34 citations · 38 across the 11 of their papers we have counts for
6 papers · 1 filter
Predict, Don't Iterate: Efficient Adaptive-Length Infilling for Diffusion Language Models
Haobo Xu, Sirui Chen, Yuanchen Bei +5
Diffusion language models (DLMs) have emerged as a promising alternative to the auto-regressive paradigm. With bidirectional attention and any-order generation, DLMs naturally fit…
Astar: Learning to Propose Evolution Directions for Self-Evolving Industrial AI Systems
Jinxin Hu, Hao Deng, Haibo Xing +12
Modern AI systems advance through continuous iteration: a loop of proposing evolution directions, implementing code, training, and evaluation. While the latter three stages are inc…
Trustworthy Recommendation in the Era of Large Language Models: Opportunities and Challenges
Bohao Wang, Yu Cui, Zhenxiang Xu +13
The field of recommender systems (RS) is currently undergoing two profound paradigm shifts. From the perspective of objectives, the goal has shifted beyond mere recommendation accu…
Trie-Aware Transformers for Generative Recommendation
Zhenxiang Xu, Jiawei Chen, Sirui Chen +5
Generative recommendation (GR) aligns with advances in generative AI by casting next-item prediction as token-level generation rather than score-based ranking. Most GR methods adop…
Influence-Preserving Proxies for Gradient-Based Data Selection in LLM Fine-tuning
Sirui Chen, Yunzhe Qi, Mengting Ai +4
Supervised fine-tuning (SFT) relies critically on selecting training data that most benefits a model's downstream performance. Gradient-based data selection methods such as TracIn…
TopKGAT: A Top-K Objective-Driven Architecture for Recommendation
Sirui Chen, Jiawei Chen, Canghong Jin +4
Recommendation systems (RS) aim to retrieve the top-K items most relevant to users, with metrics such as Precision@K and Recall@K commonly used to assess effectiveness. The archite…