4 papers
Nexusformer: Nonlinear Attention Expansion for Stable and Inheritable Transformer Scaling
Weijie Zhao, Mingquan Liu, Bolun Wang +4
Scaling Transformers typically necessitates training larger models from scratch, as standard architectures struggle to expand without discarding learned representations. We identif…
InternVLA-A1: Unifying Understanding, Generation and Action for Robotic Manipulation
Junhao Cai, Zetao Cai, Jiafei Cao +39
Prevalent Vision-Language-Action (VLA) models are typically built upon Multimodal Large Language Models (MLLMs) and demonstrate exceptional proficiency in semantic understanding, b…
InternVLA-M1: A Spatially Guided Vision-Language-Action Framework for Generalist Robot Policy
Xinyi Chen, Yilun Chen, Yanwei Fu +26
We introduce InternVLA-M1, a unified framework for spatial grounding and robot control that advances instruction-following robots toward scalable, general-purpose intelligence. Its…
Gated Slot Attention for Efficient Linear-Time Sequence Modeling
Yu Zhang, Songlin Yang, Ruijie Zhu +9
Linear attention Transformers and their gated variants, celebrated for enabling parallel training and efficient recurrent inference, still fall short in recall-intensive tasks comp…