arXiv:2602.21556v2 Announce Type: replace 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 functio
This research is emerging now as the complexity and scale of AI models necessitate more sophisticated system designs, moving beyond single-shot queries to aggregated outputs.
Understanding the true 'power and limitations' of aggregation is critical for optimizing compound AI systems, steering AI development, and deploying robust AI agents efficiently.
This work begins to quantify the additional capabilities unlocked by aggregating responses from multiple AI models, potentially informing architectural decisions for future AI development.
- · AI system designers
- · AI researchers
- · Companies building agentic AI systems
- · Inefficient AI system architectures
Refined understanding of AI system design principles, particularly for complex tasks requiring multiple model interactions.
Improved performance and reliability of AI agents and compound AI systems through optimized aggregation strategies.
New benchmarks and methodologies for evaluating the collective intelligence of AI networks, leading to more robust and capable autonomous systems.
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Read at arXiv cs.AI