VisualScratchpad: Inference-time Visual Concepts Analysis in Vision Language Models
High-performing vision language models still produce incorrect answers, yet their failure modes are often difficult to explain. To make model internals more accessible and enable systematic debugging, we introduce VisualScratchpad, an interactive interface for visual concept analysis during inference. We apply sparse autoencoders to the vision encoder and link the resulting visual concepts to text tokens via text-to-image attention, allowing us to examine which visual concepts are both captured by the vision encoder and utilized by the language model. VisualScratchpad also provides a token-latent heatmap view that suggests a sufficient set of latents for effective concept ablation in caus...
| Authors | Hyesu Lim, Jinho Choi, Taekyung Kim et al. |
|---|---|
| Primary category | cs.AI |
| Published | 7 March 2026 |
| Last updated at source | 7 March 2026 |
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