activity
20202026
most citedDeVLBert: Learning Deconfounded Visio-Linguistic Representations

64 citations · 66 across the 9 of their papers we have counts for

collaborators

9 papers

cs.IR2026

MGRetrieval: Memory-Guided Reflective Retrieval for Long-Term Dialogue Agents

Tan Wang, Yunwei Dong

Large Language Models (LLMs) have made significant progress in dialogue, yet redundant memory contexts severely limit their effectiveness in long-term dialogue agents. External mem…

cs.CV2026

OmniWeaving: Towards Unified Video Generation with Free-form Composition and Reasoning

Kaihang Pan, Qi Tian, Jianwei Zhang +12

While proprietary systems such as Seedance-2.0 have achieved remarkable success in omni-capable video generation, the academic research community lags far behind: most of its model…

cs.CL2026

Grounded Token Initialization for New Vocabulary in LMs for Generative Recommendation

Daiwei Chen, Zhoutong Fu, Chengming Jiang +12

Language models (LMs) are increasingly extended with new learnable vocabulary tokens for domain-specific tasks, such as Semantic-ID tokens in generative recommendation. The standar…

cs.CL2025

LANTERN: Scalable Distillation of Large Language Models for Job-Person Fit and Explanation

Zhoutong Fu, Yihan Cao, Yi-Lin Chen +16

Large language models (LLMs) have achieved strong performance across a wide range of natural language processing tasks. However, deploying LLMs at scale for domain specific applica…

cs.SE2025

Large Language Model Unlearning for Source Code

Xue Jiang, Yihong Dong, Huangzhao Zhang +9

While Large Language Models (LLMs) excel at code generation, their inherent tendency toward verbatim memorization of training data introduces critical risks like copyright infringe…

cs.CV2024

TIGeR: Unifying Text-to-Image Generation and Retrieval with Large Multimodal Models

Leigang Qu, Haochuan Li, Tan Wang +4

How humans can effectively and efficiently acquire images has always been a perennial question. A classic solution is text-to-image retrieval from an existing database; however, th…