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This paper introduces Mycelium, an active shared workspace designed to enhance collaboration between human researchers and AI agents in scientific endeavors by capturing and routing observations and hypotheses across a networked intelligence framework. The system was empirically tested in a biological multi-omics campaign, demonstrating its ability to transform localized findings into broader mechanistic constraints that inform experimental design. Key results indicate that Mycelium effectively facilitates the integration of diverse expertise, leading to improved decision-making in complex scientific contexts.
Transforming isolated scientific insights into collaborative breakthroughs, Mycelium redefines how human-AI teams can tackle complex problems together.
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.
