Semantic Tube Prediction

https://arxiv.org/pdf/2602.22617 I’ve been reading the STP paper and I’m confused about the theoretical justification for the loss function. The core claim is that hidden state trajectories trace locally linear trajectories (but being argmax-ed to tokens introduced noise). But the actual loss enforces something much stronger — it picks three random indices s < r < t from anywhere in the sequence and penalizes the angle between (h_r - h_s) and (h_t - h_r). That’s requiring that r lies on (well, near) the straight line from s to t. That seems like effectively *global* linearity to me, since the