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Matching papers

Scaling Native Multimodal Pre-Training From Scratch

21 upvotes · 24 JUL 2026 · Haoyuan Wu, Aoqi Wu, Hai Wang et al.

This paper investigates how to scale large language models to also understand and interact with the physical world by training them on multiple types of data from scratch, allowing them to reason about both text and images. Practitioners might care because this could lead to more robust and versatile AI systems that can handle a wider range of tasks.

transformer-based modelsmultimodal learningscalabilityvision-language modelsmodel sizecomputational budget

DiffGI: Differentiable Geometry Images for High-Fidelity Thin-Shell 3D Generation

11 upvotes · 15 JUL 2026 · Eungjune Shim, Hansol Lee, Eunjung Ju

This paper develops a new method for generating high-fidelity 3D images of thin-shell objects, like garments, by learning a continuous surface representation. Practitioners caring about 3D generation and object modeling might care about this approach because it achieves better results with fewer computational resources.

3D-to-2D mappinggeometry imagescontinuous signed distance functionsdifferentiable Marching Squareslatent diffusion modelstransformer-based models