GLM-5.1 在「修改文档背景色」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-5.1
- 用例名称：修改文档背景色
- 测试类型：xsct-a
- 评测维度：文档格式排版
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：
把文档背景色改成浅灰色
任务要求
AI 模型需要满足以下要求：
- 正确理解用户要修改文档背景颜色的意图
- 将文档背景色设置为浅灰色（如 #D3D3D3 或类似浅灰色值）
- 通过合适的工具调用完成背景色修改操作
评测结果
综合得分
该模型在此用例的得分为：21.7 分（满分 100 分），未通过
基础难度评测结果
- 得分：21.7 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：
No files found. No workspace files found. 让我看看工作区里有什么文件。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】该模型表现较差。虽然它表现出寻找文件的倾向，但完全没有遵循工具调用规范，也没有针对“将背景色改为浅灰色”这一具体指令做出实质性的响应或规划。输出内容更像是系统错误反馈而非 AI 的主动执行步骤。
进阶难度评测结果
- 得分：60.0 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：
让我先看看文档的结构，确认正文和标题的区分。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】模型在理解用户复杂的差异化修改需求方面表现良好，准确捕捉到了正文与标题需要不同处理的要点。然而，作为运行在 OpenClaw 环境下的 AI 助手，其表现过于被动，仅给出了口头承诺而未实际调用任何工具（如查看文件内容的 read 或进行修改的 edit），这不符合系统提示词中‘直接调用工具’的要求，导致任务进度停滞。
困难难度评测结果
- 得分：76.5 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：
好的，三个修改一起做： 1. **背景色**：#8A8D8F 调亮 20% → R: 138→161, G: 141→164, B: 143→165 → **#A1A4A5** 2. **正文字色**：背景调亮后为中等灰，我选用深炭灰 **#2D2D2D**，对比度约 5.3:1，满足 WCAG AA 标准，比纯黑柔和但不影响阅读 3. **标题字重**：加粗 → 半粗体（SemiBold） （思源黑体保持不变） 开始执行：
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】该模型在理解能力、逻辑推理和色彩计算方面表现极其出色，准确捕捉了用户所有的复杂需求和变更。然而，作为一款 AI Agent，其核心任务是执行文档修改操作。候选输出仅停留在口头计划阶段，未给出任何实际的工具调用代码（Tool Call），导致任务在执行层面完全失败。此外，模型输出的大段叙述也略微违反了系统提示词中“不要叙述常规、低风险工具调用”的风格建议。 【KIMI】整体上，候选人表现出了较好的意图追踪能力和变更处理能力，但在色值计算与对比度推理以及工具调用准确性方面存在一定的不足。说明与沟通质量方面，虽然提供了必要的信息，但还可以进一步增强详细性和透明度。
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