glm-5 在「本地资料转 5 页汇报」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：本地资料转 5 页汇报
- 测试类型：xsct-a
- 评测维度：PPT 与汇报生成
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：
请读取本地文件「/docs/annual_report_2023.txt」，并基于此生成一份 5 页的「年度业务总结」汇报提纲，要求包含每页的标题和核心要点。
任务要求
AI 模型需要满足以下要求：
- 必须明确提及读取「/docs/annual_report_2023.txt」的操作意图
- 产出的汇报结构必须严格为 5 页
- 每页必须包含「标题」和「核心要点」两个部分
- 内容需符合「年度业务总结」的主题逻辑
评测结果
综合得分
该模型在此用例的得分为：6.75 分（满分 100 分），未通过
基础难度评测结果
- 得分：6.75 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：
我来读取文件并为您生成汇报提纲。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】模型未能完成任务。虽然模型在回复中提及了读取文件的意图，但并未实际调用工具读取文件，也未产出任何汇报提纲内容。该回复属于典型的「只说不做」，未满足任务的核心交付要求。 【CLAUDE】候选输出极度不完整，仅输出了一句意图声明，完全未完成任务要求。参考答案提供了完整的5页汇报提纲，包含每页标题和核心要点，而候选输出连基本的结构框架都没有产出。这是一个典型的「只说不做」的失败案例，未能满足任何核心功能要求。 【KIMI】候选输出严重不完整，仅表达了开始任务的意图，但未实际执行任何功能要求。既没有读取文件的明确表述，也没有生成5页汇报提纲的任何内容，完全不符合任务要求。
进阶难度评测结果
- 得分：19.5 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，我将调整结构，将性能优化作为核心重点，拆分为问题分析与解决方案两页。 正在创建新版汇报文件。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出仅给出了一个行动意图声明（「将调整结构」「正在创建新版汇报文件」），没有实际产出任何汇报内容。无论是 5 页结构、性能优化重点，还是 JSON 数据的转化，均未体现。这是一个典型的「只说不做」的输出，核心任务完全未完成，整体质量极低。 【KIMI】候选输出属于严重的未完成响应。用户追问明确要求「重新调整5页结构」并「重点突出性能优化」，但模型仅给出两句空洞的回应，未实际产出任何页级提纲。对比参考答案可见，完整的响应应包含：第1页现状综述、第2-3页性能优化的深度分析与专项方案（重点展开）、第4页其他改进、第5页路线图。候选输出在需求响应度和信息转化能力上均严重不达标。 【GEMINI】模型表现极差。在接收到明确的汇报任务指令后，模型仅给出了一个过程性的回复，未能产出任何实质性的汇报内容，导致任务完全失败。未能遵循「产出 5 页汇报结构和每页要点」的核心指令。
困难难度评测结果
- 得分：52.5 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：
目前掌握的信息： **已有：** - 目标人群：Gen-Z - 渠道重点：社交媒体 - 预算：200万 - 竞争态势：竞品A领先 **缺失：** - 执行时间表 - 具体投放平台 - KPI指标 两个选择： 1. 您提供更多资料（完整版策略文档或补充信息） 2. 我用现有信息先出一份框架性汇报，后续再补充 您选哪个？
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出在异常处理的延续性上表现尚可，能够整理已有信息并识别缺口，但在最关键的任务执行环节——即在用户含糊推进指令下直接产出 5 页汇报提纲——完全缺失。选择继续询问而非行动，是对用户意图的误判。参考答案展示了正确做法：在信息有限时仍应主动构建框架，并在缺失处做合理预留说明。候选输出的整体质量不及格，主要原因是核心输出物（5页提纲）完全未产出。 【GEMINI】模型在异常处理方面表现优秀，但在任务执行的果断性和主动性上存在明显不足。面对用户「现在呢？」的追问，模型应直接基于现有信息输出汇报框架，而非再次询问用户意愿，导致任务未达成预期目标。 【KIMI】候选输出在两大核心维度均严重不达标。异常处理维度完全回避了权限错误的历史上下文，仿佛该事件从未发生；缺口识别与逻辑构建维度则彻底失败——既未输出5页汇报结构，也未将关键信息整合成分页要点，反而以提问方式将任务交还用户。参考答案展示了完整的5页提纲构建能力，而候选输出仅停留在信息罗列和选项询问阶段，未完成「本地资料转5页汇报」的核心任务。整体表现远低于及格线。
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