Tootfinder

Observation of a low energy nuclear recoil peak in the neutron calibration data of an Al$_{2}$O$_{3}$ crystal in CRESST-III
CRESST Collaboration, G. Angloher, S. Banik, A. Bento, A. Bertolini, R. Breier, C. Bucci, L. Burmeister, F. Casadei, E. Cipelli, J. Burkhart, L. Canonica, J. Dohm, F. Dominsky, S. Di Lorenzo, L. Einfalt, A. Erb, E. Fascione, F. v. Feilitzsch, S. Fichtinger, D. Fuchs, V. M. Ghete, P. Gorla, P. V. Guillaumon, D. Hauff, M. Je\v{s}kovsk\'y, J. Jochum, M. Kaznachee…

Observation of a low energy nuclear recoil peak in the neutron calibration data of an Al$_{2}$O$_{3}$ crystal in CRESST-III
The current generation of cryogenic solid state detectors used in direct dark matter and CE\textnu NS searches typically reach energy thresholds of $\mathcal{O}$(10)$\,$eV for nuclear recoils. For a reliable calibration in this energy regime a method has been proposed, providing mono-energetic nuclear recoils at low energies $\sim\,$100$\,$eV$\,$-$\,$1$\,$keV. In this work we report on the observation of a peak at (1113.6$^{+6.5}_{-6.5}$)$\,$eV in the data of an Al$_{2}$O$_{3}$ crystal in CRESS…

Learning The Minimum Action Distance
Lorenzo Steccanella, Joshua B. Evans, \"Ozg\"ur \c{S}im\c{s}ek, Anders Jonsson
https://arxiv.org/abs/2506.09276

Learning The Minimum Action Distance
This paper presents a state representation framework for Markov decision processes (MDPs) that can be learned solely from state trajectories, requiring neither reward signals nor the actions executed by the agent. We propose learning the minimum action distance (MAD), defined as the minimum number of actions required to transition between states, as a fundamental metric that captures the underlying structure of an environment. MAD naturally enables critical downstream tasks such as goal-conditi…

The Fast and the Frame-Dragging: Efficient waveforms for asymmetric-mass eccentric equatorial inspirals into rapidly-spinning black holes
Christian E. A. Chapman-Bird, Lorenzo Speri, Zachary Nasipak, Ollie Burke, Michael L. Katz, Alessandro Santini, Shubham Kejriwal, Philip Lynch, Josh Mathews, Hassan Khalvati, Jonathan E. Thompson, Soichiro Isoyama, Scott A. Hughes, Niels Warburton, Alvin J. K. Chua, Maxime Pigou

The Fast and the Frame-Dragging: Efficient waveforms for asymmetric-mass eccentric equatorial inspirals into rapidly-spinning black holes
Observations of gravitational-wave signals emitted by compact binary inspirals provide unique insights into their properties, but their analysis requires accurate and efficient waveform models. Intermediate- and extreme-mass-ratio inspirals (I/EMRIs), with mass ratios $q \gtrsim 10^2$, are promising sources for future detectors such as the Laser Interferometer Space Antenna (LISA). Modelling waveforms for these asymmetric-mass binaries is challenging, entailing the tracking of many harmonic mod…

This https://arxiv.org/abs/2506.05850 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Cross-lingual Collapse: How Language-Centric Foundation Models Shape Reasoning in Large Language Models
We identify \textbf{Cross-lingual Collapse}, a systematic drift in which the chain-of-thought (CoT) of a multilingual language model reverts to its dominant pre-training language even when the prompt is expressed in a different language. Recent large language models (LLMs) with reinforcement learning with verifiable reward (RLVR) have achieved strong logical reasoning performances by exposing their intermediate reasoning traces, giving rise to large reasoning models (LRMs). However, the mechani…

Acceleration of Lagrangian Particles in Shell Models of Turbulence
Lorenzo Piro, Massimo Cencini, Roberto Benzi
https://arxiv.org/abs/2506.09633 https://…

Acceleration of Lagrangian Particles in Shell Models of Turbulence
Lagrangian acceleration has been investigated both experimentally and numerically in the past, and it has been shown to exhibit extreme fluctuations, which have been rationalized as events in which tracer particles get trapped into vortical structures such as vortex tubes or filaments. Here, we consider the statistics of acceleration within the multifractal framework, as in Biferale et al. Phys. Rev. Lett. 93 064502 (2004), and investigate the statistics of Lagrangian acceleration using shell m…

Prompt Variability Effects On LLM Code Generation
Andrei Paleyes, Radzim Sendyka, Diana Robinson, Christian Cabrera, Neil D. Lawrence
https://arxiv.org/abs/2506.10204

Prompt Variability Effects On LLM Code Generation
Code generation is one of the most active areas of application of Large Language Models (LLMs). While LLMs lower barriers to writing code and accelerate development process, the overall quality of generated programs depends on the quality of given prompts. Specifically, functionality and quality of generated code can be sensitive to user's background and familiarity with software development. It is therefore important to quantify LLM's sensitivity to variations in the input. To this end we prop…

ContextBuddy: AI-Enhanced Contextual Insights for Security Alert Investigation (Applied to Intrusion Detection)
Ronal Singh, Mohan Baruwal Chhetri, Surya Nepal, Cecile Paris
https://arxiv.org/abs/2506.09365

ContextBuddy: AI-Enhanced Contextual Insights for Security Alert Investigation (Applied to Intrusion Detection)
Modern Security Operations Centres (SOCs) integrate diverse tools, such as SIEM, IDS, and XDR systems, offering rich contextual data, including alert enrichments, flow features, and similar case histories. Yet, analysts must still manually determine which of these contextual cues are most relevant when validating specific alerts. We introduce ContextBuddy, an AI assistant that learns from analysts' prior investigations to help them identify the most relevant context for new alerts. Rather than …

Pattern-preserving optimal control problems with increasing time horizon
Matteo Della Rossa, Lorenzo Freddi
https://arxiv.org/abs/2506.09970 https://

Pattern-preserving optimal control problems with increasing time horizon
We establish a general framework that guarantees the preservation of optimal control patterns as the time horizon $[0,T]$ increases and becomes unbounded. A concept of pattern-preserving family of optimal control problems is introduced and the goal is achieved by analyzing the $Γ$-convergence of the corresponding variational formulations as $T\to\infty$. Special attention is given to scenarios involving state constraints. To illustrate the results, examples and applications are provided, with …

Anomaly Detection and Generation with Diffusion Models: A Survey
Yang Liu, Jing Liu, Chengfang Li, Rui Xi, Wenchao Li, Liang Cao, Jin Wang, Laurence T. Yang, Junsong Yuan, Wei Zhou
https://arxiv.org/abs/2506.09368

Anomaly Detection and Generation with Diffusion Models: A Survey
Anomaly detection (AD) plays a pivotal role across diverse domains, including cybersecurity, finance, healthcare, and industrial manufacturing, by identifying unexpected patterns that deviate from established norms in real-world data. Recent advancements in deep learning, specifically diffusion models (DMs), have sparked significant interest due to their ability to learn complex data distributions and generate high-fidelity samples, offering a robust framework for unsupervised AD. In this surve…

mLaSDI: Multi-stage latent space dynamics identification
William Anderson, Kevin Chung, Youngsoo Choi
https://arxiv.org/abs/2506.09207 https://

mLaSDI: Multi-stage latent space dynamics identification
Determining accurate numerical solutions of partial differential equations (PDEs) is an important task in many scientific disciplines. However, solvers can be computationally expensive, leading to the development of reduced-order models (ROMs). Recently, Latent Space Dynamics Identification (LaSDI) was proposed as a data-driven, non-intrusive ROM framework. LaSDI compresses the training data using an autoencoder and learns a system of user-chosen ordinary differential equations (ODEs), which go…