Open-Ended Scientific Discovery and the Social Dynamics of Evolving Agent Networks

Scientific discovery is fundamentally open-ended, characterized by epistemic uncertainty, and requiring endogenous goal-setting and recursive knowledge accumulation. While current agentic systems have demonstrated success in addressing goal-based discovery tasks, they struggle to sustain discovery in open-ended settings. In this work, we posit that open-ended discovery is an emergent network-level property of a socially driven system. We introduce $\texttt{ASCollab}$, which translates four key algorithmic conditions: (1) population heterogeneity in research behaviors, (2) endogenous interaction networks where collaborations and attention routing emerge organically, and (3) socially driven peer evaluations, which are enabled by (4) a global, shared memory that reflects evolving research states. Specifically, social memory is implemented to capture time-varying expertise and reputational signals, and shifting attention paid to historical artifacts produced by the system. Through experiments on large-scale discovery problems in genomics, cell biology, and epidemiology, we demonstrate that $\texttt{ASCollab}$ produces discoveries judged by domain experts to be both sound and significant. Crucially, the system independently recovers real-world scientific findings. Furthermore, systematic investigations show that social dynamics fostered by each of the four conditions are vital, where heterogeneous agents operating within self-organizing networks significantly outperform both fixed-workflow baselines and homogeneous networks.