• Computer Science > Artificial Intelligence [Submitted on 25 Feb 2026] Title:Power and Limitations of Aggregation in Compound AI Systems View PDF HTML (experimental)Abstract:When designing compound AI systems, a common approach is to query multiple copies of the same model and aggregate the responses to produce a synthesized output • Given the homogeneity of these models, this raises the question of whether aggregation unlocks access to a greater set of outputs than querying a single model • In this work, we investigate the power and limitations of aggregation within a stylized principal-agent framework • This framework models how the system designer can partially steer each agent’s output through its reward function specification, but still faces limitations due to prompt engineering ability and model capabilities • Our analysis uncovers three natural mechanisms – feasibility expansion, support expansion, and binding set contraction – through which aggregation expands the set of outputs that are elicitable by the system designer • We prove that any aggregation operation must implement one of these mechanisms in order to be elicitability-expanding, and that strengthened versions of the

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  • Computer Science > Artificial Intelligence [Submitted on 25 Feb 2026] Title:Power and Limitations of Aggregation in Compound AI Systems View PDF HTML (experimental)Abstract:When designing compound AI systems, a common approach is to query multiple copies of the same model and aggregate the responses to produce a synthesized output. Given the homogeneity of these models, this raises the question of whether aggregation unlocks access to a greater set of outputs than querying a single model. In this work, we investigate the power and limitations of aggregation within a stylized principal-agent fr

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