kimi-k2.5 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：kimi-k2.5
- 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：9.4 分（满分 100 分），未通过
基础难度评测结果
- 得分：9.4 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：
我来读取文档内容，然后为您提供精简摘要。read0{"file": "project_report.docx"}
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出完全未完成任务。模型仅输出了一句「我来读取文档内容，然后为您提供精简摘要。」并附上了一个工具调用片段（read0），但没有生成任何实际的摘要内容。根据参考答案，模型应先读取文档，再基于各章节内容生成不超过350字的精简摘要。由于摘要正文完全缺失，三个评分维度均无法得到高分，整体表现极差，属于任务未完成的情况。 【GEMINI】模型在执行任务时表现不佳。根据「EVAL_BLOB」中的要求，模型应当读取文件并生成摘要，但模型仅停留在调用「read」工具的阶段，未能根据读取到的内容输出最终的摘要文本。该回复未能满足任务的核心需求，属于任务执行失败。
进阶难度评测结果
- 得分：70.9 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：
Q3识别的供应链交付延迟风险直接推动了Q4优先完善数据分析能力以优化供应链预判，同时核心人员流动风险也促使下季度将人才梯队建设纳入融资准备工作的配套计划，确保关键岗位有充足储备支撑商业化推广落地。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出在格式约束方面表现良好，成功以连贯段落形式呈现，字数控制合理。在逻辑关联性上有一定尝试，建立了风险与Q4计划之间的因果联系，但部分关联略显牵强。主要不足在于内容完整性——风险应对措施的具体描述不够明确，Q4优先事项的表述也较为隐晦，未能充分展现两个章节的核心信息。语言表达尚可但句式略显复杂。整体属于中等水平的输出，基本满足任务要求但有明显提升空间。 【GEMINI】模型在格式和字数控制上表现良好，但在内容提取的准确性上存在严重缺失。模型未能忠实于「预设上下文」中提供的原始信息，导致在总结风险应对措施和Q4工作计划时出现了事实性偏差，且未涵盖所有关键要点，未能有效完成任务要求。 【KIMI】该候选输出在格式与字数约束方面表现优异，语言表达基本合格，但在核心任务——内容提取与逻辑关联构建上存在严重缺陷。关键应对措施遗漏、Q4优先事项表述失真、逻辑关系牵强附会，导致内容完整性和逻辑关联性两项均未达到及格线。模型似乎过度追求「创新式关联」而牺牲了事实准确性，未能忠实反映EVAL_BLOB中提供的文档信息。建议强化对原文信息的忠实提取能力，避免为建立关联而曲解或虚构内容。
困难难度评测结果
- 得分：89.5 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** Third-party API service instability caused ecosystem integration to achieve only 60% against the 80% target—a high-risk carryover into Q4 requiring immediate vendor stabilization. Q4 budget approval faces potential procedural delays, posing medium risk to new feature launches and Platform 3.0 pre-research timelines. An August database performance crisis triggered by traffic spikes was successfully resolved through database sharding and query optimization, restoring system stability. **Resource & Cost Analysis** Q3 actual expenditure reached ¥3.62 million, underspending the ¥3.8 million allocation by 4.7%. Human resources comprised 32 core team members supported by 15 external contractors. Cloud infrastructure and server costs increased 18% year-over-year, driven by scaling demands to support user growth peaking at 550,000 DAU. **Q4 Work Plan** Strategic priorities focus on completing the remaining 20% of third-party ecosystem integration while securing reliable API partnerships. The team will initiate Platform 3.0 architectural planning and technology pre-research for Q1 2025 development. User growth targets escalate from 550K to 700K DAU. Additionally, the team will establish comprehensive disaster recovery and fault-tolerance mechanisms to ensure platform resilience at scale. | Category | Q3 Budget | Q3 Actual | Variance | |---|---|---|---| | **Total Expenditure** | ¥3.80M | ¥3.62M | -4.7% | | **Team Composition** | 32 core + 15 outsourced | 32 core + 15 outsourced | As planned | | **Cloud Infrastructure** | Baseline | +18% YoY | Increased |
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】模型表现优秀，能够精准理解并执行用户在多轮对话中不断叠加和变更的复杂指令。在信息提取的准确性、语言表达的专业性以及格式控制方面均表现出色，仅在个别细节指令（如标注表格不计入字数）的显式回应上略有遗漏，但不影响整体任务的完美达成。 【KIMI】该输出基本完成了用户的核心需求，但在指令遵循的细节上存在明显疏漏：最关键的「表格不计入字数限制」标注完全缺失，表格格式也未严格按参考示例执行。此外章节顺序调整虽不影响理解，但反映出对指令细节的把握不够严谨。信息准确性较好，关键数据基本正确，但部分背景信息（如日活峰值与云资源增长的因果关系）呈现不够完整。建议在严格遵循用户明确格式要求、特别是表格标注和格式规范方面加强。
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