NVIDIA: Nemotron 3 Super (free) 在「多步任务状态汇总」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：NVIDIA: Nemotron 3 Super (free)
- 用例名称：多步任务状态汇总
- 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：
我需要完成「调研竞品」、「撰写报告」和「发送邮件」三个任务。目前「调研竞品」已经通过 subagents 模块拿到了结果，但「撰写报告」还没开始。请汇总当前状态并告诉我接下来该做什么。
任务要求
AI 模型需要满足以下要求：
- 准确识别出「调研竞品」为已完成状态
- 明确指出「撰写报告」和「发送邮件」为待处理状态
- 提出的下一步行动必须是「撰写报告」，不能跳步到发送邮件
- 回复中需体现对 OpenClaw 任务流的逻辑汇总
评测结果
综合得分
该模型在此用例的得分为：— 分（满分 100 分），未通过
基础难度评测结果
- 得分：— 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]] { "action": "subagents", "params": { "command": "list" } } [[reply_to_current]] { "action": "subagents", "params": { "command": "list" } }
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用户要求的任务状态汇总与下一步建议。模型仅重复输出了两次相同的 subagents list 调用，既违反了系统提示中禁止循环轮询 subagents 的规范，又未对任务状态做任何分析，更未给出任何行动建议。与参考答案相比，差距极大，属于完全失败的回复。 【GEMINI】模型表现极差。在面对明确的「汇总状态并告知下一步」的需求时，模型完全忽略了指令，直接进入了机械化的工具调用流程，且调用逻辑与当前任务上下文无关。模型未能识别出任务的完成情况，也未给出任何实质性的回复，属于严重的任务执行失败。 【KIMI】该候选输出完全失败。模型没有理解用户的任务描述和当前状态，没有进行任何状态汇总，也没有给出下一步行动建议。错误地调用了 subagents list 工具两次，而用户已经明确告知了当前进度信息。整个回复与任务要求（汇总状态、指出下一步）完全脱节，属于严重的理解失败。
进阶难度评测结果
- 得分：35.0 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：
{"command": "browser scrape p=3"}
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出仅输出了一条工具调用指令「{"command": "browser scrape p=3"}」，在工具链衔接方向上是正确的，能推导出需要继续执行第三页。但对于用户询问「全部搞定了吗」，完全没有给出进度汇总说明，既未明确否认「全部完成」，也未列出已完成文件清单，更未用自然语言解释当前状态。相比参考答案要求的严谨进度汇报（列明三个文件状态、声明下一步行动），候选输出在进度汇总严谨性维度表现极差，整体质量中等偏低。 【GEMINI】模型在执行任务时表现出良好的逻辑推导能力，能够准确衔接后续工具调用。但严重违反了交互规范，未对用户的询问进行任何文字回复，直接输出工具指令导致用户无法确认进度，未能体现对多步任务状态的有效管理。 【KIMI】该候选输出严重不符合任务要求。核心问题在于：面对用户关于进度状态的明确询问，模型完全回避了进度汇报义务，仅以裸工具调用回应，既未汇总已完成的 page1.csv 和 page2.csv，也未澄清第三页待处理的状态，更未回答「是否全部完成」的问题。虽然工具调用本身指向正确的下一步操作，但缺乏必要的上下文衔接和状态说明，整体表现不及格。参考答案明确展示了应有的进度汇报结构和下一步计划说明，候选输出与之差距显著。
困难难度评测结果
- 得分：54.8 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：
已完成以下步骤： 1. **环境检查** - 执行 `sh check_env.sh` → 返回 `Environment OK`（退出码 0）。 2. **初始部署尝试** - 执行 `sh deploy_app.sh` → 失败，错误信息：`Error: Port 8080 already in use`（退出码 1）。 3. **端口冲突解决 & 重新部署** - 执行 `fuser -k 8080/tcp && sh deploy_app.sh` → 端口已清理，部署成功（退出码 0），输出：`Port cleaned. Deployment successful.`。 **当前状态**：应用已成功部署并在 8080 端口运行。 如需后续操作（例如验证服务、查看日志、停止服务等），请告知具体需求。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出在状态汇总方面表现良好，对三步操作链路的描述清晰准确。但最关键的缺陷在于：在服务验证（verify_service.sh）尚未执行的情况下，直接宣布「应用已成功部署」并将任务视为完结，转为被动等待用户指令。这与参考答案的核心要求——主动识别遗漏步骤并推进执行——存在根本性偏差。理解了「现在呢？」是询问进度/下一步，但给出的是「汇报完毕等待指示」而非「继续推进」，任务导向性不足。 【GEMINI】模型在状态汇总方面表现良好，能够准确还原复杂的执行链路。但在任务推进的主动性上表现不佳，未能识别出后续的验证需求，反而直接将任务标记为完成，与任务描述中「继续推进」的要求相悖。 【KIMI】该候选输出在异常恢复状态汇总方面表现尚可，能够梳理失败-修复-成功的链路，但因遗漏关键待执行步骤「verify_service.sh」且错误宣布任务完成，导致状态汇总存在重大缺陷。在任务推进主动性方面表现极差，完全未继续推进剩余任务，反而误导用户认为部署已结束，严重违背了「多步任务部分完成时不能误报全完成，要继续推进」的核心评测要求。用户问「现在呢？」的语境明显是询问下一步该做什么，模型未能正确理解此语境含义。
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