most citedAIvaluateXR: An Evaluation Framework for on-Device AI in XR with Benchmarking Results

1 citations · 1 across the 7 of their papers we have counts for

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7 papers

cs.CV2026

ContextMaster: Interactive Multi-Shot Video Creation via Fixed-Budget Sparse Context Routing

Xu Guo, Zhengxuan Wei, Xinghui Li +11

Recent video models increasingly support generation, reference conditioning, and editing within a single model, yet typically expose them as separate operations over fixed inputs.…

cs.CV2026

Lumos-Nexus: Efficient Frequency Bridging with Homogeneous Latent Space for Video Unified Models

Jiazheng Xing, Hangjie Yuan, Lingling Cai +9

Connector-based video unified models have demonstrated strong capability in instruction-grounded video synthesis, but integrating a large high-fidelity generator into the unified t…

cs.CV2026

COPRA: Conditional Parameter Adaptation with Reinforcement Learning for Video Anomaly Detection

Darryl Cherian Jacob, Xinyu Liu, Kai Wang +1

Vision-language models (VLMs) have shown strong performance in video anomaly detection (VAD) while providing interpretable predictions. However, existing VLM-based VAD methods suff…

cs.DC20261 cited

AIvaluateXR: An Evaluation Framework for on-Device AI in XR with Benchmarking Results

Dawar Khan, Xinyu Liu, Omar Mena +3

The deployment of large language models (LLMs) on extended reality (XR) devices has great potential to advance the field of human-AI interaction. In the case of direct, on-device m…

cs.CV2026

ClickAIXR: On-Device Multimodal Vision-Language Interaction with Real-World Objects in Extended Reality

Dawar Khan, Alexandre Kouyoumdjian, Xinyu Liu +3

We present ClickAIXR, a novel on-device framework for multimodal vision-language interaction with objects in extended reality (XR). Unlike prior systems that rely on cloud-based AI…

cs.CV2026

XAttnRes: Cross-Stage Attention Residuals for Medical Image Segmentation

Xinyu Liu, Qing Xu, Zhen Chen

In the field of Large Language Models (LLMs), Attention Residuals have recently demonstrated that learned, selective aggregation over all preceding layer outputs can outperform fix…