GLM-5.1 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：GLM-5.1
  • 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：

  1. 将 CSV 数据正确转换为 Markdown 表格格式
  2. 表头与数据行完整保留，不丢失任何字段或记录
  3. 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
  4. 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：6.7 分（满分 100 分），未通过

基础难度评测结果

  • 得分：6.7 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：

{"name": "read", "arguments": {"file_path": "orders_q3.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 C006,刘芳,ORD-2024-0811,2024-08-23,鼠标垫,5,25,125,已完成 C004,张晓燕,ORD-2024-0812,2024-08-25,U盘,4,85,340,已完成 C001,赵建国,ORD-2024-0813,2024-08-27,文件柜,1,960,960,已完成 C007,孙磊,ORD-2024-0814,2024-08-28,白板,2,420,840,已完成 C003,王鹏,ORD-2024-0815,2024-08-30,投影仪,1,3500,3500,已完成 C005,陈伟,ORD-2024-0901,2024-09-01,台灯,3,150,450,已完成 C002,李梅,ORD-2024-0902,2024-09-02,碎纸机,1,780,780,配送中 C001,赵建国,ORD-2024-0903,2024-09-03,订书机,4,35,140,已完成 C006,刘芳,ORD-2024-0904,2024-09-04,计算器,2,120,240,已完成 C004,张晓燕,ORD-2024-0905,2024-09-05,扫描仪,1,2800,2800,已完成 C007,孙磊,ORD-2024-0906,2024-09-06,插线板,3,65,195,已完成 C003,王鹏,ORD-2024-0907,2024-09-08,摄像头,2,290,580,已完成 C002,李梅,ORD-2024-0908,2024-09-09,墨盒×10,2,45,90,退款中 C005,陈伟,ORD-2024-0909,2024-09-10,显示器,1,1800,1800,已完成 C001,赵建国,ORD-2024-0910,2024-09-11,办公椅,1,580,580,已完成 C006,刘芳,ORD-2024-0911,2024-09-12,打印机,1,2300,2300,已完成 C004,张晓燕,ORD-2024-0912,2024-09-13,键盘,2,320,640,已完成 C008,周丽,ORD-2024-0914,2024-09-14,耳机,1,260,260,已完成 C007,孙磊,ORD-2024-0915,2024-09-15,笔记本支架,2,180,360,已完成 C003,王鹏,ORD-2024-0916,2024-09-16,移动硬盘,1,450,450,配送中 C005,陈伟,ORD-2024-0917,2024-09-17,路由器,2,380,760,已完成 C002,李梅,ORD-2024-0918,2024-09-18,办公桌,1,1200,1200,已完成 C001,赵建国,ORD-2024-0919,2024-09-19,显示器,1,1800,1800,已完成 C008,周丽,ORD-2024-0920,2024-09-20,鼠标垫,6,25,150,已完成 C006,刘芳,ORD-2024-0921,2024-09-21,U盘,3,85,255,已完成 C004,张晓燕,ORD-2024-0922,2024-09-22,文件柜,1,960,960,已完成...

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【GEMINI】模型在理解阶段表现出色，准确识别了 Markdown 转换、居中对齐以及处理全量数据的要求，并成功执行了读取原始文件的初步操作。然而，在执行阶段，模型在获取数据后仅进行了口头承诺，未能实际生成 Markdown 表格，也未调用 `write` 工具进行文件输出。由于该任务的核心目标（转换并保存文件）完全未达成，整体评价较低。

进阶难度评测结果

  • 得分：100.0 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：

```python import csv from docx import Document from docx.shared import Pt, 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_ALIGN_VERTICAL def set_cell_border(cell, **kwargs): """ 为单个单元格设置边框。 kwargs 可包含 top, bottom, left, right, insideH, insideV 等键， 每个键对应一个 dict，包含 sz, color, val 等属性。 """ tc = cell._tc tcPr = tc.get_or_add_tcPr() tcBorders = tcPr.find(qn('w:tcBorders')) if tcBorders is None: tcBorders = parse_xml(''.format(nsdecls('w'))) tcPr.append(tcBorders) for edge, attrs in kwargs.items(): element = tcBorders.find(qn(f'w:{edge}')) if element is None: element = parse_xml(''.format(edge, nsdecls('w'))) tcBorders.append(element) for attr_name, attr_val in attrs.items(): element.set(qn(f'w:{attr_name}'), str(attr_val)) def set_row_header(row): """设置表格行为跨页重复表头行。""" tr = row._tr trPr = tr.get_or_add_trPr() # 移除已有的 tblHeader，避免重复 existing = trPr.find(qn('w:tblHeader')) if existing is not None: trPr.remove(existing) trPr.append(parse_xml(''.format(nsdecls('w')))) def set_cell_shading(cell, color_hex): """设置单元格背景色。""" shading = parse_xml( ''.format(nsdecls('w'), color_hex) ) tcPr = cell._tc.get_or_add_tcPr() # 移除已有 shading，避免重复 existing = tcPr.find(qn('w:shd')) if existing is not None: tcPr.remove(existing) tcPr.append(shading) def format_cell(cell, bold=False, font_size=10, alignment=WD_ALIGN_PARAGRAPH.CENTER, vertical_alignment=WD_ALIGN_VERTICAL.CENTER): """ 格式化单元格：对齐方式、字体加粗、字号。 """ # 垂直居中 cell.vertical_alignment = vertical_alignment # 遍历段落设置水平居中 + 字体属性 for paragraph in cell.paragraphs: paragraph.alignment = alignment for run in paragraph.runs: run.font.size = Pt(font_size) run.font.bold = bold run.font.name = '宋体' # 中文字体设置 run._element.rPr.rFonts.set(qn('w:eastAsia'), '宋体') def main(): # ── 读取 CSV ────────────────────────────────────────────── with open('orders_q3.csv', encoding='utf-8') as f: reader = csv.reader(f) header = next(reader) # 表头 data_rows =...

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【GEMINI】模型展现了极高水平的理解与执行能力。它不仅准确完成了本轮新增的对齐和字体格式要求，还通过底层 XML 操作解决了 python-docx 库在处理边框和跨页表头时的局限性，提供的代码专业且健壮，是一个完美的实现方案。

困难难度评测结果

  • 得分：100.0 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：

好的，我来一次性完成所有修改，生成最终版本。 ```python import csv from docx import Document from docx.shared import Pt, Cm, RGBColor, Emu 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 KEEP_COLS = ['客户姓名', '订单编号', '商品名称', '数量', '总金额', '状态'] doc = Document() # 页面方向改为横向 section = doc.sections[0] section.orientation = WD_ORIENT.LANDSCAPE # 交换页面宽高 new_width, new_height = section.page_height, section.page_width section.page_width = new_width section.page_height = new_height doc.add_heading('第三季度订单报告', level=1) 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] rows = [[row[i] for i in col_indices] for row in reader] # 计算合计 total_qty = sum(int(row[3]) for row in rows) total_amount = sum(float(row[4]) for row in rows) # 表格：1表头 + N数据 + 1合计 table = doc.add_table(rows=1 + len(rows) + 1, cols=len(KEEP_COLS)) table.alignment = WD_TABLE_ALIGNMENT.CENTER # ========== 跨页表头重复 ========== tbl = table._tbl tr_header = tbl[0] trPr = tr_header.get_or_add_trPr() trPr.append(parse_xml(''.format(nsdecls('w')))) # ========== 边框（0.5pt黑色实线） ========== borders = parse_xml( '' ' ' ' ' ' ' ' ' ' ' ' ' ''.format(nsdecls('w')) ) tbl.tblPr.append(borders) # ========== 表头行 ========== # 固定行高 1.2cm trPr_header = tr_header.get_or_add_trPr() trHeight_header = parse_xml( ''.format( nsdecls('w'), int(Cm(1.2) / 635) # Cm→twip: 1cm=567twip, but Cm() returns EMU, 1cm=360000EMU, 1twip=635EMU ) ) trPr_header.append(trHeight_header) for i, h in enumerate(KEEP_COLS): cell = table.rows[0].cells[i] cell.text = h cell.paragraphs[0].alignment = WD_ALIGN_PARAGRAPH.CENTER cell.vertical_alignment = WD_CELL_VERTICAL_ALIGNMENT.CENTER # 深蓝背景 shading = parse_xml(''.format(nsdecls('w'))) cell._tc.get_or_add_tcPr().append(shading) run =...

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【GEMINI】这是一份极高水平的交付结果。模型展现了卓越的指令遵循能力，能够处理复杂的逻辑叠加和底层的文档格式控制。在处理 Word 表格这种 python-docx 原生 API 较难直接支持的高级格式（如固定行高、跨页表头、特定边框 XML）时，模型表现得游刃有余且准确。代码结构清晰，计算逻辑（如合计项汇总）与格式设置完全匹配用户需求。

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