30 citations · 32 across the 6 of their papers we have counts for
8 papers
SchemaWeaver: Accelerating On-Device Mobile Agentic AI via Schema-Guided Parallel LLM Decoding
Boyuan Yang, Wei Gao
Large language models (LLMs) on mobile devices can act as agents that call external software tools to complete complex tasks, but the major challenge of supporting on-device mobile…
Spatial Reasoning in Multimodal Large Language Models: A Survey of Tasks, Benchmarks and Methods
Weichen Liu, Qiyao Xue, Haoming Wang +3
Spatial reasoning, which requires ability to perceive and manipulate spatial relationships in the 3D world, is a fundamental aspect of human intelligence, yet remains a persistent…
ProGait: A Multi-Purpose Video Dataset and Benchmark for Transfemoral Prosthesis Users
Xiangyu Yin, Boyuan Yang, Weichen Liu +4
Prosthetic legs play a pivotal role in clinical rehabilitation, allowing individuals with lower-limb amputations the ability to regain mobility and improve their quality of life. G…
Never Start from Scratch: Expediting On-Device LLM Personalization via Explainable Model Selection
Haoming Wang, Boyuan Yang, Xiangyu Yin +1
Personalization of Large Language Models (LLMs) is important in practical applications to accommodate the individual needs of different mobile users. Due to data privacy concerns,…
PhyT2V: LLM-Guided Iterative Self-Refinement for Physics-Grounded Text-to-Video Generation
Qiyao Xue, Xiangyu Yin, Boyuan Yang +1
Text-to-video (T2V) generation has been recently enabled by transformer-based diffusion models, but current T2V models lack capabilities in adhering to the real-world common knowle…
Achieving Sparse Activation in Small Language Models
Jifeng Song, Kai Huang, Xiangyu Yin +2
Sparse activation, which selectively activates only an input-dependent set of neurons in inference, is a useful technique to reduce the computing cost of Large Language Models (LLM…
ElasticTrainer: Speeding Up On-Device Training with Runtime Elastic Tensor Selection
Kai Huang, Boyuan Yang, Wei Gao
On-device training is essential for neural networks (NNs) to continuously adapt to new online data, but can be time-consuming due to the device's limited computing power. To speed…
Modality Plug-and-Play: Elastic Modality Adaptation in Multimodal LLMs for Embodied AI
Kai Huang, Boyuan Yang, Wei Gao
Large Language Models (LLMs) are capable of reasoning over diverse input data modalities through pre-trained encoders. However, the growing diversity of input data modalities preve…