GenXR-Bench paper: 10 models tested on XR dev, best scores 50.9% Pass@1
rohanpaul_ai · x · 2026-10-27
arXiv paper "GenXR-Bench: Benchmarking Coding Agents for Interactive XR Development" (William Huang et al., 16 authors from Google XR Labs, UCLA, CMU, Oxford, MIT and more) presents the first end-to-end benchmark for coding agents authoring XR apps.
Benchmark
- 112 expert-authored tasks: 24 feature implementations, 21 bug repairs, 67 application generations.
- Held-out tests combine scripted/adaptive interactions with programmatic and visual assertions; the suite matches blinded expert judgments of 124 apps run on consumer XR hardware at 96.5% accuracy.
Results
- Across 10 models, the strongest configuration achieves only 50.9% overall Pass@1.
- Failures cluster around async sensor lifecycles, physical grounding in 3D space, and embodied interaction semantics.
Since XR runtime robustness depends on continuous user motion, spatial input, and environmental sensing, traditional unit tests can't cover it—GenXR-Bench offers a reproducible testbed for spatial computing agents.
Related event: GenXR-Bench: best coding agents pass only 50.9% on XR development tasks(2 posts)→
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