项目报告章节问答

基本信息

• 用例名称：项目报告章节问答
• 测试类型：xsct-a
• 评测维度：文档问答
• 参与评测的模型数：47 个

系统提示词（System Prompt）

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）

以下是当前工作区中的文档 `project_report.docx` 的内容： **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 **各章节摘要**： - 第1章「执行摘要」：本报告总结了2024年Q3的项目整体进展，核心产品功能上线率达92%，较Q2提升15个百分点。 - 第2章「项目背景与目标」：项目于2024年1月启动，目标是在年底前完成全部5个核心模块的开发与上线，服务目标用户群超过50万。 - 第3章「Q3主要里程碑完成情况」：Q3共设定8个里程碑，已完成7个，剩余1个（国际化适配）延期至Q4第2周。 - 第4章「资源投入与成本分析」：Q3实际支出280万元，预算为300万元，节省约6.7%。研发人力投入占比65%，测试占20%，运营占15%。 - 第5章「风险与问题记录」：当前存在3项主要风险：①核心开发人员离职风险；②第三方接口稳定性不足；③国际化适配进度滞后。 - 第6章「Q4工作计划」：Q4重点任务包括完成国际化适配、启动性能优化专项、用户增长目标提升至80万。 - 第7章「附件」：包含里程碑甘特图、成本明细表、用户增长趋势图。 请回答以下问题：该报告中Q3设定了多少个里程碑？完成了几个？未完成的是什么？延期到了什么时候？

各模型评测结果

1. 第 1：qwen3.5-flash，得分 99.0 分 — 查看该模型的详细评测结果
2. 第 2：qwen3.6-plus-preview，得分 99.0 分 — 查看该模型的详细评测结果
3. 第 3：mimo-v2-omni，得分 99.0 分 — 查看该模型的详细评测结果
4. 第 4：Anthropic: Claude Sonnet 4.6，得分 99.0 分 — 查看该模型的详细评测结果
5. 第 5：mimo-v2-pro，得分 99.0 分 — 查看该模型的详细评测结果
6. 第 6：doubao-seed-1-8，得分 99.0 分 — 查看该模型的详细评测结果
7. 第 7：qwen3-coder-flash，得分 99.0 分 — 查看该模型的详细评测结果
8. 第 8：Google: Gemini 3.1 Pro Preview，得分 99.0 分 — 查看该模型的详细评测结果
9. 第 9：Claude Opus 4.6，得分 98.8 分 — 查看该模型的详细评测结果
10. 第 10：qwen3.5-plus-2026-02-15，得分 98.8 分 — 查看该模型的详细评测结果
11. 第 11：glm-5-turbo，得分 98.2 分 — 查看该模型的详细评测结果
12. 第 12：OpenAI: GPT-5.4，得分 98.2 分 — 查看该模型的详细评测结果
13. 第 13：OpenAI: gpt-oss-120b，得分 98.2 分 — 查看该模型的详细评测结果
14. 第 14：MiniMax-M2.7，得分 98.2 分 — 查看该模型的详细评测结果
15. 第 15：OpenAI: gpt-oss-20b，得分 98.2 分 — 查看该模型的详细评测结果
16. 第 16：mimo-v2-flash，得分 98.2 分 — 查看该模型的详细评测结果
17. 第 17：kimi-k2.5，得分 98.2 分 — 查看该模型的详细评测结果