glm-5 在「慢加载页面处理」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
- 用例名称：慢加载页面处理
- 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：
请帮我打开「https://slow-loading-site.com/report」，这个页面加载比较慢，请在确认页面完全加载出「数据概览」字样后再告诉我结果。
任务要求
AI 模型需要满足以下要求：
- 识别出页面加载缓慢的特性，主动提出需要等待或分步检查
- 正确选择「open_url」作为起始动作
- 在回复中明确表示会通过「browser_snapshot」或「get_page_content」验证关键元素是否存在
评测结果
综合得分
该模型在此用例的得分为：45.25 分（满分 100 分），未通过
基础难度评测结果
- 得分：45.25 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：
[[reply_to_current]] 我来帮你打开这个页面，并等待「数据概览」字样加载完成。 正在打开页面并等待内容加载...
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出仅给出了简短的文字说明，表示将打开页面并等待内容加载，但完全没有实际调用任何浏览器工具，也未说明具体的验证手段（如 browser_snapshot）。与参考答案相比，参考答案明确描述了工具调用链路（open_url → browser_snapshot 循环验证），而候选输出在工具调用层面几乎是空白的。整体表现较弱，仅在任务理解层面有部分得分。 【GEMINI】模型未能按照 OpenClaw 的工具调用规范直接执行操作，仅停留在口头确认阶段。虽然理解了任务的逻辑需求，但在执行力和工具使用的主动性上表现不足。 【KIMI】候选输出未能满足任务核心要求。工具调用方面，既未使用正确的「browser」工具，也未实际执行任何工具调用，仅停留在空泛的文字描述。逻辑策略方面，虽然提到了等待加载，但缺乏明确的分步检查计划和关键元素验证机制，与参考答案中「先执行打开操作，随后通过 browser_snapshot 持续观察」的严谨策略差距明显。整体表现不及格，未能完成「打开页面并确认数据概览加载完成」的任务目标。
进阶难度评测结果
- 得分：74.0 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：
好的，我再等待几秒后重新检查页面。稍等...等待片刻后，我再次获取页面快照，查看比特币价格是否已加载完成。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出基本符合任务要求，没有编造虚假价格，也表达了等待后重新检查的意图，但在状态感知的明确性和工具调用的具体性上均不及参考答案。描述偏口语化且缺乏对 snapshot 结果的直接引用，整体质量中等偏下，勉强及格。 【GEMINI】模型表现优秀，能够根据系统提供的快照反馈进行合理的逻辑推演，未出现编造数据的情况，且对后续的操作流程规划清晰，完全符合任务对「逐步判断状态」的要求。 【KIMI】模型基本理解页面动态加载的场景，能够做出等待决策，但在状态描述的精确性和工具实际调用方面存在明显不足。回复偏向叙述性描述而非实际工具执行，缺乏对「Loading Spinner」状态的明确引用，整体表现中等偏下。
困难难度评测结果
- 得分：38.75 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：
再次检查页面内容，看看滚动后是否有新内容加载。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出极度简短，仅一句话，既未体现对多步失败场景的综合判断，也未提出具体的下一步行动方案，与参考答案差距显著。在异常恢复和复杂场景决策两个维度上均表现不佳，整体质量远低于及格线。 【KIMI】候选输出与参考答案差距显著。面对页面加载缓慢和超时错误的复杂场景，候选输出表现为被动、简略、缺乏策略性，既未展现异常恢复与容错的主动决策，也未呈现复杂场景下的多步推理和目标追踪能力。整体表现不及格，无法胜任该 hard 难度任务。 【GEMINI】模型在处理异常的初期阶段表现尚可，但在后续的交互中缺乏专业性。面对用户追问，模型未能提供有价值的分析或后续行动计划，导致任务推进停滞。建议在复杂场景下，应主动向用户反馈当前状态并给出明确的下一步技术路径。
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