doubao-seed-1-8 在「论文排版格式调整」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：doubao-seed-1-8
- 用例名称：论文排版格式调整
- 测试类型：xsct-a
- 评测维度：文档格式排版
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：
请帮我调整毕业论文《基于机器学习的交通流量预测分析》的排版格式，按照常见的本科毕业论文格式要求进行排版。
任务要求
AI 模型需要满足以下要求：
- 正确识别用户意图为毕业论文格式排版调整
- 应涵盖标题、正文、页眉页脚、页边距、行距、字体字号等常见排版要素的设置说明或操作
- 格式要求应符合常见本科毕业论文规范（如中文宋体/黑体、英文Times New Roman、小四号正文、1.5倍行距等）
- 对论文各级标题的格式层级做出明确区分和设置
评测结果
综合得分
该模型在此用例的得分为：55.2 分（满分 100 分），未通过
基础难度评测结果
- 得分：55.2 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：
我先查看当前工作目录中的文件，确认毕业论文文档是否存在： [{"name":"ls","parameters":{"cmd":"ls -la"}}]
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出仅完成了任务的第一步——使用 ls 工具查看工作目录，属于典型的「只开了头」的不完整回答。虽然方向正确，但完全未进入格式规范设置的核心环节，既没有读取文件内容，也没有给出任何排版格式调整的规范说明或实际操作，与参考答案的完整执行流程相差甚远。整体输出质量较低，不符合任务完成要求。 【GEMINI】模型在任务执行的初始阶段表现良好，能够通过工具调用来获取上下文信息。但由于当前仅处于交互的第一步，尚未对具体的排版格式要求进行说明或执行，导致在「格式规范完整性」维度得分较低。建议模型在后续交互中，在确认文件后，主动向用户列出拟执行的排版规范，以体现专业性。 【KIMI】候选输出展示了正确的意图理解和合理的工具使用起点，但内容过于简略，仅完成了查看目录的第一步，尚未涉及任何具体的格式调整内容或完整的工具执行流程。从输出本身无法评估其对本科毕业论文格式规范的掌握程度，工具调用格式也存在非标准标记的问题。
进阶难度评测结果
- 得分：66.0 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：
我将为你提供详细的论文排版调整指南，包含手动操作步骤和自动化脚本两种方案，你可以根据自己的情况选择： ### 方案一：手动Word操作步骤（推荐） #### 1. 基础格式设置 - **打开文档**：打开thesis.docx - **设置页边距**： - 页面布局 → 页边距 → 自定义边距 - 上：2.54cm，下：2.54cm，左：3.17cm，右：3.17cm - **设置正文格式**： - 全选正文（Ctrl+A）→ 字体：宋体小四 → 段落 → 行距：固定值20磅 #### 2. 标题样式设置 - **章标题格式**： - 选中所有章标题（如"第1章 绪论"）→ 字体：黑体三号 → 段落：居中对齐 - 点击"开始"选项卡→"样式"→"标题1"→右键→"更新标题1以匹配所选内容" - **节标题格式**： - 选中所有节标题→字体：黑体四号→段落：左对齐 - 点击"开始"选项卡→"样式"→"标题2"→右键→"更新标题2以匹配所选内容" #### 3. 页码与页眉设置 - **插入分节符**： - 将光标移到"第1章 绪论"前→页面布局→分隔符→下一页分节符 - **设置罗马数字页码**： - 光标移到摘要页面→插入→页码→设置页码格式→罗马数字(I,II,III...)→应用于：本节 - **设置阿拉伯数字页码**： - 光标移到第1章页面→插入→页码→设置页码格式→阿拉伯数字(1,2,3...)→起始页码：1→应用于：本节 - **设置页眉**： - 双击页眉区域→输入论文题目"基于机器学习的交通流量预测方法研究"→设置字体格式 #### 4. 生成自动目录 - 将光标移到摘要前→引用→目录→自动目录1 - 右键目录→更新域→更新整个目录 ### 方案二：Python自动化脚本（需安装python-docx库） ```python from docx import Document from docx.shared import Pt, Cm from docx.enum.text import WD_ALIGN_PARAGRAPH from docx.enum.section import WD_SECTION from docx.enum.text import WD_LINE_SPACING # 打开文档 doc = Document("thesis.docx") # 1. 设置页边距 sections = doc.sections for section in sections: section.top_margin = Cm(2.54) section.bottom_margin = Cm(2.54) section.left_margin = Cm(3.17) section.right_margin = Cm(3.17) # 2. 设置标题样式 for paragraph in doc.paragraphs: # 章标题格式（匹配"第X章"开头的段落） if paragraph.text.startswith("第") and "章" in paragraph.text: paragraph.style = doc.styles["Heading 1"] run = paragraph.runs[0] run.font.name = "黑体" run.font.size = Pt(16) # 三号字体对应16磅 paragraph.alignment = WD_ALIGN_PARAGRAPH.CENTER # 节标题格式（匹配"1.1"、"2.2"等开头的段落） elif paragraph.text and paragraph.text[0].isdigit() and "." in paragraph.text.split()[0]: paragraph.style = doc.styles["Heading 2"] run = paragraph.runs[0] run.font.name = "黑体" run.font.size = Pt(14) # 四号字体对应14磅 paragraph.alignment = WD_ALIGN_PARAGRAPH.LEFT # 正文格式 else: for run in paragraph.runs: run.font.name = "宋体" run.font.size = Pt(12) # 小四字体对应12磅 paragraph.paragraph_format.line_spacing_rule = WD_LINE_SPACING.EXACTLY paragraph.paragraph_format.line_spacing = Pt(20)...
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】模型在理解层面表现非常优秀，能够精准捕捉用户的所有排版细节要求。然而，在执行层面存在重大缺陷：它未能按照 OpenClaw 环境的要求主动调用工具完成任务，而是以对话式助手的方式提供了操作教程和不完整的脚本。这种‘只说不做’的表现不符合 AI Agent 在该测试场景下的核心职能要求，尤其是在处理分节符和页码等核心难点时选择了规避。 【KIMI】候选人输出全面覆盖了用户提出的格式调整需求，理解准确，提供了手动和自动化两种方案。但在自动化脚本的实现上不够完整，特别是页码和页眉的设置需要手动完成，影响了工具调用的准确性和格式约束的严格遵守。整体而言，候选人表现出较强的理解和问题解决能力，但在自动化实现上还有待完善。
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