A paper on arXiv introduces MJev-Spatial, a "System 1" multimodal decision model that unifies three kinds of spatial understanding — multiple-choice, numerical prediction and 2D point prediction — as classification: candidates are scored directly with the backbone's pretrained query and key projections, adding no parameters, and each decision takes a single forward pass that generates no answer tokens. Built on Qwen3.5-4B and trained on 12.3M question-answer pairs converted from existing spatial datasets, the authors report the highest overall score (65.64%) among eight tested models, first place on seven of twelve benchmarks, and better results than its unmodified autoregressive base model on all twelve, while running 2.5x faster than the fastest autoregressive baseline and 5.6x faster than the base model. The paper also reports that joint supervision of coarse-to-fine stages plus length-aware global batching and cross-GPU load balancing raised training throughput by 30.8% over the original batching strategy.

MJev-Spatial·Qwen3.5-4B