activity
20242026
most citedTalk Before You Retrieve: Agent-Led Discussions for Better RAG in Medical QA

1 citations · 1 across the 24 of their papers we have counts for

collaborators

27 papers

cs.CV2026

Does Your ViT Still Need U-Net for Segmentation?

Xin Li, Wenhui Zhu, Xuanzhao Dong +6

Medical image segmentation is dominated by U-Net-style encoder-decoder architectures. Vision Transformers (ViTs) overcome the limited receptive field of convolutional networks thro…

cs.AI2026

S-SPPO: Semantic-Calibrated Self-Play Preference Optimization

Xiwen Chen, Wenhui Zhu, Jingjing Wang +13

Aligning Large Language Models (LLMs) with human preferences is often formulated via Direct Preference Optimization (DPO). However, the standard Bradley-Terry instantiation of DPO…

cs.CV2026

Hierarchical Mesh Transformers with Topology-Guided Pretraining for Morphometric Analysis of Brain Structures

Yujian Xiong, Mohammad Farazi, Yanxi Chen +8

Representation learning on large-scale unstructured volumetric and surface meshes poses significant challenges in neuroimaging, especially when models must incorporate diverse vert…

cs.CL2026

Your Agent is More Brittle Than You Think: Uncovering Indirect Injection Vulnerabilities in Agentic LLMs

Wenhui Zhu, Xuanzhao Dong, Xiwen Chen +7

The rapid deployment of open-source frameworks has significantly advanced the development of modern multi-agent systems. However, expanded action spaces, including uncontrolled pri…

cs.CV2026

Bridging Restoration and Diagnosis: A Comprehensive Benchmark for Retinal Fundus Enhancement

Xuanzhao Dong, Wenhui Zhu, Xiwen Chen +8

Over the past decade, generative models have demonstrated success in enhancing fundus images. However, the evaluation of these models remains a challenge. A benchmark for fundus im…

cs.CV2026

OTPrune: Distribution-Aligned Visual Token Pruning via Optimal Transport

Xiwen Chen, Wenhui Zhu, Gen Li +9

Multi-modal large language models (MLLMs) achieve strong visual-language reasoning but suffer from high inference cost due to redundant visual tokens. Recent work explores visual t…