collaborators

8 papers

cs.AI2026

UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models

Lei Xin, Bin Gu, Peize Li +9

Sparse mixture-of-experts (MoE) layers expand recommendation capacity through conditional computation, yet a trained checkpoint still stores and routes over its full expert bank. W…

cs.CV2026

Ivy-Fake: A Unified Explainable Framework and Benchmark for Image and Video AIGC Detection

Changjiang Jiang, Wenhui Dong, Zhonghao Zhang +8

The rapid development of Artificial Intelligence Generated Content (AIGC) techniques has enabled the creation of high-quality synthetic content, but it also raises significant secu…

cs.CV2026

Fake-HR1: Rethinking Reasoning of Vision Language Model for Synthetic Image Detection

Changjiang Jiang, Xinkuan Sha, Fengchang Yu +5

Recent studies have demonstrated that incorporating Chain-of-Thought (CoT) reasoning into the detection process can enhance a model's ability to detect synthetic images. However, e…

cs.CL2026

JoyAI-LLM Flash: Advancing Mid-Scale LLMs with Token Efficiency

Aichen Cai, Anmeng Zhang, Anyu Li +66

We introduce JoyAI-LLM Flash, an efficient Mixture-of-Experts (MoE) language model designed to redefine the trade-off between strong performance and token efficiency in the sub-50B…

cs.CV2026

SpineBench: A Clinically Salient, Level-Aware Benchmark Powered by the SpineMed-450k Corpus

Ming Zhao, Wenhui Dong, Yang Zhang +23

Spine disorders affect 619 million people globally and are a leading cause of disability, yet AI-assisted diagnosis remains limited by the lack of level-aware, multimodal datasets.…

cs.IR2026

HyTRec: A Hybrid Temporal-Aware Attention Architecture for Long Behavior Sequential Recommendation

Lei Xin, Yuhao Zheng, Ke Cheng +3

Modeling long sequences of user behaviors has emerged as a critical frontier in generative recommendation. However, existing solutions face a dilemma: linear attention mechanisms a…