Tootfinder

Self-interacting dark matter in the center of a Local Group dwarf galaxy and its satellites
Thales A. Gutcke, Giulia Despali, Stephanie O'Neil, Mark Vogelsberger, Azadeh Fattahi, David B. Sanders
https://arxiv.org/abs/2510.05258

Self-interacting dark matter in the center of a Local Group dwarf galaxy and its satellites
We present a detailed comparison of a Local Group dwarf galaxy analogue evolved in two cosmological models: the standard $Λ$CDM and a self-interacting dark matter (SIDM) model with a velocity-dependent cross-section. Both simulations are run with the high-resolution, hydrodynamical LYRA galaxy formation model, allowing us to explore the global and substructure properties of the dwarf in a consistent context. While the overall halo growth, final mass, and subhalo mass functions remain largely u…

Retrieving the structure of probabilistic sequences from EEG data during the goalkeeper game
P. R. Cabral-Passos, P. S. Azevedo, V. H. Moraes, B. L. Ramalho, A. Duarte, C. D. Vargas
https://arxiv.org/abs/2510.06344

Retrieving the structure of probabilistic sequences from EEG data during the goalkeeper game
This work draws on the conjecture that fingerprints of stochastic event sequences can be retrieved from electroencephalographic data (EEG) recorded during a behavioral task. To test this, we used the Goalkeeper Game (game.numec.prp.usp.br). Acting as a goalkeeper, the participant predicted each kick in a probabilistic sequence while EEG activity was recorded. At each trial, driven by a context tree, the kicker chose one of three options: left, center, or right. The goalkeeper then predicted the…

CyberSOCEval: Benchmarking LLMs Capabilities for Malware Analysis and Threat Intelligence Reasoning
Lauren Deason, Adam Bali, Ciprian Bejean, Diana Bolocan, James Crnkovich, Ioana Croitoru, Krishna Durai, Chase Midler, Calin Miron, David Molnar, Brad Moon, Bruno Ostarcevic, Alberto Peltea, Matt Rosenberg, Catalin Sandu, Arthur Saputkin, Sagar Shah, Daniel Stan, Ernest Szocs, Shengye Wan, Spencer Whitman, Sven Krasser, Joshua Saxe

CyberSOCEval: Benchmarking LLMs Capabilities for Malware Analysis and Threat Intelligence Reasoning
Today's cyber defenders are overwhelmed by a deluge of security alerts, threat intelligence signals, and shifting business context, creating an urgent need for AI systems to enhance operational security work. While Large Language Models (LLMs) have the potential to automate and scale Security Operations Center (SOC) operations, existing evaluations do not fully assess the scenarios most relevant to real-world defenders. This lack of informed evaluation impacts both AI developers and those apply…