arXiv:2605.31176v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) systems typically rely on a single retriever and a single set of hyperparameters, despite facing highly heterogeneous queries that range from simple factoid questions to complex multi-hop reasoning. We propose a method that automatically selects a small, diverse subset of retrievers (a portfolio) from a large pool of candidates, to cover different regions of the target query distribution. We formalize this setting via an expected best-of-$k$ objective over the query distribution and show that it admits an effi
The proliferation of RAG systems and their varied performance across diverse query types necessitates more robust and adaptive retrieval mechanisms to improve practical AI applications.
Improving RAG's ability to handle heterogeneous queries significantly enhances the reliability, accuracy, and utility of AI systems for critical functions, reducing 'hallucinations' and improving user trust.
RAG systems can now dynamically select optimized retrievers, moving away from static, single-retriever configurations, leading to more resilient and performant AI agents.
- · AI developers
- · Enterprises deploying RAG
- · Users of AI applications
- · Specialized retriever model developers
- · AI systems with static RAG configurations
- · General-purpose retriever models (if specialised models gain traction)
Adaptive RAG architectures become a standard practice, improving the overall quality and reliability of AI agent output.
Increased adoption of AI agents in complex, high-stakes tasks due to enhanced accuracy and reduced failure rates.
The development of a market for 'retriever portfolio' optimization tools and services to manage and orchestrate diverse retrieval strategies.
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Read at arXiv cs.LG