摘要：当前大多数科学AI系统侧重于通过更好的模型、更大的上下文窗口、长程自主执行或数字合作科学家与单一主要用户协作来扩展单个推理过程。然而，具有挑战性的科学问题很少由单个推理者独自解决。它们由团队解决，团队成员会带来不同的先验知识、实验背景、隐性认知和领域训练出的直觉。因此，待解决的核心问题不仅在于如何扩展模型，更在于如何培育网络化智能：即扩展人类与AI系统之间的连接，使在一个上下文中产生的结果或假设能够触达另一个可据此行动的人、代理、仪器或机器人。我们介绍了 Mycelium，一个主动共享工作空间，能够作为多用户合作科学家自动连接研究人员和AI代理。当人类用户和代理工作时，该系统会捕获重要的观察结果和假设，追踪它们与团队演化模型的关联，并将其路由至下一步决策可受其启发的个人或代理。我们在首次实证测试中评估了 Mycelium，该测试为一项生物多组学研究实践，其中经过路由的共享上下文将一项局部分析发现转化为跨专家的机制性约束，并最终成为一项实验设计。我们还将网络化智能从计算层面解释为分布式科学上下文上的稀疏条件计算。这一解释区分了在何种情况下大规模独立代理可以匹敌网络，以及在何种情况下独立的专业知识和不可合并的上下文使网络变得不可替代。

Abstract:Most AI-for-science systems focus on scaling a single reasoning process through 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 bring different priors, experimental backgrounds, tacit knowledge, and domain-trained intuitions. The open problem is therefore not only how to scale models, but how to cultivate 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 a multi-user co-scientist. As human users and agents work, the system captures important observations and hypotheses, tracks how they relate to the team's evolving model, and routes them to the person or agent whose next decision they can inform. We evaluate Mycelium in its first empirical test, a biological multi-omics campaign in which routed shared context turned a local analytical finding into a cross-expert mechanistic constraint and ultimately into an experimental design. We also give networked intelligence a computational account as sparse conditional computation over distributed scientific contexts. This account distinguishes when a scaled standalone agent can match the network from when independent expertise and non-mergeable contexts make the network irreducible.