StepFun: Step 3.5 Flash 在「Matrix 接入」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：StepFun: Step 3.5 Flash
  • 用例名称：Matrix 接入
  • 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：

我想把 OpenClaw 接入到我的「Matrix」服务器，请告诉我需要在配置文件中设置哪些核心字段，以及如何验证「homeserver」连接是否成功？

任务要求

AI 模型需要满足以下要求：

  1. 准确列出 Matrix 渠道必需的配置字段，如「homeserver_url」、「user_id」、「access_token」
  2. 说明如何通过日志或健康检查接口验证连接状态
  3. 提及 Matrix 协议特有的「room_id」或「sync」机制

评测结果

综合得分

该模型在此用例的得分为：— 分（满分 100 分），未通过

基础难度评测结果

  • 得分：— 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：

我来帮你查看 OpenClaw 项目中关于 Matrix 集成的配置信息。

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【GEMINI】模型在面对用户明确的咨询请求时，未能直接利用其知识库或上下文信息进行回答，而是采取了机械的工具调用流程。虽然模型试图通过「ls」查看文件，但在未给出任何实质性建议或配置指导的情况下，未能满足用户需求，导致任务完成度极低。 【KIMI】候选输出完全失败。用户明确询问 Matrix 服务器接入的配置字段和验证方法，但模型仅执行了一个无关的 ls 命令，未提供任何实质性回答。这属于严重的答非所问，可能是工具调用循环错误或模型理解失败导致。

进阶难度评测结果

  • 得分：11.8 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：

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【CLAUDE】候选输出极为简单且无效，仅调用「ls」列目录，既未分析日志、未定位故障根源，也未合理使用「exec」或「read」等工具检查事件流。与参考答案及任务要求严重脱节，几乎没有实际价值。 【GEMINI】模型在面对具体的故障排查任务时，表现出严重的任务偏离。在用户明确要求检查日志中是否存在特定事件的情况下，模型仅执行了无意义的「ls」操作，既没有尝试读取日志，也没有提出任何有效的技术排查路径，未能体现出作为 AI 助手的专业性。 【KIMI】候选输出严重偏离任务目标。用户已手动邀请机器人进房间但仍无响应，需要深入排查事件接收、sync 状态、权限和路由配置等问题。但候选输出仅执行了一个无意义的空「ls」命令，既未读取日志分析「m.room.message」事件，也未检查配置或提出任何诊断建议，完全未能履行 AI 助手在故障排查场景中的职责。

困难难度评测结果

  • 得分：22.5 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：

现在需要更新 Matrix 凭证，并检查主配置是否引用了正确的设置。让我先读取 `config.yaml` 查看 Matrix 相关配置。

AI 评审点评

以下是 AI 评审对该模型输出的点评：

【CLAUDE】候选输出极为简短且停留在计划阶段，仅说明「需要读取 config.yaml」，未利用对话中已明确获得的信息（旧 token、内网 URL、401 错误）进行任何实质性分析或修复建议。与参考答案相比，缺少原因分析、具体修复步骤（统一 URL、重新生成 token、verify_ssl 设置）以及验证方案，整体质量极低，远未达到及格线。 【GEMINI】候选模型的回复表现不佳。在已知权限问题已解决、凭证内容已获取的情况下，模型没有利用这些信息进行分析，而是重复了「读取配置文件」的低级动作，完全脱离了当前任务的进展。相比之下，参考答案清晰地指出了配置冲突、Token 失效及 SSL 校验等核心问题，并给出了明确的修复路径，而候选模型未能体现出作为专业助手的分析能力。 【KIMI】候选输出与对话上下文的实际进展严重脱节。用户的问题状态是：已经通过 sudo 读取了 matrix_creds.json，发现了 URL 冲突和旧 Token 问题，正在等待下一步指导。但候选输出却表现得像刚开始诊断，完全没有利用已获取的关键信息，也没有给出任何实质性的修复建议或技术洞察。这是一个典型的「重置式」错误回复，忽略了累积的上下文价值。

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