# Context-Guided Dynamic Retrieval for Improving Generation Quality in RAG Models

arXiv: https://arxiv.org/abs/2504.19436 · HTML: https://mlsanity.com/paper/2504.19436/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Placed in RAG robustness and adaptive retrieval, one of 15 directions in Retrieval-augmented generation.
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 28 Apr 2025.

## Placed in

- Retrieval-augmented generation / [RAG robustness and adaptive retrieval](https://mlsanity.com/rag/rag-robustness-and-adaptive-retrieval/)

## Builds on (most cited first, 9 of 9)

- [DRAGIN: Dynamic Retrieval Augmented Generation based on the Information Needs of Large Language Models](https://mlsanity.com/paper/2403.10081/) (2024)
- [Self-Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information Networks](https://mlsanity.com/paper/2410.17617/) (2024)
- [Enhancing Retrieval and Managing Retrieval: A Four-Module Synergy for Improved Quality and Efficiency in RAG Systems](https://mlsanity.com/paper/2407.10670/) (2024)
- [DR-RAG: Applying Dynamic Document Relevance to Retrieval-Augmented Generation for Question-Answering](https://mlsanity.com/paper/2406.07348/) (2024)
- [Social Network User Profiling for Anomaly Detection Based on Graph Neural Networks](https://mlsanity.com/paper/2503.19380/) (2025)
- [Retrieval Augmented Generation for Dynamic Graph Modeling](https://mlsanity.com/paper/2408.14523/) (2024)
- [Context-Aware Adaptive Sampling for Intelligent Data Acquisition Systems Using DQN](https://mlsanity.com/paper/2504.09344/) (2025)
- [DynamicER: Resolving Emerging Mentions to Dynamic Entities for RAG](https://mlsanity.com/paper/2410.11494/) (2024)
- [Improving Harmful Text Detection with Joint Retrieval and External Knowledge](https://mlsanity.com/paper/2504.02310/) (2025)

## Built on by (most cited first, 8 of 8)

- [A Survey of Context Engineering for Large Language Models](https://mlsanity.com/paper/2507.13334/) (2025)
- [Structured Memory Mechanisms for Stable Context Representation in Large Language Models](https://mlsanity.com/paper/2505.22921/) (2025)
- [Deep Probabilistic Modeling of User Behavior for Anomaly Detection via Mixture Density Networks](https://mlsanity.com/paper/2505.08220/) (2025)
- [Collaborative Distillation Strategies for Parameter-Efficient Language Model Deployment](https://mlsanity.com/paper/2507.15198/) (2025)
- [Time-Series Learning for Proactive Fault Prediction in Distributed Systems with Deep Neural Structures](https://mlsanity.com/paper/2505.20705/) (2025)
- [BudgetLeak: Membership Inference Attacks on RAG Systems via the Generation Budget Side Channel](https://mlsanity.com/paper/2511.12043/) (2025)
- [Uncertainty-Aware Dynamic Knowledge Graphs for Reliable Question Answering](https://mlsanity.com/paper/2601.09720/) (2025)
- [Interpretability Framework for LLMs in Undergraduate Calculus](https://mlsanity.com/paper/2510.17910/) (2025)

## Abstract (arXiv)

This paper focuses on the dynamic optimization of the Retrieval-Augmented Generation (RAG) architecture. It proposes a state-aware dynamic knowledge retrieval mechanism to enhance semantic understanding and knowledge scheduling efficiency in large language models for open-domain question answering and complex generation tasks. The method introduces a multi-level perceptive retrieval vector construction strategy and a differentiable document matching path. These components enable end-to-end joint training and collaborative optimization of the retrieval and generation modules. This effectively addresses the limitations of static RAG structures in context adaptation and knowledge access. Experiments are conducted on the Natural Questions dataset. The proposed structure is thoroughly evaluated across different large models, including GPT-4, GPT-4o, and DeepSeek. Comparative and ablation experiments from multiple perspectives confirm the significant improvements in BLEU and ROUGE-L scores. The approach also demonstrates stronger robustness and generation consistency in tasks involving semantic ambiguity and multi-document fusion. These results highlight its broad application potential and practical value in building high-quality language generation systems.