activity
20242026
most cited"Generate" the Future of Work through AI: Empirical Evidence from Online Labor Markets

3 citations · 3 across the 1 of their papers we have counts for

collaborators

6 papers

cs.AI20263 cited

"Generate" the Future of Work through AI: Empirical Evidence from Online Labor Markets

Jin Liu, Xingchen Xu, Xi Nan +2

Large Language Model (LLM)-based generative AI systems are general-purpose tools capable of augmenting or even automating a wide range of job functions, positioning them to reshape…

cs.CY2026

Learning to Adopt Generative AI

Lijia Ma, Xingchen Xu, Yumei He +1

Recent advancements in generative AI, such as ChatGPT, have dramatically transformed how people access information. Despite its powerful capabilities, the benefits it provides may…

cs.CL2026

Open-Source Multimodal Moxin Models with Moxin-VLM and Moxin-VLA

Pu Zhao, Arash Akbari, Xuan Shen +16

Recently, Large Language Models (LLMs) have undergone a significant transformation, marked by a rapid rise in both their popularity and capabilities. Leading this evolution are pro…

cs.IR2025

When Content is Goliath and Algorithm is David: The Style and Semantic Effects of Generative Search Engine

Lijia Ma, Juan Qin, Xingchen Xu +1

Generative search engines (GEs) leverage large language models (LLMs) to deliver AI-generated summaries with website citations, establishing novel traffic acquisition channels whil…

cs.CL2025

7B Fully Open Source Moxin-LLM/VLM -- From Pretraining to GRPO-based Reinforcement Learning Enhancement

Pu Zhao, Xuan Shen, Zhenglun Kong +16

Recently, Large Language Models (LLMs) have undergone a significant transformation, marked by a rapid rise in both their popularity and capabilities. Leading this evolution are pro…

cs.AI2024

Algorithmic Collusion or Competition: the Role of Platforms' Recommender Systems

Xingchen Xu, Stephanie Lee, Yong Tan

Recent scholarly work has extensively examined the phenomenon of algorithmic collusion driven by AI-enabled pricing algorithms. However, online platforms commonly deploy recommende…