arXiv cs.AI· William Huang, David Li, Xiang 'Anthony' Chen, Xun Qian, Nels Numan, Zhongyi Zhou, Hyunsung Cho, Salman Muin Kayser Chishti, Ding Xia, Xincheng Huang, Jiahao Ren, Faraz Faruqi, Tianyu Xu, Yang Zhang, David Kim, Ruofei Du·· 13 小时前AI 评分38
GenXR-Bench：首个面向交互式 XR 开发的编码智能体基准
GenXR-Bench: Benchmarking Coding Agents for Interactive XR Development
AI 导读
GenXR-Bench 是首个端到端评估编码智能体 XR 软件开发能力的基准，包含 112 个专家编写任务，覆盖 24 项功能实现、21 个 bug 修复和 67 个应用生成任务。其测试套件对 124 个候选应用与盲审专家判断的一致率达 96.5%，但在 10 个模型上最强配置的总体 Pass@1 仅 50.9%。失败分析显示模型反复误解异步传感器生命周期、物理接地和具身交互语义。
正文 · 原文
Authors:William Huang, David Li, Xiang 'Anthony' Chen, Xun Qian, Nels Numan, Zhongyi Zhou, Hyunsung Cho, Salman Muin Kayser Chishti, Ding Xia, Xincheng Huang, Jiahao Ren, Faraz Faruqi, Tianyu Xu, Yang Zhang, David Kim, Ruofei Du
Abstract:Coding agents are rapidly advancing from single-file generation to long-horizon repository-scale engineering, yet the evaluation of their capabilities to author interactive extended reality (XR) applications remains an open challenge. Unlike traditional software, XR applications operate within dynamic 3D physical environments where runtime robustness depends jointly on continuous user motion, spatial input, and environmental sensing. We introduce GenXR-Bench, the first benchmark for the end-to-end evaluation of coding agents in XR software development. GenXR-Bench contains 112 expert-authored tasks spanning 24 feature implementations, 21 bug repairs, and 67 application generation tasks. Held-out tests combine scripted and adaptive interactions with programmatic and visual assertions to evaluate core functionality and runtime behavior. GenXR-Bench's testing suite achieves 96.5% accuracy against blinded expert judgments of 124 candidate applications executed on consumer XR hardware. Evaluating 10 models on GenXR-Bench, we find that the strongest configuration achieves only 50.9% overall Pass@1. Failure analysis identifies recurring misunderstandings in asynchronous sensor lifecycles, physical grounding, and embodied interaction semantics. GenXR-Bench establishes a reproducible testbed for evaluating coding agents in the interactive and physically situated conditions of spatial computing.
| Comments: | 37 pages, 10 figures. Code available at this https URL |
| Subjects: | Artificial Intelligence (cs.AI); Graphics (cs.GR); Human-Computer Interaction (cs.HC) |
| Cite as: | arXiv:2610.29014 [cs.AI] |
| (or arXiv:2610.29014v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.29014 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: William Huang [view email]
[v1]
Wed, 21 Oct 2026 00:28:16 UTC (30,978 KB)
来源：arXiv cs.AI · arxiv.org