Author

Ling Zhang

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Preprint Aug 2026

SeVeR: Selective Visual Exposure and Retrieval for 3D Medical Image Question Answering

SeVeR is proposed, a selective visual exposure framework that compresses dense volumes into modality-wise prototypes and retrieves complementary multi-level evidence with change-aware gated attention during decoding, trained with a marginal-utility self-consistency objective that suppresses unhelpful retrieval.

Yao-Jun Hu, Danyang Tu, Yang Liu et al. · 0 citations

Preprint Aug 2026

HounsWorld: A Multimodal World Model for Hidden Patient-State Readout, Reconstruction, and Simulation

HounsBench is introduced, a computed tomography (CT) centric patient-state benchmark that unifies these three task families with patient-disjoint splits and per-family metrics, and HounsWorld, a 3B multimodal world model that treats volumetric scans and language as observations of the shared state through Joint Understanding-Generation Learning.

Yunhao Bai, Zhongwei Qiu, Guangyu Guo et al. · 0 citations

Jul 2026

RadSight: Towards Perceptually Reliable Multimodal Radiology Image Understanding

RadSight is proposed, a perception-driven MLLM built upon a dual 2D/3D encoder architecture that preserves native imaging spatial structures that achieves consistent improvements on public 2D and 3D medical benchmarks, further demonstrating that robust low-level visual perception is a critical foundation for reliable clinical understanding.

Jianqi Liu, Weiwei Cao, Wan-Xing Chang et al. · arXiv.org · 0 citations