Gorka Azkune

Clustering outputs in representation space reveals that self-consistency is more about geometric proximity than exact matches, leading to better performance in open-ended tasks.

Dual-encoder VLMs like CLIP may be bag-of-words systems not because of representation deficiencies, but because global cosine similarity matching at inference prevents them from exploiting compositional information already present in the embeddings.