Power and Limitations of Aggregation in Compound AI Systems
arXiv:2602.21556v1 Announce Type: new 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 i
arXiv:2602.21556v1 Announce Type: new 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 these mechanisms provide necessary and sufficient conditions that fully characterize elicitability-expansion. Finally, we provide an empirical illustration of our findings for LLMs deployed in a toy reference-generation task. Altogether, our results take a step towards characterizing when compound AI systems can overcome limitations in model capabilities and in prompt engineering.
Executive Summary
This article explores the power and limitations of aggregation in compound AI systems, specifically compound models of the same architecture. By analyzing a stylized principal-agent framework, the authors uncover three natural mechanisms that enable aggregation to expand the set of elicitable outputs: feasibility expansion, support expansion, and binding set contraction. The authors prove that any aggregation operation must implement these mechanisms to be elicitability-expanding and provide empirical evidence using large language models in a reference-generation task. The study's findings contribute to the understanding of when compound AI systems can overcome model capabilities and prompt engineering limitations, but also highlight the need for further research on the subject.
Key Points
- ▸ Aggregation in compound AI systems expands the set of elicitable outputs through three natural mechanisms: feasibility expansion, support expansion, and binding set contraction.
- ▸ These mechanisms are necessary and sufficient conditions for elicitability-expansion in aggregation operations.
- ▸ Empirical evidence from large language models in a reference-generation task supports the study's findings.
Merits
Theoretical Foundation
The study provides a rigorous theoretical framework for understanding the power and limitations of aggregation in compound AI systems.
Empirical Evidence
The authors provide empirical evidence from large language models to support their theoretical findings.
Practical Implications
The study's findings have practical implications for the design and implementation of compound AI systems.
Demerits
Limited Scope
The study focuses on compound models of the same architecture and may not be generalizable to other types of AI systems.
Assumptions and Simplifications
The stylized principal-agent framework used in the study may not accurately reflect real-world scenarios and assumes certain simplifications.
Expert Commentary
While the study provides valuable insights into the power and limitations of aggregation in compound AI systems, it is essential to consider the broader context of AI development and deployment. The study's findings highlight the need for further research on the subject, particularly in terms of understanding the long-term consequences of compound AI systems on society and the economy. Additionally, policymakers should consider the implications of compound AI systems on the regulation of AI development and deployment. Ultimately, the study's findings contribute to a deeper understanding of the complex relationships between AI systems, data, and decision-making processes.
Recommendations
- ✓ Further research is needed to understand the long-term consequences of compound AI systems on society and the economy.
- ✓ Policymakers should consider the implications of compound AI systems on the regulation of AI development and deployment.
