Balance reasoning performance with inference efficiency in RAG

Determine effective approaches to balance reasoning performance with inference efficiency when integrating reasoning large language models into Retrieval-Augmented Generation systems for multi-hop question answering, especially under latency-sensitive deployment constraints.

Background

The paper studies how reasoning models (e.g., o1, DeepSeek-R1, Qwen) improve multi-hop QA within Retrieval-Augmented Generation but at the cost of increased tokens and latency due to explicit intermediate reasoning steps. In real-world applications, especially those requiring low latency, the trade-off between stronger reasoning and efficient inference is critical. The authors highlight that achieving a practical balance remains unresolved and propose LiR3AG as a lightweight framework to mitigate this trade-off.

This open problem frames the broader challenge of designing systems that retain the benefits of reasoning-enhanced RAG while keeping inference tractable. It motivates techniques that reduce redundancy, improve evidence structuring, and optimize token usage without sacrificing answer quality.

References

Balancing reasoning performance with inference efficiency remains an open challenge, particularly for real-world deployment in latency-sensitive scenarios.

— LIR$^3$AG: A Lightweight Rerank Reasoning Strategy Framework for Retrieval-Augmented Generation  (2512.18329 - Chen et al., 20 Dec 2025) in Abstract

Designing lightweight architectures or retrieval-augmented modules that operate under strict computational budgets without sacrificing reasoning depth remains a critical open problem.

— Commonsense Reasoning in Computer Vision: Foundations, Recent Advancements, and Future Directions  (2609.05257 - Mahmud et al., 4 Sep 2026) in Section 8.2, Scalability and Efficiency in Real-Time Systems