Reframe the lead around language compositionality

README.md

@@ -22,13 +22,13 @@ Representation Learning. EMNLP 2026, main conference.
 
 [Paper][paper] · [Project page][project] · [Code][code]
 
-PRISM decomposes each video clip into a view-invariant latent `z_vi` and a view-variant
-latent `z_vv`, then recomposes the two under language supervision: `z_vi` of clip A paired
-with `z_vv` of clip B is trained to match the embedding of the correspondingly recombined
-caption. That composition never occurs in the training data, so semantics that leaked
-between the streams cannot survive it. The clip embedding this model returns is the
-view-invariant stream, which makes cosine similarity a measure of what is happening rather
-than of where the camera is.
+PRISM separates what an action is from the viewpoint and scene it happens to be seen in.
+Language is what makes that separation trainable: descriptions recombine freely, so the
+action of one clip can be put together with the context of another into a composition that
+never occurred in the training data, and the model is asked to predict the meaning of that
+composition. Semantics that leaked between the two factors cannot survive the swap. What
+this model returns is the view-invariant side of the split, so cosine similarity measures
+what is happening rather than where the camera is.
 
 This checkpoint is trained on EgoExo4D, using both ego and exo views and captions generated
 by Gemini 3.