arXiv AI By Jerry Yao-Chieh Hu, Yu-An Chen, Maojiang Su, Han Liu

On the Statistical Mechanics of Attention

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arXiv AI
Sep 2

Energy-Based Transformers as Predictors of Reading Difficulty

The paper investigates energy-based transformers as predictors of reading difficulty, extending the use of transformer language models in psycholinguistics. It demonstrates that the energy measure from these models robustly predicts reading times across multiple corpora, outperforming traditional metrics like surprisal and attention entropy. In a controlled experiment on relative clause processing, energy captures known asymmetries, suggesting it may unify previously complementary predictors.

By Jakub Dotlacil, Ece Takmaz
arXiv Machine Learning
Oct 19

Anisotropic Representation Dynamics in Transformers

The paper introduces a Mixture‑Gated Theory of Anisotropic Representation Dynamics that decomposes Transformer self‑attention into value transport, query gating, and key gating. It shows that the geometry of attended representations can cause a representation difference to contract or expand, a phenomenon termed gating‑induced regime switching. The authors derive a spectral criterion to predict switching, quantify its strength, and identify subspaces where switching occurs, validating these predictions experimentally across four Transformer architectures.

By Nguyen Do, My T. Thai
arXiv Machine Learning
Oct 22

Kinetic theory for Transformers and the lost-in-the-middle phenomenon

The paper investigates causal self‑attention dynamics in decoder Transformers, modeling them as a non‑exchangeable interacting particle system. Using cumulant expansions and Glauber calculus, the authors prove a quantitative mean‑field limit and a next‑order correlation characterization. For i.i.d. uniformly distributed tokens, they solve the limiting correlation equation in closed form and rigorously explain the empirically observed lost‑in‑the‑middle phenomenon, showing a U‑shaped token retrieval profile with primacy, recency, and a unique interior minimum under a smallness condition.

By Mitia Duerinckx, Borjan Geshkovski, Stefano Rossi