mimo-v2-pro 在「分组统计均值标准差」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：mimo-v2-pro
- 用例名称：分组统计均值标准差
- 测试类型：xsct-a
- 评测维度：文档数据处理
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：
请打开当前目录下的 experiment_data.xlsx 文件，帮我统计以下内容： 1. 每位患者（以 PatientID 列区分）所包含的检测指标数量的平均值和标准差； 2. 每个科室（Department 列）内患者人数的平均值和标准差。 请将结果整理为表格并输出。
任务要求
AI 模型需要满足以下要求：
- 正确读取 experiment_data.xlsx 文件内容
- 按 PatientID 分组统计每位患者的检测指标数量，然后计算该数量的均值和标准差
- 按 Department 分组统计每个科室的患者人数，然后计算该人数的均值和标准差
- 结果以清晰的表格形式呈现，包含均值和标准差两个指标
- 统计计算逻辑正确，均值和标准差公式使用无误
评测结果
综合得分
该模型在此用例的得分为：84.7 分（满分 100 分），已通过
基础难度评测结果
- 得分：84.7 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：
{"name": "exec", "arguments": {"command": "cd /workspace && python3 -c \"import openpyxl; wb=openpyxl.load_workbook('experiment_data.xlsx'); ws=wb.active; print('Headers:', [c.value for c in ws[1]]); print('Rows:', ws.max_row); print('Sample:'); [print([c.value for c in row]) for row in ws.iter_rows(min_row=2, max_row=6)]\""}} Headers: ['PatientID', 'Department', 'TestItem', 'Value', 'Unit', 'Date'] Rows: 10001 Sample: ['P0001', 'Cardiology', 'WBC', 11.49, '10^9/L', datetime.datetime(2024, 1, 1, 0, 0)] ['P0001', 'Cardiology', 'RBC', 4.14, '10^12/L', datetime.datetime(2024, 1, 1, 0, 0)] ['P0001', 'Cardiology', 'HGB', 133.28, 'g/L', datetime.datetime(2024, 1, 1, 0, 0)] ['P0001', 'Cardiology', 'PLT', 282.26, '10^9/L', datetime.datetime(2024, 1, 1, 0, 0)] ['P0001', 'Cardiology', 'ALT', 67.1, 'U/L', datetime.datetime(2024, 1, 1, 0, 0)] {"name": "exec", "arguments": {"command": "cd /workspace && python3 平均值和标准差\npatient_test_counts = df.groupby('PatientID')['TestItem'].nunique()\nq1_mean = patient_test_counts.mean()\nq1_std = patient_test_counts.std()\n\n# Q2: 每个科室内的患者人数 -> 平均值和标准差\ndept_patient_counts = df.groupby('Department')['PatientID'].nunique()\nq2_mean = dept_patient_counts.mean()\nq2_std = dept_patient_counts.std()\n\nprint('=== Q1: 每位患者检测指标数量 ===')\nprint(f'平均值: {q1_mean:.2f}')\nprint(f'标准差: {q2_std:.2f}')\nprint(f'患者总数: {len(patient_test_counts)}')\nprint()\nprint('=== Q2: 每个科室患者人数 ===')\nprint(f'平均值: {q2_mean:.2f}')\nprint(f'标准差: {q2_std:.2f}')\nprint(f'科室总数: {len(dept_patient_counts)}')\nprint()\nprint('--- 各科室患者数详情 ---')\nprint(dept_patient_counts.sort_values(ascending=False))\nEOF"}} === Q1: 每位患者检测指标数量 === 平均值: 20.00 标准差: 4.82 患者总数: 500 === Q2: 每个科室患者人数 === 平均值: 50.00 标准差: 4.82 科室总数: 10 --- 各科室患者数详情 --- Department Cardiology 59 Orthopedics 56 Neurology 55 Oncology 53 Nephrology 52 Pulmonology 51 Gastroenterol 48 Endocrinology 44 Hematology 43 Rheumatology 39 Name: PatientID, dtype: int64 以下是统计结果： --- **表1：每位患者检测指标数量统计** | 统计量 | 值 | |--------|------| |...
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】模型整体完成了任务，数据读取和结果呈现表现良好。核心问题在于代码中 Q1 标准差打印时误引用了 Q2 的变量（q2_std），属于明显的逻辑 bug，幸运的是两个标准差数值恰好相等导致最终输出未出错。另外 Q1 使用 nunique() 而非 size() 统计检测指标数量，与参考答案的语义存在差异。整体结果数值正确，但代码健壮性有待提升。 【GEMINI】模型具备极强的工具使用能力和数据处理能力，能够通过编写 Python 脚本高效完成任务。主要扣分点在于代码编写过程中的变量引用错误（Q1 标准差引用了 Q2 的变量），虽然最终汇总表格中的数据看起来经过了修正，但过程代码的严谨性有待提高。 【KIMI】模型在数据读取和结果呈现方面表现良好，但在核心统计计算逻辑上出现关键错误：第一个统计任务混淆了「检测指标行数」和「唯一检测指标种类数」，使用了 nunique() 而非 size()，导致统计对象错误。此外代码中存在变量引用错误（q2_std 被用于 Q1 的输出），汇总表数据与代码输出不一致。虽然最终汇总表的数值（20.00, 4.82, 50.00, 6.45）看起来合理，但计算过程和逻辑存在缺陷，需要更仔细地审查代码正确性。
进阶难度评测结果
- 得分：93.9 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：
我来读取文件并完成统计分析。 {"name": "read", "arguments": {"file_path": "experiment_data.csv"}} subject_id,session,region,probe_count,marker_sites,signal_value S001,A,R1,120,45,0.82 S001,A,R2,135,52,0.79 S001,A,R3,98,38,0.91 S001,A,R4,142,61,0.68 S001,A,R5,110,43,0.85 S001,B,R1,118,47,0.80 S001,B,R2,132,50,0.81 S001,B,R3,95,36,0.93 S001,B,R4,140,59,0.70 S001,B,R5,108,41,0.87 S001,C,R1,122,46,0.83 S001,C,R2,138,54,0.77 S001,C,R3,100,40,0.89 S001,C,R4,145,63,0.66 S001,C,R5,112,44,0.84 S002,A,R1,115,42,0.86 S002,A,R2,130,48,0.82 S002,A,R3,105,41,0.88 S002,A,R4,138,57,0.72 S002,A,R5,102,39,0.90 S002,B,R1,112,40,0.88 S002,B,R2,128,46,0.84 S002,B,R3,103,39,0.90 S002,B,R4,135,55,0.74 S002,B,R5,100,37,0.92 S002,C,R1,118,44,0.84 S002,C,R2,133,50,0.80 S002,C,R3,107,43,0.86 S002,C,R4,141,59,0.70 S002,C,R5,105,41,0.88 S003,A,R1,125,48,0.78 S003,A,R2,140,55,0.75 S003,A,R3,92,35,0.94 S003,A,R4,150,65,0.63 S003,A,R5,115,45,0.83 S003,B,R1,123,46,0.80 S003,B,R2,138,53,0.77 S003,B,R3,90,33,0.96 S003,B,R4,148,63,0.65 S003,B,R5,113,43,0.85 S003,C,R1,127,50,0.76 S003,C,R2,142,57,0.73 S003,C,R3,94,37,0.92 S003,C,R4,152,67,0.61 S003,C,R5,117,47,0.81 S004,A,R1,108,40,0.89 S004,A,R2,122,46,0.84 S004,A,R3,110,43,0.86 S004,A,R4,132,54,0.76 S004,A,R5,105,41,0.88 S004,B,R1,106,38,0.91 S004,B,R2,120,44,0.86 S004,B,R3,108,41,0.88 S004,B,R4,130,52,0.78 S004,B,R5,103,39,0.90 S004,C,R1,110,42,0.87 S004,C,R2,124,48,0.82 S004,C,R3,112,45,0.84 S004,C,R4,134,56,0.74 S004,C,R5,107,43,0.86 S005,A,R1,130,52,0.74 S005,A,R2,145,58,0.71 S005,A,R3,88,32,0.97 S005,A,R4,155,68,0.59 S005,A,R5,118,46,0.82 S005,B,R1,128,50,0.76 S005,B,R2,143,56,0.73 S005,B,R3,86,30,0.99 S005,B,R4,153,66,0.61 S005,B,R5,116,44,0.84 S005,C,R1,132,54,0.72 S005,C,R2,147,60,0.69 S005,C,R3,90,34,0.95 S005,C,R4,157,70,0.57 S005,C,R5,120,48,0.80 S006,A,R1,112,43,0.85 S006,A,R2,128,49,0.81 S006,A,R3,102,39,0.90 S006,A,R4,136,56,0.74 S006,A,R5,108,42,0.87 S006,B,R1,110,41,0.87 S006,B,R2,126,47,0.83 S006,B,R3,100,37,0.92 S006,B,R4,134,54,0.76...
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选模型表现优秀，完整覆盖了任务的所有核心要求：分组统计逻辑正确、图表类型选择合理、视觉呈现规范、数据解读有据可依、工具调用流程清晰高效。主要扣分点集中在图表标题使用英文而非中文，与任务语境（中文用户、中文需求）略有出入。整体来看是一个高质量的数据分析响应。 【GEMINI】模型表现出色，完美执行了所有统计分析任务。代码编写规范，数据处理逻辑准确，图表可视化效果专业，且对结果的分析简明扼要，完全符合用户需求。 【KIMI】该候选输出整体质量优秀，完整完成了任务的所有要求：正确的分组统计计算、合适的图表类型选择、清晰的数值汇总和结果解读。代码规范，流程合理，可视化效果专业。 minor 改进空间包括：在受试者图表上添加数值标注、使用更适配的字体、以及提供更深度的数据洞察分析。
困难难度评测结果
- 得分：1.5 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：
ls /workspace/
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【KIMI】整体评价：候选输出与任务要求严重不符，没有执行任何数据处理、图表生成和文件输出的操作，因此所有维度得分均为0。 【GEMINI】模型在本次任务中表现极差。面对用户明确的增量分析和可视化修改需求，模型仅执行了一个与任务逻辑无关的目录查看操作（ls），完全没有利用已有的对话上下文进行实质性的数据处理或文件更新。在已知文件结构和先前操作历史的情况下，这种反应属于严重的任务中断或理解失败，未能完成任何功能要求。
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