Tootfinder

I can help but feel this "feature" should raise more concerns than it does alleviate them.
"Accelerated recovery for managing public DNS records addresses this need by targeting DNS changes that customers can make within 60 minutes of a service disruption in the US East (N. Virginia) Region."

Amazon Route 53 launches Accelerated recovery for managing public DNS records | Amazon Web Services
Amazon Route 53 now offers Accelerated recovery for managing public DNS records, a new business continuity feature designed to provide a 60-minute recovery time objective (RTO) during service disruptions in the US East (N. Virginia) Region. This enhancement enables customers to continue making critical DNS changes and provisioning infrastructure even during regional outages, ensuring greater resilience for mission-critical applications.

Constraining GREA, an alternative theory accounting for the present cosmic acceleration
R. Calderon, J. Garcia-Bellido, B. Vos-Gines, V. Gonzalez-Perez, A. Shafieloo, J. Aguilar, S. Ahlen, D. Bianchi, D. Brooks, T. Claybaugh, A. de la Macorra, J. E. Forero-Romero, E. Gazta\~naga, S. Gontcho A Gontcho, G. Gutierrez, K. Honscheid, C. Howlett, M. Ishak, R. Joyce, R. Kehoe, T. Kisner, A. Kremin, O. Lahav, A. Lambert, M. Landriau, M. Manera, R. Miquel, F. Prada, I. Perez-Rafols, E. Sanchez,…

Constraining GREA, an alternative theory accounting for the present cosmic acceleration
The origin of the Universe's late-time accelerated expansion remains unknown. The General Relativistic Entropic Acceleration (GREA) theory offers a compelling alternative to $Λ$CDM, attributing cosmic acceleration to entropy growth associated with cosmic and black hole horizons, without invoking a cosmological constant. We test GREA against the latest DESI DR2 Baryon Acoustic Oscillations (BAO), multiple Type Ia supernova compilations (Union3, Pantheon$\texttt{+}$, DES-SN5YR), and cosmic micro…

A Riemannian Accelerated Proximal Gradient Method
Shuailing Feng, Yuhang Jiang, Wen Huang, Shihui Ying
https://arxiv.org/abs/2509.21897 https://arxiv.org/p…

A Riemannian Accelerated Proximal Gradient Method
Riemannian accelerated gradient methods have been well studied for smooth optimization, typically treating geodesically convex and geodesically strongly convex cases separately. However, their extension to nonsmooth problems on manifolds with theoretical acceleration remains underexplored. To address this issue, we propose a unified Riemannian accelerated proximal gradient method for problems of the form $F(x) = f(x) + h(x)$ on manifolds, where $f$ can be either geodesically convex or geodesica…

GeoSketch: A Neural-Symbolic Approach to Geometric Multimodal Reasoning with Auxiliary Line Construction and Affine Transformation
Shichao Weng, Zhiqiang Wang, Yuhua Zhou, Rui Lu, Ting Liu, Zhiyang Teng, Xiaozhang Liu, Hanmeng Liu
https://arxiv.org/abs/2509.22460

GeoSketch: A Neural-Symbolic Approach to Geometric Multimodal Reasoning with Auxiliary Line Construction and Affine Transformation
Geometric Problem Solving (GPS) poses a unique challenge for Multimodal Large Language Models (MLLMs), requiring not only the joint interpretation of text and diagrams but also iterative visuospatial reasoning. While existing approaches process diagrams as static images, they lack the capacity for dynamic manipulation - a core aspect of human geometric reasoning involving auxiliary line construction and affine transformations. We present GeoSketch, a neural-symbolic framework that recasts geome…

Nonlinear Optimization with GPU-Accelerated Neural Network Constraints
Robert Parker, Oscar Dowson, Nicole LoGiudice, Manuel Garcia, Russell Bent
https://arxiv.org/abs/2509.22462

Nonlinear Optimization with GPU-Accelerated Neural Network Constraints
We propose a reduced-space formulation for optimizing over trained neural networks where the network's outputs and derivatives are evaluated on a GPU. To do this, we treat the neural network as a "gray box" where intermediate variables and constraints are not exposed to the optimization solver. Compared to the full-space formulation, in which intermediate variables and constraints are exposed to the optimization solver, the reduced-space formulation leads to faster solves and fewer iterations i…

A profile of Substrate, which uses particle acceleration to handle lithography, claiming to solve one of tech's toughest problems, and aims to compete with ASML (Bloomberg)
https://www.bloomberg.com/news/articles/20

Particle Acceleration and Transport in the Large-scale Current Sheet under an Erupting Magnetic Flux Rope
Hao Wu, Yang Guo, Rony Keppens, Chun Xia, Yang Su, Xiangliang Kong, Mingde Ding
https://arxiv.org/abs/2509.22265

Particle Acceleration and Transport in the Large-scale Current Sheet under an Erupting Magnetic Flux Rope
We investigate the acceleration and transport of electrons in the highly fine-structured current sheet that develops during magnetic flux rope (MFR) eruptions. Our work combines ultra-resolved MHD simulations of MFR eruption, with test-particle studies performed using the guiding center approximation. Our grid-adaptive, fully three-dimensional, high-resolution magnetohydrodynamic simulations model MFR eruptions that form complex current sheet topologies, serving as background electromagnetic fi…

AxLLM: accelerator architecture for large language models with computation reuse capability
Soroush Ahadi, Mehdi Modarressi, Masoud Daneshtalab
https://arxiv.org/abs/2509.22512 …

AxLLM: accelerator architecture for large language models with computation reuse capability
Large language models demand massive computational power and memory resources, posing significant challenges for efficient deployment. While quantization has been widely explored to reduce model size and computation, this paper demonstrates an additional benefit: quantization increases parameter locality, creating opportunities for computation reuse. Building on this insight, we propose AxLLM, a hardware accelerator architecture designed for quantized models. Axllm introduces a novel redundancy…

InvBench: Can LLMs Accelerate Program Verification with Invariant Synthesis?
Anjiang Wei, Tarun Suresh, Tianran Sun, Haoze Wu, Ke Wang, Alex Aiken
https://arxiv.org/abs/2509.21629

InvBench: Can LLMs Accelerate Program Verification with Invariant Synthesis?
Program verification relies on loop invariants, yet automatically discovering strong invariants remains a long-standing challenge. We introduce a principled framework for evaluating LLMs on invariant synthesis. Our approach uses a verifier-based decision procedure with a formal soundness guarantee and assesses not only correctness but also the speedup that invariants provide in verification. We evaluate 7 state-of-the-art LLMs, and existing LLM-based verifiers against the traditional solver UAu…