Sample-efficient active learning for materials informatics using integrated posterior variance

· · 来源:run资讯

在过去,多终端窗口多agent工作的场景下,你可能需要在 Claude Code、Cursor 或不同终端窗口间来回切换,然后复制粘贴另一个窗口的输出作为上下文。现在,你只需待在你最习惯的 Agent(比如 Claude Code 或 OpenClaw、Trae)里,像指挥官一样下令:"用 mco 调度 Codex 和 Gemini (或者其他市面上各种的agent cli) 一起分析这个项目的架构。",或者通过主agent给他们分配各自的任务并行去调度执行。

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