1 citations · 2 across the 5 of their papers we have counts for
7 papers
OGAM: Connecting Systematic Testing to Runtime Assurance through Object-Grounded Attention Monitoring for VLA Policies
Haki Darwish, Xiangyu Yin, Changwen Li +3
Benchmarks expose vision-language-action (VLA) policies to few canonical instructions, while exhaustive deployment testing is impossible. We introduce Object-Grounded Attention Mon…
A Cross-Architecture Audit of Direction-Based Inference-Time Defences in Vision-Language Models
Xiangyu Yin, Tora Bodin, Rohan Menon +1
Inference time defences against vision language model jailbreaks often subtract a calibrated direction from the residual stream at a chosen decoder layer. We compare five defence c…
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…