Tootfinder

Update von der oberen Etage!
Der Boden wird! Im kleinen Zimmer (erste Bilder) sind die Dielen nun fertig verlegt. Im großen Zimmer sind's so 40%.
Es sieht langsam nach Wohnraum aus. 😁
#Hausn

2025 Position Breakdown: Ashton Jeanty brings lofty expectations to rushing attack https://www.raiders.com/news/2025-position-breakdown-running-backs-ashton-jeanty-raheem-mostert-zamir-white

How Well Does GPT-4o Understand Vision? Evaluating Multimodal Foundation Models on Standard Computer Vision Tasks
Rahul Ramachandran, Ali Garjani, Roman Bachmann, Andrei Atanov, O\u{g}uzhan Fatih Kar, Amir Zamir
https://arxiv.org/abs/2507.01955

How Well Does GPT-4o Understand Vision? Evaluating Multimodal Foundation Models on Standard Computer Vision Tasks
Multimodal foundation models, such as GPT-4o, have recently made remarkable progress, but it is not clear where exactly these models stand in terms of understanding vision. In this paper, we benchmark the performance of popular multimodal foundation models (GPT-4o, o4-mini, Gemini 1.5 Pro and Gemini 2.0 Flash, Claude 3.5 Sonnet, Qwen2-VL, Llama 3.2) on standard computer vision tasks (semantic segmentation, object detection, image classification, depth and surface normal prediction) using establ…

Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning
Shenzhi Wang, Le Yu, Chang Gao, Chujie Zheng, Shixuan Liu, Rui Lu, Kai Dang, Xionghui Chen, Jianxin Yang, Zhenru Zhang, Yuqiong Liu, An Yang, Andrew Zhao, Yang Yue, Shiji Song, Bowen Yu, Gao Huang, Junyang Lin
https://arx…

Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning
Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a powerful approach to enhancing the reasoning capabilities of Large Language Models (LLMs), while its mechanisms are not yet well understood. In this work, we undertake a pioneering exploration of RLVR through the novel perspective of token entropy patterns, comprehensively analyzing how different tokens influence reasoning performance. By examining token entropy patterns in Chain-of-Thought (CoT) reasoning, we observe that o…

Poster: FedBlockParadox -- A Framework for Simulating and Securing Decentralized Federated Learning
Gabriele Digregorio, Francesco Bleggi, Federico Caroli, Michele Carminati, Stefano Zanero, Stefano Longari
https://arxiv.org/abs/2506.02679

Poster: FedBlockParadox -- A Framework for Simulating and Securing Decentralized Federated Learning
A significant body of research in decentralized federated learning focuses on combining the privacy-preserving properties of federated learning with the resilience and transparency offered by blockchain-based systems. While these approaches are promising, they often lack flexible tools to evaluate system robustness under adversarial conditions. To fill this gap, we present FedBlockParadox, a modular framework for modeling and evaluating decentralized federated learning systems built on blockcha…

Torus covers with controlled volume and diameter
Sergio Zamora
https://arxiv.org/abs/2506.00763 https://arxiv.org/pdf/2506.00763

Torus covers with controlled volume and diameter
We show that under a lower Ricci curvature bound and an upper diameter bound, a torus admits a finite-sheeted covering space with volume bounded from below and diameter bounded from above. This partially recovers a result of Kloeckner and Sabourau, whose original proof contains a serious gap that currently lacks a resolution.

Numerical treatment of annual modulation of relic neutrinos
Fabian Zimmer, Shin'ichiro Ando
https://arxiv.org/abs/2507.01505 https://

Numerical treatment of annual modulation of relic neutrinos
We present a numerical treatment of the annual modulation of relic neutrinos due to the Sun's gravitational influence. Extending our previously developed N-1-body simulation framework from Milky Way scales to solar system dynamics, we model how cosmic neutrino background densities might vary throughout Earth's orbital cycle. We validate our numerical approach against analytical expectations from previous studies that assumed idealized relic neutrino populations. Our results suggest that the pri…

Tarallo: Evading Behavioral Malware Detectors in the Problem Space
Gabriele Digregorio, Salvatore Maccarrone, Mario D'Onghia, Luigi Gallo, Michele Carminati, Mario Polino, Stefano Zanero
https://arxiv.org/abs/2506.02660

Tarallo: Evading Behavioral Malware Detectors in the Problem Space
Machine learning algorithms can effectively classify malware through dynamic behavior but are susceptible to adversarial attacks. Existing attacks, however, often fail to find an effective solution in both the feature and problem spaces. This issue arises from not addressing the intrinsic nondeterministic nature of malware, namely executing the same sample multiple times may yield significantly different behaviors. Hence, the perturbations computed for a specific behavior may be ineffective for…

2025 Position Breakdown: Ashton Jeanty brings lofty expectations to rushing attack https://www.raiders.com/news/2025-position-breakdown-running-backs-ashton-jeanty-raheem-mostert-zamir-white

Insider Suggests Raiders Week 1 Starter From Last Season Is on Chopping Block https://heavy.com/sports/nfl/las-vegas-raiders/zamir-white-cut-candidate/?adt_ei=[email]