Google: Gemma 4 26B A4B 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：Google: Gemma 4 26B A4B
- 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：92.3 分（满分 100 分），已通过
基础难度评测结果
- 得分：92.3 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：
这份《2024年第三季度项目进展报告》的核心要义可概括为： 该报告由产品运营部编写，全面回顾了项目在2024年Q3阶段的执行情况。其核心内容涵盖了**项目目标的达成度（里程碑完成情况）**、**资源与成本的投入效率**、**当前面临的风险与问题**，并以此为基础规划了**第四季度的行动路线图**。 **简而言之：报告通过对比目标与实际进度，评估了项目的资源使用效率与潜在风险，并明确了下一阶段的工作重点。**
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】这是一份高质量的文档概括。模型不仅准确识别并罗列了文档的组成部分，还通过专业且凝练的语言对报告的实质意义（评估效率、管理风险、规划未来）进行了深度提炼。结构布局合理，非常符合专业AI助手的定位。 【KIMI】整体而言，该概括很好地覆盖了文档的核心内容，语言简明扼要，结构清晰。但在可读性方面还有一定提升空间。
进阶难度评测结果
- 得分：100.0 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：
这份报告的核心要义精炼如下： 1. **项目进展：** Q3整体完成率87%，用户增长与体验优化均达标。 2. **财务状况：** 预算执行良好，实际支出462万元，节余18万元。 3. **后续计划：** Q4重点在于数据中台上线及冲刺600万MAU目标。 **最突出的风险项：** 数据中台项目因技术选型争议导致进度延期。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】该模型的表现非常出色，完美执行了用户提出的所有复杂指令和字数约束。在内容提取上做到了高度精准，在格式控制上严丝合缝，风险识别抓住了重点，是一份高质量的概括。虽然对话上下文中未显示其调用工具读取文件的过程，但基于用户最后提供的文本，其生成结果无可挑剔。
困难难度评测结果
- 得分：95.8 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：
call:write{content: "# Q3 Project Progress Briefing / 2024年第三季度项目进展简报 ## 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提前上线且DAU提升18%，数据分析看板原型按期交付并获评优秀，但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%。 **English:** In Q3 2024, the Integrated Digital Operations Platform project managed by the Product Operations Department progressed smoothly overall. Among the three core modules, the User Growth Module V2.0 was launched ahead of schedule, resulting in an 18% increase in DAU. The Data Analysis Dashboard prototype was delivered on time with an "excellent" rating. However, the Supply Chain Management module's entry into the UAT phase was delayed by two weeks due to third-party interface integration issues. Total actual expenditure was 8.17 million RMB against a budget of 8.52 million RMB. --- ## 风险分析 / Risk Analysis ### 高风险 / High Risk - **风险描述 / Description:** 供应链模块第三方接口稳定性不足 / Insufficient stability of the third-party interface for the supply chain module. - **应对措施 / Mitigation:** 已启动备选供应商评估 / Initiated evaluation of alternative suppliers. - **⚠️ 影响Q4交付 / ⚠️ Impact on Q4 Delivery:** 是 / Yes. ### 中风险 / Medium Risk - **风险描述 / Description:** 核心开发人员离职风险（2人提出离职意向） / Risk of core developers leaving (2 employees expressed intention to resign). - **应对措施 / Mitigation:** 已启动内部轮岗与外部招聘 / Initiated internal rotation and external recruitment. - **⚠️ 影响Q4交付 / ⚠️ Impact on Q4 Delivery:** 是 / Yes. ### 低风险 / Low Risk - **风险描述 / Description:** 数据分析模块在大数据量场景下响应偏慢 / Slow response of the data analysis module under large data volumes. - **应对措施 / Mitigation:** 计划Q4进行性能优化 / Performance optimization is planned for Q4. - **⚠️ 影响Q4交付 / ⚠️ Impact on Q4 Delivery:** 可能 / Possible. ",file_path: "briefing_q3.md"}
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】模型展现了极高的一致性和指令遵循能力。它不仅准确提取并整合了多次交互产生的信息，还精准地执行了中英文内容差异化处理这一高难度指令。文档结构清晰，双语对应准确，工具调用无误，是一次非常出色的生成。 【KIMI】模型整体表现优秀，准确理解并执行了用户的多轮复杂指令，信息提取准确，结构化输出规范，工具调用和任务完成度较高。在意图追踪与变更处理、信息提取准确性方面表现尤为出色，很好地处理了中英文节余百分比的矛盾指令。
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