A Benchmarking Framework for Context-aware XR Interfaces

Everyday Extended Reality (XR) systems aim to provide context-aware access to the right functionalities at the right time and place, with minimal manual reconfiguration as users switch context. Yet these interfaces are hard to evaluate: current prototyping and user-study workflows offer no systematic, repeatable way to compare adaptation methods across users and scenarios. We present ContextXR a novel benchmarking framework for context-aware XR interfaces. ContextXR represents an XR application as a connected graph of functional facets, each a semantically coherent group of related capabilities that together support a shared user intent. On this representation, we build MineXR++, a dataset augmenting prior XR interface data with facet-level annotations, and formulate three canonical tasks of context-aware suggestion: context factor analysis, initial facet suggestion, and next facet suggestion. Our evaluation protocol scores suggestion methods by a simulated interaction metric, the navigation and search cost of reaching the desired functionality. Through experiments benchmarking global popularity, relational retrieval, and LLM-based methods, we demonstrate that ContextXR enables the systematic, reproducible evaluation of context-aware XR interfaces.

@inproceedings {Cho26ContextXR, 
 author = {Cho, Hyunsung and Yun, Sarah Yewon and Sun, Nancy Ruonan and Lafreniere, Ben and Parent, Mark and Todi, Kashyap and Jonker, Tanya R. and Benko, Hrvoje and Wu, Tongshuang and Lindlbauer, David}, 
 title = {A Benchmarking Framework for Context-aware XR Interfaces}, 
 year = {2026}, 
 publisher = {Association for Computing Machinery}, 
 address = {New York, NY, USA}, 
 keywords = {Extended Reality, context-aware interfaces, functional suggestion, adaptive user interfaces, computational interaction, benchmarking}, 
 location = {Detroit, MI, USA}, 
 doi = {10.1145/3830398.3830675}, 
 series = {UIST '26} 
 }