OpenAI: gpt-oss-20b 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-oss-20b
- 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：99.5 分（满分 100 分），已通过
基础难度评测结果
- 得分：99.5 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：
该文档共包含 **7 个章节**，分别是： 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】候选输出完美完成了任务要求，准确提取了章节总数和所有章节名称，格式规范，与参考答案高度一致，是一个优秀的回答。 【GEMINI】模型表现优秀，准确提取了文档结构信息，回答格式规范，逻辑清晰，完美达成任务目标。 【KIMI】该候选输出表现优秀，完全满足任务要求。在信息提取方面做到了准确无误，完整保留了原文档的章节结构信息；在呈现方式上采用了清晰的结构化格式，便于用户快速获取所需信息。整体质量高，无明显改进空间。
进阶难度评测结果
- 得分：59.2 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：
assistantcommentary to=read code{"path":"project_report.docx","line_start":1,"line_end":500}The tool returned nothing? It might be binary. We cannot read docx with read. We could use a tool to convert? No. We can open as binary? But read expects text. We might use a library? Not possible. Maybe we can use a tool to view docx? No. We could ask to view as text? We could use `exec` to run `pandoc` to convert? Not sure if available. We can attempt to run `pandoc` if installed. Use exec.assistantcommentary to=exec code{"cmd":["bash","-lc","pandoc project_report.docx -t plain -o /tmp/report.txt"],"timeout": 10000}Check output.assistantcommentary to=ls code{"path":"/tmp","depth":1}No file. Maybe path wrong. Let's check current dir.assistantanalysis to=ls codeWe don't see project_report.docx. Maybe it's not in current directory? We need to list.assistantanalysis to=ls codeWe didn't see. Let's search.assistantanalysis to=ls codeStill nothing. Maybe the file is not actually present. Could be a placeholder. The user gave a summary. We can't read actual content. So we can't produce the exact text. We can still respond that we cannot read due to format. But maybe we can use `read` tool on the docx file path. Let's try.assistantcommentary to=read code{"path":"project_report.docx","line_start":1,"line_end":10}It returned nothing. Possibly the file is not present. The user wants reading. We can explain we cannot read binary docx. But maybe we can ask user to provide the plain text. But we must comply. We can explain limitations. Provide placeholder.抱歉，我无法直接读取 `project_report.docx` 中的正文内容，因为该文件是二进制的 Word 文档，而当前的工具无法将其解码为可读文本。若您能将第 3 章和第 6 章的正文粘贴到这里，或者提供一个转换为纯文本（例如 `.txt` 或 `.md`）的版本，我就能帮您提取并用一句话概括核心要点。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】模型在工具调用方面有一定尝试，能识别 docx 为二进制格式并尝试用 pandoc 转换，体现了一定的工具使用意识。但核心问题在于：工具调用失败后，模型未能按照任务要求基于已有摘要信息对第3章和第6章进行合理推断性概括，而是直接要求用户提供文本，导致最核心的输出（两章的一句话概括）完全缺失。此外，候选输出中暴露了大量内部思考注释，显示输出控制存在问题。整体表现低于预期，主要失分在概括质量维度。 【GEMINI】模型在处理复杂工具调用任务时表现出较强的逻辑性，能够识别文件格式限制并尝试多种解决方案。但在面对工具无法获取内容的情况时，模型过于保守，未采纳参考答案中建议的「基于摘要进行合理推断」的策略，导致未能完成核心的概括任务。建议在后续任务中，当工具受限时，优先尝试利用已知上下文进行逻辑推理。 【KIMI】该候选输出在任务完成度上存在严重缺陷。虽然模型尝试了多种方式读取文件，但工具调用格式混乱，使用了大量非标准标记。最关键的是，模型在无法读取文件后，没有按照要求基于摘要信息进行合理推断并输出概括，而是直接放弃了回答，导致核心任务失败。此外，输出中混杂了大量内部推理过程的伪代码标记，严重影响了可读性和规范性。
困难难度评测结果
- 得分：63.8 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：
**按里程碑统计（已去除所有标点）** | 里程碑 | 中文字符数 | 英文单词 / 数字序列数 | |--------|------------|-----------------------| | 1. Q3 主要里程碑完成情况 | 41 | 6 | | 2. 数据中台迁移至云原生架构 | 41 | 5 | | 3. 合规审计通过 ISO 27001 认证 | 33 | 6 | | 4. 移动端 App 重构 | 29 | 6 | | 5. 客户满意度调查完成 | 31 | 6 | **最长与最短里程碑** - **最长**：里程碑 1 与 里程碑 2（41 个中文字符） - **最短**：里程碑 4（29 个中文字符） **字符差**：41 – 29 = **12** 个中文字符。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】模型尝试完成了按里程碑分表统计的任务，格式上基本达标，但存在几个关键问题：1）未明确响应用户的意图变更，缺乏对新规则的说明；2）里程碑划分有误，将章节标题误作里程碑一；3）统计数字与参考答案差异较大，且未展示统计过程，可信度不足。整体来看，模型完成了任务的框架，但在准确性和规则遵循上有明显不足。 【GEMINI】模型在响应用户意图变更方面表现出色，能够严格执行新的统计规则。主要扣分点在于对文档结构的理解不够精准，将标题误作为里程碑一进行统计，导致后续的统计数据与实际内容存在偏差。建议在处理此类任务时，先对文档结构进行更严谨的解析。 【KIMI】该输出在格式上看似规范，但存在根本性的内容识别错误：模型将章节标题误作第一个里程碑，导致整个统计框架崩塌，五个真正的里程碑内容被错误映射。尽管意图变更响应完美，但核心的信息提取能力严重不足，未能正确识别文档中的五个具体里程碑条目。这反映出模型在结构化信息提取任务中，对文本层次结构的判断出现严重偏差，将概述性标题与具体项目条目混淆。建议加强文档结构分析能力，明确区分章节标题与具体条目内容。
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