Tootfinder

"The Civil War still turned states to ash, and killed hundreds of thousands of Andrew Jackson’s generation’s kids and grand-kids. Bad compromises are bad because they don’t meaningfully or persistently solve anything."
https://stuff.davidaugust.com/tyrants-fall/

Tyrants Fall
it is their defining characteristic How many enemies dreamed of destroying the White House. How many soldiers wished they could flatten it. How many generals fantasized about razing the American People’s house. This one man beat them all. Same man brought the confederate flag into the Capitol, same man

Dimension-Free Minimax Rates for Learning Pairwise Interactions in Attention-Style Models
Shai Zucker, Xiong Wang, Fei Lu, Inbar Seroussi
https://arxiv.org/abs/2510.11789 https:…

Dimension-Free Minimax Rates for Learning Pairwise Interactions in Attention-Style Models
We study the convergence rate of learning pairwise interactions in single-layer attention-style models, where tokens interact through a weight matrix and a non-linear activation function. We prove that the minimax rate is $M^{-\frac{2β}{2β+1}}$ with $M$ being the sample size, depending only on the smoothness $β$ of the activation, and crucially independent of token count, ambient dimension, or rank of the weight matrix. These results highlight a fundamental dimension-free statistical efficie…

Do Large Language Models Understand Word Senses?
Domenico Meconi, Simone Stirpe, Federico Martelli, Leonardo Lavalle, Roberto Navigli
https://arxiv.org/abs/2509.13905 https://…

Do Large Language Models Understand Word Senses?
Understanding the meaning of words in context is a fundamental capability for Large Language Models (LLMs). Despite extensive evaluation efforts, the extent to which LLMs show evidence that they truly grasp word senses remains underexplored. In this paper, we address this gap by evaluating both i) the Word Sense Disambiguation (WSD) capabilities of instruction-tuned LLMs, comparing their performance to state-of-the-art systems specifically designed for the task, and ii) the ability of two top-p…

Why the noise model matters: A performance gap in learned regularization
Sebastian Banert, Christoph Brauer, Dirk Lorenz, Lionel Tondji
https://arxiv.org/abs/2510.12521 https://…

Why the noise model matters: A performance gap in learned regularization
This article addresses the challenge of learning effective regularizers for linear inverse problems. We analyze and compare several types of learned variational regularization against the theoretical benchmark of the optimal affine reconstruction, i.e. the best possible affine linear map for minimizing the mean squared error. It is known that this optimal reconstruction can be achieved using Tikhonov regularization, but this requires precise knowledge of the noise covariance to properly weight …