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20242026
most citedA Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models

15 citations · 15 across the 11 of their papers we have counts for

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6 papers · 1 filter

cs.CV2026

AI Smart Glasses for Wearable Intelligence: From Egocentric Sensing to Agentic Personalization

Xu Yuan, Yi Wang, Zhuohang Jiang +8

Recent advances in artificial intelligence (AI) are reshaping smart glasses from egocentric capture and display devices into platforms for wearable intelligence. Smart glasses incr…

cs.IR2026

FashionKG-RAG: Knowledge Graph-Enhanced Retrieval-Augmented Generation for Fashion Question Answering

Yujuan Ding, Linyin Luo, Shijie Wang +5

Fashion is a knowledge-intensive domain in which effective decision-making depends on integrating multiple types of knowledge. Although Large Language Models (LLMs) have transforme…

cs.IR2026

Diffusion Language Model for Recommendation

Chengyi Liu, Yongqi Zhou, Junwei Pan +8

Large language model (LLM)-empowered recommender systems have emerged as a promising paradigm for generative recommendation, leveraging their strong semantic reasoning and generati…

cs.CR2026

Inference Cost Attacks for Retrieval-Augmented Large Language Models

Chengliang Liu, Liangbo Ning, Yujuan Ding +1

Retrieval-Augmented Generation (RAG)-enhanced LLM systems, while powerful, introduce substantial inference costs due to the inclusion of an extra multi-stage pipeline that dynamica…

cs.IR2026

Mixture-of-Experts Knowledge Graph Retrieval-Augmented Generation for Multi-Agent LLM-based Recommendation

Shijie Wang, Chengyi Liu, Yujuan Ding +4

Large language models (LLMs) have recently been adopted for recommendations due to their ability to understand user intent and item semantics. However, LLM-based recommender system…

cs.AI2026

Beyond Description: A Multimodal Agent Framework for Insightful Chart Summarization

Yuhang Bai, Yujuan Ding, Shanru Lin +1

Chart summarization is crucial for enhancing data accessibility and the efficient consumption of information. However, existing methods, including those with Multimodal Large Langu…