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"While preparing for my Black Hat and DEF CON talks in July of this year, I found the most impactful #EntraID #vulnerability that I will probably ever find. This vulnerability could have allowed me to compromise every Entra ID tenant in the world (except probably those in national cloud deploymen…

Parallelizable Feynman-Kac Models for Universal Probabilistic Programming
Michele Boreale (University of Florence), Luisa Collodi (University of Florence)
https://arxiv.org/abs/2509.14092

Parallelizable Feynman-Kac Models for Universal Probabilistic Programming
We study provably correct and efficient instantiations of Sequential Monte Carlo (SMC) inference in the context of formal operational semantics of Probabilistic Programs (PPs). We focus on universal PPs featuring sampling from arbitrary measures and conditioning/reweighting in unbounded loops. We first equip Probabilistic Program Graphs (PPGs), an automata-theoretic description format of PPs, with an expectation-based semantics over infinite execution traces, which also incorporates trace weigh…

FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale
Boris Bonev, Thorsten Kurth, Ankur Mahesh, Mauro Bisson, Jean Kossaifi, Karthik Kashinath, Anima Anandkumar, William D. Collins, Michael S. Pritchard, Alexander Keller
https://arxiv.org/abs/2507.12144…

FourCastNet 3: A geometric approach to probabilistic machine-learning weather forecasting at scale
FourCastNet 3 advances global weather modeling by implementing a scalable, geometric machine learning (ML) approach to probabilistic ensemble forecasting. The approach is designed to respect spherical geometry and to accurately model the spatially correlated probabilistic nature of the problem, resulting in stable spectra and realistic dynamics across multiple scales. FourCastNet 3 delivers forecasting accuracy that surpasses leading conventional ensemble models and rivals the best diffusion-ba…

Probabilistic Model Checking: Applications and Trends
Marta Kwiatkowska, Gethin Norman, David Parker
https://arxiv.org/abs/2509.12968 https://arxiv.org/pdf…

Probabilistic Model Checking: Applications and Trends
Probabilistic model checking is an approach to the formal modelling and analysis of stochastic systems. Over the past twenty five years, the number of different formalisms and techniques developed in this field has grown considerably, as has the range of problems to which it has been applied. In this paper, we identify the main application domains in which probabilistic model checking has proved valuable and discuss how these have evolved over time. We summarise the key strands of the underlyin…

MAPS: A Mode-Aware Probabilistic Scheduling Framework for LPV-Based Adaptive Control
Taehun Kim, Guntae Kim, Cheolmin Jeong, Chang Mook Kang
https://arxiv.org/abs/2509.12695 htt…

MAPS: A Mode-Aware Probabilistic Scheduling Framework for LPV-Based Adaptive Control
This paper proposes Mode-Aware Probabilistic Scheduling (MAPS), a novel adaptive control framework tailored for DC motor systems experiencing varying friction. MAPS uniquely integrates an Interacting Multiple Model (IMM) estimator with a Linear Parameter-Varying (LPV) based control strategy, leveraging real-time mode probability estimates to perform probabilistic gain scheduling. A key innovation of MAPS lies in directly using the updated mode probabilities as the interpolation weights for onli…

Interesting,
"One Token to rule them all - obtaining Global Admin in every Entra ID tenant via Actor tokens"
https://dirkjanm.io/obtaining-global-admin-in-every-entra-id-tenant-with-actor-tokens/

Dependency Pairs for Expected Innermost Runtime Complexity and Strong Almost-Sure Termination of Probabilistic Term Rewriting
Jan-Christoph Kassing, Leon Spitzer, J\"urgen Giesl
https://arxiv.org/abs/2507.12918

Dependency Pairs for Expected Innermost Runtime Complexity and Strong Almost-Sure Termination of Probabilistic Term Rewriting
The dependency pair (DP) framework is one of the most powerful techniques for automatic termination and complexity analysis of term rewrite systems. While DPs were extended to prove almost-sure termination of probabilistic term rewrite systems (PTRSs), automatic complexity analysis for PTRSs is largely unexplored. We introduce the first DP framework for analyzing expected complexity and for proving positive or strong almost-sure termination (SAST) of innermost rewriting with PTRSs, i.e., finite…