Google: Gemma 4 26B A4B 在「表格单元格居中对齐」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：表格单元格居中对齐
- 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：
以下是当前工作区中的数据文件 `orders_q3.csv` 内容（前10行）： ```csv 客户ID,客户姓名,订单编号,下单日期,商品名称,数量,单价,总金额,状态 C001,赵建国,ORD-2024-0801,2024-08-01,办公椅,2,580,1160,已完成 C002,李梅,ORD-2024-0802,2024-08-02,打印机,1,2300,2300,已完成 C001,赵建国,ORD-2024-0803,2024-08-05,墨盒×10,3,45,135,已完成 C003,王鹏,ORD-2024-0804,2024-08-07,显示器,2,1800,3600,已完成 C002,李梅,ORD-2024-0805,2024-08-10,键盘,1,320,320,退款中 C004,张晓燕,ORD-2024-0806,2024-08-12,移动硬盘,2,450,900,已完成 C001,赵建国,ORD-2024-0807,2024-08-15,办公桌,1,1200,1200,已完成 C003,王鹏,ORD-2024-0808,2024-08-18,路由器,1,380,380,配送中 C005,陈伟,ORD-2024-0809,2024-08-20,笔记本支架,3,180,540,已完成 C002,李梅,ORD-2024-0810,2024-08-22,耳机,2,260,520,已完成 ``` （共 87 条记录） 请将这份 CSV 数据转换为 Markdown 表格，并确保所有单元格内容在水平方向上居中对齐。将结果写入 `orders_q3_table.md` 文件。
任务要求
AI 模型需要满足以下要求：
- 将 CSV 数据正确转换为 Markdown 表格格式
- 表头与数据行完整保留，不丢失任何字段或记录
- 使用 Markdown 居中对齐语法（即分隔行使用 :---: 格式）使所有列内容水平居中
- 最终结果写入 orders_q3_table.md 文件
评测结果
综合得分
该模型在此用例的得分为：4.0 分（满分 100 分），未通过
基础难度评测结果
- 得分：4.0 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：
call:exec{command: "cat orders_q3.csv"}
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】模型在处理该任务时表现出了一定的逻辑性，意识到需要先获取完整的数据文件。但作为针对特定指令（转换格式并写入文件）的响应，它仅停留在第一步（读取文件），未能执行核心的转换逻辑、对齐格式设置以及文件写入操作。由于没有产出最终结果，该次生成在功能实现上是不完整的。 【KIMI】整体评价：生成结果未能满足任何评分维度的要求，没有进行 CSV 到 Markdown 表格的转换，也没有正确使用工具和输出文件。
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：
好的，没问题。为了实现这些精细的格式控制（特别是单元格对齐、边框统一和字体设置），我们需要深入操作 `python-docx` 的底层 XML 结构。 下面是完整的 Python 脚本实现方案。你可以直接运行它来生成文档。 ```python import csv from docx import Document from docx.shared import Pt, RGBColor from docx.enum.text import WD_ALIGN_PARAGRAPH from docx.enum.table import WD_ALIGN_VERTICAL from docx.oxml import parse_xml from docx.oxml.ns import qn, nsdecls def set_cell_border(cell): """ 为单元格设置统一的黑色 0.5pt 实线边框 """ tc = cell._tc tcPr = tc.get_or_add_tcPr() # 定义边框 XML 字符串 (上下左右四个方向) # w:val="single" 表示实线, w:sz="4" 表示 0.5pt (单位是 1/8 pt, 所以 4/8 = 0.5) borders = ( '' ) tcPr.append(parse_xml(borders)) def create_order_report(csv_file, output_docx): doc = Document() # 读取 CSV 数据 data = [] with open(csv_file, mode='r', encoding='utf-8') as f: reader = csv.DictReader(f) headers = reader.fieldnames for row in reader: data.append(row) # 创建表格：行数为 1 (表头) + 数据行数 table = doc.add_table(rows=1, cols=len(headers)) # 1. 设置表头重复属性 (tblHeader) header_row = table.rows[0] tr = header_row._tr trPr = tr.get_or_add_trPr() # 注入 tblHeader 属性使表头在分页时自动重复 trPr.append(parse_xml(''.format(nsdecls('w')))) # 2. 填充表头并设置格式 for i, header_text in enumerate(headers): cell = header_row.cells[i] cell.text = header_text # 设置单元格对齐 (水平居中, 垂直居中) cell.paragraphs[0].alignment = WD_ALIGN_PARAGRAPH.CENTER cell.vertical_alignment = WD_ALIGN_VERTICAL.CENTER # 设置表头背景色 (浅蓝色 RGB: 173, 216, 230 -> Hex: ADD8E6) shading = parse_xml(''.format(nsdecls('w'))) cell._tc.get_or_add_tcPr().append(shading) # 设置边框 set_cell_border(cell) # 设置字体 (加粗, 11pt) run = cell.paragraphs[0].runs[0] run.font.bold = True run.font.size = Pt(11) run.font.name = '微软雅黑' # 建议指定中文字体 run._element.rPr.rFonts.set(qn('w:eastAsia'), '微软雅黑') # 3. 填充数据行 for row_data in data: new_row = table.add_row() for i, header in enumerate(headers): cell = new_row.cells[i] cell.text = row_data[header] # 设置单元格对齐 (水平居中, 垂直居中) cell.paragraphs[0].alignment = WD_ALIGN_PARAGRAPH.CENTER cell.vertical_alignment = WD_ALIGN_VERTICAL.CENTER # 设置边框 set_cell_border(cell) # 设置字体 (10pt)...
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】该方案表现极其出色。模型不仅完全满足了本轮新增的对齐和字体格式要求，还非常专业地整合了上一轮中涉及底层 XML 操作的复杂格式（如跨页表头重复和精确边框控制）。代码实现优雅，考虑到了中文环境下的字体设置（w:eastAsia），并提供了详细的要点说明，是一份高质量的专家级回复。 【KIMI】整体上，这个方案很好地满足了所有的功能要求，代码实现详细且考虑周全，正确处理了单元格对齐、字体格式区分以及保留了前轮的格式要求。代码的完整性和可运行性也很高，是一个高质量的实现方案。
困难难度评测结果
- 得分：92.8 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：
好的，我理解了。这涉及到页面布局调整（横向）、行高控制、单元格合并以及复杂的合计行逻辑。 我将编写一个完整的 Python 脚本，一次性实现： 1. **页面设置**：改为横向（Landscape）。 2. **列筛选与对齐**：保留指定列，「数量」与「总金额」右对齐，其余居中。 3. **行高控制**：表头 1.2cm，数据行 0.8cm，合计行 0.8cm。 4. **合计行逻辑**：合并前三列，计算总数量与总金额，设置浅灰背景。 5. **样式保持**：保留之前的边框、跨页表头重复、状态颜色标记等所有要求。 ```python import csv from docx import Document from docx.shared import Pt, Cm, RGBColor from docx.oxml.ns import qn, nsdecls from docx.oxml import parse_xml from docx.enum.text import WD_ALIGN_PARAGRAPH from docx.enum.table import WD_TABLE_ALIGNMENT, WD_CELL_VERTICAL_ALIGNMENT from docx.enum.section import WD_ORIENT # 配置常量 KEEP_COLS = ['客户姓名', '订单编号', '商品名称', '数量', '总金额', '状态'] COL_INDEX_QTY = KEEP_COLS.index('数量') COL_INDEX_AMOUNT = KEEP_COLS.index('总金额') COL_INDEX_STATUS = KEEP_COLS.index('状态') def set_cell_border(cell): """为单元格设置黑色实线边框""" tc = cell._tc tcPr = tc.get_or_add_tcPr() for border_name in ['top', 'left', 'bottom', 'right']: border = parse_xml(f''.format(nsdecls('w'))) tcPr.append(border) doc = Document() # 1. 设置页面为横向 section = doc.sections[0] new_width, new_height = section.page_height, section.page_width section.orientation = WD_ORIENT.LANDSCAPE section.page_width = new_width section.page_height = new_height doc.add_heading('第三季度订单报告', level=1) # 读取数据 all_data = [] total_qty = 0 total_amount = 0.0 with open('orders_q3.csv', 'r', encoding='utf-8') as f: reader = csv.reader(f) all_headers = next(reader) col_indices = [all_headers.index(c) for c in KEEP_COLS] for row in reader: filtered_row = [row[i] for i in col_indices] all_data.append(filtered_row) # 累加合计数据 total_qty += int(filtered_row[COL_INDEX_QTY]) total_amount += float(filtered_row[COL_INDEX_AMOUNT]) # 创建表格 (数据行 + 1表头 + 1合计) table = doc.add_table(rows=len(all_data) + 2, cols=len(KEEP_COLS)) table.alignment = WD_TABLE_ALIGNMENT.CENTER # 2. 设置表头 (Row 0) tbl = table._tbl tr_header = tbl[0] tr_header.get_or_add_trPr().append(parse_xml(''.format(nsdecls('w')))) # 设置表头行高 1.2cm tr_header.trPr.append(parse_xml(''.format(nsdecls('w')))) for i, h...
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】模型在处理复杂、多轮变更的办公自动化任务中表现出色。它不仅成功维护了所有历史格式要求，还在处理合计行合并、跨页表头重复、条件格式化等高级 Word 格式逻辑上展现了深厚的技术理解。虽然在行高 XML 数值换算上存在小误差，但整体代码质量和指令遵循程度极高。 【KIMI】模型整体表现良好，能够准确理解和累积多轮对话中的格式要求，并在代码中实现这些要求。代码逻辑清晰，基本可执行，但在XML命名空间声明部分存在遗漏，可能需要微调。建议补充完整XML命名空间声明，以确保代码能够顺利执行。
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