Constrained Belief Updates and Geometric Structures in Transformer Representations for the RRXOR Process
Brianna Grado-White
1
In this work, we seek to further understand the computational structure formed by transformers trained to predict the next token of a data set. In particular, we extend the work of (Piotrowski et al., 2025) by analyzing if and how these transformers implement constrained Bayesian updating, focusing on data generated by a Random-Random-XOR (RRXOR) process. Unlike the MESS3 processes studied originally in (Piotrowski et al., 2025), the RRXOR process leads to slightly more nuanced theoretical and practical models. Nevertheless, we verify some of the main claims of (Piotrowski et al., 2025). Namely, we find attention patterns that decay exponentially with distance. We further analyze the behavior of OV vectors.
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@misc {
title={
(HckPrj) Constrained Belief Updates and Geometric Structures in Transformer Representations for the RRXOR Process
},
author={
Brianna Grado-White
},
date={
7/27/25
},
organization={Apart Research},
note={Research submission to the research sprint hosted by Apart.},
howpublished={https://apartresearch.com}
}
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