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most citedNVILA: Efficient Frontier Visual Language Models

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cs.CV2026

Ablate-to-Validate: Are Vision-Language Models Really Using Continuous Thought Tokens?

Tianyi Zhang, Mahtab Bigverdi, Ranjay Krishna

Vision-language models (VLMs) are increasingly augmented with continuous or latent non-textual tokens intended to support "visual thinking." Despite improved task accuracy, this al…

cs.CV2026

RefDecoder: Enhancing Visual Generation with Conditional Video Decoding

Xiang Fan, Yuheng Wang, Bohan Fang +2

Video generation powers a vast array of downstream applications. However, while the de facto standard, i.e., latent diffusion models, typically employ heavily conditioned denoising…

cs.CV2026

Posterior Augmented Flow Matching

George Stoica, Sayak Paul, Matthew Wallingford +6

Flow matching (FM) trains a time-dependent vector field that transports samples from a simple prior to a complex data distribution. However, for high-dimensional images, each train…

cs.CV20261 cited

NVILA: Efficient Frontier Visual Language Models

Zhijian Liu, Ligeng Zhu, Baifeng Shi +24

Visual language models (VLMs) have made significant advances in accuracy in recent years. However, their efficiency has received much less attention. This paper introduces NVILA, a…

cs.CV2026

ThinkMorph: Emergent Properties in Multimodal Interleaved Chain-of-Thought Reasoning

Jiawei Gu, Yunzhuo Hao, Huichen Will Wang +5

Multimodal reasoning requires iterative coordination between language and vision, yet it remains unclear what constitutes a meaningful interleaved chain of thought. We posit that t…

cs.CV2025

Synthetic Visual Genome

Jae Sung Park, Zixian Ma, Linjie Li +9

Reasoning over visual relationships-spatial, functional, interactional, social, etc.-is considered to be a fundamental component of human cognition. Yet, despite the major advances…