# Thinking with Visual Latents

Xiaomi released Xiaomi-Lens-0, a 4B multimodal model that decodes region masks from its own visual representations instead of relying on an extra segmentation encoder. Earlier multimodal models either took spatial detail from a separate segmentation encoder or kept the vision encoder frozen, so mask loss never reached the shared features. Xiaomi-Lens-0 pairs vision-encoder patch features with visual latents, the language model's states at generated mask tokens, in a lightweight decoder, so pixel-level supervision reaches the backbone shared with language generation; it gains 5.6 points on average over Qwen3.5 across all eight RefCOCO, RefCOCO+, and RefCOCOg splits and reaches the highest segmentation cIoU among compared models.

Xiaomi released Xiaomi-Lens-0, a 4B multimodal model that decodes region masks from its own visual representations instead of relying on an extra segmentation encoder. Earlier multimodal models either took spatial detail from a separate segmentation encoder or kept the vision encoder frozen, so mask loss never reached the shared features. Xiaomi-Lens-0 pairs vision-encoder patch features with visual latents, the language model's states at generated mask tokens, in a lightweight decoder, so pixel-level supervision reaches the backbone shared with language generation; it gains 5.6 points on average over Qwen3.5 across all eight RefCOCO, RefCOCO+, and RefCOCOg splits and reaches the highest segmentation cIoU among compared models.