GLM-5.1 在「项目报告摘要撰写」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：GLM-5.1
- 用例名称：项目报告摘要撰写
- 测试类型：xsct-a
- 评测维度：文档内容生成
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 请根据以上文档信息，帮我撰写一段200字以内的项目季度总结，用于在部门周会上做简要汇报。要求语言正式、条理清晰，涵盖项目背景、Q3完成情况、存在的问题和Q4计划四个方面。
任务要求
AI 模型需要满足以下要求：
- 总字数控制在200字以内
- 语言风格正式，适合部门周会汇报场景
- 内容需涵盖四个方面：项目背景、Q3完成情况、存在的问题、Q4计划
- 条理清晰，结构分明，便于口头汇报
评测结果
综合得分
该模型在此用例的得分为：98.8 分（满分 100 分），已通过
基础难度评测结果
- 得分：98.8 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：
2024年第三季度，本部门围绕既定战略目标持续推进核心项目，旨在提升产品运营效能与用户服务体验。Q3期间，各项主要里程碑均按计划达成，项目整体进展符合预期；资源投入与成本支出总体可控，保障了关键节点的顺利交付。当前仍存在若干风险与待解决问题，需持续跟踪并推动闭环。Q4将聚焦剩余目标攻坚、风险化解及成果巩固，确保项目按期高质量收官。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】这是一份高质量的项目季度总结。模型精准地提炼了文档结构中的核心信息，并将其转化为适合口头汇报的正式语言。字数控制得当，结构严谨，除了对项目背景目标进行了合理的逻辑推断外，完全忠实于提供的文档大纲。
进阶难度评测结果
- 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：
Q3项目进展顺利，核心功能上线率95%，用户活跃度环比提升12%，成本执行率92%；存在技术交付延迟风险，Q4将全力冲刺年度目标达成。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】模型完美执行了指令要求。在大幅缩减篇幅的同时，不仅守住了字数红线，还精准地保留了多项关键量化数据和未来计划，语言精炼专业，是高质量的摘要生成。
困难难度评测结果
- 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：
| 维度 | 高层汇报邮件 | 内部通报 | |------|-------------|---------| | **语气** | 正式、简洁、客观，以汇报口吻陈述事实 | 亲和、热情、鼓舞士气，用"我们""小伙伴"拉近距离 | | **内容侧重** | 强调目标达成与成本管控，突出数据支撑与决策参考 | 强调团队协作与个人贡献，增加感谢认可，弱化管控色彩 | | **风险表述** | 直面风险，明确列出供应链延期和技术不确定性，强调需关注 | 弱化风险措辞，用"课题""优化""打磨"替代，强调"尽在掌握"以消除焦虑 | --- **Q4动员口号** 三季度已创佳绩，四季度再铸辉煌！ 产品3.0启新程，年度目标必达成！ 齐心协力冲到底，漂亮收官赢全年！
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】模型在本次任务中表现卓越。它不仅能够敏锐地捕捉到不同受众群体下文案风格的细微差别，并进行逻辑清晰的对比总结，还能在创意写作环节提供高质量、符合场景要求的口号。完全遵循了所有指令约束，包括字数限制、格式要求和内容侧重点。
相关链接
您可以通过以下链接查看更多相关内容：
