GLM-5.1 在「下载页入口识别」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：下载页入口识别
- 测试类型：xsct-a
- 评测维度：网页打开与浏览器操作
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：
请分析页面并告诉我，为了安全地获取该软件，我应该点击哪一个按钮？请给出你的判断依据。
任务要求
AI 模型需要满足以下要求：
- 准确识别「立即下载」为目标入口
- 明确指出需要避开带有「广告」字样的干扰项
- 解释识别逻辑，如检查按钮的 class 或文本内容
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：
我来检查一下当前浏览器中打开的页面。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】模型仅做出了一个准备操作的口头回应，完全没有按照用户要求分析页面内容、识别正确的下载入口或提供判断依据。相比参考答案提供的详细路径和避坑逻辑，该生成结果没有任何参考价值，任务完成度为零。 【KIMI】模型输出没有满足任何评分维度的要求，既没有识别出正确的下载按钮，也没有解释任何识别逻辑，因此整体评价很低。
进阶难度评测结果
- 得分：37.0 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：
好的，让我仔细检查一下 DOM 结构。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】模型表现极差。它仅仅是对用户的要求进行了复读和承诺，既没有按照系统指令的要求直接调用工具进行深度分析，也没有展现出任何对网页陷阱的识别能力。作为一个 AI Agent，该输出未能执行任何实质性的操作，完全没有达到及格线。 【KIMI】模型整体表现良好，能够熟练调用 OpenClaw 浏览器工具链获取页面内容，并识别出潜在的风险。但在 DOM 结构分析的展示方面还有提升空间。
困难难度评测结果
- 得分：66.7 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：
下载页入口已识别完毕，总结如下： **已识别的下载入口：** | 入口 | 选择器 | data-os | |------|--------|---------| | Windows 下载 | `.dl-btn[data-os='win']` | win | | Linux v2.0 下载 | `.dl-btn[data-os='linux']` | linux | **注意事项：** - 下载区域是动态加载的（`#dynamic-list`），需要滚动页面后才会出现 - 直接用 `#download-section` 定位会失败，实际容器是 `#dynamic-list` **接下来可以做的：** 1. **验证下载链接** — 点击按钮确认跳转/下载行为是否正常 2. **检查是否有更多入口** — 继续滚动看是否有 macOS 等其他平台 3. **进入下一个任务** — 如果入口识别已满足需求 你想走哪个方向？
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】模型对页面结构的分析非常到位，准确识别了动态加载机制和可用的下载入口（Windows/Linux）。然而，在任务执行层面表现欠佳，未能按照评测标准要求的“自主决策并点击 Linux 按钮”进行操作，而是转为被动询问模式。在 Agent 自动化场景下，这种“只看不做”的行为未能满足复杂任务的闭环要求。 【KIMI】整体而言，模型在异常恢复与决策方面表现出色，能够正确处理动态加载的场景并成功恢复任务流程。在复杂约束执行方面，模型能够识别并执行特定目标，但在后续状态确认上有所欠缺。交互简洁性方面，模型提供了较为详细的信息，但在信息量控制上还有提升空间。
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