Day 5 Multivectors for Late Interaction Models Many embedding models represent data as a single vector. Transformer-based encoders achieve this by pooling the per-token vector matrix from the final layer into a single vector. That works great for most cases. But when your documents get more complex, cover multiple topics, or require context sensitivity, that one-size-fits-all compression starts to break down. You lose granularity and semantic alignment (though chunking and learned pooling mit...
