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Noch einige der zuletzt hier besonders häufig geteilten #News:
Sparkasse: Fehler der S-pushTAN-App legt Online-Banking lahm

This https://arxiv.org/abs/2505.23436 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Emergent Risk Awareness in Rational Agents under Resource Constraints
Advanced reasoning models with agentic capabilities (AI agents) are deployed to interact with humans and to solve sequential decision-making problems under (approximate) utility functions and internal models. When such problems have resource or failure constraints where action sequences may be forcibly terminated once resources are exhausted, agents face implicit trade-offs that reshape their utility-driven (rational) behaviour. Additionally, since these agents are typically commissioned by a h…

InSight-R: A Framework for Risk-informed Human Failure Event Identification and Interface-Induced Risk Assessment Driven by AutoGraph
Xingyu Xiao, Jiejuan Tong, Peng Chen, Jun Sun, Zhe Sui, Jingang Liang, Hongru Zhao, Jun Zhao, Haitao Wang
https://arxiv.org/abs/2507.00066

InSight-R: A Framework for Risk-informed Human Failure Event Identification and Interface-Induced Risk Assessment Driven by AutoGraph
Human reliability remains a critical concern in safety-critical domains such as nuclear power, where operational failures are often linked to human error. While conventional human reliability analysis (HRA) methods have been widely adopted, they rely heavily on expert judgment for identifying human failure events (HFEs) and assigning performance influencing factors (PIFs). This reliance introduces challenges related to reproducibility, subjectivity, and limited integration of interface-level da…

Creep failure in heterogeneous materials from the barrier landscape
Juan Carlos Verano-Espitia, Tero M\"akinen, Mikko J. Alava, J\'er\^ome Weiss
https://arxiv.org/abs/2507.00661

Creep failure in heterogeneous materials from the barrier landscape
Stressed under a constant load, materials creep with a final acceleration of deformation and for any given applied stress and material, the creep failure time can strongly vary. We investigate creep on sheets of paper and confront the statistics with a simple fiber bundle model of creep failure in a disordered landscape. In the experiments, acoustic emission event times $t_j$ were recorded, and both this data and simulation event series reveal sample-dependent history effects with log-normal st…

This https://arxiv.org/abs/2408.09008 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Approximations to worst-case data dropping: unmasking failure modes
A data analyst might worry about generalization if dropping a very small fraction of data points from a study could change its substantive conclusions. Checking this non-robustness directly poses a combinatorial optimization problem and is intractable even for simple models and moderate data sizes. Recently various authors have proposed a diverse set of approximations to detect this non-robustness. In the present work, we show that, even in a setting as simple as ordinary least squares (OLS) li…

SEMNAV: A Semantic Segmentation-Driven Approach to Visual Semantic Navigation
Rafael Flor-Rodr\'iguez, Carlos Guti\'errez-\'Alvarez, Francisco Javier Acevedo-Rodr\'iguez, Sergio Lafuente-Arroyo, Roberto J. L\'opez-Sastre
https://arxiv.org/abs/2506.01418

SEMNAV: A Semantic Segmentation-Driven Approach to Visual Semantic Navigation
Visual Semantic Navigation (VSN) is a fundamental problem in robotics, where an agent must navigate toward a target object in an unknown environment, mainly using visual information. Most state-of-the-art VSN models are trained in simulation environments, where rendered scenes of the real world are used, at best. These approaches typically rely on raw RGB data from the virtual scenes, which limits their ability to generalize to real-world environments due to domain adaptation issues. To tackle …

This https://arxiv.org/abs/2310.00468 has been replaced.
link: https://scholar.google.com/scholar?q=a

When Should a Leader Act Suboptimally? The Role of Inferability in Repeated Stackelberg Games
When interacting with other decision-making agents in non-adversarial scenarios, it is critical for an autonomous agent to have inferable behavior: The agent's actions must convey their intention and strategy. We model the inferability problem using Stackelberg games with observations where a leader and a follower repeatedly interact. During the interactions, the leader uses a fixed mixed strategy. The follower does not know the leader's strategy and dynamically reacts to the statistically infe…

This https://arxiv.org/abs/2411.09063 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

The typical elasticity of a quadratic order
For an atomic domain $D$, the $elasticity$ $ρ(D)$ of $D$ is defined as $\sup\{r/s: π_1\cdots π_r = ρ_1 \cdots ρ_s,~ \text{where each $π_i, ρ_j$ is irreducible}\}$; the elasticity provides a concrete measure of the failure of unique factorization in $D$. Fix a quadratic number field $K$ with discriminant $Δ_K$, and for each positive integer $f$, let $\mathcal{O}_f = \mathbb{Z} + f\mathcal{O}_K$ denote the order of conductor $f$ in $K$. Results of Halter-Koch imply that $\mathcal{O}_f$ ha…

This https://arxiv.org/abs/2504.00587 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMA_…

AgentNet: Decentralized Evolutionary Coordination for LLM-based Multi-Agent Systems
The rapid advancement of large language models (LLMs) has enabled the development of multi-agent systems where multiple LLM-based agents collaborate on complex tasks. However, existing systems often rely on centralized coordination, leading to scalability bottlenecks, reduced adaptability, and single points of failure. Privacy and proprietary knowledge concerns further hinder cross-organizational collaboration, resulting in siloed expertise. We propose AgentNet, a decentralized, Retrieval-Augme…

Image Generation from Contextually-Contradictory Prompts
Saar Huberman, Or Patashnik, Omer Dahary, Ron Mokady, Daniel Cohen-Or
https://arxiv.org/abs/2506.01929

Image Generation from Contextually-Contradictory Prompts
Text-to-image diffusion models excel at generating high-quality, diverse images from natural language prompts. However, they often fail to produce semantically accurate results when the prompt contains concept combinations that contradict their learned priors. We define this failure mode as contextual contradiction, where one concept implicitly negates another due to entangled associations learned during training. To address this, we propose a stage-aware prompt decomposition framework that gui…