ScAle: Attention Head Scaling as a Minimal Adapter for Spatial Reasoning in Vision Language Models
Spatial reasoning remains a persistent challenge for many vision language models (VLMs), and improving it typically requires fine-tuning with substantial additional parameters. Our preliminary analysis reveals that rescaling activations in selected transformer layers-without modifying pretrained weights-can significantly influence downstream performance. Motivated by this observation, we propose ScAle, an ultra-lightweight adaptation method that learns a small set of scalar coefficients to modulate last-token attention and MLP activations in a fully frozen backbone. We evaluate our method on the synthetic spatial reasoning benchmark SpatialEval and on real-world VQA datasets (COCOQA and V...
| Authors | Rahul Chowdhury, Timothy A Rupprecht, Xuan Shen et al. |
|---|---|
| Primary category | cs.CV |
| Published | 28 June 2026 |
| Last updated at source | 28 June 2026 |
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