GAVAGAI: Referential Alignment for Infant-Scale Vision-Language Models via Balanced Optimal Transport

A child learns which words name which things from roughly 100 million words of input. Vision-language models trained at a comparable scale bind words to referents far less reliably, and we argue that part of the gap is structural: the usual next-token captioning objective has no variable standing fo