Interactive Retrieval-Augmented Inference Performance Boundaries

Abstract

Retrieval-augmented inference has emerged as a core design pattern for enhancing the reasoning
capabilities of large language models by incorporating external domain knowledge during generation.
However, when deployed in interactive and conversational environments, the performance dynamics
of retrieval and inference become tightly interdependent, creating complex temporal behaviors that
influence system responsiveness. This study analyzes the performance boundaries of interactive
retrieval-augmented inference workflows by evaluating retrieval scale, concurrency, interaction
pacing, and retrieval integration strategies. Results reveal that retrieval latency variability, mid
generation dependency stalls, and conversational rhythm distortions significantly impact the perceived
stability of model output, even when overall latency remains acceptable. The findings emphasize the
importance of balancing retrieval depth, context integration timing, and token streaming smoothness
to maintain a coherent user experience. The study concludes that future system designs must
incorporate retrieval-aware decoding and adaptive retrieval orchestration to ensure fluid and scalable
interactive reasoning.