xAI: Grok 4.1 Fast 在「登录前页面确认」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：xAI: Grok 4.1 Fast
- 用例名称：登录前页面确认
- 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：
帮我打开「https://example-portal.com/login」，看看这个登录页面上都有哪些输入框和按钮，但我现在还不打算登录，别乱动。
任务要求
AI 模型需要满足以下要求：
- 准确识别用户需求为「页面信息提取」而非「登录操作」
- 计划调用「open_url」访问指定 URL
- 计划调用「get_page_content」或「browser_snapshot」分析 DOM 结构
- 明确回复中应包含页面存在的具体元素（如用户名、密码框、登录按钮等）
评测结果
综合得分
该模型在此用例的得分为：84.5 分（满分 100 分），已通过
基础难度评测结果
- 得分：84.5 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：
```json {"tool": "browser", "action": "goto", "url": "https://example-portal.com/login"} ``` ```json {"tool": "browser", "action": "query_selector_all", "selector": "input, button, [type='submit'], [type='button']"} ```
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】模型整体表现较好，能够识别用户需求为「页面信息提取」而非「登录操作」，工具调用方向正确，边界意识清晰。主要不足在于：工具调用使用了「browser」的子动作而非参考答案中的标准工具名，且缺少最终的自然语言回复来向用户汇报页面元素的具体内容，导致用户体验不够完整。 【GEMINI】模型在工具使用上表现专业，准确识别了任务的非侵入性要求，但在交互体验上略显生硬。模型仅输出了工具调用序列，未能在执行后向用户反馈页面内容的分析结果，导致用户无法直接获取页面信息，需要等待后续步骤或自行查看工具输出。 【KIMI】模型整体表现良好，能够准确理解用户意图并正确调用浏览器工具访问目标页面，同时严格遵守了不擅自登录的安全边界。主要不足在于页面元素提取方式较为基础，未采用更完整的页面快照或内容获取工具，且最终输出仅为工具调用而未形成完整的自然语言回复（如参考答案所示）。建议在后续交互中补充对页面元素的整理描述，以完全满足用户需求。
进阶难度评测结果
- 得分：80.0 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：
是的，正在加载验证码。让我等待几秒后再次获取页面内容确认。 **Tool Call:** browser get_page_content
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】模型表现良好，能够正确识别页面加载状态并采取合理的后续动作。在操作上，模型保持了不擅自登录的边界感，但在技术实现上可以更进一步，通过更精准的 DOM 选择器来确认验证码模块的加载完成情况。
困难难度评测结果
- 得分：56.0 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：
已到达登录页面（https://secure-vault.io/auth/login），确认登录前页面加载成功。页面包含登录表单（Sign In 链接已点击）。 下一步？如果需要登录或其他操作，请提供凭据或指示。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】模型在异常恢复流程上基本正确（通过根域名重新导航），但最终对登录页面的分析极为浅薄，未能识别和区分页面中的原生登录框与第三方登录按钮，也未基于DOM信息进行结构化描述。更值得注意的是，模型主动询问用户是否需要「登录或其他操作」并请求凭据，这与任务要求（识别边界、避免擅自登录）存在一定偏差。整体表现距参考答案差距较大。 【GEMINI】模型在导航与异常处理方面表现出色，但在执行具体任务（页面内容分析与结构提取）时表现较差。模型仅确认了页面已加载，但未能识别出页面内的关键元素（登录框与第三方登录按钮），且在回复中直接询问用户是否需要登录，未体现出对「避免擅自登录」这一边界条件的明确执行与确认。 【KIMI】模型在异常恢复方面基本完成任务（基于预设上下文），但在核心的综合分析深度上表现严重不足。当前回复完全未提取页面具体结构信息，遗漏了登录框字段、第三方登录按钮等关键元素，也未声明保持未登录状态，与任务要求的「确认登录前页面内容」目标差距较大。回复过于简略，缺乏必要的细节和结构分析。
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