# SpatialCORE

SpatialCORE is a GRPO-based post-training framework that weights the geometric matching quality of each predicted bounding box by the model's own coordinate-token uncertainty in generating that grounding, and uses an answer gate to tie grounding optimization to final-answer correctness, so the model learns to reason from task-relevant objects that are both accurately and confidently localized; on the held-out OmniSpatial test set SpatialCORE-8B reaches the highest weighted-average accuracy among open-source and specialized spatial-reasoning models and leads zero-shot on the unseen SpatiaLab, while ablations show that removing confidence weighting drops accuracy from 56.33% to 52.33%.

SpatialCORE is a GRPO-based post-training framework that weights the geometric matching quality of each predicted bounding box by the model's own coordinate-token uncertainty in generating that grounding, and uses an answer gate to tie grounding optimization to final-answer correctness, so the model learns to reason from task-relevant objects that are both accurately and confidently localized; on the held-out OmniSpatial test set SpatialCORE-8B reaches the highest weighted-average accuracy among open-source and specialized spatial-reasoning models and leads zero-shot on the unseen SpatiaLab, while ablations show that removing confidence weighting drops accuracy from 56.33% to 52.33%.