doubao-seed-1-8 在「报告章节摘要提取」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：doubao-seed-1-8
- 用例名称：报告章节摘要提取
- 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 以下是文档第4章「资源投入与成本分析」的内容： 本季度项目总预算为 850 万元，实际支出 792 万元，预算执行率为 93.2%。其中人力成本占比最高，达 58%（约 459 万元），主要用于核心研发团队 32 人的薪酬及外包测试团队 15 人的费用；硬件与云服务费用占比 27%（约 214 万元），包括新增 3 台 GPU 服务器采购及阿里云 ECS 实例扩容；其余 15% 为差旅、培训及行政支出。与 Q2 相比，总支出增长 12%，主要原因是 8 月份紧急引入外包团队应对交付压力。成本节约方面，通过优化 CI/CD 流水线减少了约 18 万元的云资源浪费。 请帮我回答以下问题：这一章节中，本季度的预算执行率是多少？人力成本具体包含哪些组成部分？与上季度相比支出变化的主要原因是什么？
任务要求
AI 模型需要满足以下要求：
- 准确回答预算执行率为93.2%
- 明确指出人力成本包含核心研发团队32人薪酬和外包测试团队15人费用两部分
- 说明与Q2相比总支出增长12%，主要原因是8月份紧急引入外包团队应对交付压力
- 回答结构清晰，逐一对应三个问题
评测结果
综合得分
该模型在此用例的得分为：99.3 分（满分 100 分），已通过
基础难度评测结果
- 得分：99.3 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：
### 问题解答： 1. 本季度的预算执行率为 **93.2%**（总预算850万元，实际支出792万元）。 2. 人力成本具体包含：核心研发团队32人的薪酬及外包测试团队15人的费用，这部分支出约459万元，占实际总支出的58%。 3. 与上季度（Q2）相比支出增长12%的主要原因是：8月份紧急引入外包团队以应对项目交付压力。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出在信息提取准确性、回答完整性和表达清晰度三个维度均表现优秀。所有关键数据和事实均准确无误地从原文提取，三个问题均得到完整回答，结构清晰、格式规范，是一份高质量的文档信息提取与归纳回答。 【GEMINI】模型表现优秀，能够精准地从给定的文档摘要中提取核心信息并进行归纳，完全符合任务要求。回答结构清晰，事实准确，语言简洁专业。 【KIMI】候选输出表现优秀，准确完整地提取并回答了文档中的三个核心问题，数据精确无误，结构清晰。在表达上采用了简洁的编号列表形式，关键信息突出，整体质量很高。
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