OpenAI: GPT-5.4 在「文档章节结构提取」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：OpenAI: GPT-5.4
- 用例名称：文档章节结构提取
- 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` 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 请问这份文档一共包含几个章节？请逐一列出每个章节的名称。
任务要求
AI 模型需要满足以下要求：
- 准确回答章节总数为7个
- 完整列出全部7个章节名称，不遗漏不多加
- 章节名称与文档摘要中的一致，不随意改写
评测结果
综合得分
该模型在此用例的得分为：99.0 分（满分 100 分），已通过
基础难度评测结果
- 得分：99.0 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]] 这份文档共包含 **7 个章节**： 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】候选输出表现优秀，完整、准确地提取了文档摘要中的所有章节信息，章节总数和名称均与参考答案完全一致。回答格式清晰规范，使用加粗和编号列表增强了可读性。整体质量接近满分。 【GEMINI】模型表现完美，准确提取了所有结构性信息，并以规范、清晰的格式进行了呈现，完全符合任务要求。 【KIMI】模型表现优秀，完全准确地从文档摘要中提取了章节信息，回答格式规范、清晰易读，符合任务要求。
进阶难度评测结果
- 得分：5.0 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：
我先读取文档内容，定位第3章和第6章正文后再做一句话概括。 我先检查工作区里是否有这个文档，并尝试读取可提取的正文内容。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【KIMI】该候选输出存在严重的执行失败问题。模型在对话中口头承诺会读取文档，但实际并未调用任何工具，也未生成用户要求的章节概括内容。这种「只说不做」的行为模式在工具使用场景中属于典型缺陷。根据 EVAL_BLOB 中的参考答案，即使工具读取失败，模型也应基于摘要信息做出合理推断并输出概括，但本输出完全未进入该分支。建议评分系统将此视为严重功能失效案例。
困难难度评测结果
- 得分：87.2 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：
按你最新规则重新统计： - **不统计标点符号** - **中文字符**：逐字计数 - **英文单词/数字**： - `v2.0`、`18.5`、`4.3`、`5.0`、`0.2` 都按 **1 个整体** - `A/B` 按 **1 个整体** - `ISO`、`App`、`Q3`、`Q4` 也各按 **1 个** - 纯数字串如 `2847`、`27001` 也各按 **1 个** 我将“每个里程碑”理解为从“里程碑一”到“里程碑五”各自对应的一整句描述。 ## 一、按里程碑统计 | 里程碑 | 中文字符数 | 英文单词/数字数 | 合计 | |---|---:|---:|---:| | 里程碑一：用户增长平台 v2.0 上线 | 40 | 6 | 46 | | 里程碑二：数据中台迁移至云原生架构 | 31 | 5 | 36 | | 里程碑三：合规审计通过 ISO 27001 认证 | 29 | 5 | 34 | | 里程碑四：移动端 App 重构 | 27 | 5 | 32 | | 里程碑五：客户满意度调查完成 | 33 | 7 | 40 | ## 二、总计 5 个里程碑描述合计： - **中文字符数：160** - **英文单词/数字数：28** - **总计：188** ## 三、最长与最短 按“描述文字总长度（中文字符 + 英文单词/数字）”比较： - **最长**：里程碑一，共 **46** - **最短**：里程碑四，共 **32** - **两者相差：14** 如果你愿意，我也可以继续把**第3章整段（包括标题、总述句，不仅仅是5个里程碑）**按这个新规则重新统计一遍。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】模型对用户意图变更的响应非常准确，格式表达清晰规范，最长/最短里程碑的判断方向正确。主要不足在于具体字符统计数字偏低，与参考答案存在系统性偏差（中文字符普遍少计约3-5个），这可能是因为模型未能对原文进行真实逐字计数而是估算所致。整体而言，方法论正确，结构完整，但精确度有待提升。 【GEMINI】模型表现出色，不仅完美响应了用户关于统计规则的变更，还通过清晰的表格和逻辑严密的分析完成了复杂的数据处理任务。在处理「排除标点」和「特定词汇合并」等细节规则时表现出极高的准确性。 【KIMI】模型整体表现良好，能够正确响应用户意图变更，采用清晰的表格格式呈现统计结果，并准确识别最长和最短的里程碑。主要不足在于具体字符计数的精确度，各里程碑的中文字符数与参考值存在一定偏差，可能源于对标点排除范围或具体字符边界的理解差异。建议在统计类任务中更加精确地界定计数边界。
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