Object-ordering is a distributed linear direction that globally corrects spatial errors

Cui et al. train linear probes on vision-encoder tokens that decode object ordinal position near-perfectly, with the signal extending to background "strip" tokens beyond object regions [cui-etal-2026-the-dual-mechanisms-of-spatial-reasoning-in-vision-language-models] Interchange-intervention activation patching shows ordering forms at layers 20-22 and color at 23-27 in Qwen2-VL-7B, and patching object-strip tokens flips the output [cui-etal-2026-the-dual-mechanisms-of-spatial-reasoning-in-vision-language-models] Globally amplifying the probe-derived ordering direction corrects over 50% of previously-incorrect predictions on Gemma-3-4b-it and over 30% on Qwen2-VL-7B-Instruct [cui-etal-2026-the-dual-mechanisms-of-spatial-reasoning-in-vision-language-models] No distributed-alignment-search is used; the method is linear probing plus activation patching and probe-direction amplification [cui-etal-2026-the-dual-mechanisms-of-spatial-reasoning-in-vision-language-models]