ViPER: Empowering the Self-Evolution of Visual Perception Abilities in Vision-Language Models
Juntian Zhang · Song Jin · Chuanqi Cheng · Yuhan Liu · Yankai Lin · Xun Zhang · Yufei zhang · Fei Jiang · Guojun Yin · Shuai Chen · Rui Yan
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Juntian Zhang · Song Jin · Chuanqi Cheng · Yuhan Liu · Yankai Lin · Xun Zhang · Yufei zhang · Fei Jiang · Guojun Yin · Shuai Chen · Rui Yan
Shufang Xie · Qizhi Pei · Ang Lv · Jingyang Hu · Lijun Wu · Rui Yan
AI development is often framed as the outcome of isolated research and engineering efforts, yet evidence from deployed systems suggests that language models interact through a shared data ecosystem. While the optimization of individual models is extensively studied, the emergent properties of thi...
Jia-Nan Li · Jian Guan · Songhao Wu · Wei Wu · Rui Yan
Large language models (LLMs) have traditionally been aligned through one-size-fits-all approaches that assume uniform human preferences, fundamentally overlooking the diversity in user values and needs. This paper introduces a comprehensive framework for scalable personalized alignment of LLMs. W...
Song Jin · Juntian Zhang · Xun Zhang · Zeying Tian · Fei Jiang · Guojun Yin · Shuai Chen · Yong Liu · Rui Yan
Recent advancements in Vision-Language Models (VLMs) have revolutionized general visual understanding. However, their application in the food domain remains constrained by benchmarks that rely on coarse-grained categories, single-view imagery, and inaccurate metadata. To bridge this gap, we intro...
Songhao Wu · Ang Lv · Ruobing Xie · Samm Sun · Di Wang · Rui Yan · Yankai Lin
Mixture-of-Experts (MoE) models rely on an external router to assign tokens to experts. This design inherently separates the routing decision from each expert’s internal capabilities, leading to suboptimal performance. In this work, we address this limitation with Union-of-Experts (UoE), an MoE v...
Kaiyi Zhang · Ang Lv · Jinpeng Li · Yongbo Wang · Feng Wang · Haoyuan hu · Rui Yan
Reinforcement learning with verifiable rewards (RLVR) is a promising approach for improving the complex reasoning abilities of large language models (LLMs). However, current RLVR methods face two significant challenges: the near-miss reward problem, where a small mistake can invalidate an otherwi...
Lang Gao · Xuhui Li · Chenxi Wang · Mingzhe Li · Wei Liu · Zirui Song · Jinghui Zhang · Rui Yan · Preslav Nakov · Xiuying Chen
As large language models (LLMs) increasingly imitate personal writing styles, personalization has become a key challenge for machine-generated text (MGT) detection. Yet personalized MGT detection remains largely underexplored. In this work, we introduce StyloBench, the first benchmark for evaluat...
Xueran Han · Yuhan Liu · Mingzhe Li · WEI LIU · Sen Hu · Rui Yan · Zhiqiang Xu · Xiuying Chen
Great novels create immersive worlds with rich character arcs, well-structured plots, and nuanced writing styles. However, current novel generation methods often rely on brief, simplistic story outlines and generate details using plain, generic language.To bridge this gap, we introduce the task o...
Jixiang Hong · Yiran Zhang · Guanzhong Wang · Yi Liu · Ji-Rong Wen · Rui Yan
Songhao Wu · Zhongxin Chen · Yuxuan Liu · Heng Cui · Cong Li · Rui Yan