3 papers
cs.CV2026
Seeing Isn't Knowing: Do VLMs Know When Not to Answer Spatial Questions (and Why)?
Yue Zhang, Zun Wang, Han Lin +3
Spatial reasoning is a fundamental capability for vision-language models (VLMs) deployed in real-world environments. However, visual observations are inherently limited representat…
cs.CV2025
Exploring MLLM-Diffusion Information Transfer with MetaCanvas
Han Lin, Xichen Pan, Ziqi Huang +10
Multimodal learning has rapidly advanced visual understanding, largely via multimodal large language models (MLLMs) that use powerful LLMs as cognitive cores. In visual generation,…
cs.CV2025
Bifrost-1: Bridging Multimodal LLMs and Diffusion Models with Patch-level CLIP Latents
Han Lin, Jaemin Cho, Amir Zadeh +2
There is growing interest in integrating high-fidelity visual synthesis capabilities into large language models (LLMs) without compromising their strong reasoning capabilities. Exi…