glm-5-turbo 在「简短评价语撰写」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-turbo
- 用例名称：简短评价语撰写
- 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 `annual_review.docx` 摘要： **文档标题**：2024年度组织生活会民主评议材料 **作者**：综合管理部 **字数**：约 2800 字 **章节结构**： 1. 会议概述 2. 班子成员述职述廉情况 3. 批评与自我批评记录 4. 整改措施与下一步计划 5. 附件：考核评分表 请根据以上文档背景，为某康复医院监事会主席撰写一段年度组织生活会的简短批评意见，要求约20字左右，重点指出该同志在推动下属机构廉政文化建设方面存在的不足。
任务要求
AI 模型需要满足以下要求：
- 生成内容为批评意见，语气应体现组织生活会民主评议的正式风格
- 字数控制在15-25字左右
- 内容需聚焦于廉政文化建设方面的不足
- 应体现对监事会主席这一角色职责的理解
评测结果
综合得分
该模型在此用例的得分为：72.0 分（满分 100 分），已通过
基础难度评测结果
- 得分：72.0 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：
下属机构廉政文化建设抓得不实。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出方向正确，聚焦廉政文化建设不足，语言风格基本符合场景，但存在两个明显问题：一是字数严重不足（仅13字，低于15字下限）；二是内容过于笼统，缺乏具体问题的指向性，未能体现监事会主席的特定职责。整体质量偏低，需要在字数和内容具体性上加以改进。 【GEMINI】模型较好地完成了任务，在有限的字数内精准切中了廉政文化建设这一核心议题，语言风格符合组织生活会的正式要求。虽然字数略少于建议区间，但表达清晰有力，整体质量较高。 【KIMI】候选输出基本完成了任务要求，抓住了廉政文化建设这一核心要点，字数和风格大体合规。但内容深度不足，缺乏具体问题的细化描述，且字数偏少、个别用语略显口语化，整体质量中等偏上。
进阶难度评测结果
- 得分：87.8 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：
风险预判不足，跨部门沟通瓶颈关注不够，Q4计划缺量化指标支撑。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出整体完成了任务要求，三个批评要点均有体现，字数控制合理，语气转换准确。主要不足在于表述略显简短，缺乏正式评价文风中的称谓规范和总结性语句，与文档章节的关联也不够显性。整体属于合格但略显简略的输出。 【GEMINI】模型表现出色，完美执行了所有指令要求。在极短的字数限制下，不仅准确覆盖了所有关键批评点，还保持了极高的专业度与客观性，完全符合工作评价的场景需求。 【KIMI】该候选输出整体表现良好，核心完成了批评性评价的任务要求，三个关键批评点无遗漏，字数控制得当，且成功实现了从正面到负面评价的意图切换。主要不足在于语言表达的完整性和正式度尚有提升空间，部分表述略显简略生硬，未达到参考示例中「批评该同志……规划落地性有待加强」的完整组织评价文风。建议优化句式结构，增加主语引导和总结性判断，使评价更具组织正式文件的规范感。
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