发表机构
Pacific Northwest National Laboratory（太平洋西北国家实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究旨在培养人类与AI系统间的网络智能。提出Mycelium主动共享工作区，能自动连接人员与智能体，捕获并路由重要信息。通过生物多组学活动测试，验证其可将局部发现转化为实验设计，还对网络智能进行了计算解释。
AI 中文摘要
大多数人工智能科学系统专注于通过更好的模型、更大的上下文窗口、长期的智能体执行或与一个主要用户合作的数字共同科学家来扩展单个推理过程。然而，具有挑战性的科学问题很少由一个推理者单独解决，而是由成员具有不同先验知识、实验背景、隐性知识和领域训练直觉的团队解决。因此，开放问题不仅是如何扩展模型，而且是如何培养网络智能，即扩展人类与人工智能系统之间的连接，使在一个上下文中产生的结果或假设能传达给可以据此采取行动的其他人、智能体、仪器或机器人。我们引入了Mycelium，一个主动共享工作区，它作为多用户共同科学家自动连接研究人员和人工智能智能体。当人类用户和智能体工作时，系统捕获重要观察结果和假设，跟踪它们与团队不断发展的模型的关系，并将它们路由到可以为其下一个决策提供信息的人或智能体。我们在首次实证测试中评估了Mycelium，这是一项生物多组学活动，其中路由的共享上下文将局部分析发现转化为跨专家的机制约束，并最终转化为实验设计。我们还将网络智能作为分布式科学上下文中的稀疏条件计算进行了计算解释。这种解释区分了扩展的独立智能体何时可以与网络匹配，以及何时独立的专业知识和不可合并的上下文使网络不可简化。
英文摘要
Most AI-for-science systems focus on scaling a single reasoning process by using better models, larger context windows, long-horizon agentic execution, or digital co-scientists working with one principal user. However, challenging scientific problems are rarely solved by one reasoner alone. They are solved by teams whose members carry different priors, experimental background, tacit knowledge, and domain-trained intuitions. The open problem is therefore not only how to scale models, but how to develop "networked intelligence", scaling the connections between humans and AI systems so that a result or hypothesis produced in one context reaches another person, agent, instrument or robot that can act on it. We introduce Mycelium, an active shared workspace that automatically connects researchers and AI agents. As human users and agents work, the system captures important observations and hypotheses, tracks how they relate to the team's evolving knowledge model, and routes them to the person or agent whose next decision they can inform. We evaluate Mycelium through a real-world scientific discovery use case: a biological multi-omics campaign where shared context turned a local analytical finding into a cross-expert mechanistic constraint and ultimately into an experimental design. Finally, we describe networked intelligence as sparse conditional computation over distributed scientific contexts. This framework establishes when a scaled standalone agent is sufficient, and when isolated data and specialized expertise make a networked approach essential.
