November 2025 arXiv papers — page 19

Lewis Smith, Bilal Chughtai, Neel Nanda

Building reliable deception detectors for AI systems -- methods that could predict when an AI system is being strategically deceptive without necessarily requiring behavioural evidence -- would be valuable in mitigating risks from advanced AI systems. But evaluating the reliability and efficacy of a proposed deception detector requires examples that we can c

Raven Gassis, Matthew B. Bayliss, Keren Sharon, Guillaume Mahler

Strong lensing galaxy clusters provide a unique and powerful way to test simulation-derived structure predictions that follow from $\Lambda$ Cold Dark Matter ($\Lambda$CDM) cosmology. Specifically, the relative alignments of the dark matter (DM) halo, stars, and hot intracluster gas in these clusters offer insights into how well theoretical structure predict

Siraphob Buahong, Teeradej Kittipassorn, Jiratchaphat Nanta, Piyashat Sripratak

We consider a graph representation in the plane, called the transparent rectangle visibility graph (TRVG), where each vertex is represented by a rectangle in the plane with sides parallel to the plane axes, in a way that any two vertices are adjacent if and only if a vertical or horizontal line can be drawn from the interior of one rectangle to the other. Ex

Zeren Chen, Xiaoya Lu, Zhijie Zheng, Pengrui Li

Vision Language Models (VLMs) exhibit a fundamental semantic-to-geometric gap in spatial reasoning: they excel at qualitative semantic inference but their reasoning operates within a lossy semantic space, misaligned with high-fidelity geometry. Current paradigms fail to bridge this gap. Training-based methods suffer from an ``oracle paradox,'' learning flawe

Ajay P. Joseph, Amit Roy, Anurag Singh

We investigate the minimal free resolutions of closed neighborhood ideals of graphs within the framework of Barile-Macchia (BM) resolutions. We show that for any tree $T$, the closed neighborhood ideal $NI(T)$ is bridge-friendly, and hence its BM resolution is minimal. The combinatorial structure of trees further allows us to construct a maximal critical cel

Lin Xu, Ke Li, Dongjie Wang, Fengmao Lv

Multi-view unsupervised feature selection (MUFS) has recently emerged as an effective dimensionality reduction method for unlabeled multi-view data. However, most existing methods mainly use first-order similarity graphs to preserve local structure, often overlooking the global structure that can be captured by second-order similarity. In addition, a few MUF

Wei Xing

We show that every higher Auslander algebra $A_{n+1}^d$ of type $\mathbb{A}$ such that $\gcd(n,d)=1$ is derived equivalent to a certain replicated algebra $B=B_0^{(n+d)}$. Moreover ${\rm{gldim}} B = nd$ and $B$ admits an $nd$-cluster tilting subcategory consisting of all direct sums of projective modules and injective modules. We introduce a class of algebra

Keisuke Fujii

Out-of-time-order correlators (OTOCs) are central probes of quantum scrambling, and their generalizations have recently become key primitives for both benchmarking quantum advantage and learning the structure of Hamiltonians. Yet their behavior has lacked a unified algorithmic interpretation. We show that higher-order OTOCs naturally fit within the framework

Sanghita Sengupta

We investigate the phonon magnetochiral effect (PMCE) in chiral Weyl semimetals by deriving the nonreciprocal dispersion relations of both acoustic and non-polar optical phonons in the presence of a magnetic field. Using a semiclassical Boltzmann kinetic framework that incorporates Berry curvature, orbital magnetic moment, and node-dependent electronic struc

Houssam Metni, Laura Ruple, Lauren N. Walters, Luca Torresi

Understanding structure-property relationships in materials is fundamental in condensed matter physics and materials science. Over the past few years, machine learning (ML) has emerged as a powerful tool for advancing this understanding and accelerating materials discovery. Early ML approaches primarily focused on constructing and screening large material sp

Anthony Carreon, Vansh Sharma, Venkat Raman

Optimization methods have long advanced many fields, yet they struggle when faced with design problems where the search space and design parameters are difficult to define. Large language models (LLMs) offer a promising alternative by dynamically interpreting design spaces and leveraging encoded domain knowledge. To this end, we present AUTO: an iterative op

Stanislav Budzinskiy, Vladimir Kazeev, Maxim Olshanskii

The paper considers function-valued tensors, viewed as multidimensional arrays with entries in an abstract Hilbert space. Despite the absence of the algebraic structure of a field, the geometric inner-product structure suffices to introduce the Tucker rank, higher-order SVD, and Tucker-cross decomposition for function-valued tensors. An adaptive cross-approx

Daniel D. Reidpath

This paper explains why internal and external validity cannot be simultaneously maximised. It introduces "evidential states" to represent the information available for causal inference and shows that routine study operations (restriction, conditioning, and intervention) transform these states in ways that do not commute. Because each operation removes or reo

Ishwaryah Pandiarajan, Mohamed Mansoor Roomi Sindha, Uma Maheswari Pandyan, Sharafia N

Sustained operation of solar photovoltaic assets hinges on accurate detection and prioritization of surface faults across vast, geographically distributed modules. While multi modal imaging strategies are popular, they introduce logistical and economic barriers for routine farm level deployment. This work demonstrates that deep learning and classical machine

David Grasev

A spatially aware dictionary-free eigenfunction discovery (SADFED) framework is proposed for identification of low-rank Koopman models from data without prescribing a lifting dictionary, kernel, or neural-network eigenfunction architecture. A reference trajectory is selected and used to determine the Koopman modes by regularized least squares (LS). Then, a t

Dong-Yu Bao, Gong Cheng, Hong-Hao Song, Zheng-Cheng Gu

Recent progress in generalized symmetry and topological holography has shown that, in conformal field theory (CFT), topological data from one dimensional higher can play a key role in determining local dynamics. Based on this insight, a fixed-point (FP) tensor complex (TC) for CFT has recently been constructed. In this work, we develop a TC renormalization (

Oliver Clarke, Sean Dewar, Matteo Gallet, Georg Grasegger

We study the generic fibre of the Hadamard product of linear spaces via matroid theory and tropical geometry. To do so, we introduce the flip product, a numerical invariant associated to a pair of matroids defined via the stable intersection of their (flipped) Bergman fans. Our first main result is that the cardinality of a generic fibre for the Hadamard pro

Di Wang, Shunyu Liu, Wentao Jiang, Fengxiang Wang

Multimodal large language models (MLLMs) have undergone rapid development in advancing geospatial scene understanding. Recent studies have sought to enhance the reasoning capabilities of remote sensing MLLMs, typically through cold-start training with elaborately curated chain-of-thought (CoT) data. However, this approach not only incurs substantial annotati

Liyuan Ye

In this paper, we extend the results of Michel-Venkatesh and Hu-Michel-Nelson to establish an upper bound for triple product and Rankin-Selberg L-functions of the form $$L(\pi_1 \otimes \pi_2 \otimes \pi_3,\frac{1}{2})\ll_{\pi_3,\epsilon}C(\pi_1\otimes\pi_2)^{\frac{1}{2} + \epsilon} \left( \frac{C(\pi_1 \otimes \pi_2)}{C(\pi_2 \otimes \pi_2)}\right)^{-\delta

Huan Wu

This paper develops a general framework for identifying causal effects in settings with spillovers, where both outcomes and endogenous treatment decisions are influenced by peers within a known group. It introduces the generalized local average controlled spillover and direct effects (LACSEs and LACDEs), which extend the local average treatment effect framew

Ruiqi Gao, Pinchen Xie, Roberto Car

In molecular simulations, machine-learning force fields can achieve ab initio accuracy at a lower cost but remain limited in the explicit modeling of electrons. In this work, we develop an electron-aware machine-learning force field, in which an excess electron of interest is modeled quantum mechanically, while the remaining short-range interactions and long

Riccardo De Santi, Marin Vlastelica, Ya-Ping Hsieh, Zebang Shen

Adapting large-scale foundation flow and diffusion generative models to optimize task-specific objectives while preserving prior information is crucial for real-world applications such as molecular design, protein docking, and creative image generation. Existing principled fine-tuning methods aim to maximize the expected reward of generated samples, while re

Feng-Jun Li, Ruixing Xia, Qianmei Deng, Yuze Lu

Holography can reconstruct immersive light fields for virtual and augmented reality by modulating optical wavefront. Due to huge pixel sizes, current spatial light modulators (SLMs) have small field-of-view (FOV) for holographic displays. Despite various methods for etendue expansion, the largest full-screen FOV for dynamic holography is only 70 {\deg} X 70

María Dias Astros, Lukáš Gráf, Stefan Vogl

Late decays of dark matter to a lighter, warm dark matter component are a known way to reduce the amplitude of the matter power spectrum on scales of $8$ Mpc. However, only very few particle physics models have been put forward that exhibit the required properties and allow to relate them to other observables. In this work, we investigate a model based on tw

Jacob Bradd, Nigel Higson, Robert Yuncken

The (maximal) Satake compactification associated to a real reductive group $G$ is the closure of the symmetric space of all maximal compact subgroups of $G$ within the compact space of all closed subgroups of $G$. We shall present three different views of a groupoid that may be associated to the Satake compactification. To begin, we shall define our Satake g

Jacob Bradd, Nigel Higson, Robert Yuncken

We give a geometric account of Harish-Chandra's principle that a tempered irreducible representation of a real reductive group is either square-integrable modulo center, or embeddable in a representation that is parabolically induced from such a representation. Our approach uses the Satake compactification, an associated groupoid that was constructed in the

Josh G. Weston, David R. Young, Stephen J. Smartt, Matt Nicholl

The upcoming Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will enable astronomers to discover rare and distant astrophysical transients. Host-galaxy association is crucial for selecting the most scientifically interesting transients for follow-up. LSST Deep Drilling Field observations will detect distant transients occurring in galaxies b

Tomasz Matulewicz, Izabela Skwira-Chalot

Two recent measurements of $^{12}$C(p,$\gamma$)$^{13}$N reaction on natural carbon were performed by detecting $\beta^+$ decay of $^{13}$N residue. It is argued that the measurements at energies above the $^{13}$C(p,n)$^{13}$N reaction threshold of 3.24 MeV proton kinetic energy can be interpreted as a consequence of 1.06\% admixture of $^{13}$C isotope in n

Sanalkumar K, Koushik Dey, Swati Meena

Effective agent shift scheduling is crucial for businesses, especially in the Contact Center as a Service (CCaaS) industry, to ensure seamless operations and fulfill employee needs. Most studies utilizing mathematical model-based solutions approach the problem as a single-step process, often resulting in inefficiencies and high computational demands. In cont

Frederick Heinz, Sascha Jähnigen, Joana-Lysiane Schäfer, Bettina G. Keller

Reliable dynamical properties from molecular dynamics simulations require careful control of thermostatting artifacts. We systematically assess how NVE, deterministic thermostats, velocity-rescale dynamics, and stochastic Langevin-type thermostats affect time-correlation functions across liquids of varying complexity. The analysis spans vibrational spectra,

Petar Žugec, Eric Andreas Vivoda, Mihael Makek, Ivica Friščić

Two photons from the ground state para-positronium annihilation are emitted in a maximally entangled singlet state of orthogonal polarizations. In case of the Compton scattering of both photons the phenomenon of quantum entanglement leads to a measurable increase in the azimuthal correlations of scattered photons, as opposed to a classical description treati

António Barros, Christoph Studer

We present a computationally-efficient algorithm for time-of-arrival (ToA) estimation that is robust under multipath propagation and strong interference. Our algorithm leverages multiple receive antennas to combine adaptive spatial filtering with autodifferentiation in order to super-resolve the tap of the first-arriving path at low computational complexity

David P Hewett

We prove best approximation error estimates for discontinuous piecewise polynomial approximation in fractional Sobolev spaces on non-Lipschitz meshes of non-Lipschitz domains. In particular, the boundary of the domain, and the boundaries of the mesh elements, can be fractal.

Raúl Martínez Bohórquez, José Navarro, Juan B. Sancho

On smooth manifolds of dimension $n \ge 4$, we prove that the torsion and curvature are, up to a scalar factor, the only pair of a vector-valued 2-form and an endomorphism-valued 2-form naturally associated with a linear connection that satisfy both the linear and differential Bianchi identities. This result extends to arbitrary linear connections a recent c

Pavel Zalesskii

We develop JSJ decomposition theory of pro-p groups.

Fukun Yin, Shiyu Liu, Yucheng Han, Zhibo Wang

Recent advances in image editing models have shown remarkable progress. A common architectural design couples a multimodal large language model (MLLM) encoder with a diffusion decoder, as seen in systems such as Step1X-Edit and Qwen-Image-Edit, where the MLLM encodes both the reference image and the instruction but remains frozen during training. In this wor

Aharon Brodutch, Gregory Baimetov, Edwin Tham, Nicolas Delfosse

Clifford noise reduction (CliNR) is a partial error correction scheme that reduces the logical error rate of Clifford circuits at the cost of a modest qubit and gate overhead. The CliNR implementation of an $n$-qubit Clifford circuit of size $s$ achieves a vanishing logical error rate if $snp^2\rightarrow 0$ where $p$ is the physical error rate. Here, we pro

Jason Kristiano, Ryo Namba, Atsushi Naruko, Ryo Saito

The analytic structure of the flat-space S-matrix provides non-perturbative constraints on low-energy effective field theories based on the properties of high-energy theory. While the analytic structure of the flat-space S-matrix is well understood, extending this framework to de Sitter space is challenging, as the expanding background complicates the defini

Alexander Valov, Baruch Meerson

We study large deviations, over a long time window $T \to \infty$, of the dynamical observables $A_n = \int_{0}^{T} x^n(t) dt$, $n=3,4,\dots$, where $x(t)$ is a centered stationary Gaussian process in continuous time. We show that, for short-correlated processes the probability density of $A_n$ exhibits an anomalous scaling $P(A_n,T) \sim \exp[-T^{\mu} f_n(\

Mark Sellke

We give a short proof that low-temperature dynamics for the Sherrington-Kirkpatrick model have mixing time exponential in the system size, based on the recently proved existence of gapped spin configurations by (Minzer-Sah-Sawhney 2023, Dandi-Gamarnik-Zdeborov\'a 2023). This result is in contrast with a well established physics prediction which posits a stre

Antonio Iannizzotto

We study an elliptic equation, with homogeneous Dirichlet boundary conditions, driven by a mixed type operator (the sum of the Laplacian and the fractional Laplacian), involving a parametric reaction and an undetermined source term. Applying a recent abstract critical point theorem of Ricceri, we prove existence of a solution for a convenient source and smal

Boyuan Chen, Sitong Fang, Jiaming Ji, Yanxu Zhu

As intelligence increases, so does its shadow. AI deception, in which systems induce false beliefs to secure self-beneficial outcomes, has evolved from a speculative concern to an empirically demonstrated risk across language models, AI agents, and emerging frontier systems. This project provides a comprehensive and up-to-date overview of the AI deception fi

Leonardo Scandurra, Pavlos Alexias, Eugene de Villiers

Time series data often contain initial transient periods before reaching a stable state, posing challenges in analysis and interpretation. In this paper, we propose a novel approach to detect and estimate the end of the initial transient in time series data. Our method leverages the reversal mean standard error (RMSE) as a metric for assessing the stability

Johan Sebastián Galindez-Acosta, Juan José Giraldo-Huertas

The integration of artificial intelligence into everyday decision-making has reshaped patterns of selective trust, yet the cognitive mechanisms behind context-dependent preferences for AI versus human informants remain unclear. We applied a Bayesian Hierarchical Sequential Sampling Model (HSSM) to analyze how 102 Colombian university students made trust deci

Meriem Arbaoui, Mohamed-el-Amine Brahmia, Abdellatif Rahmoun, Mourad Zghal

The integration of IoT and AI has unlocked innovation across industries, but growing privacy concerns and data isolation hinder progress. Traditional centralized ML struggles to overcome these challenges, which has led to the rise of Federated Learning (FL), a decentralized paradigm that enables collaborative model training without sharing local raw data. FL

Paraskevi-Antonia Theofilou, Anuhya Thota, Stefanos Kollias, Mamatha Thota

When deep learning models are sequentially trained on new data, they tend to abruptly lose performance on previously learned tasks, a critical failure known as catastrophic forgetting. This challenge severely limits the deployment of AI in medical imaging, where models must continually adapt to data from new hospitals without compromising established diagnos

Yan Yang, Bin Gao, Ya-xiang Yuan

Determinantal varieties -- the sets of bounded-rank matrices or tensors -- have attracted growing interest in low-rank optimization. The tangent cone to low-rank sets is widely studied and underpins a range of geometric methods. The second-order geometry, which encodes curvature information, is more intricate. In this work, we develop a unified framework to

Guilherme Sousa, Rinaldo Lima, Cassia Trojahn

Large Language Models (LLMs) are increasingly being integrated into various components of Ontology Matching pipelines. This paper investigates the capability of LLMs to perform ontology matching directly on ontology modules and generate the corresponding alignments. Furthermore, it is explored how a dedicated fine-tuning strategy can enhance the model's matc

João P. B. Brito, Rafael P. Bernar, Luís C. B. Crispino

We investigate the electromagnetic radiation emitted by a charged particle orbiting a four-dimensional Schwarzschild--de Sitter black hole using a semiclassical approach. We calculate the probability amplitude for the charged particle to emit a photon, from which we derive the emitted power and spectral distributions. The results are compared with those obta

Claudia Fanelli, Tiany Li, Luca Biferale, Bruno Buongiorno Nardelli

In this work, we address the super-resolution problem of satellite-derived sea surface temperature (SST) using deep generative models. Although standard gap-filling techniques are effective in producing spatially complete datasets, they inherently smooth out fine-scale features that may be critical for a better understanding of the ocean dynamics. We investi

Bo Wang, Jiehong Lin, Chenzhi Liu, Xinting Hu

We present MG-Nav (Memory-Guided Navigation), a dual-scale framework for zero-shot visual navigation that unifies global memory-guided planning with local geometry-enhanced control. At its core is the Sparse Spatial Memory Graph (SMG), a compact, region-centric memory where each node aggregates multi-view keyframe and object semantics, capturing both appeara

Xiaoyin Yang

Eye tracking has become increasingly important in virtual and augmented reality applications; however, the current gaze accuracy falls short of meeting the requirements for spatial computing. We designed a gaze collection framework and utilized high-precision equipment to gather the first precise benchmark dataset, GazeTrack, encompassing diverse ethnicities

Rowzatul Zannath Prerona

Brain metastasis segmentation in MRI remains a formidable challenge due to diminutive lesion sizes (5-15 mm) and extreme class imbalance (less than 2% tumor volume). While soft-attention CNNs are widely used, we identify a critical failure mode termed the "over-segmentation paradox," where models achieve high sensitivity (recall > 0.88) but suffer from catas

Anthony Carbery

Littlewood--Paley theory began with the classic paper of Littlewood and Paley (J.\ E.\ Littlewood, R.\ E.\ A.\ C.\ Paley, {\em Theorems on Fourier Series and Power Series}. J. Lond. Math. Soc. (1), {\bf 6} (1931), 230--33). We discuss this paper and its impact from a historical perspective. We include an outline of the results in the paper and their subseque

Paul Bastide, Carla Groenland, Lukas Michel, Clément Rambaud

A temporal graph $G$ is a sequence $(G_t)_{t \in I}$ of graphs on the same vertex set of size $n$. The \emph{temporal exploration problem} asks for the length of the shortest sequence of vertices that starts at a given vertex, visits every vertex, and at each time step $t$ either stays at the current vertex or moves to an adjacent vertex in $G_t$. Bounds on

J. A. S. Lima, M. H. Benetti

Adopting a bottom-up perspective, we propose a novel two-parametric nonadditive entropy, $S_{\kappa\ell}$, associated with a Kappa-type power-law velocity distribution, $F_{\kappa\ell}(v)$, recently derived in the literature. By formulating an extended Neo-Boltzmannian microstate counting procedure and employing standard averaging techniques, we demonstrate

Dongwoo Gang

We consider a smooth closed orientable submanifold $M \subset \mathbb{R}^D$ with narrow cycles. We embed $M$ into a scaled oriented Grassmannian bundle via the Gauss map in order to enlarge the scale of these cycles. Under mild assumptions, we show that this embedding reduces the normal curvature of the embedded submanifold in directions where the original n

Giovanni Busalacchi, Fabrizio Martino, Carla Rizzo

Let $W$ be a $G$-graded algebra over a field of characteristic zero, where $G$ is a finite group. We develope a theory of generalized $G$-graded polynomial identities satisfied by any finite-dimensional $W$-algebra $A$, by mean of the graded multiplier algebra of $A.$ In particular, we first prove that the graded generalized exponent exists and equals the or

Zhiqiang Li, Xianghui Shi

We establish a local central limit theorem for primitive periodic orbits of expanding Thurston maps, providing a fine-scale refinement of the Prime Orbit Theorem in the context of non-uniformly expanding dynamics. Specifically, we count the number of primitive periodic orbits whose Birkhoff sums for a given potential lie within a family of shrinking interval

Joaquim Roé, Stefano Urbinati

We prove semicontinuity properties for local positivity invariants of big and nef divisors. The usual definition of Seshadri constant and asymptotic order of vanishing along a subvariety is extended to include all seminorms in the Berkovich space, and we obtain semicontinuity of such constants as a function of the center seminorm. We use Shokurov's language

Mohammadreza Malekabbasi, Minghe Wang, David Bermbach

Deploying Large Language Model (LLM) services at the edge benefits latency-sensitive and privacy-aware applications. However, the stateless nature of LLMs makes managing user context (e.g., sessions, preferences) across geo-distributed edge nodes challenging. Existing solutions, such as client-side context storage, introduce network latency and bandwidth ove

Huanyu Li, Zongyuan Li, Wei Huang, Xian Guo

Large language models (LLMs) such as ChatGPT o1, ChatGPT o3, and DeepSeek R1 have shown great potential in solving difficult problems. However, current LLM evaluation benchmarks are limited to one-step interactions. Some of the existing sequence decision-making environments, such as TextStarCraftII and LLM-PySC2, are too complicated and require hours of inte

Jeffrey S. Hazboun, Joseph Simon, Jeremy Baier, Bjorn Larsen

Pulsar timing arrays (PTAs) have recently entered the detection era, quickly moving beyond the goal of simply improving sensitivity at the lowest frequencies for the sake of observing the stochastic gravitational wave background (GWB), and focusing on its accurate spectral characterization. While all PTA collaborations around the world use Fourier-domain Gau

Alexander Roskill, Sara Maleubre, David Alonso, Pedro G. Ferreira

Skew-spectra allow us to extract non-Gaussian information by taking the square of a map and finding the power spectrum of this new map with the original map. This allows us to use much of the infrastructure of power spectra and avoid the intricacies of estimating three point statistics. In this paper we present the first extension of skew-spectra to arbitrar

Dayou Huang, Feng Xue, Xurui Li, Yu Zhou

Zero-shot industrial anomaly detection (ZSAD) methods typically yield coarse anomaly maps as vision transformers (ViTs) extract patch-level features only. To solve this, recent solutions attempt to predict finer anomalies using features from ZSAD, but they still struggle to recover fine-grained anomalies without missed detections, mainly due to the gap betwe

Haoxi Zeng, Haoxuan Li, Yi Bin, Pengpeng Zeng

Contrastive Language-Image Pre-training (CLIP) has demonstrated remarkable generalization ability and strong performance across a wide range of vision-language tasks. However, due to the lack of region-level supervision, CLIP exhibits limited fine-grained semantic understanding. Although several methods attempt to mitigate this issue, they unintentionally di

Niccolò Di Eugenio, Ashley Dickson, Flyura Djurabekova, Francesco Laviano

High-Temperature Superconductors (HTS) such as YBa2Cu3O7-delta (YBCO) are essential for next-generation Tokamak fusion reactors, where Rare-Earth Barium Copper Oxides (REBCO) form the functional layers in HTS magnets. Because YBCO's superconductivity depends strongly on oxygen stoichiometry and defect structure, atomistic simulations can provide crucial insi

Şahsene Altınkaya, Masayo Fujimura, Marcelina Mocanu, Matti Vuorinen

We prove several formulas for the Hilbert metric in the unit disk and apply these results to study quasiregular mappings of the unit disk $\mathbb{B}^2$ onto a bounded convex domain $D$. The main result deals with the H\"older continuity of these mappings with respect to Hilbert metrics of $\mathbb{B}^2$ and $D$. Also several open problems are formulated.

Maya Levanon, Noa S. Goldberg, Dvir Cohen, Eran Bouchbinder

In living systems, DNA undergoes continuous and rhythmic mechanical remodeling through condensation, looping, and disentangling to regulate gene expression, segregate chromosomes, and guide morphogenesis. Here, we demonstrate a purely mechanical route to rhythmic DNA reorganization in a minimal active composite of microtubules, kinesin motors, and DNA. We em

Francesco Dolce, Christian B. Hughes

Branching Rauzy induction is a two-sided form of Rauzy induction that acts on regular interval exchange transformations (IETs). We introduce an extended form of branching Rauzy induction that applies to arbitrary standard IETs, including non-minimal ones. The procedure generalizes the branching Rauzy method with two induction steps, merging and splitting, to

Francesco Marchetti, Edoardo Legnaro, Sabrina Guastavino

In the supervised binary classification setting, score-oriented losses have been introduced with the aim of optimizing a chosen performance metric directly during the training phase, thus avoiding \textit{a posteriori} threshold tuning. To do this, in their construction, the decision threshold is treated as a random variable provided with a certain \textit{a

Yifan Du, Kun Zhou, Yingqian Min, Yue Ling

We study how different Chain-of-Thought (CoT) designs affect the acquisition of the generalizable visual reasoning ability in vision-language models (VLMs). While CoT data, especially long or visual CoT such as "think with image", has been widely used to supervise intermediate reasoning, it remains unclear why specific CoT designs help and which ones truly s

Zheng-Xu Zhu, Yuan-Hao Yang, Xin-Biao Xu, Jia-Qi Wang

Microring resonators (MRRs) are fundamental building blocks of photonic integrated circuits, yet their dynamic reconfiguration has been limited to tuning refractive index or absorption. Here, we demonstrate acoustic control over optical path topology on a lithium niobate on sapphire platform. By launching gigahertz acoustic waves into a hybrid phononic-photo

Omar El Deeb

We develop a minimal nonlinear model to investigate the oscillatory dynamics of Saturn's F ring under dual-moon forcing from Prometheus and Pandora. The model extends classical predator--prey dynamics by incorporating both a nonlinear mass aggregation term $kM^n$ and explicit dual-frequency forcing, capturing how higher-order coagulation physics interact

Chao Feng, Zihan Liu, Siddhant Gupta, Gongpei Cui

Hardware-in-the-Loop (HIL) testing is essential for automotive validation but suffers from fragmented and underutilized test artifacts. This paper presents HIL-GPT, a retrieval-augmented generation (RAG) system integrating domain-adapted large language models (LLMs) with semantic retrieval. HIL-GPT leverages embedding fine-tuning using a domain-specific data

Fumika Suzuki, Wojciech H. Zurek

The Kibble-Zurek mechanism (KZM) successfully predicts the density of topological defects deposited by the phase transitions, but it is not clear why. Its key conjecture is that, near the critical point of the second-order phase transition, critical slowing down will result in a period when the system is too sluggish to follow the potential that is changing

Matilde Marcolli, Richard Larson, Riny Huijbregts

We analyze, using the mathematical formulation of Merge within the Strong Minimalist Thesis framework, a set of linguistic phenomena, including head-to-head movement, phrasal affixes and syntactic cliticization, verb-particle alternation, and operator-variable phenomena. These are often regarded as problematic, as violations of the Extension Condition. We sh

Emily Wright, Leo Van Damme, Niklas J. Glaser, Amit Devra

State-of-the-art single-qubit gates on superconducting transmon qubits can achieve the fidelities required for error-corrected computations. However, parameter fluctuations due to qubit instabilities, environmental changes, and control inaccuracies make it difficult to maintain this performance. To mitigate the effects of these parameter variations, we numer

Ziliang Wang, Han Zhang, Axel Ringh

Inverse Optimal Control (IOC) seeks to recover an unknown cost from expert demonstrations, and it provides a systematic way of modeling experts' decision mechanisms while considering the prior information of the cost functions. Nevertheless, existing IOC methods have consistency issue with the estimator under noisy and nonlinear settings. In this paper, we c

Samuel Thio, Matthew Lewis, Spiros Denaxas, Richard JB Dobson

Electronic health record (EHR) systems present clinicians with vast repositories of clinical information, creating a significant cognitive burden where critical details are easily overlooked. While Large Language Models (LLMs) offer transformative potential for data processing, they face significant limitations in clinical settings, particularly regarding co

Mianjie Zheng, Xinquan Yang, Xuguang Li, Xiaoling Luo

The abutment is an important part of artificial dental implants, whose design process is time-consuming and labor-intensive. Long-term use of inappropriate dental implant abutments may result in implant complications, including peri-implantitis. Using artificial intelligence to assist dental implant abutment design can quickly improve the efficiency of abutm

Zhanyu Ma, Cheolhee Han, F. Pierre, Eran Sela

Charge Kondo circuits consist of metallic islands connected by single-mode quantum point contacts (QPCs). The island's charging energy makes these circuits tunable quantum simulators of various strongly interacting models. Here we propose a circuit that realizes the Kondo effect with effective Luttinger-liquid interactions, and show that it undergoes a local

Janak Kapuriya, Ali Hatami, Paul Buitelaar

Recent advancements in text-to-image generative models have improved narrative consistency in story visualization. However, current story visualization models often overlook cultural dimensions, resulting in visuals that lack authenticity and cultural fidelity. In this study, we conduct a comprehensive multicultural analysis of story visualization using curr

Matteo Bonato, Ivano Baronchelli, Gianfranco De Zotti, Leonardo Trobbiani

The ALMACAL project leverages ALMA maps of calibrator-centered fields to conduct deep mm/sub-mm surveys, enabling the detection of extragalactic sources with flux densities orders of magnitude fainter than achievable with other instruments. These faint sources are critical for refining evolutionary models, as their number counts provide key constraints. In t

Anton Rudakovskyi, Franco Vazza, Maksym Tsizh

Context. The spatial distribution of haloes in the Cosmic Web encodes a wealth of information about the underlying cosmological model. These haloes can be represented as nodes of a graph, whose structural properties reflect cosmological parameters. Aims. Using our new MAKITRA suite of cosmological magneto-hydrodynamical simulations covering a total volume of

Damian Kurpiewski, Jędrzej Michalczyk, Wojciech Jamroga, Jerzy Julian Michalski

This technical report presents a comprehensive formal verification approach for probabilistic agent systems modeling ballistic rocket flight trajectories using Probabilistic Alternating-Time Temporal Logic (PATL). We describe an innovative verification framework specifically designed for analyzing critical safety properties of ballistic rockets engineered to

Mohamad Kazouini, Janis Peter, Zisu Emily Guo, Benedikt Wilde

Photon-pressure circuits are the circuit implementation of the cavity optomechanical Hamiltonian and discussed for qubit readout, low-frequency quantum photonics and dark matter axion detection. Due to the enormous design flexibility of superconducting circuits, photon-pressure systems provide fascinating possibilities to explore unusual parameter regimes of

Zhihong Shao, Yuxiang Luo, Chengda Lu, Z. Z. Ren

Large language models have made significant progress in mathematical reasoning, which serves as an important testbed for AI and could impact scientific research if further advanced. By scaling reasoning with reinforcement learning that rewards correct final answers, LLMs have improved from poor performance to saturating quantitative reasoning competitions li

Aleksandr Pustyntsev, Muthubharathi S. Ramasamy, Marc Vanderhaeghen

We examine the sensitivity of the beam normal spin asymmetry in Bhabha scattering to beyond the Standard Model (BSM) mediators, in the context of the JLab polarized positron program. A key property of this observable is that the Standard Model contribution exhibits a zero crossing at a fixed scattering angle, providing a clean, effectively background-free po

Feyza Eksen, Stefan Oehmcke, Stefan Lüdtke

Accurate sensor placement is critical for modeling spatio-temporal systems such as environmental and climate processes. Neural Processes (NPs), particularly Convolutional Conditional Neural Processes (ConvCNPs), provide scalable probabilistic models with uncertainty estimates, making them well-suited for data-driven sensor placement. However, existing approa

Casper da Costa-Luis, Matthias J. Ehrhardt, Christoph Kolbitsch, Evgueni Ovtchinnikov

Introduction: We describe the foundation of PETRIC, an image reconstruction challenge to minimise the computational runtime of related algorithms for Positron Emission Tomography (PET). Purpose: Although several similar challenges are well-established in the field of medical imaging, there have been no prior challenges for PET image reconstruction. Methods:

Yannick Brunink, Daniel Daza, Yunjie He, Michael Cochez

Neural methods for Complex Query Answering (CQA) over knowledge graphs (KGs) are widely believed to learn patterns that generalize beyond explicit graph structure, allowing them to infer answers that are unreachable through symbolic query processing. In this work, we critically examine this assumption through a systematic analysis comparing neural CQA models

Ruiyu Han

In an earlier joint work, we studied a sequential Monte Carlo algorithm to sample from the Gibbs measure supported on torus with a non-convex energy function at a low temperature, where we proved that the time complexity of the algorithm is polynomial in the inverse temperature. However, the analysis in that torus setting relied crucially on compactness and

Keqiang Lyu, Mohamed Farhat, Ying Wu

We propose a simple yet effective method for low-frequency broadband acoustic absorption. The absorber consists of two concentric space-coiling resonators with distinct resonance frequencies, with the inner resonator characterized by a low-quality factor (Q) and the outer resonator by a high Q factor. The coupling between the two resonators enables efficient

Jørgen Bang-Jensen, Frédéric Havet, Florian Hörsch, Clément Rambaud

Given an oriented graph $D$, the inversion of a subset $X$ of vertices consists in reversing the orientation of all arcs with both endpoints in $X$. When the subset $X$ is of size $p$ (resp. at most $p$), this operation is called an $(=p)$-inversion (resp. $(\leq p)$-inversion). Then, an oriented graph is $(=p)$-invertible if it can be made acyclic by a sequ

Stefano Forte, Juan Rojo, Roy Stegeman

We present an explicit demonstration that a determination of the strong coupling constant $\alpha_s(m_Z)$ from deep-inelastic scattering and hadron collider data without a simultaneous determination of the parton distribution functions (PDFs) leads to a biased result for both the central value and the uncertainty, even in the ideal scenario (closure test) wh

Fabio Tanania

In this paper, we introduce the category of real isotropic motivic spectra, and show that the real realization functor from motivic spectra over $\mathbb{R}$ to classical spectra factors through it. We then describe its cellular subcategory as a one-parameter deformation of the category of spectra, with parameter $\rho$ corresponding to $-1 \in \mathbb{R}^{\

Sabri Koraltan, Joe Sunny, Emily Darwin, Daniel Rothhardt

Magnetic skyrmions stabilized by interfacial Dzyaloshinskii-Moriya interactions (DMI) are promising candidates for applications in memory, logic, and neuromorphic computing. Beyond planar films, theoretical studies predict that curvature can influence skyrmion stability by introducing effective chiral interactions. Here, we investigate skyrmion formation on

Cuong Mai Bui, Stephan K. Matthai

Non-Newtonian rheology is widely acknowledged in subsurface fluids, yet its presence and effects are largely ignored in current fracture-flow studies. Here, we simulate fracture flow of non-Newtonian polymer solutions on a several metre-wide millimetre-aperture network of fractures, examining the complex interplay between fluid rheology, fracture geometry, a

Yanbo Mao, Jianlong Fu, Ruoxuan Zhang, Hongxia Xie

Vision-Language-Action (VLA) models have enabled notable progress in general-purpose robotic manipulation, yet their learned policies often exhibit variable execution quality. We attribute this variability to the mixed-quality nature of human demonstrations, where the implicit principles that govern how actions should be carried out are only partially satisf