6 papers
LLM-Based Generative Retrieval for Snapchat Content Recommendation
Liam Collins, Jiwen Ren, Donald Loveland +19
Pretrained large language models (LLMs) are promising retrieval engines because they combine rich semantic priors, strong sequence modeling capabilities, and favorable scaling beha…
Unequal Trips, Unequal Places: Diagnosing and Mitigating Delay Inequity in Autonomous Vehicle Fleet Coordination
Nicole Hu, Mingtao Zhang, Haoyang LI +2
City-scale autonomous vehicle fleet coordinators are typically optimized for aggregate travel time, yet fleet averages conceal how delay is distributed across trips and regions. We…
PolyUQuest: Verifiable Structure-Aware Web RAG over Heterogeneous Graphs
Ying Liu, Yi Ye, Quanyu Feng +5
Existing retrieval-augmented generation (RAG) systems treat web pages as flat text, losing the structural and semantic signals encoded in HTML. We present PolyUQuest, a verifiable,…
Securing Retrieval-Augmented Generation: A Taxonomy of Attacks, Defenses, and Future Directions
Yuming Xu, Mingtao Zhang, Zhuohan Ge +7
Retrieval-augmented generation (RAG) extends large language models (LLMs) with external knowledge, but this access path also introduces security risks that existing work often conf…
Efficient Linear Attention for Multivariate Time Series Modeling via Entropy Equality
Mingtao Zhang, Guoli Yang, Zhanxing Zhu +2
Attention mechanisms have been extensively employed in various applications, including time series modeling, owing to their capacity to capture intricate dependencies; however, the…
Machine Learning for Evolutionary Graph Theory
Guoli Yang, Matteo Cavaliere, Mingtao Zhang +3
The stability of communities - whether biological, social, economic, technological or ecological depends on the balance between cooperation and cheating. While cooperation strength…