OpenAI: gpt-oss-20b 在「学术长句润色」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：OpenAI: gpt-oss-20b
- 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：
- 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
- 润色后的文本应为正式学术书面语风格，避免口语化表达
- 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
- 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：15.3 分（满分 100 分），未通过
基础难度评测结果
- 得分：15.3 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：
**润色后文本** The observed phenomenon can be attributed to
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出严重不完整，仅生成了润色句子的开头半句「The observed phenomenon can be attributed to」，随即截断，未完成任何实质性的润色改写任务。所有评分维度均接近最低分。这是一次明显的生成失败，模型未能完成用户要求的段落润色任务。 【GEMINI】模型在生成过程中出现了严重的截断或提前终止问题，仅输出了润色句的开头，未能完成对长句的改写任务。输出内容无法满足任何功能要求，属于严重的生成失败。 【KIMI】候选输出存在严重的完整性问题，仅提供了一个不完整的句子开头，完全未能完成润色任务。虽然片段本身风格正确，但核心语义几乎全部丢失，无法进行有效的语言质量评估。这可能是由于生成过程中断、输出截断或模型未能理解任务要求所致。建议检查生成过程的完整性，并重新执行润色任务。
进阶难度评测结果
- 得分：— 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 json{"cmd":["bash","-lc","ls -R"]}
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选模型完全未完成任务。其输出为一段格式异常的内容（疑似工具调用协议错误或输出截断），既未生成润色文本，也未调用正确的 edit 工具写入文件。所有评分维度均为0分，属于完全失败的响应。 【GEMINI】模型完全偏离了用户指令，未执行任何润色任务，也未按要求调用 edit 工具修改文档，而是执行了无关的 ls 命令，任务完成度为零。 【KIMI】候选输出完全失败。模型没有理解用户的任务要求（润色学术长句并写入文档），而是输出了格式混乱、内容无关的字符串，其中包含一个错误的 ls 工具调用。用户明确要求：1）润色特定段落；2）拆分长句；3）替换'可观测影响'；4）补充建议说明；5）使用 edit 工具写入 project_report.docx 第5章。候选输出未满足任何一项要求，属于严重的任务偏离和格式错误。
困难难度评测结果
- 得分：54.5 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：
{"path":"project_report.docx","old_text":"[Placeholder: Discussion paragraph about subgroup analysis findings]","new_text":"This observation could potentially be attributed to the routine administration of lipid‑lowering and anticoagulant therapies within the study cohort, which may have mitigated the arterial structural changes commonly associated with hyperlipidemia, while the anticoagulant treatment primarily reduces thrombin generation and clot formation rather than directly altering the hemodynamics of venous stasis; consequently, the influence of these treatments on the stratified analysis might be attenuated."}
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出在医学逻辑修正和第一段学术语体方面表现尚可，正确区分了抗凝药物的凝血通路机制与静脉淤滞的血流动力学机制，hedging语言使用适当。然而，最关键的缺陷是未能完整执行用户的四条要求：完全缺失Limitations小节的第二段内容及其对应的edit工具调用，导致多轮意图追踪和信息完整性两个维度得分极低。这是一个执行不完整的典型案例，模型只完成了约一半的任务要求。 【GEMINI】模型在医学逻辑修正和学术语体润色方面表现出色，准确理解了导师对医学机制的严谨要求。然而，在多轮指令执行上存在严重疏漏，未能按照要求将第二段内容写入指定的 Limitations 小节，导致任务未完整闭环。 【KIMI】该候选输出在多轮意图追踪与执行维度表现最差，严重遗漏了用户明确要求的分段和位置要求，仅完成了一半的任务。医学逻辑修正也未达到要求，未能清晰区分降脂药与抗凝药的不同作用机制。学术语体基本合格但hedging程度可进一步加强。信息完整性因遗漏第二段而严重受损。整体而言，该输出未能满足用户的核心需求，需要重新生成以补全Limitations段落并修正医学逻辑表述。
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