GLM-5.1 在「季度工作总结撰写」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：季度工作总结撰写
- 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 请基于这份项目报告的内容框架，帮我写一份产品运营部2024年Q3季度工作总结，要求800字左右，语言正式，适合在部门汇报会上使用。
任务要求
AI 模型需要满足以下要求：
- 生成内容为产品运营部2024年Q3季度工作总结，主题明确
- 内容需涵盖项目背景、Q3主要成果、资源与成本、风险问题、Q4展望等核心板块
- 篇幅约800字，允许±150字浮动
- 语言风格正式、专业，适合部门汇报场景
- 结构清晰，有明确的段落或小标题划分
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：
ls
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】该模型在本次任务中表现极差。用户已在提示词中提供了详细的文档摘要和框架，明确要求基于此编写总结报告。然而，模型忽略了直接生成的指令，转而尝试调用工具查看工作区目录，未能产出任何符合任务要求的文字内容。作为一份季度工作总结的生成结果，该输出完全不可用。
进阶难度评测结果
- 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：
尊敬的各位领导： 现将2024年第三季度项目进展汇报如下。 一、项目概况 本季度产品运营部围绕既定目标稳步推进，核心里程碑均按计划完成，整体进度符合预期。 二、重点成果 1. 关键里程碑按期交付，阶段达成率达标； 2. 资源投入与成本控制在预算范围内，运营效率持续提升； 3. 风险事项全面识别，应对预案已制定并启动跟踪。 三、下阶段计划 Q4将聚焦剩余目标攻坚与收尾，重点推动核心交付物落地，确保全年目标顺利达成。 详细数据请参阅附件报告，如有疑问欢迎随时沟通。 此致 敬礼 产品运营部 --- **Executive Summary** In Q3 2024, the Product Operations team achieved all key milestones on schedule, with costs well within budget. Identified risks have been addressed with mitigation plans in place. Q4 will focus on delivering remaining objectives to ensure full-year targets are met. For details, please refer to the attached report.
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】这是一份非常优秀的生成结果。模型不仅严格遵守了复杂的字数和格式约束，还在内容提炼和风格把控上表现出了极高的专业素养。对用户追问的响应非常到位，能够基于有限的背景信息生成逻辑自洽且符合商务标准的汇报邮件。
困难难度评测结果
- 得分：83.8 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：
<tool_call: write({"file_path": "q3_report_cn.md", "content": "**主题：Q3战报来了！一起看看我们这个季度的成绩单**\n\n各位小伙伴：\n\n大家好！2024年第三季度已经收官，是时候一起回顾我们这个季度的努力和成果了。先说结论——大家辛苦了，成绩非常亮眼！\n\n**整体战绩**\n\n三大核心项目整体完成率达到87%，本季度团队累计投入48人月，费用支出约320万元（较预算超支8%，主要因为智能客服模型训练的额外投入，属于值得的战略性投资）。\n\n**Q3亮点时刻**\n\n1. **用户增长平台**：核心功能9月15日如期上线！用户注册转化率直接提升了12%，这是大家连续加班攻坚的成果，值得骄傲。\n2. **数据中台二期**：数据接入覆盖率达到83%，超额完成80%的目标线，中台团队稳扎稳打，令人放心。\n3. **智能客服**：华东区试点成功上线，首月客户满意度4.2/5.0，客户的认可就是对我们最好的肯定。\n\n**需要一起攻克的挑战**\n\n当然我们也面临一些需要持续关注的问题：数据中台部分老系统接口不太稳定，已安排专项修复；智能客服方言识别准确率目前68%，还有提升空间；增长平台海外节点因合规审批可能延迟约2周。这些都在可控范围内，Q4我们一起逐个击破。\n\n**Q4冲刺目标**\n\n- 增长平台：完成海外部署，启动A/B测试优化\n- 数据中台：完成剩余17%接入，启动数据质量治理\n- 智能客服：扩展至华南、华北，方言识别目标85%\n- 新任务：启动2025年度规划预研\n\n新的季度，新的挑战。期待我们继续携手创造更好的成绩！有任何想法和建议，随时沟通交流。\n\n相信团队的力量，让我们一起向Q4进发！\n\n产品运营部\n2024年10月"})</tool_call<tool_call: write({"file_path": "q3_report_en.md", "content": "**Subject: Q3 2024 Project Progress Update from China HQ**\n\nDear Colleagues,\n\n*Context: This is a Q3 2024 project progress update from the China headquarters (Product Operations Department) covering three core initiatives under our \"Accelerated Digital Transformation\" strategy. Please review the highlights and Q4 plans below for alignment with our cross-regional collaboration.*\n\nThe third quarter of 2024 has concluded, and we are pleased to share the results of our collective efforts. The overall completion rate across our three core projects has reached 87%.\n\n**Resource Investment & Cost Overview**\n\nTotal effort this quarter: 48 person-months. Total expenditure: approximately RMB 3.2 million, representing an 8% overspend primarily due to additional model training costs for the intelligent customer service pilot — a strategic investment deemed worthwhile.\n\n**Q3 Key Milestones**\n\n1. **User Growth Platform**: Core features launched on September 15. User registration conversion rate improved by 12%.\n2. **Data Platform Phase II**: Data integration coverage reached 83%, exceeding the 80% target.\n3....
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】模型在内容理解和创作方面表现卓越，能够完美处理复杂的业务需求变更，并生成高质量的双语通报。它准确地执行了所有关于语气、数据保留和特定内容修改的指令。然而，该模型在技术执行（工具调用规范）上存在显著短板，输出的工具调用语法错误且格式不统一，这将直接导致其无法在自动化环境中完成预定的文件写入任务。
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