aiqbench analysis
This paper introduces ContextXR, a benchmarking framework designed to systematically evaluate context-aware adaptation in Extended Reality (XR) systems using graph-based representations of functional facets. By incorporating LLM-based methods into its evaluation protocol, the study addresses the critical need for reproducible metrics to compare how XR interfaces suggest functionalities based on user intent and environment. The work advances the field by moving beyond subjective user studies toward rigorous, data-driven assessment of adaptive interface capabilities.
A Benchmarking Framework for Context-aware XR Interfaces
arXiv:2609.30466v1 Announce Type: cross Abstract: 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.
Abstract: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.
Human-Computer Interaction (cs.HC); Artificial Intelligence (cs.AI)
arXiv:2609.30466 [cs.HC]
(or arXiv:2609.30466v1 [cs.HC] for this version)
https://doi.org/10.48550/arXiv.2609.30466 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
https://doi.org/10.1145/3830398.3830675 Focus to learn more DOI(s) linking to related resources
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Full text under the publisher's open licence · 276 words
