Google: Gemma 4 31B 在「项目报告精简摘要」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 31B
- 用例名称：项目报告精简摘要
- 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` 的内容摘要： **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 请阅读该文档，将其内容精简概括为一段不超过350字的摘要。
任务要求
AI 模型需要满足以下要求：
- 摘要总字数不超过350字（中文字符计数）
- 摘要需涵盖文档的核心章节要点，包括项目背景、Q3里程碑完成情况、资源与成本、风险问题、Q4计划等关键信息
- 语言精练通顺，逻辑结构清晰，具有可读性
- 不得遗漏重要章节内容，不得编造文档中未提及的具体数据
评测结果
综合得分
该模型在此用例的得分为：1.0 分（满分 100 分），未通过
基础难度评测结果
- 得分：1.0 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：
[[reply_to_current]]由于我目前无法直接读取 `.docx` 格式的二进制文件，请您将该文档的内容以文本形式发送给我，或者将其转换为 `.txt` 或 `.md` 文件并告知我文件路径。 一旦我获取到具体文本，将立即为您提供一份 350 字以内的精简摘要。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出完全未完成评测任务。模型错误地认为无法处理 .docx 文件并要求用户提供额外信息，但实际上用户消息中已提供了文档的详细内容结构（标题、作者、章节、字数等），足以让模型尝试调用 read 工具读取文件或基于已有信息生成摘要。模型应展现主动性，尝试使用可用工具（如 read）获取文档内容，而非直接拒绝任务。这是典型的任务理解失败和工具使用能力缺失，导致零分评价。 【GEMINI】模型完全未能履行摘要任务。尽管系统提示词中提供了 read、ls 等工具，模型并未尝试调用这些工具来获取 project_report.docx 的内容，而是直接给出了无法处理二进制文件的回复。这导致所有功能性考核点均未达成，任务完成度极低。 【KIMI】候选人未能直接处理文档并生成摘要，而是请求用户提供文档的文本形式或转换格式，这不符合任务要求。因此，在所有评分维度上均未能得分。
进阶难度评测结果
- 得分：93.4 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：
针对当前面临的供应链交付延迟与核心人员流动风险，团队已通过制定应急预案及人才保留方案确保项目稳健运行。基于此，Q4工作计划将把风险对冲与业务推进深度结合：在优先推进产品商业化推广与数据能力建设的同时，重点加强供应链多元化布局以消除交付隐患，并通过优化团队激励机制保障关键岗位稳定，确保下季度融资准备及业务目标的顺利达成。
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。在内容完整性方面，准确提取了两个章节的核心信息，涵盖风险点、应对措施及Q4三项优先事项；逻辑关联性清晰，通过连接词和因果表述有效展现了风险应对与Q4计划的衔接关系；格式与字数约束完全满足要求，119字的连贯段落符合规范；语言表达简洁专业，书面化程度高。唯一可优化之处在于「供应链多元化布局」的细化表述略超原文信息范围，但不影响整体质量。综合来看，该输出高质量完成了任务要求。 【GEMINI】生成结果非常出色，不仅完美遵守了所有的格式和字数约束，而且在信息提取的完整性与逻辑构建上表现优异。模型成功地将分散的风险点与未来的工作计划整合在一起，形成了一段逻辑自洽且具有洞察力的总结。 【KIMI】整体而言，输出质量较高，准确提取了核心信息，逻辑关联清晰，格式和字数控制得当，语言表达流畅。
困难难度评测结果
- 得分：92.6 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：
Here is the updated detailed summary covering the three requested sections (under 350 words), followed by the budget comparison table. **Resource Investment & Cost Analysis** Q3 actual expenditure was 3.62 million RMB, coming in 4.7% under the allocated budget of 3.8 million RMB. The workforce consisted of 32 core team members and 15 outsourced contractors. Notably, server and cloud resource costs increased by 18% year-on-year, driven primarily by the surge in user growth. **Risks & Issues** - **High Risk**: Instability of third-party API providers led to ecosystem integration delays, achieving only 60% of the 80% target. - **Medium Risk**: Potential delays in Q4 budget approval may hinder the initiation of new features. - **Resolved**: A critical database performance bottleneck in August was successfully mitigated via a sharding solution. **Q4 Work Plan** - **Ecosystem Completion**: Finalize the remaining 20% of third-party integrations. - **Platform 3.0**: Launch planning and technical pre-research for the next version. - **User Growth**: Scale Daily Active Users (DAU) from the 550K peak to 700K. - **System Resilience**: Implement a comprehensive disaster recovery and fault-tolerance mechanism. *** **Q3 Budget vs. Actual Expenditure** | Category | Budgeted (RMB) | Actual (RMB) | Variance (%) | | :--- | :--- | :--- | :--- | | Total Q3 Cost | 3,800,000 | 3,620,000 | -4.7% |
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】模型表现优秀，准确追踪了用户四轮对话中的多次意图变更，最终输出完全符合「三章节英文详细+表格」的要求。三个章节的关键数据准确完整，正文字数控制良好（约180-190词，远低于350词上限），表格格式规范。主要扣分点在于未明确标注「表格不计入字数限制」这一用户要求的说明文字。英文表达专业流畅，结构清晰，整体完成度高。 【GEMINI】模型表现非常出色。它不仅成功处理了复杂的多次指令变更，准确提取并翻译了所有关键业务数据，还敏锐地纠正了历史对话中的数据笔误。字数控制精准，格式规范，英文表达质量极高。唯一的小瑕疵是对“表格不计入字数”的标注方式略显含蓄，但整体上是一份高质量的回答。 【KIMI】模型整体表现优秀，准确理解并遵循了用户的多次变更意图，信息呈现准确完整，字数和格式符合要求，英文表达专业流畅。只是在表达简洁性上有轻微不足，总体而言是一份高质量的输出。
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