[R] Causal self-attention as a probabilistic model over embeddings

We’ve been working on a probabilistic interpretation of causal self-attention where token embeddings are treated as latent variables. In that view, the attention map induces a change-of-variables term, which leads to a barrier / degeneracy boundary in embedding space.

The resulting picture is:

• a stability-margin interpretation of causal attention
• “support tokens,” i.e. the positions closest to the degeneracy boundary
• a simple MAP-style training penalty: standard cross-entropy plus a smooth log-barrier term

Empirically, this improves robustness to input perturbations and makes the learned geometry more margin-concentrated, without much loss in clean accuracy at modest regularization strengths.

Curious whether this framing feels natural to people, or whether it reads more like a <insert-your-favorite-regularizer-here> than a genuinely probabilistic view.