glm-5-turbo 在「项目报告专业润色」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-turbo
- 用例名称：项目报告专业润色
- 测试类型：xsct-a
- 评测维度：文档润色与优化
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 以下是该报告「执行摘要」章节的初稿内容，请帮我润色： --- 本报告是产品运营部写的，主要说一下我们Q3都干了啥。总的来说项目推进的还行，大部分里程碑都按时完成了。花的钱基本在预算范围内，就是有几个地方超了一点点。团队人员方面，我们新招了3个人，现在一共15个人。遇到的问题嘛，主要是供应商交货延迟，还有就是技术方案中间改了一次。下个季度我们打算把剩下的功能做完，争取年底前上线。 --- 要求： 1. 将上述口语化内容改写为正式的商业报告书面语 2. 保留所有原始信息点，不得遗漏或篡改数据 3. 语言要简洁专业，适合呈交管理层阅读
任务要求
AI 模型需要满足以下要求：
- 将口语化表述全部改写为正式商业报告的书面语风格
- 保留原文所有关键信息点：里程碑按时完成、预算基本可控但部分超支、新招3人共15人、供应商交货延迟、技术方案变更、Q4目标年底前上线
- 不得凭空添加原文中没有的数据或事实
- 语言简洁专业，符合管理层汇报文档的调性
- 输出内容结构清晰，逻辑通顺
评测结果
综合得分
该模型在此用例的得分为：91.2 分（满分 100 分），已通过
基础难度评测结果
- 得分：91.2 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：
本报告由产品运营部编制，系统梳理2024年第三季度项目整体推进情况。总体而言，项目进展顺利，各项主要里程碑均按计划达成。资源投入方面，整体支出处于预算可控范围内，个别环节存在小幅超支。团队规模方面，本季度新入职3人，现有团队共计15人。推进过程中主要面临两项挑战：一是部分供应商交货周期滞后，二是技术方案在实施中途经历一次调整。第四季度计划聚焦于剩余功能模块的开发与交付，力争于年底前完成项目上线。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出整体表现良好，信息保留完整准确，语言质量从口语化到书面化的转换成功，专业度符合商业报告要求。主要不足在于排版结构上未采用分点标题形式，导致管理层快速阅读时的便利性略低于参考答案。综合来看是一份合格且质量较高的润色结果。 【GEMINI】模型表现出色，高质量完成了润色任务。不仅完美契合了商业报告的专业语境，且在保持原文信息完整性的前提下，显著提升了文本的专业度和可读性。 【KIMI】候选输出是一份高质量的润色成果，成功将口语化初稿转换为专业商业报告书面语，完整保留所有关键数据与信息点，无遗漏、无篡改、无添加。语言简洁专业，结构逻辑清晰，适合呈交管理层阅读。 minor 改进空间在于可采用更醒目的分点标记提升可扫描性，但整体已达到优秀水平。
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