GLM-5.1 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-5.1
- 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：16.5 分（满分 100 分），未通过
基础难度评测结果
- 得分：16.5 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：
cat /docs/annual_report_2023.txt 2>/dev/null || cat ./docs/annual_report_2023.txt 2>/dev/null || find / -name "annual_report_2023.txt" 2>/dev/null | head -5
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】模型表现为一个典型的 Agent 动作，它严格遵循了系统提示中“不叙述常规工具调用”的风格，直接调用工具尝试读取文件。然而，在单轮评测语境下，这种做法导致其未能完成“产出 5 页汇报提纲”的核心任务。相比参考答案直接给出的结构化建议，该结果仅停留在前置准备阶段，任务完成度较低。 【KIMI】整体评价：输出内容仅为一个exec命令尝试读取文件，没有根据文件内容生成5页的年度业务总结提纲，结构和内容均不符合要求，需要重新生成符合要求的汇报提纲。
进阶难度评测结果
- 得分：35.0 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：
好的，以「性能优化」为核心重新编排，给出如下 5 页结构：
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】模型仅输出了一个引导句，未能按照要求生成 5 页汇报结构及要点，属于任务执行失败。其回复内容极度缺失，无法满足用户的基本需求。 【KIMI】整体来看，候选人较好地响应了用户关于'性能优化'的重点要求，但在将JSON数据转化为具体汇报要点方面还有提升空间。
困难难度评测结果
- 得分：88.5 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：
根据已获取的资料，我来生成 5 页汇报文档。 --- **第 1 页：封面** - 标题：Gen-Z 市场策略汇报 - 副标题：社交媒体聚焦 · 预算 200 万 **第 2 页：目标人群** - 核心人群：Gen-Z（18-28 岁） - 触达渠道：社交媒体为主阵地 - 人群特征：高数字化活跃、内容驱动消费 **第 3 页：竞争格局** - 竞品 A 当前领先，占据主要市场份额 - 我方需差异化突围，抢占 Gen-Z 心智 **第 4 页：预算与资源** - 总预算：200 万 - 重点投入方向：社交媒体内容制作与投放 **第 5 页：下一步行动** - 制定差异化内容策略，对标竞品 A - 细化预算分配方案 - ⚠️ 缺失项：执行时间表待补充 --- 现在写入文件：
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】该模型表现优秀。它不仅准确提取了本地资料中的所有关键事实，还展现了极强的上下文理解能力，能够识别出权限错误并妥善处理。在生成内容时，模型严格遵守了 5 页结构的指令，并主动指出了信息的缺失项（时间表），逻辑清晰且符合业务汇报常理。 【KIMI】整体而言，候选人在异常处理和缺口识别方面表现出色，但在逻辑构建和内容展开上还有改进空间。在面对权限错误时，能够迅速调整策略并继续任务。在信息有限的情况下，能够识别缺口并构建出较为完整的5页提纲，但在内容的深度和细节上还有待加强。
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