FlexRAG: A Flexible and Comprehensive Framework for Retrieval-Augmented Generation (2025.acl-demo)
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| Challenge: | Existing frameworks for retrieval-augmented generation (RAG) lack new techniques, difficulties in algorithm reproduction and sharing, and high system overhead. |
| Approach: | They propose a retrieval-augmented generation framework specifically designed for research and prototyping that supports text-based, multimodal, and network-based RAG. |
| Outcome: | The proposed framework supports text-based, multimodal, and network-based RAG, providing comprehensive lifecycle support alongside efficient asynchronous processing and persistent caching capabilities. |
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| Challenge: | Existing RAG systems rely on flat data representations and inadequate contextual awareness . lightRAG framework incorporates graph structures into text indexing and retrieval processes . |
| Approach: | LightRAG is a framework that integrates graph structures into text indexing and retrieval processes. |
| Outcome: | The proposed framework incorporates graph structures into text indexing and retrieval processes. |
MiniRAG: A Lightweight RAG system with Small Language Models (2026.acl-long)
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| Challenge: | Existing RAG frameworks rely on Large Language Models (LLMs) for all stages of the process, resulting in high computational costs and resource demands. |
| Approach: | They propose a semantic-aware heterogeneous graph indexing mechanism that combines text chunks and named entities in a unified structure and a lightweight topology-enhanced retrieval approach that leverages graph structures for efficient knowledge discovery without requiring advanced language capabilities. |
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Enhancing Retrieval-Augmented Generation: A Study of Best Practices (2025.coling-main)
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| Challenge: | Retrieval-augmented generation systems have shown remarkable advancements by integrating retrieval mechanisms into language models, enhancing their ability to produce more accurate and contextually relevant responses. |
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Searching for Best Practices in Retrieval-Augmented Generation (2024.emnlp-main)
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Xiaohua Wang, Zhenghua Wang, Xuan Gao, Feiran Zhang, Yixin Wu, Zhibo Xu, Tianyuan Shi, Zhengyuan Wang, Shizheng Li, Qi Qian, Ruicheng Yin, Changze Lv, Xiaoqing Zheng, Xuanjing Huang
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TableRAG: A Retrieval Augmented Generation Framework for Heterogeneous Document Reasoning (2025.emnlp-main)
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| Challenge: | Existing approaches to retrievalaugmented generation (RAG) are limited when applied to heterogeneous documents . flattening tables and chunking strategies disrupt tabular structure, leads to information loss, and undermines reasoning capabilities of LLMs in multi-hop, global queries. |
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FunnelRAG: A Coarse-to-Fine Progressive Retrieval Paradigm for RAG (2025.findings-naacl)
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| Challenge: | Retrieval-Augmented Generation (RAG) is widely adopted in Large Language Models, but is flat and has limitations such as a significant burden on one retriever and constant granularity limits the ceiling of retrieval performance. |
| Approach: | They propose a progressive retrieval paradigm with coarse-to-fine granularity for RAG, termed FunnelRAG, so as to balance effectiveness and efficiency. |
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RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation (2024.emnlp-demo)
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Xuanwang Zhang, Yun-Ze Song, Yidong Wang, Shuyun Tang, Xinfeng Li, Zhengran Zeng, Zhen Wu, Wei Ye, Wenyuan Xu, Yue Zhang, Xinyu Dai, Shikun Zhang, Qingsong Wen
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| Outcome: | The proposed approach reduces the need for expensive open-source tools and lacks fair comparisons between novel RAG algorithms. |
UniversalRAG: Retrieval-Augmented Generation over Corpora of Diverse Modalities and Granularities (2026.acl-long)
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| Challenge: | Retrieval-Augmented Generation (RAG) has shown substantial promise in improving factual accuracy by grounding model responses with external knowledge relevant to queries. |
| Approach: | They propose a framework to retrieve and integrate knowledge from heterogeneous sources with diverse modalities and granularities. |
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VideoRAG: Retrieval-Augmented Generation over Video Corpus (2025.findings-acl)
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| Challenge: | Existing approaches to generating models rely on text and images, but video content is a rich source of multimodal knowledge. |
| Approach: | They propose a framework that dynamically retrieves videos based on their relevance with queries . they use large video language models to represent video content for retrieval . |
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RouteRAG: Efficient Retrieval-Augmented Generation from Text and Graph via Reinforcement Learning (2026.findings-acl)
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| Challenge: | Existing graph-based or hybrid systems lack the ability to integrate supplementary evidence as reasoning unfolds. |
| Approach: | They propose a framework that integrates non-parametric knowledge into Large Language Models . they use a RL-based framework to optimize the entire generation process via RL . |
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