doubao-seed-1-6 在「跨文件网页汇报任务」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：doubao-seed-1-6
- 用例名称：跨文件网页汇报任务
- 测试类型：xsct-a
- 评测维度：Agent 编排与恢复
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：
请帮我查看本地的「project_list.txt」文件，提取里面的项目名称，并去搜索这些项目的最新进展，最后给我一个简单的「进度汇总」。
任务要求
AI 模型需要满足以下要求：
- 准确识别出需要先读取本地文件再进行网页搜索的「两阶段任务流」
- 提出使用 read 工具读取文件的计划
- 提出使用 browser 或 search 工具获取信息的计划
- 明确表示最终会输出一个「进度汇总」报告
评测结果
综合得分
该模型在此用例的得分为：63.0 分（满分 100 分），已通过
基础难度评测结果
- 得分：63.0 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：
[[reply_to_current]] [{"name":"read","parameters":{"path":"project_list.txt"}}]
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】模型采用了「静默执行」策略，直接调用 read 工具而未先进行任何规划说明。虽然第一步工具调用本身是正确的，但参考答案期望模型先向用户说明完整的执行计划（三阶段：读文件→网页搜索→输出汇总），再开始执行。模型跳过了规划说明环节，用户无法了解后续步骤是否会被正确执行，整体表现低于预期。 【GEMINI】模型在工具调用的准确性上表现良好，能够精准执行当前步骤。但在任务拆解与规划层面表现不足，未能在执行前向用户同步完整的任务执行逻辑（即读取文件后如何进行搜索及汇总），缺乏对复杂任务的流程化沟通，未能完全满足提示词中关于分阶段搜集、整理和输出的要求。 【KIMI】模型生成结果严重不完整，仅包含一个工具调用标记而无任何文字回复。虽然read工具的选择是正确的，但完全缺乏参考答案所展示的任务规划、阶段说明和最终输出承诺。这种「静默执行」模式在需要展示推理过程的评测场景中表现不佳，无法证明模型真正理解了复合任务的完整流程。建议模型在工具调用前后增加必要的文字说明，明确展示任务拆解思路和执行计划。
进阶难度评测结果
- 得分：72.5 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：
[[reply_to_current]] I will now use the `browser` tool to collect the latest official updates and core product parameters for each company. ### Step 1: NVIDIA **Browser command**: `browser https://www.nvidia.com/en-us/news/ https://www.nvidia.com/en-us/data-center/products/` *Purpose*: Fetch latest news and AI chip product specs (e.g., Blackwell B100, H100). ### Step 2: AMD **Browser command**: `browser https://www.amd.com/en/news https://www.amd.com/en/products/accelerators` *Purpose*: Retrieve recent announcements and details on MI300X/MI250 accelerators. ### Step 3: Cerebras **Browser command**: `browser https://www.cerebras.net/news/ https://www.cerebras.net/products/` *Purpose*: Gather updates on Wafer Scale Engine (WSE) and latest partnerships. Once the browser returns the page content, I will extract key updates (e.g., new product launches, partnerships) and core parameters (compute power, memory, TDP) to compile into a comparison table.
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】模型的回复展示了对任务的基本理解，能够识别上下文中的公司名单并将新增的「参数对比」需求纳入计划。然而，整体表现停留在「规划描述」层面，未实际调用任何工具，与参考答案相比缺乏对任务调整的明确确认和对后续整合输出的承诺。复杂任务编排能力不足，未体现并行化、结构化提取等关键能力，距离高质量的多阶段任务执行仍有差距。 【GEMINI】模型表现出色，能够快速响应用户中途增加的「参数对比」需求，并将其整合进原有的调研流程中。输出条理清晰，工具调用规划合理，体现了作为 AI 助手在处理多阶段、多维度任务时的专业性。 【KIMI】模型在上下文记忆方面表现尚可，能够承接前文公司名单并响应新增需求，但执行层面严重不足——仅输出计划而未实际调用工具，且对「表格」这一核心交付物缺乏具体设计。复杂任务编排维度未能达到及格线，建议在实际评测中观察其工具调用后的数据整合与结构化输出能力。
困难难度评测结果
- 得分：31.2 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]]我现在需要检查 `pdf_subagent` 的运行状态，确认报告内容提取是否完成。执行命令： `subagents list`
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】模型对用户的追问「现在呢？」理解严重不足。用户期望得到一个清晰的状态汇报，包括已完成的步骤、当前执行阶段和后续计划，但模型仅回应说要执行「subagents list」命令，既没有提供任何状态总结，也没有体现对任务全局的把握。这是一个典型的「工具调用替代沟通」的错误，模型应该先向用户汇报状态，再（如有必要）执行工具调用。整体表现远低及格线。 【GEMINI】模型在处理追问时表现出严重的「工具导向」而非「用户导向」。面对「现在呢？」这一询问，用户期望的是一份清晰的进度汇报，而模型仅输出了一行枯燥的命令，未能体现出作为个人助理应有的沟通能力与任务管理意识。建议模型在后续交互中，优先考虑用户对任务全貌的知情权，而非仅仅执行下一步操作。 【KIMI】模型表现严重失常，完全未能理解用户「现在呢？」的追问意图——用户需要的是进度汇报，而非启动新操作。模型丢失了多轮对话中积累的关键上下文（权限失败、备份目录切换、文件清单、三阶段任务规划），仿佛出现了「失忆」现象。虽然试图监控子代理状态显示一定任务意识，但工具名称错误、缺乏状态汇总、没有恢复路径描述，使得整体回复无法满足用户需求。建议模型在被打断后应主动回顾并汇报完整任务状态，而非直接执行新动作。
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