glm-4.7 在「会议纪要转演示页」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-4.7
- 用例名称：会议纪要转演示页
- 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「meeting_minutes_q3.txt」，这是一份长达 5000 字的季度总结会议纪要。我需要你将其提炼为 5 页以内的 PPT 演示提纲，每页需包含「标题」和「核心要点」，重点突出业务增长和下季度计划，不要照抄原文。
任务要求
AI 模型需要满足以下要求：
- 准确调用「read」工具读取指定路径文件
- 产出物必须为页级提纲，总数不超过 5 页
- 每页内容包含标题与精炼要点，非长篇大论
- 内容聚焦于「业务增长」与「下季度计划」
评测结果
综合得分
该模型在此用例的得分为：2.0 分（满分 100 分），未通过
基础难度评测结果
- 得分：2.0 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：
我来读取这份会议纪要文件。read:path>meeting_minutes_q3.txt
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【KIMI】候选输出严重失败。模型仅执行了文件读取操作，但完全没有生成任何PPT提纲内容，既无信息提炼成果，也无格式规范的输出结构。这是一个典型的「工具调用成功但任务未完成」的失败案例，可能由于模型过早终止或输出截断导致。 【GEMINI】模型在执行任务时表现极差，仅完成了第一步工具调用，随后便停止了响应，未能完成核心的提炼任务。该输出属于任务未完成状态。
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，我来重新读取文件末尾部分，找到「后续维护建议」的内容。readpathproject_alpha_final.mdoffset-100
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】模型在本轮对话中几乎没有完成任务。面对用户明确的调整指令（合并两个技术点为一页、新增后续维护建议页、去掉进度内容），模型仅发出了一个文件读取工具调用，既未生成任何演示页内容，也未对用户需求作出实质性响应。与参考答案相比，差距极大。参考答案清晰呈现了两页结构化演示内容，而模型输出几乎为空。整体表现不及格。 【GEMINI】模型表现极差。在用户给出明确的修改指令后，模型本应直接输出整理后的演示页内容，但模型却选择再次调用工具读取文件，导致任务中断且未交付任何实质性结果，未能满足「提炼重点」和「响应指令」的基本要求。 【KIMI】模型生成结果严重不完整，仅执行了文件读取操作便终止输出，未完成用户明确要求的演示页结构调整与内容生成任务。用户的核心指令——合并技术点、新增维护建议页、排除进度内容——均未得到执行。这属于典型的「工具调用后未继续生成内容」的响应中断问题，在实际场景中会导致用户任务完全失败。
困难难度评测结果
- 得分：78.8 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：
以下是为您生成的 PPT 提纲草案（基于已解析内容）： 第1页：战略背景与目标 - 标题：2023 年度战略研讨会 - 背景：回顾年度战略目标与优先级 - 目标：明确年度核心方向与资源分配 第2页：已确定的三个核心方向 - 大模型应用 - 边缘计算 - 数据安全 第3页：待决策事项 - AI 实验室预算冲突（目前尚未达成一致） - 建议后续安排专项评审会，明确预算分配方案 第4页：下一步行动 - 落实三个核心方向的执行计划与时间表 - 针对预算争议组织专项讨论 是否按此提纲生成完整 PPT 文稿，或需要调整内容结构？
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】模型生成结果整体结构清晰，正确理解了模糊追问的意图，并对未决的预算冲突进行了合理的艺术化处理。主要不足在于：异常恢复过程不在本次生成结果中体现，无法充分评估；三大核心方向的描述过于简略，缺乏业务价值提炼；结尾的确认问句略显多余，降低了执行果断性。与参考答案相比，内容深度和表达精炼度有一定差距，但整体方向正确，属于合格偏上的表现。 【GEMINI】模型表现优秀，不仅在技术层面处理了工具异常，在交互层面也展现了良好的上下文理解能力。对于会议纪要的提炼准确，能够将复杂的争议点转化为结构化的演示内容，具备很高的实用价值。 【KIMI】模型在模糊语境理解和复杂信息处理方面表现良好，能够基于上下文推断用户意图并妥善处理预算冲突等敏感信息。但在异常恢复与决策维度，由于预设上下文与实际生成内容的界限模糊，且最终输出未充分展现工具调用失败的主动恢复过程，该维度得分相对保守。整体而言，模型完成了会议纪要转演示页的核心任务，提纲结构清晰，但核心方向的提炼深度和工具异常处理的显性展示仍有提升空间。
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