OpenAI: gpt-oss-120b 在「学术长句润色」的评测结果

基本信息

• 模型名称：OpenAI: gpt-oss-120b
• 用例名称：学术长句润色
• 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 请帮我润色以下段落，这段话出现在报告第5章「风险与问题记录」中。要求保持学术书面语风格，提升句子的清晰度和可读性，但不能改变原意。原文如下： This phenomenon might be attributed to the fact that the standardized deployment of resource optimization strategies and redundancy failover mechanisms across the project portfolio, which could have effectively counteracted the operational disruptions ordinarily precipitated by budget constraints and personnel turnover, thereby attenuating their discernible influence during the quarterly risk assessment.

任务要求

AI 模型需要满足以下要求：

1. 润色后的文本必须完整保留原文的核心语义：标准化的资源优化策略和冗余容灾机制抵消了预算限制和人员流动带来的运营中断，从而在季度风险评估中减弱了这些因素的可见影响
2. 润色后的文本应为正式学术书面语风格，避免口语化表达
3. 应提升原文的清晰度和可读性，例如拆分过长从句、减少不必要的修饰词、使逻辑层次更分明
4. 润色结果仍为英文

评测结果

综合得分

该模型在此用例的得分为：95.8 分（满分 100 分），已通过

基础难度评测结果

• 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：

This phenomenon can be attributed to the standardized implementation of resource‑optimization strategies and redundancy‑failover mechanisms throughout the project portfolio. These measures likely mitigated the operational disruptions typically caused by budget constraints and staff turnover, thereby diminishing their observable impact in the quarterly risk assessment.

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，在原意保留、语言质量提升和风格适配三个维度上均表现出色，与参考答案的改写思路高度一致。主要亮点在于将复杂长句有效拆分为两句、去除冗余修饰、保留全部核心语义要素，同时维持了正式学术书面语风格。细微差异（如「staff turnover」vs「personnel turnover」、「can be」vs「may be」）均属合理的同义替换，不影响整体质量。 【GEMINI】模型表现出色，成功地将原文复杂的长难句转化为逻辑清晰、表达简洁且学术性强的专业文本。改写不仅提升了可读性，还精准地保留了所有关键信息点，达到了极高的润色质量。 【KIMI】候选输出是一份高质量的学术润色结果，在保留原意、提升可读性和保持学术风格三方面均表现优秀。成功将冗长复杂的原文重构为清晰流畅的两句话，核心语义完整无损，语言精炼专业，符合任务要求。

进阶难度评测结果

• 得分：1.1 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：

assistantcommentary to=ls code{"path": ""}

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【CLAUDE】候选模型的输出完全未完成任务。输出内容仅为一个格式异常的 ls 工具调用片段，既没有生成润色后的文本，也没有替换「可观测影响」，更没有补充建议性说明，也未正确调用 edit 工具写入文件。整体表现极差，几乎所有评分维度均为零分。 【GEMINI】模型完全未能理解并执行用户的指令。在面对明确的润色任务和工具调用要求时，模型仅输出了一个无关的 ls 工具调用，未提供任何润色后的文本内容，也未对 project_report.docx 进行任何编辑操作，任务完成度为零。 【KIMI】候选输出严重失败，仅显示了一个未完成且格式错误的工具调用片段，未产生任何实际的润色文本，完全未满足用户在语义保留、句式拆分、术语替换、建议补充及工具调用等方面的全部要求。模型似乎陷入了某种内部状态或格式错误，未能完成基本任务。

困难难度评测结果

• 得分：— 分
• 通过状态：未通过

AI 评审点评

以下是 AI 评审对该模型输出的点评：

模型返回空输出，已跳过 AI 评分（finish_reason=stop）

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：「学术长句润色」
• 查看该模型的所有评测结果：OpenAI: gpt-oss-120b
• 浏览同维度的其他用例：「文档润色与优化」
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