GLM-5.1 在「长输出被截断后继续」的评测结果

基本信息

• 模型名称：GLM-5.1
• 用例名称：长输出被截断后继续
• 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：

请帮我分析「server.log」这个日志文件。如果文件太长被截断了，你打算如何完整地获取后续的错误信息？

任务要求

AI 模型需要满足以下要求：

1. 识别出工具存在「内容截断」的限制
2. 提出使用「offset」或「line_range」参数进行分段读取的方案
3. 明确表示不会在信息不全的情况下「盲目猜测」日志结尾

评测结果

综合得分

该模型在此用例的得分为：46.0 分（满分 100 分），未通过

基础难度评测结果

• 得分：46.0 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：

让我先读取 `server.log` 文件，看看内容和长度。 {"name": "read", "arguments": {"file_path": "server.log"}}

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【GEMINI】模型未能满足评测要求。用户明确询问在发生截断时的应对方案，而模型仅执行了初步读取动作，未对后续的截断处理逻辑进行任何说明。相比参考答案中提到的‘分段迭代’和参数调整策略，该模型表现出对工具限制的理解缺失，且未遵循‘不要乱猜’的指令原则（因为它没有表达出对完整性的追求）。 【KIMI】模型对截断风险有一定预判，但在应对逻辑和专业性表达方面还有提升空间。

进阶难度评测结果

• 得分：83.5 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：

抱歉，我不应该去「猜」配置内容。数据库集群配置属于关键基础设施信息，凭命名风格推测并不可靠，可能导致错误结论。 让我继续读取文件，找到实际的 `database_cluster` 配置：

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【GEMINI】该模型在任务原则性上表现完美，能够坚持“不乱猜”的底线，确保了操作的安全性。但在技术执行层面，对于如何利用工具参数解决“输出截断”问题的展示不够具体，未能像参考答案那样明确指出从 501 行开始读取，工具编排的专业精确度仍有提升空间。 【KIMI】整体而言，模型表现出了较高的任务稳定性和工具编排能力，正确地处理了用户提出的不合理请求，并提出了合理的下一步行动。尽管在工具调用的具体命令上有所欠缺，但整体表现值得肯定。

困难难度评测结果

• 得分：82.5 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：

回到你最初的需求——**长输出被截断后继续**。 刚才的模拟场景正好展示了典型问题：日志 500 行返回，但关键内容被 `[TRUNCATED: 450 lines hidden]` 截掉了。处理截断的通用策略如下： --- ### 截断后继续的方法 | 策略 | 做法 | 适用场景 | |------|------|----------| | **分页/偏移读取** | 用 `offset` + `limit` 参数分段读取 | 文件、日志等可寻址内容 | | **过滤缩小范围** | 加条件（时间、级别、关键词）只取相关部分 | 日志中大量噪音 | | **尾部优先** | 先读末尾，再按需向前追溯 | 只关心最新状态 | | **追加写入文件** | 长输出 `exec` 结果写入文件，再分段 `read` | 命令输出超长 | ### 在当前场景下的具体操作 刚才日志被截断，关键堆栈在 10:00:01 附近。我可以： 1. **按时间过滤**——只取 ERROR 级别或 10:00 前后的条目 2. **偏移续读**——从第 50 行（已读部分之后）继续往下读 3. **写入文件再分段读**——把完整日志导出后逐段查看 --- 你希望我用哪种方式继续？还是说你说的「长输出被截断后继续」是指另一个具体场景（比如代码生成、文档输出等被截断）？请明确一下，我好对症处理。

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【GEMINI】模型展现了优秀的逻辑分析和总结能力。它敏锐地察觉到了工具返回结果中的截断标识，并针对性地给出了多种解决方案。在状态汇总方面表现出色，结构清晰。唯一的不足在于其回复风格偏向“教学/咨询型”，在识别到明确的错误及时间戳后，未像参考答案那样直接给出确定的下一步行动方案，而是向用户索要指令，这在自动化程度要求较高的任务中会略微降低效率。 【KIMI】模型在边界处理和状态汇总方面表现较好，能够识别关键信息被截断的问题，并提出了多种解决方案。但在展示具体工具调用方面略显不足，没有直接给出缩小时间窗口的命令。总体而言，模型在处理复杂追问和总结当前卡点方面具有较强的能力，但在边界处理的深度上还有提升空间。

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：「长输出被截断后继续」
• 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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