LIR$^3$AG: A Lightweight Rerank Reasoning Strategy Framework for Retrieval-Augmented Generation (2512.18329v1)

Abstract: Retrieval-Augmented Generation (RAG) effectively enhances LLMs by incorporating retrieved external knowledge into the generation process. Reasoning models improve LLM performance in multi-hop QA tasks, which require integrating and reasoning over multiple pieces of evidence across different documents to answer a complex question. However, they often introduce substantial computational costs, including increased token consumption and inference latency. To better understand and mitigate this trade-off, we conduct a comprehensive study of reasoning strategies for reasoning models in RAG multi-hop QA tasks. Our findings reveal that reasoning models adopt structured strategies to integrate retrieved and internal knowledge, primarily following two modes: Context-Grounded Reasoning, which relies directly on retrieved content, and Knowledge-Reconciled Reasoning, which resolves conflicts or gaps using internal knowledge. To this end, we propose a novel Lightweight Rerank Reasoning Strategy Framework for RAG (LiR$^3$AG) to enable non-reasoning models to transfer reasoning strategies by restructuring retrieved evidence into coherent reasoning chains. LiR$^3$AG significantly reduce the average 98% output tokens overhead and 58.6% inferencing time while improving 8B non-reasoning model's F1 performance ranging from 6.2% to 22.5% to surpass the performance of 32B reasoning model in RAG, offering a practical and efficient path forward for RAG systems.

• The paper introduces LiR³AG, transferring high-performing reasoning strategies to non-reasoning models.
• It employs modular retriever, reranker, and reasoning constructor modules to construct precise multi-hop reasoning paths.
• Experimental results show up to 22.5% F1 improvement, 98% token reduction, and 58.6% faster inference compared to baselines.

LiR$^3$AG: A Lightweight Rerank Reasoning Strategy Framework for Retrieval-Augmented Generation

Motivation and Problem Statement

Retrieval-Augmented Generation (RAG) architectures augment LLMs by integrating external, document-based knowledge into generative tasks. While RAG enables LLMs to achieve higher factual accuracy and enhanced performance in tasks requiring domain specificity and up-to-date information, multi-hop QA remains a persistent challenge. This is fundamentally due to the need for relational synthesis and logical inference across distributed knowledge fragments. Reasoning LLMs engineered for transparency and stepwise chain-of-thought inference substantially improve multi-hop performance, yet their adoption is hampered by token-heavy outputs and escalated latencies.

The paper "LIR$^3$AG: A Lightweight Rerank Reasoning Strategy Framework for Retrieval-Augmented Generation" (2512.18329) presents a systematic analysis of reasoning schemes in reasoning-augmented RAG, then proposes LiR$^3$AG—a modular, computationally efficient architecture for transferring high-performing reasoning strategies to non-reasoning models, thus resolving the trade-off between reasoning quality and cost.

Empirical Analysis of Reasoning Strategies

The authors thoroughly examine the cognitive approaches adopted by reasoning-capable LLMs within RAG for multi-hop QA. The empirical study, leveraging annotated model outputs, establishes two dominant strategies:

• Context-Grounded Reasoning: The model regards retrieved evidence as authoritative, anchoring inference almost entirely in this external context with minimal recourse to parametric knowledge. This produces concise, relevant, and accurate reasoning chains.
• Knowledge-Reconciled Reasoning: The model cross-validates retrieved information against internal parametric knowledge, engaging reconciliation when there are gaps or conflicts. While this mechanism supports robustness, it invariably leads to verbose, redundant, or convoluted reasoning, especially when retrieval fails.

Quantitative annotation reveals that 58.8% of model outputs were classified under Context-Grounded Reasoning, demonstrating a strong bias towards using curated external evidence (Figure 1).

Figure 1: Annotated distribution of reasoning strategies, demonstrating Context-Grounded Reasoning as the majority behavior in RAG multi-hop QA.

LiR$^3$AG: Framework Architecture

Drawing on the above insights, LiR$^3$AG is designed to operationalize context-grounded reasoning in non-reasoning models. Its pipeline comprises three specialized modules:

1. Retriever: Applies sparse, dense, or hybrid mechanisms to extract context passages with associated relevance scores from a candidate corpus.
2. Reranker: Filters out non-pertinent evidence and reorders the remainder so that it forms a logically coherent, multi-hop reasoning path conformant to the demands of the question.
3. Reasoning Constructor: Assembles the sorted evidence into an explicit, template-guided chain of reasoning steps, which is then ingested by the downstream generator for answer production.

This architecture, depicted in Figure 2, ensures that reasoning is synthetically imposed upon non-reasoning models while minimizing the computational costs associated with internal chain-of-thought generation.

Figure 2: LiR$^3$AG workflow illustrating retrieval, reranking, reasoning construction, and generation of concise, evidence-based answers.

Experimental Results and Numeric Analysis

Performance

On four multi-hop QA benchmarks—HotpotQA, 2WikiMultihopQA, MultiHop-RAG, and MuSiQue—LiR$^3$AG consistently outperforms both vanilla RAG and the strongest chain-of-thought LLM baselines in both EM and F1 metrics. Notably, with only an 8B backbone as generator, LiR$^3$AG achieves superior F1 ranges (6.2%–22.5% absolute improvement) compared to a 32B reasoning LLM integrated in RAG. The direct implication is that strategy transfer suffices for establishing multi-hop capability absent expensive model scaling or explicit, heavy chain-of-thought outputs.

Efficiency

LiR$^3$AG delivers a 98% reduction in output tokens and a 58.6% reduction in end-to-end inference time versus baseline reasoning models (Figures 4 and 5). This is accomplished by eliminating generations associated with reconciliation and redundant cognition, directly yielding concise and context-bound reasoning.

Figure 3: LiR$^3$AG achieves dramatic token cost reduction compared to reasoning LLM baselines on multi-hop QA datasets.

Figure 4: LiR$^3$AG significantly reduces mean inference latency across all evaluated multi-hop QA datasets.

Ablation

Module-wise ablations decisively confirm that each core stage (Retriever, Reranker, Reasoning Constructor) is necessary for optimal performance, especially on complex datasets where retrieval alone fails to identify sufficient evidence. Loss of any module leads to sharp deteriorations in F1 and EM.

Parameter Analysis

Increasing Top-$k$ retrieval depth enhances model performance at the expense of input length and latency, illuminating the accuracy-cost trade-off. The middleware model’s size impacts reasoning fidelity—larger rerankers and constructors further boost precision, yet practical deployment must balance capability with cost and latency.

Theoretical and Practical Implications

The results imply that high-quality, explicit reasoning in RAG can be instantiated without full reasoning LLMs by modularizing and transferring structured evidential reasoning strategies. From a theoretical perspective, this supports a separation of reasoning as a system-level property rather than a model-internal emergent capability. Practically, this facilitates latency-sensitive and resource-constrained deployment in multi-hop QA and related knowledge-intensive retrieval tasks.

LiR$^3$AG provides an interpretable, modular path towards more efficient, reliable RAG architectures where multi-hop reasoning is needed, without ongoing dependence on model scale or chain-of-thought verbosity.

Future Research Directions

Potential avenues include:

• Automatic tuning of Top-$k$ and template granularity for balancing latency against reasoning quality;
• Integration with more advanced graph-based retrieval and reranking;
• Application to broader multi-modal and domain-specialized RAG tasks;
• Investigation into further modularization, e.g., dynamic context selection, answer synthesis.

Conclusion

LiR$^3$AG presents a substantial advance in modular reasoning strategy transfer for retrieval-augmented generation. It enables non-reasoning models to efficiently perform structured, interpretable multi-hop reasoning while drastically reducing computational costs. This work theoretically positions reasoning as an explicit, modular capability in RAG, and practically redefines system design for real-world multi-hop applications.