1 citations · 2 across the 8 of their papers we have counts for
9 papers
One Token Is Enough: Improving Diffusion Language Models with a Sink Token
Zihou Zhang, Zheyong Xie, Li Zhong +3
Diffusion Language Models (DLMs) have emerged as a compelling alternative to autoregressive approaches, enabling parallel text generation with competitive performance. Despite thes…
RedOne 2.0: Rethinking Domain-specific LLM Post-Training in Social Networking Services
Fei Zhao, Chonggang Lu, Haofu Qian +9
As a key medium for human interaction and information exchange, social networking services (SNS) pose unique challenges for large language models (LLMs): heterogeneous workloads, f…
RedOne: Revealing Domain-specific LLM Post-Training in Social Networking Services
Fei Zhao, Chonggang Lu, Yue Wang +22
As a primary medium for modern information dissemination, social networking services (SNS) have experienced rapid growth, which has proposed significant challenges for platform con…
Pet-Bench: Benchmarking the Abilities of Large Language Models as E-Pets in Social Network Services
Hongcheng Guo, Zheyong Xie, Shaosheng Cao +6
As interest in using Large Language Models for interactive and emotionally rich experiences grows, virtual pet companionship emerges as a novel yet underexplored application. Exist…
SNS-Bench-VL: Benchmarking Multimodal Large Language Models in Social Networking Services
Hongcheng Guo, Zheyong Xie, Shaosheng Cao +5
With the increasing integration of visual and textual content in Social Networking Services (SNS), evaluating the multimodal capabilities of Large Language Models (LLMs) is crucial…
Redefining Machine Translation on Social Network Services with Large Language Models
Hongcheng Guo, Fei Zhao, Shaosheng Cao +8
The globalization of social interactions has heightened the need for machine translation (MT) on Social Network Services (SNS), yet traditional models struggle with culturally nuan…
Cluster-Driven Expert Pruning for Mixture-of-Experts Large Language Models
Hongcheng Guo, Juntao Yao, Boyang Wang +5
Mixture-of-Experts (MoE) architectures have emerged as a promising paradigm for scaling large language models (LLMs) with sparse activation of task-specific experts. Despite their…
Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models
Wenxuan Huang, Bohan Jia, Zijie Zhai +7
DeepSeek-R1-Zero has successfully demonstrated the emergence of reasoning capabilities in LLMs purely through Reinforcement Learning (RL). Inspired by this breakthrough, we explore…
Dynamic-LLaVA: Efficient Multimodal Large Language Models via Dynamic Vision-language Context Sparsification
Wenxuan Huang, Zijie Zhai, Yunhang Shen +6
Multimodal Large Language Models (MLLMs) have achieved remarkable success in vision understanding, reasoning, and interaction. However, the inference computation and memory increas…