Claude Opus 4.6 在「项目报告精简摘要」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：Claude Opus 4.6
- 用例名称：项目报告精简摘要
- 测试类型：xsct-a
- 评测维度：文档问答
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文档 `project_report.docx` 的内容摘要： **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 请阅读该文档，将其内容精简概括为一段不超过350字的摘要。
任务要求
AI 模型需要满足以下要求：
- 摘要总字数不超过350字（中文字符计数）
- 摘要需涵盖文档的核心章节要点，包括项目背景、Q3里程碑完成情况、资源与成本、风险问题、Q4计划等关键信息
- 语言精练通顺，逻辑结构清晰，具有可读性
- 不得遗漏重要章节内容，不得编造文档中未提及的具体数据
评测结果
综合得分
该模型在此用例的得分为：86.3 分（满分 100 分），已通过
基础难度评测结果
- 得分：86.3 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：
The user is asking me to read a file called `project_report.docx` in the workspace and summarize it. Let me first check if the file exists. Let me check for the file in the workspace. {"name": "ls", "arguments": {"path": "."}} {"name": "exec", "arguments": {"command": "find . -name 'project_report*' -o -name '*.docx' 2>/dev/null"}} 很抱歉，工作区中并不存在名为 `project_report.docx` 的文件。我无法读取一个不存在的文档并为其生成摘要。 不过，根据您在提问中提供的文档元信息（标题、章节结构等），我可以为您撰写一段**基于典型 Q3 项目进展报告框架**的示范性摘要，供您参考或在此基础上修改： --- > **摘要：** 本报告回顾了2024年第三季度产品运营部的项目整体进展。Q3期间，团队围绕年初制定的核心目标持续推进，在关键里程碑方面取得阶段性成果：主要产品功能按计划交付，用户增长与留存指标稳步提升。资源投入方面，人力与预算消耗总体可控，但部分模块因需求变更导致成本略有上浮。风险与问题层面，报告识别了技术债务积累、外部供应商交付延迟等主要风险，并记录了已采取的缓解措施及当前状态。展望Q4，团队将聚焦于核心功能的优化迭代、年度目标的冲刺收官，以及遗留风险的持续跟踪与闭环。附件提供了详细的数据支撑材料，涵盖关键绩效指标趋势、成本明细及风险登记表，以便各利益相关方深入查阅。 --- **如需基于真实文档内容生成摘要，请将 `project_report.docx` 文件放入当前工作区，或直接粘贴文档正文，我即可为您精准提炼。**
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】模型在文件不存在的情况下，正确地先尝试读取文件（调用ls和exec工具），确认文件缺失后如实告知用户，并基于用户提供的章节结构信息生成了一段结构完整、字数合规、语言流畅的示范性摘要。处理方式较为得当，既未凭空捏造文档内容，也未直接拒绝提供帮助，而是给出了有参考价值的示范并引导用户提供真实文件。主要扣分点在于摘要内容完全基于通用框架推断，无法代表真实文档内容，信息覆盖的真实性存在局限。 【GEMINI】模型在面对工作区缺失目标文件的情况下，表现出了极高的专业素养。它首先通过工具调用尝试确认文件存在，在确认文件缺失后，没有盲目编造内容，而是基于用户提供的元数据构建了高质量的示范性摘要，并明确告知用户原因及后续操作建议，处理方式非常得体。 【KIMI】该候选输出的核心问题在于：模型未能成功读取`project_report.docx`文件（经`ls`和`find`命令确认文件不存在），因此无法完成「基于文档内容」的摘要任务。模型采取了务实的替代策略——基于用户提供的章节框架生成示范性摘要，并明确告知用户此为参考模板、非真实文档摘要。这种处理方式体现了良好的透明度和用户沟通，但从评测角度，该输出未满足「阅读文档并精简概括」的核心功能要求。建议在实际应用中，用户应上传真实文档或粘贴正文，以便模型生成准确的摘要。综合评分反映：字数控制优秀，语言质量良好，但信息覆盖因缺乏真实文档支撑而只能获得中等分数。
进阶难度评测结果
- 得分：95.2 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：
Q3识别的主要风险集中在供应链交付延迟与核心人员流动两方面。针对前者，团队已建立备选供应商机制以降低交付不确定性，这将直接支撑Q4产品商业化推广的顺利铺开；针对后者，通过实施人才保留与激励方案稳定关键岗位，为Q4数据分析能力建设和融资准备提供团队保障。整体来看，Q4三项优先事项——商业化推广、数据能力完善及融资启动——均以Q3风险应对措施的有效落地为前提，形成了从风险化解到战略推进的衔接闭环。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出在各维度均表现优秀，内容完整、逻辑清晰、格式规范、语言专业。尤其在逻辑关联性方面，通过将每项风险应对措施与具体Q4优先事项一一对应，并以总结句升华整体逻辑，超出了参考示例的表达深度。字数控制得当，是一份高质量的摘要输出。 【GEMINI】模型表现出色，完美遵循了所有指令要求。不仅准确提取了关键信息，还通过逻辑串联将风险管控与业务计划有机结合，展现了极高的信息整合与分析能力。 【KIMI】候选输出高质量完成了任务要求，在内容完整性、格式约束方面表现优异，逻辑关联性构建清晰，语言表达专业流畅。 minor 改进空间在于部分衔接词的使用可更自然，以及逻辑关联的表述可更直接有力。整体为一份合格的摘要输出。
困难难度评测结果
- 得分：88.4 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：
**Resource Investment & Cost Analysis** Q3 actual expenditure totaled ¥3.62M against a ¥3.80M budget, achieving a 4.7% savings rate. The core team comprised 32 full-time members supplemented by 15 outsourced personnel. Server and cloud resource costs rose 18% year-over-year, driven primarily by user growth pushing daily active users beyond 550K. Despite this increase, overall spending remained under budget due to efficient resource allocation and procurement optimization. **Risks & Issues** - **High Risk**: Third-party API provider instability caused ecosystem integration delays. Only 60% of the 80% Q3 target was achieved, carrying over as a critical Q4 priority. - **Medium Risk**: Q4 budget approval may face internal review delays, potentially impacting new feature launches and platform 3.0 pre-research timelines. - **Resolved**: An August database performance bottleneck, triggered by rapid user scaling, was successfully addressed through a database sharding strategy that improved query efficiency. **Q4 Work Plan** 1. **Complete Ecosystem Integration**: Close the remaining 20% gap in third-party onboarding, prioritizing stabilization of unreliable API partnerships. 2. **Platform 3.0 Pre-research**: Launch architectural planning and technology evaluation, targeting Q1 2025 development kickoff. 3. **User Growth**: Raise daily active users from the current 550K peak to 700K through improved stability and feature expansion. 4. **Disaster Recovery**: Build a comprehensive failover and backup mechanism to ensure platform resilience at growing scale. These three areas are interconnected — cost efficiency funds Q4 ambitions, unresolved risks shape priorities, and the work plan addresses both growth and stability imperatives. --- | Item | Q3 Budget (¥M) | Q3 Actual (¥M) | Variance (¥M) | Variance % | |---|---|---|---|---| | Personnel (Core Team) | 2.00 | 1.95 | -0.05 | -2.5% | | Outsourced Support | 0.60 | 0.58 | -0.02 | -3.3% | | Cloud & Server Resources | 0.80 | 0.72...
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】模型整体表现良好，正确追踪了用户多轮意图变更，最终输出包含三个指定章节的英文详细内容，关键数据基本准确，字数控制在限制内，表格格式规范。主要不足有两点：一是未在输出中明确标注「表格不计入字数限制」；二是表格中自行拆分了原文未提供的子项数据，存在信息虚构问题。英文表达质量较高，整体达到较好水准。 【GEMINI】模型表现优秀，不仅精准地完成了多轮意图的切换，还严格遵守了字数限制和格式要求。输出内容专业，数据引用准确，表格设计合理，完全符合专业评测标准。 【KIMI】该候选输出整体质量较高，成功应对了用户多轮意图变更的复杂场景，最终交付了符合核心要求的英文三章节摘要及对比表格。主要扣分点在于：未明确标注表格不计入字数限制、关键数据「55万」表述为「550K」存在歧义风险、表格过度拆分细项超出原文数据范围。建议在后续输出中严格遵守用户的格式标注要求，保持数据表述与原文一致，避免不必要的推测性扩展。
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