Transcoders Trace Visual Grounding and Hallucinations in Vision-Language Models

A new arXiv paper proposes using transcoders, an alternative to sparse autoencoders, to study how vision-language models turn image inputs into text. The authors argue that sparse autoencoders decompose static representations and miss the flow of information, while transcoders can follow that transformation more directly. The method is used to locate where visual grounding happens and where hallucinated content originates in these models.