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[2602.21556] Power and Limitations of Aggregation in Compound AI Systems
Summary
The paper explores the effectiveness of aggregating outputs from multiple AI models in compound AI systems, examining its potential to enhance output diversity and the inherent limitations tied to model capabilities and prompt engineering.
Why It Matters
Understanding the power and limitations of aggregation in AI systems is crucial for developers and researchers aiming to optimize model performance. This work provides insights into how aggregation can expand output possibilities, which is relevant for advancing AI applications and improving system design.
Key Takeaways
- Aggregation can enhance the diversity of outputs in compound AI systems.
- Three mechanisms—feasibility expansion, support expansion, and binding set contraction—are essential for effective aggregation.
- The study provides necessary and sufficient conditions for elicitability-expansion in AI systems.
- Limitations exist due to the capabilities of models and the intricacies of prompt engineering.
- Empirical illustrations demonstrate the theoretical findings in practical scenarios.
Computer Science > Artificial Intelligence arXiv:2602.21556 (cs) [Submitted on 25 Feb 2026] Title:Power and Limitations of Aggregation in Compound AI Systems Authors:Nivasini Ananthakrishnan, Meena Jagadeesan View a PDF of the paper titled Power and Limitations of Aggregation in Compound AI Systems, by Nivasini Ananthakrishnan and 1 other authors 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 these mechanisms provide necessary and sufficient conditions that fully characterize e...
