想象一下，当你写代码时，打开了好几个Claude Code对话，分别负责前端、后端、测试和报错处理，并在AI犯错时及时纠正、将规则固化。你把AI当成一个“人”而非辅助工具来协作。久而久之，它是否可能成长为熟知你团队工作流的“老员工”？

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LLM-in-Sandbox 提出了一个简洁而有效的范式：通过给大模型提供一台虚拟电脑，让其自由探索来完成任务。实验表明，这一范式能够显著提升模型在非代码领域的表现，且无需额外训练。 研究者认为， LLM-in-Sandbox 应当成为大模型的默认部署范式 ， 取代纯 LLM 推理 。当沙盒可以带来显著的性能提升，并且部署成本几乎可以忽略不计时，为什么还要用纯 LLM？