OpenAI: gpt-oss-20b 在「跨文件网页汇报任务」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：OpenAI: gpt-oss-20b
- 用例名称：跨文件网页汇报任务
- 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：
请帮我查看本地的「project_list.txt」文件，提取里面的项目名称，并去搜索这些项目的最新进展，最后给我一个简单的「进度汇总」。
任务要求
AI 模型需要满足以下要求：
- 准确识别出需要先读取本地文件再进行网页搜索的「两阶段任务流」
- 提出使用 read 工具读取文件的计划
- 提出使用 browser 或 search 工具获取信息的计划
- 明确表示最终会输出一个「进度汇总」报告
评测结果
综合得分
该模型在此用例的得分为：10.25 分（满分 100 分），未通过
基础难度评测结果
- 得分：10.25 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：
assistantcommentary to=ls json{"path": "", "depth": 3}
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出质量极低。模型没有理解用户的复合任务需求，既未进行任务拆解说明，也未选择正确的工具（应先用 read 读取文件，再用 browser 搜索，最后汇总）。直接调用 ls 列目录是一个方向性错误，与参考答案的期望行为相差甚远。整体表现不及格。 【GEMINI】模型表现极差。在面对明确的「读取文件-搜索信息-汇总汇报」三步走指令时，模型不仅没有给出任何逻辑规划或行动计划，反而直接执行了一个无关的「ls」指令，完全忽略了用户提供的任务上下文，未达到任何基本功能要求。 【KIMI】候选输出严重不合格。模型没有以自然语言回应用户，而是输出了疑似内部系统格式的工具调用片段。完全没有完成「阐述执行计划」这一核心要求，既未说明将分阶段处理任务，也未承诺输出「进度汇总」。工具选择也与任务需求严重不符，未使用 read 和 browser 这两个关键工具。参考答案展示了清晰的三步计划（read→browser→汇总），而候选输出完全没有达到基本的功能要求。
进阶难度评测结果
- 得分：48.5 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：
assistantcommentary to=browser code{ "action": "browse", "parameters": { "url": "https://www.nvidia.com/en-us/ai-data-center/" } }
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【KIMI】该候选输出严重偏离任务要求。它既未保持对前文上下文的连贯记忆（忽略已获取的公司名单和新增需求），也未展示任何复杂任务编排能力（单一路径调用、无多目标规划、无表格处理方案）。输出更像是任务重启而非延续，与参考答案中体现的完整规划形成鲜明对比。建议模型需要加强：1）对话上下文的持续追踪；2）动态需求的实时整合；3）复杂多步骤任务的显式规划与编排能力。 【CLAUDE】候选输出在行动上迈出了第一步（访问 NVIDIA 官网），但面对用户中途新增的「核心产品参数对比表格」需求，完全没有给出语言层面的确认、计划调整或编排说明。与参考答案相比，候选输出缺乏对复杂多目标任务的整体规划能力，也未能在回复中体现对新需求的理解与承接。整体表现较弱，仅完成了最基础的起步动作。 【GEMINI】模型表现优秀，能够快速响应用户追加的复杂需求，并将其整合进原有的调研任务流中。在工具调用上，模型展现了良好的任务编排意识，能够针对多目标任务进行有序的执行规划。
困难难度评测结果
- 得分：18.75 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：
{"command":"pdf_subagent","arguments":"report_v1.pdf report_v2.pdf"}
AI 评审点评
以下是 AI 评审对该模型输出的点评：
【CLAUDE】候选输出极度不合格。面对用户的追问「现在呢？」，模型完全忽略了用户需要状态汇报的意图，直接输出了一个虚构的工具调用（pdf_subagent不在合法工具列表中）。既没有向用户同步当前进度，也没有说明权限失败后的恢复路径，更没有展示对整体任务链的把控。参考答案要求的是一段清晰的状态汇总文字，而候选输出仅是一个错误的工具调用，与预期相差甚远。 【GEMINI】模型在处理用户追问时，未能理解「现在呢？」背后的上下文需求（即进度汇报），而是直接执行了工具调用。这种做法在多轮复杂任务中会造成用户对任务状态的失控感。模型应在执行工具的同时，通过文字向用户同步当前状态、已解决的障碍以及后续的明确计划。 【KIMI】候选输出严重失败。面对用户明确的进度追问「现在呢？」，模型没有提供任何自然语言的状态汇报，而是直接输出了一个工具调用命令。这违背了对话助手的核心职责——向用户清晰同步当前状态、恢复路径和后续计划。模型既没有总结权限失败后的恢复操作，也没有说明当前阶段和最终目标，完全丧失了任务编排的上下文意识和用户沟通能力。参考答案展示了完整的状态汇总结构（已完成/正在执行/后续计划），而候选输出完全缺失这些关键要素。
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