most citedGenerative Archetype-Grounded Item Representations for Sequential Recommendation

1 citations · 1 across the 2 of their papers we have counts for

collaborators

8 papers

cs.CL2026

Augmenting Molecular Language Models with Local -gram Memory

Xinni Zhang, Zijing Liu, He Cao +2

Transformer-based language models for SMILES strings suffer from a locality gap: standard character-level tokenization fragments chemically meaningful motifs, forcing models to rep…

cs.IR20261 cited

Generative Archetype-Grounded Item Representations for Sequential Recommendation

Yifan Li, Jiahong Liu, Xinni Zhang +5

Sequential recommendation aims to predict users' next interaction with items by analyzing their historical behavior. However, the limited quality of item representations remains a…

cs.LG2026

Recent Advances of Multimodal Continual Learning: A Comprehensive Survey

Dianzhi Yu, Xinni Zhang, Yankai Chen +4

Continual learning (CL) aims to empower machine learning models to learn continually from new data, while building upon previously acquired knowledge without forgetting. As models…

cs.LG2026

ConSurv: Multimodal Continual Learning for Survival Analysis

Dianzhi Yu, Conghao Xiong, Yankai Chen +6

Survival prediction of cancers is crucial for clinical practice, as it informs mortality risks and influences treatment plans. However, a static model trained on a single dataset f…

cs.AI2025

Embracing Trustworthy Brain-Agent Collaboration as Paradigm Extension for Intelligent Assistive Technologies

Yankai Chen, Xinni Zhang, Yifei Zhang +6

Brain-Computer Interfaces (BCIs) offer a direct communication pathway between the human brain and external devices, holding significant promise for individuals with severe neurolog…

cs.CL2025

RECODE-H: A Benchmark for Research Code Development with Interactive Human Feedback

Chunyu Miao, Henry Peng Zou, Yangning Li +28

Large language models (LLMs) show the promise in supporting scientific research implementation, yet their ability to generate correct and executable code remains limited. Existing…