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cs.CV2025★ 1 cited
From Pixels to Words -- Towards Native Vision-Language Primitives at Scale
Haiwen Diao, Mingxuan Li, Silei Wu +6
The edifice of native Vision-Language Models (VLMs) has emerged as a rising contender to typical modular VLMs, shaped by evolving model architectures and training paradigms. Yet, t…
cs.CV2025★ 1 cited
EchoBench: Benchmarking Sycophancy in Medical Large Vision-Language Models
Botai Yuan, Yutian Zhou, Yingjie Wang +9
Recent benchmarks for medical Large Vision-Language Models (LVLMs) emphasize leaderboard accuracy, overlooking reliability and safety. We study sycophancy -- models' tendency to un…
cs.CV2025
Streamline Without Sacrifice -- Squeeze out Computation Redundancy in LMM
Penghao Wu, Lewei Lu, Ziwei Liu
Large multimodal models excel in multimodal tasks but face significant computational challenges due to excessive computation on visual tokens. Unlike token reduction methods that f…