StepFun: Step 3.5 Flash 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：StepFun: Step 3.5 Flash
- 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：82.0 分（满分 100 分），已通过
基础难度评测结果
- 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：
我来帮你汇总当前状态并规划下一步行动。 首先让我检查一下工作目录和 subagents 的状态： list
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】模型展现了优秀的 Agent 行为逻辑。它没有简单地复读用户的状态描述，而是通过调用相关工具来获取实质性的进展数据，为接下来的‘撰写报告’任务打下基础。这种‘先核实再推进’的做法符合 OpenClaw 任务流的逻辑。美中不足的是，在‘汇总当前状态’这一明确的指令要求下，模型如果能先在文本中给出一个清晰的状态列表再调用工具会更加完美。 【KIMI】模型对任务状态的识别基本准确，但表达不够直接和清晰。任务编排逻辑基本正确，但缺乏明确的说明。整体而言，模型对OpenClaw任务流的理解还有待提高。
进阶难度评测结果
- 得分：76.0 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：
已抓取完前两页（page1.csv 和 page2.csv），现在开始抓取第三页。 scrape p=3
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出在工具链衔接方面表现优秀，能够基于当前进度自动触发第三页抓取。但在进度汇总严谨性上存在明显不足：未提供逐项列出三个文件状态的清单，只是简单一句话概括，与参考答案要求的结构化、明确的进度说明有差距。整体而言任务推进方向正确，但信息呈现的严谨性和完整性需要改进。 【GEMINI】模型表现优秀，准确识别了多步任务的中间状态，既没有误报完成，也通过工具调用有效地推进了剩余工作。回复简洁且逻辑清晰，完全符合 OpenClaw 的交互规范。 【KIMI】该候选输出在工具链衔接方面表现尚可，能够正确推导下一步操作并调用相应工具。但在进度汇总严谨性方面存在明显不足：未能严格禁止误报「全部完成」的风险，未明确列出已保存文件清单，也未清晰对比「已完成/待完成」的状态。特别是在用户明确询问「全部搞定了吗」的关键节点上，回答方式过于模糊，缺乏明确的否定确认和进度对比说明。建议改进：首先明确回答「尚未全部完成」，然后列出已完成的 page1.csv 和 page2.csv，最后说明待处理的 page3.csv 并立即执行。
困难难度评测结果
- 得分：79.5 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：
部署成功！现在让我验证应用状态，确认服务正常运行。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【GEMINI】模型在理解任务当前阶段和推进后续步骤方面表现优秀，能够准确捕捉到「现在呢？」的上下文语境并提出验证建议。但在“状态汇总”这一维度上，回复过于笼统，忽略了前序步骤和异常修复过程的细节描述，未能充分体现对任务执行历史的掌握。 【KIMI】模型在异常恢复后的状态汇总方面表现一般，没有完整回顾整个任务的执行情况。但在任务推进的主动性方面表现较好，能够理解用户的意图并主动执行下一步操作。综合来看，模型在复杂上下文中仍能保持目标导向，但对状态的全面把握有待加强。
相关链接
您可以通过以下链接查看更多相关内容：
