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arXiv Computer Vision By Luozheng Qin, Yuhan Zhu, Changhai Zhou, Qian Qiao, Jun Gao, Yuhua Zhou, Harry Zhang, Xiangyu Zeng, Yuxuan Liu, Kaijie Chen, Andrew Chen, Pony Ma

MJev-Spatial: System One Spatial Intelligence via Native Attention Matching

Read the original on arXiv Computer Vision →

MJev-Spatial is a multimodal decision model that unifies spatial intelligence tasks—choice, numerical, and point prediction—into a single classification framework. It treats each candidate as a class, scoring them with pretrained query and key projections without adding parameters, and achieves predictions in a single forward pass with no answer tokens. Trained on 12.3 M question‑answer pairs, it outperforms eight baseline models, ranks first on seven of twelve benchmarks, and runs 2.5× faster than the fastest autoregressive baseline.

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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