How Many Scientists? Epistemic Diversity in Expanding-Frontier Discovery

Scientific discovery is inherently sequential: each experiment updates beliefs and changes which hypotheses are worth testing next. As budget-limited discovery systems such as laboratory-in-the-loop agents are deployed at scale, this raises a basic organizational question: should discovery be driven by a single shared belief state, or distributed across several persistent searches? We formalize this question as an adaptive search process over an expanding hypothesis graph, and introduce $\text{ISLAND}$ (Independent Search over Local Adaptive Neighborhoods for Discovery), which allocates a fixed experimental budget across persistent epistemic states with locally expanding frontiers. Multiple states can preserve distinct search directions and local coverage, but they fragment the budget each trajectory needs to resolve prerequisite hypotheses. Theoretically, we show that fragmentation limits what each isolated trajectory can reach, while heterogeneous commitments and prior diversity can offset this loss, yielding a budget-dependent crossover from concentrated to distributed search. Controlled simulations recover this regime structure, and on the SciGym systems-biology benchmark, distributed search likewise overtakes pooled search as the experimental budget grows. Applying these principles to gene regulatory network recovery on CausalBench, we find that persistent local search states improve over a matched single-state variant and existing baselines on K562. Together, these results identify the number and isolation of persistent search states as design variables for sequential scientific discovery.