Google: Gemma 4 26B A4B 在「技术邮件翻译」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：技术邮件翻译
- 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：
请将以下中文内容翻译成英文： 你好，马克，感谢你的来信。我会将此问题转交给我们的工程团队进行评估，稍后会把技术参数表发给你。另外请注意，外壳部分必须采用UL94 V0等级的阻燃材料。
任务要求
AI 模型需要满足以下要求：
- 翻译结果为英文，语句通顺自然
- 准确翻译专业术语，如'技术参数表'(technical data sheet)、'UL94 V0等级'、'阻燃材料'(flame-retardant material)等
- 保留原文的商务邮件语气和礼貌用语
- 信息完整，不遗漏任何要点
评测结果
综合得分
该模型在此用例的得分为：96.7 分（满分 100 分），已通过
基础难度评测结果
- 得分：96.7 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 English translation: "Hi Mark, thank you for your email. I will forward this issue to our engineering team for evaluation and will send you the technical data sheet later. Additionally, please note that the housing must be made of UL94 V0 grade flame-retardant material."
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】这是一份非常优秀的翻译。不仅准确地转换了语言，还精准地捕捉到了专业工业术语，并保持了商务邮件应有的专业和礼貌语气。完全达到了任务要求。 【KIMI】整体来看，该翻译结果准确、完整地传达了原文信息，专业术语翻译准确，语言流畅自然，符合商务邮件的语体要求。
进阶难度评测结果
- 得分：96.5 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：
Hi Eva，感谢您的反馈。我们会对此进行进一步分析。关于连接器——请确保其防水等级符合 IP67 标准，且工作电压范围应为 9V 至 36V DC。散热模块必须支持至少 15W 的连续功率，且结温（junction temperature）不得高于 125°C。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】模型在处理含专业术语的商务翻译任务中表现非常出色。特别是在第二轮英译中翻译中，能够精准捕捉技术细节并以标准的中文技术规范呈现，同时严格执行了保留参数原样的约束性指令。整体翻译质量高，术语规范，完全满足专业商务沟通的需求。 【KIMI】整体来看，翻译质量很高，准确保留了所有技术参数和单位，专业术语翻译规范，信息完整性好。语言流畅性整体不错，仅有少量地方可优化。工具使用和流程合理，符合要求。
困难难度评测结果
- 得分：96.0 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：
Subject: Regarding Material Test Results for Batch KG-2024-0712 (PC-GF30) Dear Eva Mueller, I hope this email finds you well. We have recently completed the comprehensive material evaluation for the next-generation EV charging station enclosure samples (Sample ID: PC-GF30-2024-A, Batch No.: KG-2024-0712) provided by Kunststoff GmbH. First, I would like to share some positive news. The mechanical and thermal performance of the material is excellent; specifically, the tensile strength (62 MPa), flexural modulus (7800 MPa), and Heat Deflection Temperature (142°C) all exceeded our required standards. We are very satisfied with these aspects of the material. However, we encountered a discrepancy regarding the flame retardancy rating. While your previous communication indicated that this material has achieved the UL 94 V-0 rating, our internal laboratory testing yielded a result of V-1. As the UL 94 V-0 rating is a non-negotiable mandatory requirement for this project's safety standards, we must resolve this inconsistency before proceeding. To help us clarify this matter, could you please provide the official UL 94 V-0 test report issued by an accredited third-party laboratory? Additionally, please send us the latest technical data sheets (TDS) for this specific material grade. We would appreciate it if you could provide these documents and your feedback within the next two weeks. Thank you for your continued cooperation and support. We look forward to hearing from you soon. Best regards, [Your Name] [Your Title/Company] *** **内部备注 (Internal Note):** 如果供应商在两周内无法提供有效的第三方 UL 94 V-0 认证报告，请采购团队立即启动备选供应商的评估流程，以确保项目进度不受影响。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】这是一份近乎完美的生成结果。模型不仅准确地执行了当前指令中的所有细节要求，还展现了极强的上下文整合能力，将前几轮对话中发现的矛盾点巧妙地融入商务邮件中。技术术语使用精准，数据引用无误，语气拿捏得当，完全符合专业AI助手的定位。 【KIMI】整体而言，模型很好地完成了任务，体现了对整个对话上下文的理解和追踪，生成的邮件内容全面，语气专业友好，格式规范，很好地满足了用户的各项要求。只是在邮件标题和结尾签署语上还有些许提升空间。
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