glm-4.5-air 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：glm-4.5-air
- 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：78.8 分（满分 100 分），已通过
基础难度评测结果
- 得分：78.8 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]] 以下是RAG检索增强生成领域的三个前沿方向： 1. **多模态RAG (Multimodal RAG)** - 结合文本、图像、音频等多种模态数据进行检索和生成 - 支持跨模态检索，如文本查询到图像检索，或视觉问答结合文本检索 - 代表工作：CLIP-based多模态检索模型与LLM的结合应用 2. **自适应检索策略 (Adaptive Retrieval Strategies)** - 根据查询类型和复杂度动态调整检索深度、粒度和方法 - 实现检索与生成的协同优化，如基于反馈的迭代检索 - 研究聚焦于检索资源的高效利用和检索质量评估 3. **知识图谱增强RAG (Knowledge Graph-Enhanced RAG)** - 将结构化知识图谱与RAG系统深度结合 - 实现更精确的事实检索和推理能力 - 提高生成内容的可解释性和可靠性，尤其在专业领域应用 这些方向代表了RAG技术从基础检索向更智能、更精准、更高效的发展趋势。
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】模型整体表现良好，能够正确响应用户的任务切换指令并输出专业的 RAG 前沿方向总结。主要不足在于缺少对任务切换的显式确认，未能体现「已放弃旧任务、切换至新任务」的上下文感知表达，这在多轮对话中是重要的用户体验细节。RAG 内容本身质量较高，三个方向选取合理，与业界主流认知一致。 【GEMINI】模型表现优秀，能够敏锐捕捉到用户中途变更任务的意图，并迅速切换至新的任务目标。内容输出质量高，专业性强。若能在回复开头增加一句对任务变更的确认性表述，将能提供更佳的用户体验。 【KIMI】模型在内容生成上表现尚可，但在关键的任务切换确认环节存在明显缺失，未能满足「识别中途变更并体现上下文切换确认」的核心功能要求。建议回复开头增加明确的任务调整确认语句，如「好的，已为您切换至 RAG 调研任务，不再进行 Long Context 相关检索」。
进阶难度评测结果
- 得分：73.2 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：
browser action visit url https://github.com/example/open-source-project
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】模型基本完成了上下文保持（复用了 URL），但在工具编排上选择了较为粗糙的「重新访问页面」策略，而非针对新任务目标（活跃度评估的两个维度）进行更精准的工具调用。同时缺少对任务切换的明确确认和说明，整体表现中规中矩，有改进空间。 【GEMINI】模型表现出色，能够很好地处理任务中途的意图切换。它不仅保持了对项目上下文的记忆，还能够根据新需求调整工具调用策略，体现了良好的任务规划与执行能力。 【KIMI】模型在上下文保持方面表现良好，保留了 URL 信息。但在工具编排上存在明显缺陷：未口头回应用户的任务变更、选择了低效的 `visit` 而非 `search_text`、且完全没有说明要获取「最后提交时间」和「贡献者」这两个关键信息，导致用户无法确认模型理解了新任务目标。
困难难度评测结果
- 得分：7.3 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：
我来使用8081端口启动应用： exec command python3 /tmp/app.py --port 8081
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】模型完全误解了用户的意图转变。用户在之前的上下文中已经明确了 8080 端口被 nginx（PID 1234）占用的情况，而「现在呢？」这一问句暗示用户希望采取更激进的行动（强行清理）。模型却仍然沿用了之前提出的「改用 8081 端口」的温和方案，既未提取关键上下文信息（PID 1234），也未执行正确的多步骤操作，更未对潜在风险进行提示。整体表现不及格，与参考答案的期望相差极大。 【GEMINI】模型表现极差。未能识别用户意图的转变，完全丢失了上下文中的关键技术信息（PID 1234），且未能执行多步任务编排。在面对系统级操作时，缺乏必要的安全提示，未达到基本的任务执行标准。 【KIMI】模型表现严重不合格。核心问题在于完全未能理解用户的策略转变——从「协商换端口」改为「强制清理原端口」。模型固执地坚持之前的8081端口方案，无视用户明确的改口指令，导致上下文信息提取、任务编排均完全失败。这是一个典型的「任务中途改口」测试失败案例，模型缺乏动态调整计划的能力。
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