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20242026
most citedThe Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG)

6 citations · 17 across the 10 of their papers we have counts for

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cs.IR2026

Paper2Data: Large-Scale LLM Extraction and Metadata Structuring of Global Urban Data from Scientific Literature

Runwen You, Tong Xia, Jingzhi Wang +5

Urban data support a wide range of applications across multiple disciplines. However, at the global scale, there is no unified platform for urban data discovery. As a result, resea…

cs.IR2026

Fix Before Search: Benchmarking Agentic Query Visual Pre-processing in Multimodal Retrieval-augmented Generation

Jiankun Zhang, Shenglai Zeng, Kai Guo +4

Multimodal Retrieval-Augmented Generation (MRAG) has emerged as a key paradigm for grounding MLLMs with external knowledge. While query pre-processing (e.g., rewriting) is standard…