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arXiv Computer Vision By Runyi Yang, Murat Akkoyun, Di Wen, Ruiping Liu, Yufan Chen, Junwei Zheng, Xiaoye Wang, Kailun Yang, Danda Pani Paudel, Luc Van Gool, Kunyu Peng

Do LiDAR Language Models Really Understand Spatio-temporal Relationships?

Read the original on arXiv Computer Vision →

The paper introduces LiDAR-Hallu, a benchmark with 10,000 questions designed to test 4D LiDAR language models on spatio-temporal reasoning. It shows that models often achieve high multiple-choice accuracy by exploiting trivial patterns, such as always selecting the same option or relying on candidate duration, rather than truly understanding object relationships. Detailed analysis reveals systematic failures, especially in lateral-motion cases and opposite-answer scenarios, indicating that aggregate accuracy masks underlying reasoning gaps.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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