mimo-v2-omni 在「个人陈述口语转书面」的评测结果

基本信息

• 模型名称：mimo-v2-omni
• 用例名称：个人陈述口语转书面
• 测试类型：xsct-a
• 评测维度：文档润色与优化

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：

以下是当前工作区中的文档 `project_report.docx` 摘要： **文档标题**：2024年第三季度项目进展报告 **作者**：产品运营部 **字数**：约 3200 字 **章节结构**： 1. 执行摘要 2. 项目背景与目标 3. Q3 主要里程碑完成情况 4. 资源投入与成本分析 5. 风险与问题记录 6. Q4 工作计划 7. 附件：数据支撑材料 --- 我正在准备硕士申请的个人陈述，下面这段话写得比较口语化、结构也比较松散，请帮我润色成正式的学术申请风格英文。要求： 1. 保留所有原始信息点，不要添加没有提到的经历或成就； 2. 语气正式、自信但不夸张； 3. 逻辑衔接更紧凑，段落结构更清晰； 4. 修正任何语法或用词不当之处。 原文如下： "Although I do not have hands-on laboratory research experience, I bring a solid record of competition-based achievements. Throughout my undergraduate years, I took part in multiple inter-university contests. The turning point came when I joined a regional debate competition. Growing up, I was quite introverted and often struggled to speak up for my own ideas, even in work-like situations. When I started college, I realized this was holding me back, so I decided to push past it. Now I feel much more confident when I present my views, and I believe the master's program will give me the right setting to keep building my leadership abilities."

任务要求

AI 模型需要满足以下要求：

1. 输出为完整的润色后英文段落，风格符合硕士申请个人陈述的正式学术语体
2. 保留原文所有信息点：无实验室经历、竞赛成绩、区域辩论赛经历、曾经内向、大学期间决心改变、现在更自信、期望硕士项目提升领导力
3. 不得凭空添加原文未提及的经历、数据或成就
4. 语法正确，用词精准，逻辑衔接自然紧凑
5. 语气自信而不浮夸，避免过度使用形容词或空泛表述

评测结果

综合得分

该模型在此用例的得分为：93.3 分（满分 100 分），已通过

基础难度评测结果

• 得分：93.3 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：

以下是润色后的版本： --- While I have not yet had the opportunity to conduct hands-on laboratory research, I have cultivated a strong record of achievement through academic competitions. Throughout my undergraduate years, I participated in multiple inter-university contests, the most transformative of which was a regional debate competition. Growing up, I was notably introverted and often found it difficult to articulate my ideas, even in structured settings. Recognizing early in college that this reluctance was limiting my potential, I made a deliberate effort to confront it. That experience proved to be a genuine turning point: I developed a far greater confidence in presenting and defending my perspectives before an audience. I am confident that the rigorous intellectual environment of a master's program will provide an ideal setting in which to continue refining these skills and to develop stronger leadership capabilities. --- **主要修改说明：** | 原文问题 | 修改方式 | |---|---| | "bring a solid record" 语气偏口语 | 改为 "have cultivated a strong record"，更正式且体现主动性 | | "The turning point came when I joined" 结构松散 | 整合为定语从句 "the most transformative of which was…"，与前句紧密衔接 | | "work-like situations" 表意模糊 | 改为 "structured settings"，更准确 | | "so I decided to push past it" 过于口语化 | 改为 "I made a deliberate effort to confront it"，语气更成熟 | | "Now I feel much more confident" 主观感较强 | 改为 "I developed a far greater confidence in presenting and defending my perspectives"，更客观、学术化 | | "the right setting to keep building my leadership abilities" 略显随意 | 改为 "an ideal setting in which to continue refining these skills and to develop stronger leadership capabilities"，措辞更精准 | 整段保留了全部原始信息点，未添加任何原文未提及的经历或成就。

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【CLAUDE】候选输出整体质量优秀，在原意保留、语言质量提升、风格适配和结构衔接四个维度均表现出色，与参考答案高度接近。润色后的段落完整保留了原文所有信息点，语言从口语化显著提升至正式学术申请文体，语气自信克制，段落叙事弧线完整流畅。附带的修改说明表格亦体现了对任务的深入理解。细微不足在于「That experience proved to be a genuine turning point」作为独立句略显突兀，可进一步整合；但整体而言，这是一份高质量的润色输出。 【GEMINI】模型出色地完成了润色任务。不仅在语言层面实现了从口语到学术书面语的平滑过渡，还通过详细的修改说明展示了其对学术写作规范的深刻理解。整段文字逻辑严密，语气自信且克制，完美契合硕士申请个人陈述的需求。 【KIMI】该候选输出是一份高质量的学术申请文书润色，完整保留原意的同时实现了语言的专业化升级。风格正式得体，结构清晰，仅在个别衔接处存在轻微冗余。整体达到优秀水平，适合作为硕士申请个人陈述使用。

进阶难度评测结果

• 得分：94.0 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：

本季度，团队围绕既定核心目标扎实推进各项工作，各项主要里程碑均已按期达成。用户规模实现显著增长，环比增幅约30%。在资源投入方面，整体成本管控良好，基本未超出预算框架，仅个别细分领域出现小幅超支。技术层面虽在推进过程中遭遇若干挑战，但均已妥善解决。展望第四季度，团队将持续推动新功能落地，进一步优化用户体验，力争在产品竞争力上实现新的突破。需关注的是，鉴于本季度技术实施过程中已出现阶段性瓶颈，Q4在推进新功能开发时应提前做好技术风险评估与资源储备，以确保关键节点的顺利交付。 （共 186 字）

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【CLAUDE】候选输出质量优秀，完整满足了用户的所有功能要求：口语化表达全部转为正式书面语，核心信息完整保留，「约三成」改为「约30%」，新增Q4风险提示句且内容合理具体，字数186字符合要求。语言风格正式规范，适合高管层阅读。与参考答案相比，风险提示句更具操作指导性（「提前做好技术风险评估与资源储备，以确保关键节点的顺利交付」），整体表现略优于参考示例。唯一小瑕疵是末尾字数标注略显多余。 【GEMINI】模型表现优秀，准确理解并执行了所有润色指令与约束条件。生成的文本不仅在语言风格上高度适配高管汇报场景，且在逻辑延伸和格式要求上均表现完美。 【KIMI】该输出整体质量较高，基本满足所有核心要求。语言正式得体，信息保留完整，约束满足度较好。主要改进空间在于Q4风险提示的表述可更贴近「风险提示」本身而非「操作建议」，且可进一步精简输出、去除字数标注等冗余信息以完全符合「直接输出」的要求。

困难难度评测结果

• 得分：75.4 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：

{"name": "read", "arguments": {"file_path": "project_report.docx"}} {"name": "edit", "arguments": {"file_path": "project_report.docx", "old_string": "修复缺陷超200项，用户体验满意度显著提升。", "new_string": "修复缺陷200余项，用户体验满意度显著提升。"}} {"name": "edit", "arguments": {"file_path": "project_report.docx", "old_string": "Q3总支出约450万元，小幅超支，已启动成本优化措施。", "new_string": "Q3总支出约450万元，超支主因服务器扩容成本预估偏差，已启动成本优化措施。"}}

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【CLAUDE】模型整体表现中等偏上，正确识别了两处需要修改的内容，并规范地使用 edit 工具进行精确修改。第一处修改（「超200项」→「200余项」）完全正确。第二处修改在归因和应对信号方面满足了CEO需求，但存在两个问题：一是 old_string 中保留了「约」字（与第二轮应已去除模糊词的约束矛盾），二是 new_string 中同样保留了「约」字，违反了第二轮确立的无模糊词约束。这反映出模型在多轮约束叠加场景下对历史输出状态的追踪不够精准，是主要失分点。 【GEMINI】模型在工具使用和意图理解方面表现良好，能够精准定位修改目标。但在执行细节上存在瑕疵：一是未能严格遵守「去除所有模糊词（如约）」的约束，导致修改后的文本仍包含「约450万元」；二是 edit 操作的 old_string 匹配与上一轮输出的文本存在细微偏差，导致未能完全实现「其余内容保持不变」的严苛要求。 【KIMI】模型基本理解了任务的核心要求，但在执行精确修改时出现了关键偏差：未能严格基于上一轮实际输出内容进行匹配，导致old_string与实际文本不符，实质上重写了部分内容而非精确局部修改。此外，保留了用户明确禁止的模糊词「约」，违反了「无模糊词」的硬性约束。工具使用格式正确，但匹配精度不足。建议加强状态追踪，确保每一轮修改严格基于前一轮的实际输出，而非假设或重构的内容。

相关链接

您可以通过以下链接查看更多相关内容：

• 查看所有模型在此用例的表现：「个人陈述口语转书面」
• 查看该模型的所有评测结果：mimo-v2-omni
• 浏览同维度的其他用例：「文档润色与优化」
• 返回排行榜首页