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
Hour-scale audio-visual question answering is constrained by a context dilemma: dense whole-recording encoding rapidly exhausts context limits, whereas uniform temporal compression severely dilutes fine-grained acoustic and visual evidence. We introduce LEAP, a framework where the model retrieves its own evidence witho...
Ju-Yi Lin, Zhi-Qiang Lao, Jia-Li Cui et al.· 0 citations
ForGE is proposed, a unified framework for adapting frozen models through per-query routing over a joint action space that spans both support form and thinking depth and improves accuracy at 42-45% lower token cost on both main hosts.
Xi Xiao, Yun-Bei Zhang, Chen Liu et al.· 0 citations
Dynamic Hub-and-Spoke Memory is proposed, a training-free framework that represents distant history as structured textual memory while preserving the recent frames as visual tokens for fine-grained perception in streaming video understanding.
Xin-Ru Jiang, Lin Zhao, Xi Xiao et al.· 4 citations
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