Multi-Bounce Attention Explained in 3 Minutes! | Understanding Information Flow in Transformers

Описание: 🧠 What if transformer attention is not just a matrix… but a dynamical system?

Attention is the core mechanism behind modern transformers, yet most analyses only look at direct token interactions.

This video explores a powerful new interpretation where attention matrices are viewed as discrete-time Markov chains, revealing how information actually flows across tokens over multiple steps.

Instead of analyzing attention statically, this perspective models attention as a probabilistic transition process.

By propagating attention through multiple transitions, we uncover higher-order relationships, global token importance, and a steady-state representation called TokenRank.

In this video, we cover:

✅ Why attention matrices behave like stochastic transition systems
✅ Multi-bounce attention and higher-order token interactions
✅ TokenRank and global token importance
✅ Why eigenvalues reveal meaningful attention heads
✅ How this improves segmentation, visualization, and diffusion models

This interpretation provides a deeper theoretical understanding of transformers and offers practical tools for explainability and downstream improvements.

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