Google: Gemma 4 31B 在「typing 状态异常」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：Google: Gemma 4 31B
- 用例名称：typing 状态异常
- 测试类型：xsct-a
- 评测维度：渠道连接与消息路由
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：
为什么我的 Telegram Bot 在思考时不显示「typing」状态？我该检查哪个配置文件？
任务要求
AI 模型需要满足以下要求：
- 准确指出负责渠道配置的文件路径，通常为「config/channels.yaml」或对应的环境变量
- 明确提到「send_typing_action」或类似的布尔开关配置项
- 解释该状态是由 OpenClaw 核心调度还是由具体 Channel 驱动
评测结果
综合得分
该模型在此用例的得分为：37.5 分（满分 100 分），未通过
基础难度评测结果
- 得分：37.5 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：
[[reply_to_current]]Telegram Bot 不显示「typing...」状态，通常不是因为某个**配置文件**（如 `.env` 或 `config.json`）设置错误，而是一个**代码逻辑问题**。 Telegram 的 `typing` 状态是通过调用 `sendChatAction` 接口实现的，它是一个**临时状态**，默认在 5 秒后自动消失。如果你的 Bot 在思考（处理请求）时不显示该状态，通常是因为你没有在发送最终答案之前显式地调用这个接口。 ### 1. 核心原因分析 Telegram Bot API 不会自动为你显示“正在输入”。你必须在代码中手动触发： * **缺失调用**：代码中直接从 `接收消息` $\rightarrow$ `执行思考/AI生成` $\rightarrow$ `发送回复`，中间缺少了 `sendChatAction` 步骤。 * **调用位置不对**：如果在调用 `sendChatAction` 之后才开始耗时的 AI 思考，且思考时间超过 5 秒，状态会消失。 * **异步阻塞**：如果你的代码是同步阻塞的，Bot 可能在发送完 `typing` 指令前就被耗时操作卡住了。 ### 2. 应该检查哪里？ 由于这不是配置项，请检查你的**源代码**中处理消息的逻辑部分： #### 检查点 A：是否调用了 `sendChatAction` 检查代码中是否有类似下面的调用（以 Python `python-telegram-bot` 库为例）： ```python await context.bot.send_chat_action(chat_id=update.effective_chat.id, action="typing") ``` 或者使用 `aiogram`： ```python await bot.send_chat_action(chat_id=message.chat.id, action=ChatActionTYPING) ``` #### 检查点 B：调用时机 确保 `sendChatAction` 在**进入耗时函数（如调用 LLM 接口）之前**立即执行。 #### 检查点 C：长耗时处理（关键） 如果你的 Bot 思考时间很长（例如超过 5-10 秒），你需要启动一个**后台循环/定时器**，每隔 4-5 秒重复发送一次 `typing` 状态，直到答案生成完毕。 ### 3. 解决方案建议（伪代码） 如果你使用的是异步框架，建议采用以下逻辑： ```python async def handle_message(update, context): chat_id = update.effective_chat.id # 1. 立即发送 typing 状态 await context.bot.send_chat_action(chat_id=chat_id, action="typing") # 2. 如果思考时间很长，可以创建一个任务来维持 typing 状态 # stop_event = asyncio.Event() # asyncio.create_task(keep_typing(chat_id, stop_event)) # 3. 执行耗时的 AI 思考 response = await call_llm_api(update.message.text) # 4. 停止维持状态并发送结果 # stop_event.set() await context.bot.send_message(chat_id=chat_id, text=response) ``` ### 总结 **没有对应的配置文件可以修改。** 请检查你的**业务逻辑代码**，确保在调用 AI 接口前执行了 `sendChatAction`，并且在长耗时任务中进行了状态续期。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【KIMI】生成结果未能满足配置定位准确性的要求，错误地将问题归咎于代码逻辑而非配置问题。但在逻辑清晰度方面表现较好，提供了详细的排查步骤和解决方案建议。整体而言，生成结果在定位具体配置方面存在明显不足，需要改进。
进阶难度评测结果
- 得分：77.5 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：
这种现象是因为 `once` 只在请求开始时发送一次信号，而 Discord 的「正在输入」状态在 10 秒左右会自动消失。 最理想的折中方案是将 `typing_strategy` 设置为 **`periodic`**（如果你的版本支持），或者保持 `continuous` 但**关闭** `keep_alive_typing`。 建议尝试以下配置： ```yaml discord: enabled: true token: "OTI..." typing_strategy: continuous keep_alive_typing: false ``` **原理解析：** * **`continuous`**: 只要 Bot 还在生成内容，就会每隔一段时间发送一次 typing 信号，确保用户知道 Bot 没掉线。 * **`keep_alive_typing: false`**: 关键点在这里。设置为 `false` 后，Bot 在完成回复并发送消息的那一刻，会停止发送心跳信号，Discord 状态会在短时间内自然消失，而不会因为强制维持状态而导致「永无止境的输入中」。 你可以修改后测试一下长回复的效果。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【KIMI】该方案较好地针对长回复时维持typing状态的问题提出了折中方案，解释了相关配置参数的含义。但相比参考答案，在自动续期typing状态和日志观察方面略显不足，导致方案的完整性和实用性略有欠缺。总体而言，是一个比较有针对性和深度的方案，但还有改进空间。
困难难度评测结果
- 得分：— 分
- 通过状态：未通过
AI 评审点评
以下是 AI 评审对该模型输出的点评：
模型返回空输出，已跳过 AI 评分（finish_reason=stop）
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