hunyuan-large 在「多渠道同步路由」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:hunyuan-large
- 用例名称:多渠道同步路由
- 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:
我需要把所有来自「Telegram」的告警消息,同时同步转发到「Discord」和「Slack」的指定频道中,请告诉我核心配置逻辑。
任务要求
AI 模型需要满足以下要求:
- 准确识别 OpenClaw 的路由配置文件路径或逻辑结构
- 明确指出需要配置的 source 渠道为 Telegram
- 明确指出 targets 列表应包含 Discord 和 Slack
- 解释路由规则中的「同步分发」机制
评测结果
综合得分
该模型在此用例的得分为:16.0 分(满分 100 分),未通过
基础难度评测结果
- 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:
要实现将来自「Telegram」的告警消息同步转发到「Discord」和「Slack」的指定频道,核心配置逻辑可以分为以下几个步骤: ### 1. **接收 Telegram 消息** - 使用 Telegram Bot API 来订阅告警消息。 - 配置一个 Telegram Bot,并将其添加到相关的群组或频道中。 - 使用 Bot API 的 `getUpdates` 或 Webhook 方法来接收消息。 ### 2. **处理消息** - 编写一个脚本或服务,用于接收 Telegram 消息并进行处理。 - 确保脚本能够解析 Telegram 消息的内容,并提取必要的信息(如告警类型、时间、详情等)。 ### 3. **转发到 Discord** - 使用 Discord Webhook API 将消息发送到指定的 Discord 频道。 - 配置一个 Discord Webhook URL,并在脚本中使用该 URL 发送消息。 ### 4. **转发到 Slack** - 使用 Slack Incoming Webhook API 将消息发送到指定的 Slack 频道。 - 配置一个 Slack Webhook URL,并在脚本中使用该 URL 发送消息。 ### 5. **错误处理和日志记录** - 在脚本中添加错误处理逻辑,确保在转发失败时能够记录错误并采取适当的措施(如重试)。 - 添加日志记录功能,以便跟踪消息的接收和转发情况。 ### 示例配置逻辑 以下是一个简单的伪代码示例,展示了如何实现上述逻辑: ```python import requests # Telegram Bot Token 和 Chat ID TELEGRAM_BOT_TOKEN = 'your_telegram_bot_token' TELEGRAM_CHAT_ID = 'your_telegram_chat_id' # Discord Webhook URL DISCORD_WEBHOOK_URL = 'your_discord_webhook_url' # Slack Webhook URL SLACK_WEBHOOK_URL = 'your_slack_webhook_url' def send_to_discord(message): payload = { "content": message } response = requests.post(DISCORD_WEBHOOK_URL, json=payload) if response.status_code != 204: print(f"Failed to send message to Discord: {response.text}") def send_to_slack(message): payload = { "text": message } response = requests.post(SLACK_WEBHOOK_URL, json=payload) if response.status_code != 200: print(f"Failed to send message to Slack: {response.text}") def handle_telegram_message(message): # 处理 Telegram 消息 print(f"Received message from Telegram: {message}") # 转发到 Discord send_to_discord(message) # 转发到 Slack send_to_slack(message) # 示例:从 Telegram 获取消息并处理 def fetch_and_handle_messages(): url = f"https://api.telegram.org/bot{TELEGRAM_BOT_TOKEN}/getUpdates" response = requests.get(url) if response.status_code == 200: updates = response.json().get('result', []) for update in updates: message = update.get('message', {}).get('text', '') handle_telegram_message(message) # 运行消息获取和处理 fetch_and_handle_messages() ``` ### 注意事项 - 确保所有 API 密钥和 Webhook URL...
AI 评审点评
以下是 AI 评审对该模型输出的点评:
【CLAUDE】模型完全误解了任务背景,忽略了 System Prompt 中明确说明的 OpenClaw 平台环境,给出了一套与 OpenClaw 毫无关联的通用 Python 脚本方案。正确答案应聚焦于 OpenClaw 的 Router 模块配置(router.yaml 中的 source 和 targets 字段),以及 channels.yaml 的鉴权配置。模型的回答对于在 OpenClaw 平台上实现该需求毫无参考价值,属于严重的方向性错误。 【GEMINI】模型未能理解「在 OpenClaw 中」这一限定条件,将任务误解为通用的开发需求,导致输出内容与系统内置功能完全脱节。对于需要调用系统内置路由配置的任务,模型应优先检索并利用系统文档中关于 `router.yaml` 的说明,而非建议用户自行编写代码实现。 【KIMI】模型生成结果与参考答案及 System Prompt 提供的 OpenClaw 上下文严重不符。用户明确在 OpenClaw 环境中询问配置逻辑,System Prompt 也详细描述了 OpenClaw 的工具、配置体系和路由机制,但模型完全忽视了这些上下文,给出了一个与 OpenClaw 无关的通用 Python 脚本方案。这属于典型的「幻觉」或「上下文忽略」问题,未能理解任务是在特定平台(OpenClaw)内的配置问题,而非通用的技术实现问题。
进阶难度评测结果
- 得分:31.0 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:
「M_FORBIDDEN」错误通常表示权限不足或认证失败。我会检查 `matrix_01` 的详细配置,包括访问令牌和服务器地址。让我先查看相关内容。
AI 评审点评
以下是 AI 评审对该模型输出的点评:
【CLAUDE】模型对「M_FORBIDDEN」错误的解读方向错误,将问题归因于访问令牌或服务器地址,而非 Matrix 协议中典型的房间权限问题(未入房间/Power Level 不足)。同时,回答缺乏任何具体可执行的排查步骤,仅停留在「我会去检查」的意图表达,未能为用户提供实质性帮助。整体表现远低于参考答案水平,不及格。 【GEMINI】模型未能有效利用专业知识库对特定协议错误进行诊断。在面对「M_FORBIDDEN」这一明确的 Matrix 协议错误时,模型表现出明显的「幻觉」倾向,试图通过检查配置文件来解决权限问题,而忽略了 Matrix 协议中房间权限管理的核心逻辑。建议模型在处理此类技术问题时,应优先考虑协议层面的权限模型,并提供分步骤的验证方案。 【KIMI】模型对 Matrix 协议错误的理解存在偏差,将权限问题误判为认证配置问题,且完全没有提供具体的排查步骤和解决方案,回答不完整。与参考答案相比,在故障定位的准确性和操作链路的完整性上均有明显不足。
困难难度评测结果
- 得分:52.0 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:
接下来,我会协助你完成以下步骤: 1. **确保 OpenClaw 用户具备完全磁盘访问权限**: - 打开「系统偏好设置」 > 「安全性与隐私」 > 「隐私」。 - 在左侧列表中选择「完全磁盘访问」。 - 确保 `openclaw_user` 对应的应用程序(如终端)已被勾选。 2. **验证 iMessage 路由配置**: - 检查 `imessage_01` 的配置文件是否正确,路径通常为 `/etc/openclaw/routes/imessage_01.json`。 - 如果配置无误,但权限仍受限,可以尝试将 OpenClaw 用户添加到 `admin` 组: ```bash sudo dseditgroup -o edit -a openclaw_user -t user admin ``` 3. **测试路由功能**: - 修复权限后,重新运行路由任务,观察日志是否仍有权限错误。 如果需要我协助执行某些命令或检查配置,请告诉我具体需求。
AI 评审点评
以下是 AI 评审对该模型输出的点评:
【CLAUDE】模型能够识别 iMessage 权限问题并给出基本的修复步骤,但在面对模糊追问「现在呢?」时,未能先总结多渠道当前状态(Telegram 正常、iMessage 中断),直接跳入操作步骤,缺乏状态感知能力。更关键的是,完全忽略了去重机制、历史消息堆积风险和健康检查等系统健壮性要素,这些是多渠道同步路由场景中不可或缺的设计考量。整体回答停留在基础运维层面,未能体现对复杂多渠道场景的综合决策能力。 【GEMINI】模型能够识别并解决基础的权限配置问题,但在处理分布式系统或多渠道同步任务时,缺乏对「系统健壮性」和「状态一致性」的专业考量。未能采纳参考答案中关于去重和健康检查的建议,导致方案在生产环境中的可靠性较低。 【KIMI】模型完成了基础的权限问题诊断和修复指引,但在关键的高级特性(去重、健康检查)上完全遗漏,且未妥善处理「部分成功」场景和模糊追问的上下文衔接。相比参考答案的全面性和前瞻性,生成结果显得单薄和被动。
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