mimo-v2-omni 在「项目报告核心概括」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：项目报告核心概括
- 测试类型：xsct-a
- 评测维度：文档问答
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 请用简明扼要的语言概括这份文档的核心要义。
任务要求
AI 模型需要满足以下要求：
- 概括内容需涵盖文档的主题（Q3项目进展报告）
- 需提及文档的关键组成部分，如里程碑完成情况、成本分析、风险记录、Q4计划等
- 语言简明扼要，避免冗长，总结控制在合理篇幅内
- 不得编造文档中未提及的具体数据或结论
评测结果
综合得分
该模型在此用例的得分为：93.6 分（满分 100 分），已通过
基础难度评测结果
- 得分：93.6 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：
这份文档是**产品运营部**对 **2024年第三季度**工作的阶段性复盘与下季度规划，核心要义可概括为三句话： 1. **回顾成果**：对照 Q3 既定里程碑，逐项汇报完成进度，展示关键交付物。 2. **盘点资源**：梳理人力与资金投入，进行成本效益分析，同时识别当前存在的风险与阻碍。 3. **部署下阶段**：基于 Q3 的进展与问题，制定 Q4 工作计划，并附上数据材料作为决策支撑。 **一句话概括**：这是一份标准的季度项目管理报告——**"Q3 做了什么、花了多少、有什么风险、Q4 接着干什么"**。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出在内容覆盖、表达简洁和结构组织三个维度均表现优异。三点式拆解配合「一句话概括」的双层结构，既满足了详细阅读需求，也提供了快速理解入口，整体质量明显高于参考答案的平铺直叙风格。 【GEMINI】模型表现出色，能够精准提取文档核心要义，概括逻辑清晰，语言表达既专业又具有高度的可读性，完美满足了任务要求。 【KIMI】该候选输出是一份高质量的文档概括范例。在严格遵循「不编造、不冗长」原则的前提下，以极短的篇幅完整呈现了3200字报告的核心骨架，体现了优秀的信息提取与压缩能力。三层递进式概括加一句话点睛的结构设计，既满足功能性要求，又具备良好的可读性，符合专业助理场景下的文档摘要需求。
进阶难度评测结果
- 得分：87.2 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：
1. Q3整体完成率87%，用户增长与体验优化项目达标，数据中台延期两周。 2. 预算执行良好，支出462万元，节余18万元，用户增长ROI达1:4.3。 3. 主要风险为数据中台延期，已增援工程师；Q4将完成上线并启动增长二期。 最突出的风险项是数据中台项目因技术选型争议导致延期，需加班赶工并增派人力以确保10月中旬上线。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出整体质量较好，3个要点覆盖了核心维度，格式基本符合要求，风险识别准确。主要不足在于要点3将风险信息与Q4计划混合，导致三个维度的边界不够清晰；另有少量轻微冗余表达。与参考答案相比，内容完整性和准确性接近，格式执行略有瑕疵，属于中上水平的输出。 【GEMINI】模型表现出色，完全满足了所有任务要求。在内容概括上做到了精准覆盖，格式控制非常严谨，风险识别准确且具备针对性，语言表达简洁高效。 【KIMI】该输出基本完成了任务要求，3个要点覆盖了核心维度，风险识别准确，格式大体合规。主要改进空间在于：要点3应纯粹聚焦Q4计划而不混入风险内容，要点2应明确给出预算总额以增强信息完整性，风险说明句可更精炼避免与要点3重复。整体表现良好，达到中等偏上水平。
困难难度评测结果
- 得分：93.9 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：
briefing_q3.md # 2024年Q3项目进展简报 / Q3 2024 Project Progress Briefing --- ## 一、核心摘要 / Executive Summary **中文：** 2024年Q3，产品运营部的一体化数字运营平台项目整体进展顺利。三大模块中，用户增长模块V2.0提前3天上线，DAU提升18%；数据分析看板原型按期交付并获评"优秀"；供应链管理模块因第三方接口联调问题延迟两周进入UAT。成本方面，实际支出817万元，低于预算852万元，节余约4.1%。Q4计划重点为供应链模块灰度发布、数据看板正式上线及全平台集成测试。 **English：** In Q3 2024, the integrated digital operations platform project by the Product Operations Department progressed well overall. Among the three core modules: the User Growth Module V2.0 launched 3 days ahead of schedule, boosting DAU by 18%; the Data Analytics Dashboard prototype was delivered on time and rated "Excellent"; the Supply Chain Module entered UAT 14 days late due to third-party API integration issues. Actual expenditure was 8.17 million yuan against a budget of 8.52 million yuan. Q4 priorities include the supply chain module's gray release, the dashboard's official launch, and full-platform integration testing. --- ## 二、风险分析 / Risk Analysis ### 🔴 高风险 / High Risk **中文：** - 风险描述：供应链模块第三方接口稳定性不足 - 应对措施：已启动备选供应商评估 - ⚠️ 影响Q4交付：是——Q4首要任务为供应链模块完成UAT并灰度发布，接口问题未解决将直接影响10月灰度发布节点 **English：** - Risk: Supply chain module's third-party API lacks stability - Mitigation: Alternative supplier evaluation initiated - ⚠️ Q4 Impact: Yes — Q4 priority is supply chain UAT and gray release; unresolved API issues will directly jeopardize the October release milestone --- ### 🟡 中风险 / Medium Risk **中文：** - 风险描述：核心开发人员离职风险（2人提出离职意向） - 应对措施：已启动内部轮岗与外部招聘 - ⚠️ 影响Q4交付：是——人员缺口可能影响Q4多模块并行推进的人力保障 **English：** - Risk: Key developer attrition risk (2 staff have indicated resignation intent) - Mitigation: Internal rotation and external recruitment underway - ⚠️ Q4 Impact: Yes — staffing gaps may compromise the parallel execution of multiple Q4 deliverables --- ### 🟢 低风险 / Low Risk **中文：** - 风险描述：数据分析模块在大数据量场景下响应偏慢 - 应对措施：计划Q4进行性能优化 - ⚠️ 影响Q4交付：可能——该问题已列入Q4优化计划，若优化工作量超预期，可能影响11月数据看板V1.0正式上线节点 **English：** - Risk: Data analytics module exhibits slow response under heavy data loads - Mitigation:...
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选模型在这道多轮意图追踪+矛盾指令处理的复合任务中表现优秀。最关键的差异化细节（中英文节余数据不同）处理正确，信息提取准确无编造，结构清晰规范。主要不足在于中英双语的排列方式采用了交叉嵌套而非严格分离，以及工具参数名存在轻微偏差，整体仍属高质量输出。 【GEMINI】模型表现优秀，不仅准确理解并执行了复杂的多轮指令，还展现了对细节（如中英文差异化处理）的极强把控力。工具使用规范，输出内容结构化且准确，完全符合评测标准。 【KIMI】该候选输出整体质量优秀，完整完成了复杂的多轮意图追踪任务，精准处理了中英文内容差异的矛盾指令，信息提取准确，结构规范清晰。主要改进空间在于工具调用格式的规范性（避免HTML转义符）以及部分表述与原文的严格对应。总体而言，是一份高质量的双语简报输出。
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