Multi-agent decomposition inherently loses information as it scales—each tier passes up only a fraction of findings—but this cost is sometimes worth paying for reduced context and compute, especially when the root's memory becomes the bottleneck.
This paper analyzes how splitting tasks across multiple agents in a tree structure affects information flow from leaf agents back to the root. The authors model this as a probabilistic process and find that decomposition trades yield (fewer findings reach the top) for benefits like reduced context size and lower computational cost.