Beyond Component Strength: Synergistic Integration and Adaptive Calibration in Multi-Agent RAG Systems

TL;DR

This paper demonstrates that in retrieval-augmented generation systems, combining multiple enhancements synergistically significantly reduces abstention rates and hallucinations, emphasizing the importance of integration, standardized metrics, and adaptive calibration.

Contribution

It reveals that synergistic integration of system components outperforms individual enhancements and highlights the need for standardized metrics and adaptive calibration in RAG systems.

Findings

Synergistic enhancements reduce abstention from 40% to 2%.

Component interactions are more impactful than individual strength.

Inconsistent labeling affects hallucination rate interpretation.

Abstract

Building reliable retrieval-augmented generation (RAG) systems requires more than adding powerful components; it requires understanding how they interact. Using ablation studies on 50 queries (15 answerable, 10 edge cases, and 25 adversarial), we show that enhancements such as hybrid retrieval, ensemble verification, and adaptive thresholding provide almost no benefit when used in isolation, yet together achieve a 95% reduction in abstention (from 40% to 2%) without increasing hallucinations. We also identify a measurement challenge: different verification strategies can behave safely but assign inconsistent labels (for example, "abstained" versus "unsupported"), creating apparent hallucination rates that are actually artifacts of labeling. Our results show that synergistic integration matters more than the strength of any single component, that standardized metrics and labels are essential for correctly interpreting performance, and that adaptive calibration is needed to prevent overconfident over-answering even when retrieval quality is high.