4 citations · 6 across the 6 of their papers we have counts for
6 papers
Attention Retention for Continual Learning with Vision Transformers
Yue Lu, Xiangyu Zhou, Shizhou Zhang +3
Continual learning (CL) empowers AI systems to progressively acquire knowledge from non-stationary data streams. However, catastrophic forgetting remains a critical challenge. In t…
RISE: Self-Improving Robot Policy with Compositional World Model
Jiazhi Yang, Kunyang Lin, Jinwei Li +10
Despite the sustained scaling on model capacity and data acquisition, Vision-Language-Action (VLA) models remain brittle in contact-rich and dynamic manipulation tasks, where minor…
PhysiAgent: An Embodied Agent Framework in Physical World
Zhihao Wang, Jianxiong Li, Jinliang Zheng +6
Vision-Language-Action (VLA) models have achieved notable success but often struggle with limited generalizations. To address this, integrating generalized Vision-Language Models (…
Apple Intelligence Foundation Language Models: Tech Report 2025
Ethan Li, Anders Boesen Lindbo Larsen, Chen Zhang +395
We introduce two multilingual, multimodal foundation language models that power Apple Intelligence features across Apple devices and services: i a 3B-parameter on-device model opti…
Apple Intelligence Foundation Language Models
Tom Gunter, Zirui Wang, Chong Wang +152
We present foundation language models developed to power Apple Intelligence features, including a ~3 billion parameter model designed to run efficiently on devices and a large serv…
Less is More: Removing Text-regions Improves CLIP Training Efficiency and Robustness
Liangliang Cao, Bowen Zhang, Chen Chen +5
The CLIP (Contrastive Language-Image Pre-training) model and its variants are becoming the de facto backbone in many applications. However, training a CLIP model from hundreds of m…