qwen3-coder-flash 在「文档章节排序整理」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-coder-flash
- 用例名称：文档章节排序整理
- 测试类型：xsct-a
- 评测维度：文档问答
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` 的章节列表，但顺序被打乱了： [4] 资源投入与成本分析 [7] 附件：数据支撑材料 [2] 项目背景与目标 [5] 风险与问题记录 [1] 执行摘要 [6] Q4 工作计划 [3] Q3 主要里程碑完成情况 该文档信息如下： - 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 请根据所标示的编号顺序对这些章节进行重新排列，输出正确顺序的章节列表。
任务要求
AI 模型需要满足以下要求：
- 按照编号 [1] 到 [7] 的升序排列所有章节
- 输出结果包含全部 7 个章节，不遗漏任何一项
- 章节名称与原文保持一致，不得篡改或省略
评测结果
综合得分
该模型在此用例的得分为：100.0 分（满分 100 分），已通过
基础难度评测结果
- 得分：100.0 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：
[[reply_to_current]] 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
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出与参考答案完全吻合，排序正确且内容完整，是一次满分表现。 【GEMINI】模型准确识别了输入文档中的编号逻辑，并成功将其按升序排列，输出格式清晰，完全符合任务要求。 【KIMI】候选输出完美完成了任务要求。虽然开头包含了回复标签 [[reply_to_current]]，但随后的章节列表严格按照编号 1-7 的升序排列，且所有 7 个章节名称完整无误、与原文保持一致。整体表现优秀。
进阶难度评测结果
- 得分：63.2 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：
[[reply_to_current]] 根据重排后的章节顺序，对各章节进行分类如下： ### 回顾性内容（回顾过去的工作） 1. **项目背景与目标**（原序号2） 2. **执行摘要**（原序号1） 3. **资源投入与成本分析**（原序号4） 4. **Q3 主要里程碑完成情况**（原序号3） ### 前瞻性内容（展望未来的计划） 1. **Q4 工作计划**（原序号6） ### 辅助性内容（附件、数据等支撑材料） 1. **风险与问题记录**（原序号5） 2. **附件：数据支撑材料**（原序号7）
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出最主要的问题是将「风险与问题记录」错误归入「辅助性内容」，导致分类准确性严重失分。此外，分类内序号未使用重排后的全局序号，而是重新从1计数，与要求不符。格式上整体清晰，但上述两个错误影响了整体质量。 【GEMINI】模型在格式上表现良好，但在逻辑分类任务上出现了事实性错误，特别是将「风险与问题记录」归类错误，且在序号使用上未严格遵循「重排后的序号」这一指令，导致序号对应关系出现偏差。 【KIMI】该模型在核心任务——章节分类准确性上出现明显错误，将「风险与问题记录」错误归入辅助性内容而非回顾性内容，这是关键的功能性失误。虽然序号与名称的对应关系大体正确，输出格式也基本可读，但分类错误导致整体质量不达标。模型似乎对「风险与问题记录」的性质理解有误，该章节记录的是Q3期间已识别的风险，属于回顾过去的工作，而非附件类的辅助性内容。
困难难度评测结果
- 得分：29.6 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]]
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出仅输出了一个回复标签「[[reply_to_current]]」，没有任何实质性内容。所有评分维度均为0分。模型未能完成用户的任何要求，包括目录生成、变更记录、阅读逻辑摘要以及文档保存操作，属于完全失败的响应。 【GEMINI】模型表现优秀，能够精准理解并执行复杂的指令序列，在多轮交互中保持了高度的一致性和准确性，最终输出完全符合所有功能要求。 【KIMI】候选输出完全失败，仅返回了格式标记「[[reply_to_current]]」，未响应任何用户指令。所有四个评分维度的功能要求均未达成，包括：多轮意图追踪、最终目录生成、变更记录撰写、阅读逻辑摘要。这是一个典型的空响应或格式错误输出，得分为0。
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