Medical vision-language models (VLMs) for visual question answering (VQA) often pass long visual-token sequences to the language model, making visual-token pruning attractive but risky for medical images, where answers may depend on localized boundary and texture evidence. We study training free pre-decoding pruning an...
Meng-Chen Fan, Qi-Zhen Lan, Xi Xiao et al.· 0 citations
Brain MRI interpretation is a longitudinal clinical reasoning problem: radiologists compare serial studies, integrate information across MRI sequences, localize findings within volumetric anatomy, and translate this evidence into report-grounded assessments. Existing medical VQA and 3D imaging benchmarks capture import...
Qi-Zhen Lan, Meng-Chen Fan, Hang Zhang et al.· 2 citations
Existing LLM routers choose among models using static per-model costs. We show that open-weight inference markets introduce a second, largely ignored decision axis: after choosing a model, a client must still choose which provider serves it. Measuring live endpoints across [nummodels] open models, competing providers,...
Liang He, Jing Wen, Yi-Xiong Chen et al.· 0 citations
EvoTreeNAD is introduced, a genealogy-guided evolutionary algorithm that constructs trainable architectures without a supplied seed or a hand-specified search space that outperforms direct generation, best-of-$N$ greedy continuation, and full-family-mean routing.
Li-Shan Yu, D. Jiu, Qi-Zhen Lan et al.· 0 citations
PerturbLDM, a latent-diffusion framework for conditional generation of single-cell transcriptional responses, is introduced, showing support for conditional response generation across data scales and biological settings.
Li-Shan Yu, Kang-Lin Hsieh, Y. Chu et al.· bioRxiv· 0 citations
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