9 papers
Farewell to Item IDs: Unlocking the Scaling Potential of Large Ranking Models via Semantic Tokens
Zhen Zhao, Tong Zhang, Jie Xu +5
Recent studies on scaling up ranking models have achieved substantial improvement for recommendation systems and search engines. However, most large-scale ranking systems rely on i…
Test-time Scaling of LLMs: A Survey from A Subproblem Structure Perspective
Zhuoyi Yang, Xu Guo, Tong Zhang +2
With this paper, we survey techniques for improving the predictive accuracy of pretrained large language models by allocating additional compute at inference time. In categorizing…
LongCat-Video Technical Report
Meituan LongCat Team, Xunliang Cai, Qilong Huang +8
Video generation is a critical pathway toward world models, with efficient long video inference as a key capability. Toward this end, we introduce LongCat-Video, a foundational vid…
Edge Collaborative Gaussian Splatting with Integrated Rendering and Communication
Yujie Wan, Chenxuan Liu, Shuai Wang +5
Gaussian splatting (GS) struggles with degraded rendering quality on low-cost devices. To address this issue, we present edge collaborative GS (ECO-GS), where each user can switch…
Q-CLIP: Unleashing the Power of Vision-Language Models for Video Quality Assessment through Unified Cross-Modal Adaptation
Yachun Mi, Yu Li, Yanting Li +6
Accurate and efficient Video Quality Assessment (VQA) has long been a key research challenge. Current mainstream VQA methods typically improve performance by pretraining on large-s…
AdaptRec: A Self-Adaptive Framework for Sequential Recommendations with Large Language Models
Tong Zhang
The recent advancements in Large Language Models (LLMs) have generated considerable interest in their utilization for sequential recommendation tasks. While collaborative signals f…