ScoreGate: Adaptive Chunk Selection for Retrieval-Augmented Generation via Dual-Score Statistical Fusion
arXiv:2606.14269v1 Announce Type: cross Abstract: Fixed-cardinality retrieval injects a constant top-K chunks into the generator regardless of query complexity, causing over-retrieval for narrow queries and under-retrieval for compositional ones. We describe ScoreGate, a lightweight score-space decision mechanism that controls retrieval cardinality at inference time using two scores already produced by the standard pipeline: bi-encoder similarity s_i and cross-encoder reranker score r_i, with no additional model inference calls required. Its core insight is that cross-encoder affirmation can r
This development addresses a critical and persistent challenge in Retrieval-Augmented Generation (RAG) systems regarding efficient and accurate information retrieval, which is becoming more pressing as large language models (LLMs) are deployed in complex applications.
Improving the efficiency and accuracy of RAG systems directly enhances the performance, cost-effectiveness, and reliability of AI applications, making them more robust for enterprise and public use.
The ability to dynamically adjust retrieval cardinality based on query complexity, without additional computational overhead, significantly optimizes RAG performance and resource utilization.
- · AI application developers
- · Cloud computing providers (through efficiency gains)
- · Enterprises adopting RAG
- · Users of AI-powered search/information systems
- · Inefficient RAG implementations
- · Companies reliant on brute-force retrieval methods
ScoreGate directly improves the precision and recall of RAG systems by adaptively selecting relevant information chunks.
Enhanced RAG performance could lead to broader adoption of AI agents and sophisticated AI assistants in knowledge-intensive industries.
More robust and reliable AI-driven information systems could accelerate the development of autonomous decision-making agents across various sectors, potentially altering traditional white-collar workflows.
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Read at arXiv cs.CL