Artificial collectives of specialists and generalists excel at different tasks
Collective artificial intelligence, where multiple agents work on shared tasks, holds potential to solve expansive problems in fields from medicine to collective governance. But while prescriptive engineering solutions abound, we lack descriptive scientific understanding of artificial collectives, and therefore principles for how to design resource efficient multi-agent systems. Through systematic experiments with optimizing agents, we characterize how agent interpretive abilities, rationality bounds, and task qualities interact to shape collective performance. Agents range from specialists, with narrow interpretive abilities, to generalists, with broad ones. Collectives of specialists co...
| Authors | John Meluso, Laurent Hébert-Dufresne, Christoph Riedl et al. |
|---|---|
| Primary category | cs.MA |
| Published | 18 June 2026 |
| Last updated at source | 18 June 2026 |
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