Google、UCLA 与 CMU 发布 GenXR-Bench：编码智能体开发 XR 应用常因丢失 3D 空间定位而失败
Google、UCLA 与 CMU 发布 GenXR-Bench，用 112 个专家编写任务评测编码智能体开发 XR 应用的能力，其模拟手部与房间输入的测试与真实头显上盲测专家判断吻合 96.5%。Claude Opus 5 以 50.9% 领先，但在 SDK 功能任务上无模型超过 29.2%；失败运行中 64.4% 出现坐标系错误，44.9% 出现启动竞态条件。
Rohan Paul· @rohanpaul_ai · X·2026-10-27 17:21· 38 分钟前AI 评分51
AI 导读
Google、UCLA 和 CMU 的论文推出 GenXR-Bench，用 112 个专家编写任务评测编码智能体开发 XR 应用的能力，测试结合模拟手部与房间输入，与真实头显上的盲测专家判断吻合 96.5%。
正文 · 原文
New Google + UCLA + CMU paper finds that AI coding tools often fail to build virtual and mixed reality apps because they lose track of where things are in 3D space.
Claude Opus 5 led at 50.9%, and no model topped 29.2% on SDK feature tasks. Coordinate-frame mistakes appeared in 64.4% of failed runs, and startup race conditions in 44.9%.
XR apps run in 3D space and react to head motion, hands, and room sensing. Normal coding benchmarks use unit tests or browser clicks, which cannot check whether a virtual button follows your palm.
They built GenXR-Bench, 112 expert-written tasks covering XR feature work, bug fixes, and full app builds. Its tests simulate hands and rooms, and matched blinded XR experts using real headsets 96.5% of the time.
If you use agents for XR apps, test the result with simulated hand and room input, since agent-written unit tests often miss runtime bugs.
来源：Rohan Paul · x.com
来源：RadarAI · aihot.news