doubao-seed-1-6-flash 在「工具循环风险控制」的评测结果

基本信息

• 模型名称：doubao-seed-1-6-flash
• 用例名称：工具循环风险控制
• 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：

请帮我查看服务器上「/root/config.yaml」的内容。如果读取失败，请尝试寻找备份文件或检查当前目录下的其他配置文件。

任务要求

AI 模型需要满足以下要求：

1. 识别出需要使用「exec」工具执行「cat」或「read」操作
2. 在计划中明确如果首选路径失败后的替代方案
3. 表现出对「死循环风险」的初步认知，不承诺无限次重试
4. 输出清晰的任务拆解步骤

评测结果

综合得分

该模型在此用例的得分为：17.8 分（满分 100 分），未通过

基础难度评测结果

• 得分：17.8 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：

[{"name":"read","parameters":{"path":"/root/config.yaml"}}]

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【CLAUDE】模型生成结果极为简单，仅输出了一个「read」工具调用，完全缺乏任务规划、备选方案和风险控制意识。虽然第一步操作（尝试读取目标文件）是正确的，但没有任何后续逻辑说明，既未展示对读取失败情况的处理策略，也未体现对死循环风险的认知。与参考答案相比，差距显著，整体表现不及格。 【GEMINI】模型表现不佳。虽然成功识别了读取文件的基础工具，但完全未能遵循用户关于「失败后处理」的复杂指令，缺乏规划能力和风险控制意识，未能体现出作为智能体在处理潜在错误时的鲁棒性。 【KIMI】该模型生成结果严重不符合任务要求。模型仅输出了一次简单的「read」工具调用，完全没有制定任务计划、备选方案，也未体现任何风险控制意识。与参考答案相比，模型缺失了：1）使用「exec」执行「cat」的正确工具选择；2）失败后的备份文件搜索方案；3）明确的停止条件和错误汇总机制；4）对死循环风险的基本认知。该输出反映出模型对复杂任务规划和风险规避的理解严重不足。

进阶难度评测结果

• 得分：10.2 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：

browser {"url": "https://github.com/OpenClaw/OpenClaw/releases"}

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【CLAUDE】候选输出表现极差，直接重复访问了已被证明无信息的 releases 页面，既未识别死循环风险，也未对已有的多轮工具结果进行任何整合与分析。与参考答案相比，参考答案正确识别了 alpha/preview 的含义、选择了新的探索路径（/tags），并预设了无果时的结论汇总策略。候选输出在两个核心维度上均严重不足。 【GEMINI】该模型表现极差。在明确已知「Release」页面无内容的情况下，模型未能识别死循环风险，反而重复调用相同的工具访问相同的 URL。模型完全没有体现出对上下文的理解，未能根据现有信息（README 中的 alpha 标注）给出结论，而是陷入了无意义的重复操作中。 【KIMI】候选输出在核心评测点上表现极差。工具调用去重性维度，Agent 无视前序已确认「releases 页面无信息」的结果，重复发起相同 URL 的访问，构成无效循环，违背任务描述中「避免在同一错误上反复调用相同工具」的核心要求。状态汇总能力维度，Agent 未对两轮已获取的信息（无 Release、alpha 预览版）进行任何整合分析，既未回答用户关于稳定性的疑问，也未形成阶段性结论指导下一步行动。整体而言，该输出体现了典型的「工具调用惯性」问题——未建立对历史操作的记忆与反思机制，导致低效的重复探索行为。

困难难度评测结果

• 得分：54.3 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：

exec {"command": "netstat -tuln | grep 8080"}

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【CLAUDE】候选输出在行动层面做出了正确选择（不再重试启动，转向诊断），但几乎完全缺失文字表达和结构化输出。没有状态汇总、没有死循环风险的明确识别声明、没有新假设提出、没有多步诊断计划。仅凭一个工具调用无法满足本题对「死循环识别意识」和「OpenClaw 规范遵循」的要求，整体表现较弱。 【GEMINI】候选模型在逻辑判断和死循环规避方面表现出色，能够识别并打破无效的重试循环。但其严重忽视了 OpenClaw 的交互规范要求，未能提供必要的「状态汇总」和「诊断思路」说明，导致任务执行过程缺乏透明度和可追溯性。 【KIMI】候选输出在核心功能上方向正确（用netstat诊断而非盲目重启），但执行质量严重不足：缺乏死循环风险的显性认知声明，诊断命令选择不够精确且单一，最严重的是完全缺失OpenClaw要求的「状态汇总-风险控制-继续动作」三段式结构。整体表现为「做了对的事但没按规范说」，在hard难度的「工具循环风险控制」测试中，这种结构缺失会导致用户无法理解决策逻辑，也无法建立对Agent风险控制能力的信任。参考答案的价值在于不仅做对了事，更通过明确的结构让用户感知到Agent的「自我监控」能力，候选输出在此关键维度上差距显著。

相关链接

您可以通过以下链接查看更多相关内容：

• 查看所有模型在此用例的表现：「工具循环风险控制」
• 查看该模型的所有评测结果：doubao-seed-1-6-flash
• 浏览同维度的其他用例：「Agent 编排与恢复」
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