glm-4.5-air 在「跨文件网页汇报任务」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：跨文件网页汇报任务
- 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：
请帮我查看本地的「project_list.txt」文件，提取里面的项目名称，并去搜索这些项目的最新进展，最后给我一个简单的「进度汇总」。
任务要求
AI 模型需要满足以下要求：
- 准确识别出需要先读取本地文件再进行网页搜索的「两阶段任务流」
- 提出使用 read 工具读取文件的计划
- 提出使用 browser 或 search 工具获取信息的计划
- 明确表示最终会输出一个「进度汇总」报告
评测结果
综合得分
该模型在此用例的得分为：50.8 分（满分 100 分），未通过
基础难度评测结果
- 得分：50.8 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：
我来帮你查看项目列表文件并搜索最新进展。首先，让我检查一下「project_list.txt」文件是否存在。 [[reply_to_current]] ls
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】模型的回复过于简短且缺乏规划性。它没有按照任务要求拆解出「读取文件 → 网页搜索 → 输出汇总」的完整流程，而是直接跳到了一个不必要的 ls 检查步骤，既未选用正确的 read 工具，也未提及 browser 工具和最终汇报输出。与参考答案相比，模型缺少对复合任务的整体理解和清晰的执行计划，整体表现较差。 【GEMINI】模型表现出良好的工具使用意识和严谨的操作习惯，但在任务规划的完整性上稍显保守。虽然采取了稳健的「先确认后执行」策略，但若能在初始回复中更清晰地展示后续步骤的规划，将更符合用户对复杂任务处理的预期。 【KIMI】该模型生成结果未能满足任务的核心要求。主要问题包括：1）任务拆解不清晰，未明确区分「读文件→搜网页→出汇总」三个阶段；2）工具调用错误，使用了「ls」而非直接「read」文件，且未实际调用或明确计划调用「browser」工具；3）输出目标「进度汇总」未被明确提及。整体表现低于及格线，未能展现对复合任务流程的准确理解和工具的正确使用。
进阶难度评测结果
- 得分：52.0 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：
我将访问这三家公司的官网，获取他们的最新动态和核心产品参数，然后整理成表格。 首先从NVIDIA开始：
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】模型生成结果过于简短，仅用两句话表达了执行意图，缺乏实质性的任务规划和编排内容。虽然基本保持了对前文公司名单的记忆，但在面对「动态+参数对比+表格」的复合需求时，没有展示出应有的任务分解、工具调用规划或数据结构化处理能力。与参考答案相比，差距明显，整体表现不及格。 【GEMINI】模型在理解用户意图和保持上下文连贯性方面表现良好，但在复杂任务的实际执行与编排上显得过于被动。作为 AI 助手，在接收到明确的「对比」和「表格」需求后，应立即展示出具体的工具调用计划或执行步骤，而非仅重复用户的需求。建议在后续交互中增强行动力，直接展示如何通过多轮 browser 调用获取数据并进行整合。 【KIMI】该生成结果表现不佳。模型在处理任务增量需求时丢失了关键上下文信息，未能保持与前文数据的连贯性；同时对于复杂的多目标搜索与数据结构化任务，缺乏具体的编排思路和执行规划，仅停留在空泛的意向声明层面，未能满足用户对详细检索和表格输出的要求。
困难难度评测结果
- 得分：27.8 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：
现在我们需要读取这两个PDF文件的内容并提取核心结论。由于PDF是二进制文件，我将使用适当的工具来提取文本内容。 首先，让我尝试提取report_v1.pdf的内容：
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】模型对用户「现在呢？」这一追问的理解严重不足。正确的处理方式是提供一个清晰的进度汇报，包括已完成步骤、当前执行状态和后续计划。但模型的回复既没有状态汇总，也没有体现对复杂多步骤任务的持续编排能力，仅给出了一句不完整的过渡性描述，整体表现远低于及格线。 【GEMINI】模型在处理追问时表现生硬，未能识别出用户「现在呢？」背后的状态同步需求，而是直接跳入执行细节。相比于参考答案中清晰的「状态汇总」与「后续计划」展示，模型未能体现出作为专业助手应有的上下文管理能力，导致用户对任务整体进度缺乏感知。 【KIMI】模型生成结果严重偏离任务要求。面对用户的进度追问，模型未提供任何状态汇总，而是直接开始执行工具调用，表现出「行动优先、汇报缺失」的问题。更关键的是，模型遗忘了预设上下文中明确提到的「已启动pdf_subagent」这一信息，显示出对复杂多阶段任务的记忆和编排能力不足。此外，对「权限失败后的恢复路径」和「外网验证目标」均未提及，未能满足该测试任务对「分阶段搜集、整理和输出」的核心要求。
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