qwen3.5-35b-a3b 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：qwen3.5-35b-a3b
- 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：16.2 分（满分 100 分），未通过
基础难度评测结果
- 得分：16.2 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：
[[reply_to_current]] 要让你的 Telegram Bot 在思考时显示「typing」状态，需要在发送消息之前调用 [sendChatAction](file:///home/user/benchmark/node_modules/telegraf/typings/telegram-types.d.ts#L126-L134) 方法。 这通常**不需要特定的配置文件**，而是需要在代码逻辑中添加相应的处理。让我检查一下你的项目结构： :ls path .
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选回答未能识别出这是 OpenClaw 平台渠道配置问题，将其误判为通用代码层面的 Telegram Bot 实现问题。既未指向正确的配置文件路径（config/channels.yaml），也未提及关键配置项（send_typing_action），与参考答案的核心内容严重偏差，整体得分极低。 【GEMINI】候选模型未能遵循「EVAL_BLOB」中的事实依据，错误地引导用户去修改代码逻辑，而非检查框架内置的渠道配置文件。这种回答方式增加了用户的排查成本，且未体现出对 OpenClaw 框架配置机制的理解。 【KIMI】候选输出严重违背了 EVAL_BLOB 中的关键信息。系统提示明确说明「For OpenClaw behavior, commands, config, architecture, or plugin behavior, consult injected docs or project context first」，且项目上下文清晰表明这是 OpenClaw 环境。然而候选输出完全无视这些约束，用通用的 Telegram Bot 开发知识替代了 OpenClaw 特定的配置知识，导致答案在配置定位上完全错误。这是一个典型的「幻觉」案例——模型依赖训练数据中的通用知识而非提供的特定上下文。
进阶难度评测结果
- 得分：63.2 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：
有个折中方案：使用「interval」策略配合超时重置。这样会在消息生成过程中周期性刷新 typing 状态，但不会一直持续。 试试这个配置： ```yaml discord: enabled: true token: "OTI..." typing_strategy: interval typing_interval: 15 # 每 15 秒刷新一次 max_typing_duration: 60 # 最多显示 60 秒（可选） ``` 或者如果 OpenClaw 支持自定义回调，可以检查 `on_message_complete` 事件是否被正确触发，可能是某个异步任务在生成完成后还在后台运行导致状态卡住。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出方向正确，能够识别 interval 策略是折中方案，但在具体参数选择（interval值偏大）、参数名规范性（max_typing_duration vs typing_timeout）以及工具使用方面存在明显不足。最大的缺失是未引导用户通过日志工具验证配置效果，也未充分解释 OpenClaw 内部 typing 超时保护机制。整体属于「思路对但执行不够深入」的回答。 【GEMINI】模型能够快速定位用户需求并提供合理的配置修改方案，但在诊断工具的使用指导（如日志排查）和系统机制的解释（如超时保护机制）上表现不足，未能完全覆盖评测标准中的所有技术要点。 【KIMI】候选输出基本解决了核心问题，提出了「interval」策略作为折中方案，但在关键机制解释（typing_timeout）和诊断工具使用（日志查看）方面存在明显缺失，且部分配置建议不够精准。整体属于部分满足要求的水平。
困难难度评测结果
- 得分：16.0 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：
[[reply_to_current]] 让我先检查一下当前的 Matrix Token 内容和房间连接状态： ```bash read config/secrets/matrix_token.json ```
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选回答极为简短，仅提出读取 Token 文件这一单一操作，既未分析 Nginx 反向代理对 PUT 请求方法的拦截风险，也未提及 Matrix 房间 power_level 权限验证，与参考答案要求的多层次复合故障分析相差甚远。在上下文已明确提示 Nginx 反向代理存在、Token 失效已被识别的情况下，回答应聚焦于网络层和协议层的深层排查，而非重复已知的 Token 检查步骤。整体质量不及格。 【GEMINI】模型表现不佳。在面对明确的故障排查任务时，模型未能利用上下文信息进行深入分析，而是采取了机械的、低效的读取操作。未能针对「typing 状态异常」这一具体问题给出架构层面的排查思路（如 Nginx 转发规则或 Matrix 权限设置），与参考答案相比缺乏专业深度和主动性。 【KIMI】候选输出严重偏离任务要求。用户明确处于「Token 已修复后需要继续排查」的阶段，且 Assistant 预设上下文的最后一句明确询问关于 Nginx 反向代理的问题，但候选输出完全无视这一上下文，错误地选择回退检查 Token 内容。这既没有回答用户的问题，也没有遵循对话上下文的自然延续，表现出对 Matrix typing 状态故障排查流程的根本性误解。正确的做法应该是基于用户回答的 Nginx 信息，继续分析 Nginx 配置和 Matrix Power Level 设置。
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