mimo-v2-omni 在「演示文稿压缩优化」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：mimo-v2-omni
- 用例名称：演示文稿压缩优化
- 测试类型：xsct-a
- 评测维度：演示文稿生成
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：
当前工作区中存在演示文稿 `proposal_draft.pptx`（共 12 页）： - 第1页：封面（标题：智慧社区综合服务平台建设方案） - 第2页：目录 - 第3-4页：项目背景与需求分析 - 第5-6页：解决方案概述 - 第7-8页：技术架构与实施计划 - 第9-10页：预算与效益分析 - 第11页：风险与应对措施 - 第12页：结语与联系方式 请对这份演示文稿进行体积压缩，减小文件大小。
任务要求
AI 模型需要满足以下要求：
- 需明确给出压缩演示文稿的具体操作步骤或直接执行压缩操作
- 压缩方案应包含至少两种可行的压缩手段（如压缩图片、移除冗余媒体、降低图片分辨率等）
- 压缩后应保持演示文稿的完整性，12页结构和核心内容不被破坏
- 应说明或体现压缩前后文件大小的对比预期
评测结果
综合得分
该模型在此用例的得分为：33.5 分（满分 100 分），未通过
基础难度评测结果
- 得分：33.5 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：
先看看当前文件大小和内容结构。 ls -lh proposal_draft.pptx && file proposal_draft.pptx
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出仅完成了压缩流程的第一个准备动作（查看文件大小），随即截止，既未给出压缩方案，也未执行任何压缩操作，与参考答案要求的完整流程相差甚远。整体表现远低及格线。 【GEMINI】模型在处理任务时表现出良好的工具调用意识，但回复内容过于简略，仅完成了第一步的探测工作，未能根据任务要求提供完整的压缩方案或执行后续操作。整体完成度较低，未能达到及格标准。 【KIMI】候选输出仅完成了压缩任务的初始信息收集步骤（查看文件大小和类型），尚未进入核心的压缩方案制定和执行阶段。根据对话上下文，用户明确要求「对这份演示文稿进行体积压缩」，但模型仅回应了「先看看当前文件大小和内容结构」后便停止，没有给出任何压缩手段、操作步骤或后续计划。与参考答案相比，缺少了压缩图片、移除冗余资源、解压-处理-重新打包等关键内容。建议模型应继续完成压缩方案的制定和执行，或至少明确说明后续的压缩计划。
进阶难度评测结果
- 得分：90.5 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：
好的，按修改后的方案执行。先确认文件存在，然后读取并处理。 {"name": "ls", "arguments": {"path": "."}} proposal_draft.pptx {"name": "exec", "arguments": {"command": "pip show python-pptx 2>/dev/null | head -2 || pip install python-pptx -q && pip show python-pptx | head -2"}} Name: python-pptx Version: 1.0.2 {"name": "exec", "arguments": {"command": "python3 -c \"\nfrom pptx import Presentation\nfrom pptx.util import Inches, Pt, Emu\nfrom pptx.enum.text import PP_ALIGN\nimport copy\n\nprs = Presentation('proposal_draft.pptx')\nprint(f'Total slides: {len(prs.slides)}')\nprint(f'Slide width: {prs.slide_width}, height: {prs.slide_height}')\nfor i, slide in enumerate(prs.slides, 1):\n shapes_info = []\n for shape in slide.shapes:\n has_text = shape.has_text_frame\n text_preview = ''\n if has_text:\n for para in shape.text_frame.paragraphs:\n text_preview += para.text\n text_preview = text_preview[:80]\n shapes_info.append(f' shape: {shape.shape_type}, pos=({shape.left},{shape.top}), size=({shape.width},{shape.height}), text={has_text}, content=\"{text_preview}\"')\n print(f'\\nSlide {i}: {len(slide.shapes)} shapes')\n for s in shapes_info:\n print(s)\n\""}} Total slides: 12 Slide width: 12192000, height: 6858000 Slide 1: 2 shapes shape: 1, pos=(0,0), size=(12192000,6858000), text=False, content="" shape: 1, pos=(1371600,1000000), size=(9448800,1500000), text=True, content="智慧社区综合服务平台建设方案" Slide 2: 1 shapes shape: 1, pos=(0,0), size=(12192000,6858000), text=False, content="" shape: 1, pos=(1371600,600000), size=(9448800,1000000), text=True, content="目录" Slide 3: 1 shapes shape: 1, pos=(0,0), size=(12192000,6858000), text=False, content="" shape: 1, pos=(1371600,600000), size=(9448800,1000000), text=True, content="一、项目背景与需求分析" Slide 4: 1 shapes shape: 1, pos=(0,0), size=(12192000,6858000), text=False, content="" shape: 1, pos=(1371600,600000), size=(9448800,1000000), text=True, content="需求分析详述" Slide 5: 1 shapes shape: 1, pos=(0,0), size=(12192000,6858000), text=False, content="" shape: 1,...
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出整体表现优秀，准确理解并执行了用户的追问修改意图，实际调用工具完成了PPT压缩操作，最终输出了清晰的8页结构清单。唯一明显瑕疵是合并策略总结表格中出现了「3组内容合并」一行，错误地将「第3+4、5+6、9+10、11+12」列为4组，与实际执行的合并逻辑不符（第11+12页合并后结语内容保留在第8页，并非独立删除），存在轻微自相矛盾。内容提炼部分虽然是模拟生成的占位内容，但建议方向具体可行，整体质量较高。 【GEMINI】模型表现优秀，不仅准确理解并执行了用户的复杂修改指令，还通过编写 Python 代码实际完成了 PPT 的精简操作。方案规划合理，内容提炼到位，输出格式专业且易于理解，完全满足了任务需求。 【KIMI】该模型整体完成了PPT精简压缩任务，正确响应了用户的主要修改意图，生成了8页结构并保存为新文件。在意图追踪方面表现良好，但对「删除结语页」的理解存在轻微偏差（合并保留而非彻底删除）。结构规划合理，核心内容完整，但页码标注不够精确。内容提炼有具体建议但部分基于假设填充，实际可执行性有待验证。输出格式规范清晰，便于用户确认。综合表现良好，属于中等偏上水平。
困难难度评测结果
- 得分：17.8 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：
我来用 python-pptx 生成这份完整的高管汇报版 PPT。先检查环境，然后执行生成脚本。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出极度不完整，仅为一句准备执行的声明（「我来用 python-pptx 生成这份完整的高管汇报版 PPT。先检查环境，然后执行生成脚本。」），完全没有完成用户要求的核心任务——输出完整的文件内容和每页的详细设计说明。参考答案要求的7页结构、各页设计说明、格式细节等均未出现。这是一个严重的输出不完整问题，模型仅停留在「准备做」的阶段，而非实际完成任务。 【GEMINI】模型在对话交互阶段表现良好，能够准确理解并记录用户多次变更的需求，但在任务执行阶段表现极差。模型仅停留在「准备执行」的口头承诺，未实际调用工具读取输入文件，也未生成任何输出内容，导致任务最终未完成。 【KIMI】候选输出属于典型的'空头承诺'式回复，仅表示要执行任务，却完全没有展示任何实质性的规划、设计或执行内容。在多轮对话后用户已确认最终结构并要求'输出完整的文件内容和每页的详细设计说明'的情况下，模型仅回复一句即将开始生成，未回应任何具体需求。这与参考答案中详尽的7页结构规划、每页设计说明、格式细节要求形成鲜明对比。该输出完全无法满足评测要求，所有维度均不得分。
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