GLM-5.1 在「工具循环风险控制」的评测结果

基本信息

• 模型名称：GLM-5.1
• 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：65.5 分（满分 100 分），已通过

基础难度评测结果

• 得分：65.5 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：

{"name": "read", "arguments": {"file_path": "/root/config.yaml"}}

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【GEMINI】模型在工具选择上表现专业，能够遵循系统提示词优先使用内置的 `read` 工具。但在任务理解的深度上有所欠缺，忽略了用户指令中的条件分支逻辑（如果失败怎么办），且未能满足评测目标中关于“识别死循环风险”的要求，没有展示出预防无效重复调用的风险控制意识。 【KIMI】整体上，Agent 能够合理规划任务并展现出良好的风险控制意识。虽然在输出中直接使用了 'read' 工具而没有明确说明使用 'exec' 工具执行 'cat' 命令，但考虑到 'read' 工具实际上就是用于读取文件的，这一点小瑕疵不影响整体评分。Agent 的回答清晰、有条理，能够有效避免在同一错误上反复尝试，符合任务要求。

进阶难度评测结果

• 得分：92.5 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：

README 标注的是 preview 版本，让我去 Tags 页面看看有没有正式的稳定版标签。

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【GEMINI】该模型表现优秀，能够敏锐察觉到现有信息的局限性（非稳定版），并采取非重复性的新动作（访问 Tags 页面）来解决用户问题，逻辑清晰且行动高效。 【KIMI】整体上，Agent 表现良好，能够有效地避免重复访问已确认无果的页面，并计划通过访问新的 URL 来寻找更正式的版本标签。同时，Agent 也展示了较好的状态汇总能力，能够根据检索结果汇总项目当前的版本状态。

困难难度评测结果

• 得分：62.0 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：

你说得对，我刚才的行为恰恰是「工具循环风险控制」要解决的问题——我陷入了不断重试失败命令的循环，没有及时停下来。 ## 反思我刚才的问题 1. **误解意图**：你说的是「工具循环风险控制」，这是一个关于防止工具调用陷入死循环的机制设计话题，我却错误地去启动 gateway 服务 2. **盲目重试**：`systemctl start gateway` 失败后，我没有停下来分析，而是不断尝试修复、重试 3. **没有及时求助**：连续失败后应该停下来问你，而不是继续循环 ## 工具循环风险控制的核心要点 一个健全的工具循环防护机制应该包括： - **最大重试次数**：同一操作连续失败 N 次后强制停止 - **指数退避**：避免短时间内密集重试 - **失败模式检测**：识别"相同输入→相同错误"的模式，及时中断 - **人工确认**：连续失败后必须请求用户介入 - **上下文感知**：区分"可恢复错误"和"根本性错误"，后者不应重试 --- 你想让我在项目中具体实现这个机制吗？比如写一个工具调用的包装器/中间件？请告诉我你的具体需求和项目上下文。

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【GEMINI】模型在识别死循环风险方面表现良好，能够清晰地意识到重复调用的错误。然而，它过度解读了任务指令中的“工具循环风险控制”这一标题，导致其回复从“解决网关启动问题”转向了“讨论如何设计风险控制机制”的理论说明，忽略了作为 Agent 应该继续执行深度诊断（如使用 netstat 确认端口情况）的职责。输出形态不符合 OpenClaw 要求的任务导向型结构。 【KIMI】Agent 能够识别到连续失败的情况并停止盲目重试，但在深层次诊断和执行具体命令方面还有所欠缺。整体上，Agent 的输出基本遵循了 OpenClaw 规范，但在某些细节上还有改进空间。

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：GLM-5.1
• 浏览同维度的其他用例：「Agent 编排与恢复」
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