2.2k citations · 7.1k across the 72 of their papers we have counts for
82 papers
RADIO1D: Elastic Representations for Condensed Vision Modeling
Greg Heinrich, Mike Ranzinger, Collin McCarthy +6
This paper challenges the assumption that vision-language models (VLMs) require fixed patch-based 2D vision features. Analyzing fine-tuned vision encoders, we find that representat…
C-RADIOv4 (Tech Report)
Mike Ranzinger, Greg Heinrich, Collin McCarthy +4
By leveraging multi-teacher distillation, agglomerative vision backbones provide a unified student model that retains and improves the distinct capabilities of multiple teachers. I…
Nemotron-Research-Tool-N1: Exploring Tool-Using Language Models with Reinforced Reasoning
Shaokun Zhang, Yi Dong, Jieyu Zhang +6
Enabling large language models with external tools has become a pivotal strategy for extending their functionality beyond text space. To enhance LLMs' tool-calling abilities, previ…
Eagle 2.5: Boosting Long-Context Post-Training for Frontier Vision-Language Models
Guo Chen, Zhiqi Li, Shihao Wang +16
We introduce Eagle 2.5, a family of frontier vision-language models (VLMs) for long-context multimodal learning. Our work addresses the challenges in long video comprehension and h…
FeatSharp: Your Vision Model Features, Sharper
Mike Ranzinger, Greg Heinrich, Pavlo Molchanov +3
The feature maps of vision encoders are fundamental to myriad modern AI tasks, ranging from core perception algorithms (e.g. semantic segmentation, object detection, depth percepti…
Eagle 2: Building Post-Training Data Strategies from Scratch for Frontier Vision-Language Models
Zhiqi Li, Guo Chen, Shilong Liu +24
Recently, promising progress has been made by open-source vision-language models (VLMs) in bringing their capabilities closer to those of proprietary frontier models. However, most…