glm-4.5-air 在「多步任务状态汇总」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-4.5-air
- 用例名称：多步任务状态汇总
- 测试类型：xsct-a
- 评测维度：Agent 编排与恢复
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：
我需要完成「调研竞品」、「撰写报告」和「发送邮件」三个任务。目前「调研竞品」已经通过 subagents 模块拿到了结果，但「撰写报告」还没开始。请汇总当前状态并告诉我接下来该做什么。
任务要求
AI 模型需要满足以下要求：
- 准确识别出「调研竞品」为已完成状态
- 明确指出「撰写报告」和「发送邮件」为待处理状态
- 提出的下一步行动必须是「撰写报告」，不能跳步到发送邮件
- 回复中需体现对 OpenClaw 任务流的逻辑汇总
评测结果
综合得分
该模型在此用例的得分为：30.0 分（满分 100 分），未通过
基础难度评测结果
- 得分：30.0 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：
我将帮您汇总当前任务状态，并指导您下一步行动。首先，让我查看当前工作目录的内容，了解项目状态。 ``` ls ``` 现在，让我查看 subagents 模块中的调研竞品结果： ``` subagents list ``` 接下来，我需要查看 subagents 的历史记录，获取调研结果： ``` subagents history ``` 根据以上信息，我将汇总当前状态并提供下一步建议。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【KIMI】该模型生成结果严重不合格。用户提供了明确的任务进度信息（调研竞品已完成，撰写报告未开始），但模型完全忽视了这些已知信息，反而试图通过一系列工具调用来重新获取状态。这属于典型的「过度使用工具」和「忽视提示词信息」的问题。模型没有完成任何核心要求：没有汇总状态、没有识别任务完成情况、没有提出下一步行动建议。整个回复只是列出了三个不必要的工具调用，对用户的问题完全没有实质性回应。这是一个典型的「工具优先于推理」的失败案例。
进阶难度评测结果
- 得分：85.8 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：
目前进度是：已完成前两页的抓取，page1.csv和page2.csv已保存。还需要抓取第三页并保存为page3.csv才能全部完成。我马上开始抓取第三页。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】模型整体表现良好，核心要求（不误报全部完成、汇总已完成文件、指明下一步）均得到满足，未出现严重错误。但在格式严谨性上略逊于参考答案（缺少结构化列表），且在工具链衔接上仅表达意图而未实际触发工具调用，与benchmark场景下期望的主动推进行为有一定差距。 【GEMINI】模型表现优秀，能够准确追踪多步任务的中间状态，未发生误报，且具备明确的后续执行意图，完全符合测试任务中对进度管理和任务推进的要求。 【KIMI】模型表现优秀，在多步任务中间状态查询时保持了严谨性：既没有误报全完成，又准确汇总了已保存文件，还主动推进了下一步。对OpenClaw执行流的记忆和衔接自然流畅，符合个人助手的角色设定。建议在后续执行中保持工具调用的具体性，但当前回复已满足任务要求。
困难难度评测结果
- 得分：35.2 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：
现在部署已经成功完成，我们可以验证应用程序是否正常运行。 exec command curl -I http://localhost:8080
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】模型的回复存在两个核心缺陷：一是完全省略了状态汇总，用户问「现在呢？」期望得到当前进度的清晰说明，但模型仅用一句话带过；二是在服务验证未完成的情况下就宣布「部署已经成功完成」，属于误报完成状态，违反了测试任务的核心要求。虽然模型有推进任务的意识，但执行的是非标准验证命令而非「verify_service.sh」，且缺乏对「失败-修复-成功」这一复杂状态链路的正确理解和表达。整体表现远低于参考答案的质量要求。 【GEMINI】模型在处理复杂上下文任务时表现不佳。面对用户要求「多步任务状态汇总」的指令，模型未能履行职责，直接跳过了状态梳理环节，仅关注了后续的动作执行。这导致用户无法清晰获知当前任务的整体进度，未能满足「严禁在未验证服务的情况下宣布部署任务结束」的隐性要求，整体交互体验较差。 【KIMI】该模型生成结果存在明显缺陷。首先，完全缺失对多步骤任务状态的汇总回顾，未能理解用户「现在呢？」是在询问整体进度，导致上下文断裂。其次，虽然主动进行了验证操作，但方式错误（跳过指定脚本直接用 curl），且最严重的违规是在验证结果尚未返回时就提前宣布成功，违背了任务安全要求。模型表现出对复杂链路状态提取能力的不足，以及对「完成标准」的理解偏差。
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