Ling Li

The work proposes Spatial Latent Reasoning (SLR), which organizes pointing-gesture geometry and target-region appearance into an ordered sequence of continuous latent states: one spatial ray state supervised by fingertip position and pointing direction, followed by four states aligned with target-region features whose visual targets are built by parity pooling, which groups ROI tokens by row and column parity and averages each group; on EgoPoint-Ground it improves mIoU over same-backbone supervised fine-tuning by 2.8, 17.5, and 21.1 percentage points on Qwen3.5-4B, Qwen2.5-VL-7B, and Qwen3-VL-8B, with gains on both hard subsets, and reaches 77.6% precision at IoU 0.5 on YouRefIt.

The work proposes Spatial Latent Reasoning (SLR), which organizes pointing-gesture geometry and target-region appearance into an ordered sequence of continuous latent states: one spatial ray state supervised by fingertip position and pointing direction, followed by four states aligned with target-region features whose visual targets are built by parity pooling, which groups ROI tokens by row and column parity and averages each group; on EgoPoint-Ground it improves mIoU over same-backbone supervised fine-tuning by 2.8, 17.5, and 21.1 percentage points on Qwen3.5-4B, Qwen2.5-VL-7B, and Qwen3-VL-8B, with gains on both hard subsets, and reaches 77.6% precision at IoU 0.5 on YouRefIt.