14 citations · 15 across the 5 of their papers we have counts for
10 papers · 1 filter
i1: A Simple and Fully Open Recipe for Strong Text-to-Image Models
Boya Zeng, Tianze Luo, Shu Pu +4
Diffusion models have consistently driven progress in text-to-image generation. However, it is challenging to attribute recent progress to specific modeling and data choices: state…
WorldBench: A Challenging and Visually Diverse Multimodal Reasoning Benchmark
Yida Yin, Harish Krishnakumar, Chung Peng Lee +9
In real-world applications, models are expected to perform reliably across diverse settings. Yet, many existing multimodal benchmarks expand task types without capturing the visual…
Vero: An Open RL Recipe for General Visual Reasoning
Gabriel Sarch, Linrong Cai, Qunzhong Wang +3
What does it take to build a visual reasoner that works across charts, science, spatial understanding, and open-ended tasks? The strongest vision-language models (VLMs) suggest tha…
Out of Sight, Not Out of Context? Egocentric Spatial Reasoning in VLMs Across Disjoint Frames
Sahithya Ravi, Gabriel Sarch, Vibhav Vineet +2
An embodied AI assistant operating on egocentric video must integrate spatial cues across time - for instance, determining where an object A, glimpsed a few moments ago lies relati…
Grounding Task Assistance with Multimodal Cues from a Single Demonstration
Gabriel Sarch, Balasaravanan Thoravi Kumaravel, Sahithya Ravi +2
A person's demonstration often serves as a key reference for others learning the same task. However, RGB video, the dominant medium for representing these demonstrations, often fai…
Grounded Reinforcement Learning for Visual Reasoning
Gabriel Sarch, Snigdha Saha, Naitik Khandelwal +4
While reinforcement learning (RL) over chains of thought has significantly advanced language models in tasks such as mathematics and coding, visual reasoning introduces added compl…