most citedReinforced Preference Optimization for Recommendation

2 citations · 2 across the 3 of their papers we have counts for

collaborators

5 papers

cs.AI2026

Risky-Bench: Probing Agentic Safety Risks under Real-World Deployment

Jingnan Zheng, Yanzhen Luo, Jingjun Xu +8

Large Language Models (LLMs) are increasingly deployed as agents that operate in real-world environments, introducing safety risks beyond linguistic harm. Existing agent safety eva…

cs.CL2026

Understanding Multilingualism in Mixture-of-Experts LLMs: Routing Mechanism, Expert Specialization, and Layerwise Steering

Yuxin Chen, Zhengzhou Cai, Xiangtian Ji +4

Mixture-of-Experts (MoE) architectures have shown strong multilingual capabilities, yet the internal mechanisms underlying performance gains and cross-language differences remain i…

cs.IR2025

MiniOneRec: An Open-Source Framework for Scaling Generative Recommendation

Xiaoyu Kong, Leheng Sheng, Junfei Tan +5

The recent success of large language models (LLMs) has renewed interest in whether recommender systems can achieve similar scaling benefits. Conventional recommenders, dominated by…

cs.IR20252 cited

Reinforced Preference Optimization for Recommendation

Junfei Tan, Yuxin Chen, An Zhang +7

Recent breakthroughs in large language models (LLMs) have fundamentally shifted recommender systems from discriminative to generative paradigms, where user behavior modeling is ach…

cs.CL2025

The Emergence of Abstract Thought in Large Language Models Beyond Any Language

Yuxin Chen, Yiran Zhao, Yang Zhang +7

As large language models (LLMs) continue to advance, their capacity to function effectively across a diverse range of languages has shown marked improvement. Preliminary studies ob…