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GRAG: Graph Retrieval-Augmented Generation

Findings of the Association for Computational Linguistics: NAACL 2025
Naive Retrieval-Augmented Generation (RAG) focuses on individual documents during retrieval and, as a result, falls short in handling networked documents which are very popular in many applications such as citation graphs, social media, and knowledge graphs.
Yuntong Hu, Zhihan Lei, Zheng Zhang 0047, Bo Pan 0009, Chen Ling 0003, Liang Zhao 0002   +5 more
openaire   +4 more sources

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

FictionRAG: A Stateful Metacognitive Framework for High-Fidelity Long-Narrative Role-Playing

Algorithms
Maintaining high-fidelity character personas and tracking trusted narrative facts remain significant challenges for LLM-based role-playing systems, particularly in long-context scenarios. Traditional Retrieval-Augmented Generation (RAG) approaches, which
Yifei Deng, Yudong Zhang, Jingpu Yang, Miao Fang   +3 more
doaj   +1 more source

Smart Nanotechnologies for Multimodal Neuromodulation and Brain Interfacing

Advanced Science, EarlyView.
Recent advances in smart nanotechnologies are expanding the toolbox for brain interfacing, from wireless neuromodulation and high‐resolution sensing to targeted delivery within the central nervous system. By combining responsive nanomaterials with bioinspired design, these platforms enable multimodal interactions with neurons and glia, while also ...
Tommaso Curiale, Marie Celine Lefevre, Alessio Carmignani, Maria Cristina Ceccarelli, Matteo Battaglini, Attilio Marino, Gianni Ciofani   +6 more
wiley   +1 more source

KAQG: A Knowledge-Graph-Enhanced RAG for Difficulty-Controlled Question Generation

IEEE Access
This study introduces Knowledge Augmented Question Generation (KAQG), an educational assessment framework that integrates Item Response Theory (IRT), Bloom’s Taxonomy, and knowledge graphs into a multi-agent Retrieval-Augmented Generation (RAG ...
Ching Han Chen, Ming Fang Shiu
doaj   +1 more source

Automated Extraction of Multicomponent Alloy Data Using Large Language Models for Sustainable Design

Advanced Science, EarlyView.
A large language model (LLM) based pipeline is developed to automatically extract a comprehensive and accurate multicomponent alloy database from literature corpus. The extracted dataset is integrated with sustainability indicators to identify potential alloys that outperform existing industrial benchmark materials in terms of both performance and ...
Aravindan Kamatchi Sundaram, Mohit Chakraborty, Sai Mani Kumar Devathi, B. Pabitramohan Prusty, Rohit Batra   +4 more
wiley   +1 more source

GraphRAG-Enabled Local Large Language Model for Gestational Diabetes Mellitus: Development of a Proof-of-Concept

JMIR Diabetes
BackgroundGestational diabetes mellitus (GDM) is a prevalent chronic condition that affects maternal and fetal health outcomes worldwide, increasingly in underserved populations.
Edmund Evangelista, Fathima Ruba, Salman Bukhari, Amril Nazir, Ravishankar Sharma   +4 more
doaj   +1 more source

PEAR1 Promotes Glucose Metabolism Reprogramming in Sepsis‐Associated Acute Lung Injury via AARS1‐Mediated HIF‐1α Lactylation

Advanced Science, EarlyView.
This study revealed that a PEAR1/HIF‐1α/ glycolysis/lactate/H3K18la positive feedback loop in PMVECs that drives the development of S‐ALI. Mechanistically, PEAR1 mediates the binding of HIF‐1α to AARS1, leading to the lactylation of HIF‐1α, the primary lactylation site of which is K172.
Shuai Li, Zongqing Lu, Pu Fang, Qingqing Pang, Xianghui Wu, Bo Wang, Min Liang, Jingjing Pan, Haobo Kong, Haijian Cai, Juanjuan Li, Jiaying Kang, Dongsheng Wang, Dunling Xia, Mengmeng Xu, Qinghai You   +15 more
wiley   +1 more source

A Comparative Evaluation of RAG Architectures for Cross-Domain LLM Applications: Design, Implementation, and Assessment

IEEE Access
Retrieval-Augmented Generation (RAG) pairs large language models with external search to constrain knowledge staleness and hallucination, a critical need in finance and e-commerce where numerical precision and regulatory auditability are non-negotiable ...
Pinar Ersoy, Mustafa Ersahin
doaj   +1 more source

Benchmarking Retrieval-Augmented Generation for Chemistry

CoRR
Retrieval-augmented generation (RAG) has emerged as a powerful framework for enhancing large language models (LLMs) with external knowledge, particularly in scientific domains that demand specialized and dynamic information. Despite its promise, the application of RAG in the chemistry domain remains underexplored, primarily due to the lack of high ...
Xianrui Zhong, Bowen Jin, Siru Ouyang, Yanzhen Shen, Qiao Jin 0001, Yin Fang, Zhiyong Lu, Jiawei Han 0001   +7 more
openaire   +2 more sources