 将口语化表达全部转换为正式公文书面语，不得保留'的话''吧''差不多''一点点'等口语词
- 保留原文所有关键信息：目标基本完成、项目推进顺利、遇到问题已解决、成本超支约8%、原因为临时增加外包人员、Q4计划继续功能开发并争取年底收尾
- 改写后文字应简练准确，字数不超过原文的80%
- 使用规范公文用语，如'本季度''基本达成''予以解决'等
评测结果
综合得分
该模型在此用例的得分为：— 分（满分 100 分），未通过
基础难度评测结果
- 得分：— 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]] 我先读取文件以获取完整的文档结构，然后进行改写。 ```read file path="project_report.docx"```
困难难度评测结果
- 得分：1.2 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，我明白了。让我先读取完整文档，然后按照您的新要求进行调整。
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「好的，我明白了」式的确认回复，完全未完成用户要求的任务。用户明确要求给出「完整的修改方案」，但模型只停留在「准备读取文档」的阶段，既未调用工具读取文档，也未输出任何修改内容。这是一次严重的任务未完成，四个评分维度中有三个得分为零，整体表现极差。 【GEMINI】模型在本次任务中表现极差。面对用户的具体修改指令，模型仅给出了一个空洞的确认回复，完全没有执行读取文件、润色文本、调整结构或进行公文格式化等任何实质性操作。作为 AI 助手，未能完成用户交付的文档改写任务，导致所有评测维度均无法得分。 【KIMI】该候选输出严重不合格。用户经过4轮对话逐步明确了复杂需求（润色执行摘要→按会议纪要调整→再次调整意图变更→最终定稿要求），但模型在最终轮仅回复了一句确认性话语，完全没有执行任何实际修改操作，未输出任何修改后的文档内容或完整方案。这是一个典型的「空回复」或「虚假承诺」失败案例——模型声称理解了要求并准备执行，但实际上没有产生任何有价值的输出。所有评分维度均无法评估，因为根本没有可评估的实质内容。正确的做法应是直接输出按照第4轮指令修改后的完整文档，或至少给出详细的修改方案。
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