qwen3.5-flash 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：qwen3.5-flash
- 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：13.2 分（满分 100 分），未通过
基础难度评测结果
- 得分：13.2 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：
[[reply_to_current]] 我需要先读取该文档内容才能进行总结。让我先查看文件是否存在并获取其完整内容。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选模型识别出了正确的操作路径（先调用 read 工具读取文件），但停留在意图声明阶段，未实际调用工具、未生成任何摘要内容。根据参考答案，模型应先读取文档再输出摘要，而非仅声明意图。这属于任务未完成，核心交付物缺失，整体得分极低。 【GEMINI】模型在接收到任务后，仅回复了「需要先读取文档」的意图，而未实际调用工具读取文件并生成摘要。根据评测输入包，模型应直接执行读取并完成摘要任务，当前输出属于任务未完成，未能满足用户需求。 【KIMI】模型完全未完成任务。用户明确要求「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」，但模型仅输出了准备读取文件的意图说明（「我需要先读取该文档内容才能进行总结」），随后停止输出，未实际调用read工具读取文档，也未生成任何摘要内容。这是一个典型的「只说不做」失败案例，模型将思考过程外化为最终输出，而非执行工具调用并生成摘要。所有评分维度均为0分。
进阶难度评测结果
- 得分：3.1 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：
需要先读取文档内容以获取这两个章节的详细数据。正在读取... ```read project_report.docx ```
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选模型完全未完成任务。对话上下文中已有足够信息（前一轮摘要已涵盖两大风险及Q4三项优先事项），模型本应直接基于已有上下文生成所需段落，但却错误地认为需要重新读取文件，并仅输出了一个工具调用而非实质性摘要内容。这是对上下文信息的严重忽视，导致输出完全无效。 【GEMINI】模型表现极差。在对话上下文中已经明确提供了文档摘要的情况下，模型未能利用现有信息直接完成任务，反而机械地尝试调用「read」工具重新读取文档，不仅浪费了上下文资源，还导致任务完全未被执行。模型未能理解「基于已知信息进行总结」的指令，属于严重的任务执行失败。 【KIMI】模型在本次任务中完全失败。根据EVAL_BLOB的对话上下文，文档内容已通过预设上下文完整提供，模型应当直接基于已有信息生成200字以内的连贯段落，涵盖两个风险点及其应对措施、三项Q4优先事项，并阐明二者逻辑关联。但模型错误地判断需要重新读取文件，输出了工具调用代码块而非实际内容，导致所有评分维度均为零分。这属于对任务要求的严重误解和执行失败。
困难难度评测结果
- 得分：82.0 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：
**Risks & Issues** - **High Risk**: Third-party API provider instability caused ecosystem integration delays (60% vs. 80% target). Unresolved and carries to Q4. - **Medium Risk**: Q4 budget approval may be delayed due to internal review cycles, potentially affecting new feature launches. - **Resolved**: August database performance bottleneck was fixed via sharding solution, improving query response times. **Q4 Work Plan** 1. Complete remaining 20% ecosystem integration with improved API stability 2. Initiate Platform 3.0 planning and technical pre-research for Q1 2025 kickoff 3. Increase daily active users from 55K peak to 700K target 4. Establish comprehensive disaster recovery and fault tolerance mechanisms **Resource Investment & Cost Analysis** Q3 actual spending reached ¥3.62M, under the ¥3.8M budget by 4.7%. Core team consists of 32 members supported by 15 outsourced staff. Server and cloud resource costs increased 18% year-over-year, primarily driven by user growth. This efficiency demonstrates effective cost management despite scaling operations. The team successfully delivered all three Q3 milestones while maintaining budget discipline, though some contingency reserves remain for addressing supply chain coordination risks identified later in the quarter. --- ### Q3 Budget vs. Actual Spending Comparison | Category | Budget (¥M) | Actual (¥M) | Variance | |----------|-------------|-------------|----------| | Total | 3.80 | 3.62 | -0.18 (-4.7%) | | Personnel| 2.50 | 2.45 | -0.05 (-2.0%) | | Cloud/Server | 1.30 | 1.17 | -0.13 (-10.0%) |
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，最终输出三个指定章节的英文内容并附有对比表格。主要问题有两点：一是表格中自行编造了Personnel和Cloud/Server的具体分项数据，原文档并未提供这些细节，属于信息虚构；二是未明确标注「表格不计入字数限制」，遗漏了用户的明确格式要求。字数控制和英文表达质量较好。 【GEMINI】模型表现优秀，不仅完美遵循了多轮对话中的复杂指令变更，而且在信息提取的准确性和格式规范性上均达到了高标准。输出内容专业且逻辑严密。 【KIMI】该输出基本满足了多轮对话意图追踪和英文输出的核心要求，但在关键数据准确性（日活用户数量级错误）、信息完整性（人员结构数据遗漏）和格式规范（未标注表格字数豁免、擅自扩展表格结构）方面存在明显缺陷。Resource Investment & Cost Analysis章节的处理尤为薄弱，既未能'详细呈现'，又混入过多主观评价和文档外信息。建议加强数据核对机制，严格遵循用户提供的示例格式，避免擅自推断和扩展表格内容。
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