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20242026
most citedRAG LLMs are Not Safer: A Safety Analysis of Retrieval-Augmented Generation for Large Language Models

2 citations · 4 across the 6 of their papers we have counts for

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5 papers · 1 filter

cs.CV2025

Active Video Perception: Iterative Evidence Seeking for Agentic Long Video Understanding

Ziyang Wang, Honglu Zhou, Shijie Wang +6

Long video understanding (LVU) is challenging because answering real-world queries often depends on sparse, temporally dispersed cues buried in hours of mostly redundant and irrele…

cs.LG2025★ 1 cited

OpenThoughts: Data Recipes for Reasoning Models

Etash Guha, Ryan Marten, Sedrick Keh +47

Reasoning models have made rapid progress on many benchmarks involving math, code, and science. Yet, there are still many open questions about the best training recipes for reasoni…

cs.LG2025

Anyprefer: An Agentic Framework for Preference Data Synthesis

Yiyang Zhou, Zhaoyang Wang, Tianle Wang +13

High-quality preference data is essential for aligning foundation models with human values through preference learning. However, manual annotation of such data is often time-consum…

cs.CL2025★ 2 cited

RAG LLMs are Not Safer: A Safety Analysis of Retrieval-Augmented Generation for Large Language Models

Bang An, Shiyue Zhang, Mark Dredze

Efforts to ensure the safety of large language models (LLMs) include safety fine-tuning, evaluation, and red teaming. However, despite the widespread use of the Retrieval-Augmented…

cs.CR2025

A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment

Kun Wang, Guibin Zhang, Zhenhong Zhou +100

The remarkable success of Large Language Models (LLMs) has illuminated a promising pathway toward achieving Artificial General Intelligence for both academic and industrial communi…