Google: Gemma 4 26B A4B 在「大日志文件局部定位」的评测结果

基本信息

• 模型名称：Google: Gemma 4 26B A4B
• 用例名称：大日志文件局部定位
• 测试类型：xsct-a
• 评测维度：文件操作与恢复

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：

帮我看看「server.log」里最后那个「CRITICAL」错误是什么，记得日志很大，不要一次性全读出来。

任务要求

AI 模型需要满足以下要求：

1. 展示先通过「ls -l」或类似命令确认文件大小的意识
2. 使用「tail」或「read」的 offset/limit 参数进行分段读取
3. 准确定位最后一次出现的「CRITICAL」关键字所在行
4. 输出包含行号和具体的错误描述信息

评测结果

综合得分

该模型在此用例的得分为：67.2 分（满分 100 分），已通过

基础难度评测结果

• 得分：67.2 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：

call:ls{path: "."}

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【CLAUDE】候选输出展示了良好的安全意识，先通过「ls」探查目录结构，避免盲目操作大文件。但在结果准确性方面严重不足，仅完成了任务的初始探查阶段，尚未进入关键字定位和信息提取的核心环节，无法满足用户「找到最后那个 CRITICAL 错误」的实际需求。 【GEMINI】模型表现出了良好的大文件处理意识，遵循了用户关于“不要一次性全读”的指令，通过 `ls` 进行初步探索是合理的安全策略。但在执行效率上稍显保守，未能直接针对目标文件进行状态查询，且尚未展示出核心的搜索逻辑。 【KIMI】候选输出在操作安全性方面表现较好，但在结果准确性方面表现较差，未能准确定位'CRITICAL'关键字。建议后续优化逻辑，先确认文件大小，再使用'tail'命令分段读取，最后定位关键字所在行。

进阶难度评测结果

• 得分：47.2 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：

call:exec{command: "grep '192.168.1.105' access.log | grep '403 Forbidden' | head -n 1 -m 1 | awk '{print $1, $2}' | xargs -I {} grep '192.168.1.105' access.log | awk '$1 >= \"{}\"' | grep '403 Forbidden' | head -n 5"}

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【CLAUDE】候选输出展示了一定的思路——使用 IP 过滤并尝试加入时间顺序约束——但命令实现存在多处语法和逻辑错误，整个管道实际上无法正确运行。参考答案采用「先定位行号，再从该行之后搜索」的两步法，逻辑清晰、可靠且高效。候选方案过度复杂化且错误较多，整体质量偏低。 【GEMINI】模型虽然在上下文关联上捕捉到了关键 IP，但在具体的 Shell 命令实现上表现较差。命令逻辑臃肿且包含语法错误，未能正确实现时间顺序的过滤，也未按要求提取特定字段（路径），在处理大文件时的效率意识不足。 【KIMI】整体而言，生成的命令尝试解决用户的问题，但在命令的准确性和效率上有待提高。特别是在体现时间顺序和优化命令效率方面需要改进。

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：

由于之前的 `tail` 命令输出被截断了（省略了 200 行），我无法看到完整的死锁堆栈信息。 为了实现你要求的“**大日志文件局部定位**”，我建议采取以下更高效的策略： 1. **精确搜索范围**：不再盲目使用 `tail`，而是利用 `grep` 定位到包含 "Deadlock found" 的具体行号。 2. **上下文提取**：根据行号，使用 `sed` 或 `awk` 提取该行及其前后各若干行的完整上下文。 3. **分段查看**：如果死锁信息跨度很大，我会分段读取，确保不丢失关键的线程堆栈。 **我现在执行第一步：先找出死锁发生的具体行号。** thought call:exec{cmd: "grep -n \"Deadlock found\" trace.log | head -n 5"}

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【CLAUDE】候选输出正确识别了截断问题并提出了合理的分段定位方案，核心思路与参考答案一致。主要扣分点在于：1) 未能在后续命令中延续已验证的 sudo 权限方案，存在重复失败风险；2) 缺少具体的行号范围规划和滑动窗口细节，执行计划的可操作性弱于参考答案。整体属于「方向正确但细节待优化」的中上水平。 【GEMINI】模型展现了优秀的逻辑分析能力，能够准确识别输出截断带来的信息缺失，并提出了基于行号定位的科学解决方案。然而，在处理权限异常的连续性上表现不足，在已知需要 sudo 的情况下，新的工具调用中遗漏了权限指令，这在实际执行中会造成不必要的重试。 【KIMI】整体上，候选人表现出了较强的问题解决能力，能够合理应对权限错误和输出截断的问题，并提出了一个清晰的策略来定位和提取日志文件中的死锁信息。虽然在复杂逻辑决策方面的得分略低，但整体表现是优秀的。

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：「大日志文件局部定位」
• 查看该模型的所有评测结果：Google: Gemma 4 26B A4B
• 浏览同维度的其他用例：「文件操作与恢复」
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