Tootfinder

Zelensky doesn't see corruption as a problem, prosecuted activist says: https://benborges.xyz/2025/07/18/zelensky-doesnt-see-corruption-as.html

Apparently an older boy generated the ads with AI and ran away.
https://www.rockpapershotgun.com/nexon-respond-to-claims-theyre-using-ai-generated-tiktok-streamers-to-advertise-the-first-descendant…

Nexon respond to claims they're using AI-generated Tiktok streamers to advertise The First Descendant
The First Descendant publishers Nexon have addressed AI-looking streamer adverts for the game on TikTok, saying they and the platform are investigating.

Democrat convinces colleagues to stand up to Ron DeSantis' anti-Pride agenda with impassioned speech - LGBTQ Nation
https://www.lgbtqnation.com/2025/08/democrat-convinces-colleagues-to-stand-up-to-ron-desantis-anti-pride-agenda-with-impassioned-speech/

Harmonized Gradient Descent for Class Imbalanced Data Stream Online Learning
Han Zhou, Hongpeng Yin, Xuanhong Deng, Yuyu Huang, Hao Ren
https://arxiv.org/abs/2508.11353 https://…

Harmonized Gradient Descent for Class Imbalanced Data Stream Online Learning
Many real-world data are sequentially collected over time and often exhibit skewed class distributions, resulting in imbalanced data streams. While existing approaches have explored several strategies, such as resampling and reweighting, for imbalanced data stream learning, our work distinguishes itself by addressing the imbalance problem through training modification, particularly focusing on gradient descent techniques. We introduce the harmonized gradient descent (HGD) algorithm, which aims …

Fermat descent
Santiago Arango-Pi\~neros
https://arxiv.org/abs/2508.13059 https://arxiv.org/pdf/2508.13059

Fermat descent
Descent theory (a modern formulation of Fermat's classical method of infinite descent) is a powerful tool in arithmetic geometry. In this article, we reinterpret descent theory through the lens of quotient stacks and apply it in the setting where it first arose: the Diophantine study of generalized Fermat equations (1) \[ Ax^a + By^b + Cz^c = 0. \] We focus on understanding the arithmetic of the stacks that arise from the study of primitive integral solutions to general Fermat equat…

Report on $\mathbb{E}_\infty$-descendability
Benjamin Antieau, Germ\'an Stefanich
https://arxiv.org/abs/2508.13089 https://arxiv.org/pdf/2508.13089

Report on $\mathbb{E}_\infty$-descendability
We introduce the notion of $\mathbb{E}_\infty$-descendability as well as a derived variant. We prove that several classes of descendable maps of commutative rings are $\mathbb{E}_\infty$-descendable. As an application, we prove a variant of Tannaka duality.

Discretization and Vanishing Discount Problems for First-order Mean Field Games
Renato Iturriaga, Cristian Mendico, Kaizhi Wang, Yuchen Xu
https://arxiv.org/abs/2509.14541 https…

Discretization and Vanishing Discount Problems for First-order Mean Field Games
This article focuses two issues related to the first-order discounted mean field games system. The first is the time discretization problem. The time discretization approach enables us to prove the existence of solutions (u,m) of the system, where u is a viscosity solution of the discounted Hamilton-Jacobi equation and m is a projected minimizing measure satisfying the continuity equation in the sense of distributions. The second is the vanishing discount problems for both the discounted mean f…

A Universal Banach--Bregman Framework for Stochastic Iterations: Unifying Stochastic Mirror Descent, Learning and LLM Training
Johnny R. Zhang (Independent Researcher), Xiaomei Mi (University of Manchester), Gaoyuan Du (Amazon), Qianyi Sun (Microsoft), Shiqi Wang (Meta), Jiaxuan Li (Amazon), Wenhua Zhou (Independent Researcher)
https://arx…

A Universal Banach--Bregman Framework for Stochastic Iterations: Unifying Stochastic Mirror Descent, Learning and LLM Training
Stochastic optimization powers the scalability of modern artificial intelligence, spanning machine learning, deep learning, reinforcement learning, and large language model training. Yet, existing theory remains largely confined to Hilbert spaces, relying on inner-product frameworks and orthogonality. This paradigm fails to capture non-Euclidean settings, such as mirror descent on simplices, Bregman proximal methods for sparse learning, natural gradient descent in information geometry, or Kullb…

The square-integrability of double descent
Nozomi Ito
https://arxiv.org/abs/2508.12489 https://arxiv.org/pdf/2508.12489

The square-integrability of double descent
Double descent is a method to construct automorphic representations of classical groups. For given A-parameter $ψ$ with certain good properties, double descent constructs a space of functions orthogonal to any cuspidal representation whose A-parameter is not $ψ$ and not orthogonal to any cuspidal representation with A-parameter $ψ$. In this paper, we show that functions constructed by double descent are always square-integrable.