5 papers
GPUSimBench: Towards Scalable and Reliable GPU-Accelerated Simulators in Embodied AI
Huzhenyu Zhang, Shenghai Yuan, Wenrui Yan +4
Data-driven embodied AI is rapidly transitioning into a paradigm that scales training through massively parallel simulation, where GPU-accelerated simulators serve as the foundatio…
Ascend-RaBitQ: Heterogeneous NPU-CPU Acceleration of Billion-Scale Similarity Search with 1-bit Quantization
Fujun He, Chuyue Ye, Huaxiang Cai +12
Vector similarity search is a critical component of modern AI systems, but traditional CPU-based implementations face fundamental scalability bottlenecks for billion-scale corpora…
Revisiting the Data Sampling in Multimodal Post-training from a Difficulty-Distinguish View
Jianyu Qi, Ding Zou, Wenrui Yan +5
Recent advances in Multimodal Large Language Models (MLLMs) have spurred significant progress in Chain-of-Thought (CoT) reasoning. Building on the success of Deepseek-R1, researche…
EmbodiedBrain: Expanding Performance Boundaries of Task Planning for Embodied Intelligence
Ding Zou, Feifan Wang, Mengyu Ge +17
The realization of Artificial General Intelligence (AGI) necessitates Embodied AI agents capable of robust spatial perception, effective task planning, and adaptive execution in ph…
VisionSelector: End-to-End Learnable Visual Token Compression for Efficient Multimodal LLMs
Jiaying Zhu, Yurui Zhu, Xin Lu +5
Multimodal Large Language Models (MLLMs) encounter significant computational and memory bottlenecks from the massive number of visual tokens generated by high-resolution images or…