doubao-seed-1-8 在「项目报告多维度总结」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：项目报告多维度总结
- 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 --- 以下是文档正文关键内容： 【执行摘要】本报告总结了2024年Q3（7月-9月）产品运营部负责的3个核心项目的进展情况。Q3总投入人力为28人月，预算执行率为87%。 【项目背景与目标】公司于2024年初启动数字化转型计划，产品运营部承担用户增长平台、数据中台、智能客服三个子项目，目标在年底前完成全部上线。 【Q3主要里程碑完成情况】用户增长平台已完成核心功能开发并进入内测阶段，覆盖用户数达12万；数据中台完成架构设计与60%的开发工作；智能客服完成POC验证，客户满意度测试得分82分。整体里程碑完成率为75%。 【资源投入与成本分析】Q3实际支出为186万元，预算为214万元，节余28万元。人力方面投入28人月，其中外包人力占比35%。主要成本集中在云资源采购（72万元）和外包服务费（48万元）。 【风险与问题记录】当前存在3项主要风险：数据中台核心开发人员离职导致进度延迟约2周；智能客服依赖的第三方NLP接口响应时间不稳定；Q4预算可能因云资源价格上调而超支10%-15%。 【Q4工作计划】Q4计划完成数据中台剩余40%开发并启动联调测试；用户增长平台从内测转为公测，目标覆盖用户50万；智能客服进入正式开发阶段，计划12月底前上线Beta版。 --- 请用一段话，从项目数量、时间范围、资源投入和整体结论四个维度，简要总结这篇报告。
任务要求
AI 模型需要满足以下要求：
- 总结中必须包含项目数量信息（3个核心项目）
- 总结中必须包含时间范围信息（2024年Q3 / 7月-9月）
- 总结中必须包含资源投入信息（如28人月、186万元支出、预算执行率87%等关键数据至少提及一项）
- 总结中必须包含整体结论（如里程碑完成率75%、存在风险、Q4规划等）
- 输出应为一段连贯的文字，而非分条列举
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：
这篇2024年第三季度（7月-9月）项目进展报告，涵盖产品运营部负责的用户增长平台、数据中台、智能客服3个核心项目；Q3共投入28人月人力（外包占比35%），实际支出186万元（较214万元预算节余28万元），主要成本集中在云资源采购和外包服务费；整体里程碑完成率达75%，各项目分别完成内测推进、60%开发、POC验证等阶段目标，但存在核心人员离职致进度延迟、第三方NLP接口不稳定、Q4预算或因云资源涨价超支等风险，Q4计划完成剩余开发并推进各项目向公测、Beta版上线等阶段迈进。
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