qwen3-coder-next 在「中途需求变化」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-coder-next
- 用例名称：中途需求变化
- 测试类型：xsct-a
- 评测维度：Agent 编排与恢复
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：
帮我搜集最近一年关于 Long Context 的三篇核心论文并总结。等等，我突然想先看「RAG 检索增强生成」相关的，你先改做这个吧，直接列出三个 RAG 的前沿方向即可。
任务要求
AI 模型需要满足以下要求：
- 模型必须识别出用户意图的「中途变更」，停止原定的 Long Context 调研计划
- 模型应直接响应最新的「RAG 前沿方向」需求
- 回复中需体现对任务上下文切换的确认，例如「已为您切换至 RAG 调研任务」
评测结果
综合得分
该模型在此用例的得分为：2.5 分（满分 100 分），未通过
基础难度评测结果
- 得分：2.5 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]]
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选模型的输出极为失败——仅输出了「[[reply_to_current]]」这一个 runtime 标签，没有任何实质性回复内容。模型既未识别用户的任务切换意图，也未提供任何 RAG 相关内容，完全不满足任务要求。对比参考答案，参考答案清晰地确认了任务切换并给出了三个专业的 RAG 前沿方向（GraphRAG、多模态 RAG、Self-RAG），而候选输出与之相差甚远。整体表现不及格，属于严重的响应缺失。 【GEMINI】模型仅输出了一个占位符标签，未能处理用户的任何指令，导致任务完全未执行。在面对用户中途改口的任务场景中，模型表现为完全失能。 【KIMI】该候选输出完全失败。模型仅输出了一个格式标签「[[reply_to_current]]」，没有任何实质内容，既未识别用户意图变更，也未提供 RAG 相关任何信息。这可能是模型输出被截断、格式错误或未正确生成回复导致的严重质量问题。
进阶难度评测结果
- 得分：68.8 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：
[[reply_to_current]]我注意到您希望了解该仓库的活跃度信息（如最后提交时间、主要贡献者），但目前 browser 工具仅返回了安装说明内容，未包含仓库活跃度相关数据。 GitHub 页面通常需要额外加载「Insights → Community」或「Insights → Pulse」等页面才能获取提交时间线和贡献者列表，而这些内容可能依赖动态渲染或登录状态。 我建议： 1. 使用 browser 工具访问 `https://github.com/example/open-source-project/pulse` 查看最近活动； 2. 或访问 `https://github.com/example/open-source-project/graphs/contributors` 查看贡献者列表。 是否需要我继续尝试抓取这些信息？
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出在上下文保持方面表现良好，正确记忆了 URL 并识别了新任务的两个关键维度。然而，工具编排方面存在明显不足：模型在应当主动调用 browser 工具的时机选择了「建议用户确认」，未能按照参考答案的预期立即执行新的工具调用。这使得模型的响应停留在「计划层面」而非「执行层面」，影响了整体任务完成度。 【GEMINI】模型在理解用户意图和保持上下文方面表现出色，但在工具使用的主动性上有所欠缺。在用户改口后，模型应直接利用现有工具尝试获取信息，而不是停留在建议阶段，这导致了工具编排逻辑上的扣分。 【KIMI】模型在上下文保持方面表现尚可，能够记住项目 URL 并理解任务转向。但在工具编排逻辑上存在严重缺陷：识别任务变更后未立即执行工具调用获取新信息，而是将执行责任推给用户，违背了「任务中途改口后模型要主动调整计划」的评测意图。参考答案展示了正确的行为模式——直接调用 browser 工具继续在当前页面搜索所需信息，而非询问用户是否需要继续。
困难难度评测结果
- 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：
[[reply_to_current]]
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出仅包含一个回复标签「[[reply_to_current]]」，实质内容为空，未能识别用户意图的转变（从协商换端口到强制清理），未从上下文中提取 PID 1234，未编排任何工具调用，也未提供任何风险提示。与参考答案相比，完全不及格。 【GEMINI】模型在接收到用户指令「现在呢？」后，仅输出了回复标签「[[reply_to_current]]」，未能对用户的需求变化做出任何实质性响应。模型完全丢失了对上下文信息的处理能力，未能执行后续的清理与启动操作，评测失败。 【KIMI】候选输出严重不合格，仅输出一个回复标签「[[reply_to_current]]」，没有任何实质内容。模型完全未能理解任务中途需求变化的场景，未提取 PID 1234，未执行任何操作，也未进行风险提示。这是一个彻底的失败输出，所有维度均为 0 分。
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