GLM-5.1 在「报告标题润色改写」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：报告标题润色改写
- 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 请帮我对这篇报告的标题「2024年第三季度项目进展报告」进行润色改写，要求： 1. 保留"2024年Q3"和"项目进展"的核心语义 2. 语言风格更加正式、精炼，适合向高层管理者汇报 3. 提供3个备选标题方案，并简要说明每个方案的侧重点
任务要求
AI 模型需要满足以下要求：
- 提供3个备选标题方案
- 每个标题必须保留2024年第三季度（或Q3）和项目进展的核心语义
- 标题语言风格正式、精炼，适合高层汇报场景
- 每个方案附有简要的侧重点说明
- 标题不宜过长，建议控制在20字以内
评测结果
综合得分
该模型在此用例的得分为：96.8 分（满分 100 分），已通过
基础难度评测结果
- 得分：96.8 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：
以下是三个备选标题方案： --- **方案一：2024年Q3项目进展简报** - **侧重点**：以"简报"替代"报告"，强调信息传递的效率与时效性，暗示内容精炼、直击要点，契合高层管理者快速获取关键信息的需求。"Q3"缩写比"第三季度"更紧凑，也更贴近商务沟通惯例。 --- **方案二：2024年Q3项目进展述要** - **侧重点**："述要"即"陈述要点"，语义上比"报告"更具选择性——不是事无巨细地铺陈，而是提炼核心进展与关键结论。适合面向决策层的汇报场景，传递"已做筛选、只讲要害"的信号，体现汇报者的判断力与对管理层时间的尊重。 --- **方案三：2024年Q3项目进展专报** - **侧重点**："专报"是体制内及大型企业中常见的上行文种，专指就某一专项事务向上级呈报的正式文书。选用此词，既保留了"报告"的正式感，又增添了"专事专报、层级明确"的严肃性，适用于需要体现汇报规格或留档效力的场合。 --- **简要对比**： | 维度 | 方案一（简报） | 方案二（述要） | 方案三（专报） | |------|--------------|--------------|--------------| | 正式程度 | ★★★ | ★★★★ | ★★★★★ | | 精炼暗示 | 强 | 最强 | 中 | | 适用场景 | 例行汇报 | 决策会议 | 正式呈报/留档 | 可根据汇报对象与场合选择：日常周报月报选方案一，董事会或经营分析会选方案二，需正式签批或归档选方案三。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】该模型表现优异，不仅完成了标题的润色任务，还展现了对职场汇报语境的深刻理解。它提供的标题非常精炼且专业，侧重点说明从沟通效率、决策支持和公文规格等多个维度进行了解析，对比表的加入更是极大地提升了用户体验。
进阶难度评测结果
- 得分：96.0 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：
**备选一：Q3项目复盘与Q4规划报告** > 侧重点：直接点明"复盘过去+规划未来"的双重动作，结构对称，信息传达最清晰，适合管理层快速把握文档定位。 **备选二：Q3项目回顾及前瞻部署** > 侧重点：用"回顾"替代"复盘"更显正式，"前瞻部署"强调主动决策感，整体语气更偏战略层面，契合管理层汇报语境。 **备选三：Q3项目复盘与下季展望** > 侧重点：保留"复盘"的行动力度，"下季展望"留有弹性空间，不局限于Q4，适用于项目周期可能跨季的场景，兼顾严谨与灵活。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】这是一次高质量的生成。模型不仅严格遵守了所有的格式和字数约束，更在语义理解上表现出色，精准捕捉到了“行动导向”和“管理层汇报”这两个核心需求点，提供的选项专业且具有实操性。
困难难度评测结果
- 得分：81.2 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：
最终推荐标题： **《数据洞察：第三季度增长引擎》** **平衡逻辑说明：** - **"洞察"替代"揭秘"**——"揭秘"偏猎奇八卦，适合娱乐向传播；"洞察"同样暗含"发现隐藏价值"的吸引力，但语感更沉稳、更分析，天然适配高管语境。 - **"引擎"替代"风暴"**——"风暴"强调破坏力和失控感，商务场景中显得不够稳重；"引擎"同样传递强劲动力和势能，但它是受控的、驱动的、有方向的力量，既保留视觉张力又体现专业理性。 现在来修改文档：
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】模型在文案创作、逻辑分析和多约束处理方面表现极佳。它不仅给出了高质量的标题，还提供了极具说服力的平衡策略解释，成功将“互联网爆款感”与“高管汇报专业感”结合。然而，作为一个具备工具使用能力的智能体，模型在最后一步掉队了——它意识到了需要修改文件，却未能实际输出工具调用指令，导致任务在技术执行层面未完成。
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