13 citations · 15 across the 5 of their papers we have counts for
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cs.CV2026
SenseNova-U1: Unifying Multimodal Understanding and Generation with NEO-unify Architecture
Haiwen Diao, Penghao Wu, Hanming Deng +55
Recent large vision-language models (VLMs) remain fundamentally constrained by a persistent dichotomy: understanding and generation are treated as distinct problems, leading to fra…
cs.CV2024★ 13 cited
Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs
Shengbang Tong, Ellis Brown, Penghao Wu +11
We introduce Cambrian-1, a family of multimodal LLMs (MLLMs) designed with a vision-centric approach. While stronger language models can enhance multimodal capabilities, the design…
cs.CV2023★ 1 cited
V*: Guided Visual Search as a Core Mechanism in Multimodal LLMs
Penghao Wu, Saining Xie
When we look around and perform complex tasks, how we see and selectively process what we see is crucial. However, the lack of this visual search mechanism in current multimodal LL…