4 citations · 5 across the 13 of their papers we have counts for
16 papers
SlideDP: Scaling Host-Resident LLM Fine-Tuning Across Multiple GPUs
Ruijia Yang, Shiyuan Lin, Yulong Ao +5
Host-resident layer streaming enables full-parameter LLM fine-tuning beyond GPU memory, but data-parallel ranks compete for shared host resources. Replicated transfers amplify traf…
Orca: The World is in Your Mind
Yihao Wang, Yuheng Ji, Mingyu Cao +54
We introduce Orca, an initial instantiation of a general world foundation model. Orca learns a unified world latent space from multimodal world signals and exposes it through multi…
HetCCL: Enabling Collective Communication For Mixed-Vendor Heterogeneous Clusters
Yuejie Wang, Tao Chang, Yuanyuan Zhao +10
Training Large Language Models (LLMs) on heterogeneous clusters presents significant challenges for collective communication, as hardware from multiple vendors introduces diverse n…
FreqCache: Accelerating Embodied VLN Models with Adaptive Frequency-Guided Token Caching
Zihao Zheng, Xingyue Zhou, Zhihao Mao +7
Vision-Language-Navigation (VLN) models exhibit excellent navigation accuracy but incur high computational overhead. Token caching has emerged as a promising training-free strategy…
Think While Watching: Online Streaming Segment-Level Memory for Multi-Turn Video Reasoning in Multimodal Large Language Models
Lu Wang, Zhuoran Jin, Yupu Hao +4
Multimodal large language models (MLLMs) have shown strong performance on offline video understanding, but most are limited to offline inference or have weak online reasoning, maki…
RoboBrain 2.5: Depth in Sight, Time in Mind
Huajie Tan, Enshen Zhou, Zhiyu Li +32
We introduce RoboBrain 2.5, a next-generation embodied AI foundation model that advances general perception, spatial reasoning, and temporal modeling through extensive training on…
TrimTokenator-LC: Towards Adaptive Visual Token Pruning for Large Multimodal Models with Long Contexts
Hao Zhang, Mengsi Lyu, Bo Huang +2
Large Multimodal Models (LMMs) have proven effective on various tasks. They typically encode visual inputs into Original Model sequences of tokens, which are then concatenated with…
Emu3.5: Native Multimodal Models are World Learners
Yufeng Cui, Honghao Chen, Haoge Deng +20
We introduce Emu3.5, a large-scale multimodal world model that natively predicts the next state across vision and language. Emu3.5 is pre-trained end-to-end with a unified next-tok…
RoboOS-NeXT: A Unified Memory-based Framework for Lifelong, Scalable, and Robust Multi-Robot Collaboration
Huajie Tan, Cheng Chi, Xiansheng Chen +21
The proliferation of collaborative robots across diverse tasks and embodiments presents a central challenge: achieving lifelong adaptability, scalable coordination, and robust sche…
TrimTokenator: Towards Adaptive Visual Token Pruning for Large Multimodal Models
Hao Zhang, Mengsi Lyu, Chenrui He +2
Large Multimodal Models (LMMs) have achieved significant success across various tasks. These models usually encode visual inputs into dense token sequences, which are then concaten…
PDTrim: Targeted Pruning for Prefill-Decode Disaggregation in Inference
Hao Zhang, Mengsi Lyu, Zhuo Chen +3
Large Language Models (LLMs) demonstrate exceptional capabilities across various tasks, but their deployment is constrained by high computational and memory costs. Model pruning pr…
RoboBrain 2.0 Technical Report
BAAI RoboBrain Team, Mingyu Cao, Huajie Tan +50
We introduce RoboBrain 2.0, our latest generation of embodied vision-language foundation models, designed to unify perception, reasoning, and planning for complex embodied tasks in…
Infinity-MM: Scaling Multimodal Performance with Large-Scale and High-Quality Instruction Data
Shuhao Gu, Jialing Zhang, Siyuan Zhou +23
Recently, Vision-Language Models (VLMs) have achieved remarkable progress in multimodal tasks, and multimodal instruction data serves as the foundation for enhancing VLM capabiliti…
Emu3: Next-Token Prediction is All You Need
Xinlong Wang, Xiaosong Zhang, Zhengxiong Luo +22
While next-token prediction is considered a promising path towards artificial general intelligence, it has struggled to excel in multimodal tasks, which are still dominated by diff…
Aquila2 Technical Report
Bo-Wen Zhang, Liangdong Wang, Jijie Li +6
This paper introduces the Aquila2 series, which comprises a wide range of bilingual models with parameter sizes of 7, 34, and 70 billion. These models are trained based on an innov…
AquilaMoE: Efficient Training for MoE Models with Scale-Up and Scale-Out Strategies
Bo-Wen Zhang, Liangdong Wang, Ye Yuan +24
In recent years, with the rapid application of large language models across various fields, the scale of these models has gradually increased, and the resources required for their…