November 2025 arXiv papers — page 28

Isaías Rodríguez, Renela M. Valladares, Alexander Valladares, David Hinojosa-Romero

Twisted bilayers offer a compelling and, at times, confounding platform for the engineering of new twistronic materials. Whereas standard studies almost exclusively focus on the explicit enigma that is presented by twist-angles, perhaps better epitomized by the related phenomena that have been observed in twisted bilayer graphene, functional devices necessar

William O'Regan, Pablo Shmerkin, Hong Wang

We give a simple, short and self-contained presentation of Bourgain's discretised projection theorem from 2010, which is a fundamental tool in many recent breakthroughs in geometric measure theory, harmonic analysis, and homogeneous dynamics. Our main innovation is a short elementary argument that shows that a discretised subset of $\R$ satisfying a weak ``t

Guillaume Duprez, Mélina Durande, François Graner, Hélène Delanoë-Ayari

Collective cell flows are a hallmark of tissue dynamics in development, wound healing, and various diseases. Here, we perform experiments on epithelial MDCK cell monolayers, over tens of hours without jamming, on millimeter-scale micropatterned substrates with or without free front (a strip or a closed racetrack). During maturation in time, domains and long-

Ruisheng Han, Kanglei Zhou, Shuang Chen, Amir Atapour-Abarghouei

Action Quality Assessment (AQA) predicts fine-grained execution scores from action videos and is widely applied in sports, rehabilitation, and skill evaluation. Long-term AQA, as in figure skating or rhythmic gymnastics, is especially challenging since it requires modeling extended temporal dynamics while remaining robust to contextual confounders. Existing

Kirill Paramonov, Mete Ozay, Aristeidis Mystakidis, Nikolaos Tsalikidis

The proliferation of AI models in everyday devices has highlighted a critical challenge: prediction errors that degrade user experience. While existing solutions focus on error detection, they rarely provide efficient correction mechanisms, especially for resource-constrained devices. We present a novel system enabling users to correct AI misclassifications

Jhordan Santiago

We show that any nonclassicality criterion based on factorial moments, including several higher-order parameters such as the Mandel $Q^{(\ell)}$ parameter, the Lee antibunching function $d^{(\ell-1)}_h$, and the Agarwal--Tara parameter $A_3$, can be computed straightforwardly for photon-subtracted and photon-added states by performing operator reordering of

Francesco Capozzi, Eligio Lisi, Francesco Marcone, Antonio Marrone

Within the standard $3\nu$ framework, we discuss updated bounds on the leading oscillation parameters related to the $(\nu_1,\,\nu_2)$ states, namely, the squared mass difference $\delta m^2=m^2_2-m^2_1$ and the mixing parameter $\sin^2\theta_{12}$. A previous global analysis of 2024 oscillation data estimated $\delta m^2$ and $\sin^2\theta_{12}$ with fracti

Mohamed El Jbari, Fernando D. A. García, Hugerles S. Silva, Felipe A. P. de Figueiredo

Thermal noise communication (TNC) enables ultra-low-power wireless links for Internet of Things (IoT) devices by modulating the variance of thermal noise, rather than using active carriers. Existing analyses often rely on Gaussian approximations and overlook fading effects, which limits their accuracy. This paper presents an accurate analytical framework for

Tianchen Zhao

Let $X$ be a K3 or Enriques surface with good reduction. Let $G$ be a finite group acting (not necessarily linearly) on $X$. We give a criterion for this group action to extend to a smooth model of $X$ in terms of the action of $G$ on the second $\ell$-adic cohomology groups. In particular, we generalize the result on the extendability of Galois actions on K

Jingke Zhang, Jingyi Yin, U-Wai Lok, Lijie Huang

Three-dimensional ultrasound localization microscopy (ULM) enables comprehensive visualization of the vasculature, thereby improving diagnostic reliability. Nevertheless, its clinical translation remains challenging, as the exponential growth in voxel count for full 3D reconstruction imposes heavy computational demands and extensive post-processing time. In

Filippo de Feo, Fausto Gozzi, Andrzej Święch, Lukas Wessels

Optimal control of interacting particles governed by stochastic evolution equations in Hilbert spaces is an open area of research. Such systems naturally arise in formulations where each particle is modeled by stochastic partial differential equations, path-dependent stochastic differential equations (such as stochastic delay differential equations or stocha

R. Alonso, B. Lods, I. Tristani

We obtain the first rigorous derivation of an incompressible Navier-Stokes-Fourier system with self-consistent and time-dependent forcing terms from the inelastic hard-spheres Boltzmann equation associated to the relevant case of viscoelastic granular gases. The model's inelasticity is measured by the so-called restitution coefficient which, for viscoelastic

Robin Simmons, Maria Papageorgiou, Marios Christodoulou, Časlav Brukner

Quantum operations that are perfectly admissible in non-relativistic quantum theory can enable signalling between spacelike separated regions when naively imported into quantum field theory (QFT). Prominent examples of such "impossible measurements", in the sense of Sorkin, include certain unitary kicks and projective measurements. It is generally accepted t

Gernot Akemann, Yan V. Fyodorov, Dmitry V. Savin

Non-Hermitian random matrices with statistical spectral characteristics beyond the standard Ginibre ensembles have recently emerged in the description of dissipative quantum many-body systems as well as in non-ergodic wave transport in complex media. We investigate the class AI$^\dag$ of complex symmetric random matrices, for which available analytic results

Pablo Rocha

Let $0 \leq \alpha < n$, $N \in \mathbb{N}$, and let $X$ and $Y$ be ball quasi-Banach function spaces on $\mathbb{R}^n$. We consider operators $T_{\alpha}$ defined by convolution with kernels of type $(\alpha, N)$. Assuming that the powered Hardy-Littlewood maximal operator satisfies some Fefferman-Stein vector-valued maximal inequality on $X$ and is bounded

Michael Ruderman

Practical design and tuning of feedback controllers has often to get by without a model of the dynamic process at hand. Only some general assumptions about the system dynamics, in this work type-one stable, can be available for engineers, for instance in motion control applications and many others. This paper proposes a practical and simple in realization pr

Sudipta Chakraborty, Kamal Majee, Achintya Kumar Dutta

We present an efficient relativistic implementation of algebraic diagrammatic construction (ADC) theory up to third order for the treatment of electronic ionization potentials (IP), electron affinities (EA), and excitation energies (EE) in heavy-element systems using an exact two-component atomic mean-field (X2CAMF) Hamiltonian. The approach combines Cholesk

Nikolai A. Krylov

The Riordan group ${\cal R}$ over the field ${\mathbb F}_2$ is a split extension of the Appell subgroup by the Nottingham group ${\cal N}({\mathbb F}_2)$. Using the lower central series of the Nottingham group obtained by C. Leedham-Green and S. McKay, the lower central series of ${\cal R}({\mathbb F}_2)$ is calculated. Considering the Riordan group over an

Daniel R. Jiang, Jalaj Bhandari, Yukai Yang, Rémi Munos

Optimizing large language models (LLMs) for multi-turn conversational outcomes remains a significant challenge, especially in goal-oriented settings like AI marketing or sales agents who facilitate transactions via messaging platforms. The difficulty stems from sparse, long-horizon rewards and the discrepancy between response-level planning and token-level g

Vijay V. Vazirani

This paper unifies two foundational constructs from economics and algorithmic game theory, the Arctic Auction and the linear Fisher market, to address the efficient allocation of differentiated goods in complex markets. Our main contributions are showing that an equilibrium for the Arctic Auction is captured by a Rational Convex Program, and deriving the fir

Peter S. Hovmand, Kari O'Donnell, Callie Ogland-Hand, Brian Biroscak

AI/ML models have rapidly gained prominence as innovations for solving previously unsolved problems and their unintended consequences from amplifying human biases. Advocates for responsible AI/ML have sought ways to draw on the richer causal models of system dynamics to better inform the development of responsible AI/ML. However, a major barrier to advancing

Anantha Padmanaban Krishna Kumar

Deeper Vision Transformers often perform worse than shallower ones, which challenges common scaling assumptions. Through a systematic empirical analysis of ViT-S, ViT-B, and ViT-L on ImageNet, we identify a consistent three-phase Cliff-Plateau-Climb pattern that governs how representations evolve with depth. We observe that better performance is associated w

Ernest Ma, Partha Kumar Paul, Narendra Sahu

In the canonical type-I seesaw mechanism for neutrino masses, a residual symmetry known as lepton parity: $(-1)^L$, remains preserved. Introducing a Majorana fermion $S$ with even lepton parity renders it naturally stable, making it a viable dark matter (DM) candidate. The addition of a lepton parity odd singlet scalar $\sigma$ allows for the coupling $N S \

André de Laire, Olivier Goubet, María Eugenia Martínez, Claudio Muñoz

The Boussinesq $abcd$ system is a 4-parameter set of equations posed in $\mathbb R_t\times\mathbb R_x$, originally derived by Bona, Chen and Saut as first-order 2-wave approximations of the incompressible and irrotational, two-dimensional water wave equations in the shallow water wave regime, in the spirit of the original Boussinesq derivation. Among the var

Shuai Bai, Yuxuan Cai, Ruizhe Chen, Keqin Chen

We introduce Qwen3-VL, the most capable vision-language model in the Qwen series to date, achieving superior performance across a broad range of multimodal benchmarks. It natively supports interleaved contexts of up to 256K tokens, seamlessly integrating text, images, and video. The model family includes both dense (2B/4B/8B/32B) and mixture-of-experts (30B-

Anna Marklová, Ondřej Vinš, Martina Vokáčová, Jiří Milička

Large language models are increasingly capable of producing creative texts, yet most studies on AI-generated poetry focus on English -- a language that dominates training data. In this paper, we examine the perception of AI- and human-written Czech poetry. We ask if Czech native speakers are able to identify it and how they aesthetically judge it. Participan

Andrey Kupavskii, Georgy Sokolov

Given integers $n\ge s\ge 2$, let $e(n,s)$ stand for the maximum size of a family of subsets of an $n$-element set that contains no $s$ pairwise disjoint members. The study of this quantity goes back to the 1960s, when Kleitman determined $e(sm-1,s)$ and $e(sm,s)$ for all integer $m,s\ge 1$. The question of determining $e(n,s)$ is closely connected to its un

Yjan A. Gordon, Peter S. Ferguson, Eric J. Hooper, Michael N. Martinez

We use data from the first two epochs of the Very Large Array Sky Survey (VLASS) and the IceCube Neutrino Observatory to search for evidence of a correlation between radio variability and the detection of astrophysical neutrinos. We find an excess number of associations between flaring radio sources and neutrinos that were detected between the first and seco

Mathew Vanherreweghe, Michael H. Freedman, Keith M. Adams

Recent work by Freedman and Mulligan demonstrated that shallow multilayer perceptrons spontaneously develop Kolmogorov-Arnold geometric (KAG) structure during training on synthetic three-dimensional tasks. However, it remained unclear whether this phenomenon persists in realistic high-dimensional settings and what spatial properties this geometry exhibits. W

Hichem Sahbi

Despite the notable success of graph convolutional networks (GCNs) in skeleton-based action recognition, their performance often depends on large volumes of labeled data, which are frequently scarce in practical settings. To address this limitation, we propose a novel label-efficient GCN model. Our work makes two primary contributions. First, we develop a no

N. Schneider, S. Dannhauer, E. Keilmann, S. Kabanovic

We investigated an isolated, globule-shaped object (0.37x0.11 pc), located near the centre of the Cygnus OB2 cluster and named proplyd #7 in optical observations. The source can be a massive star (with or without disc) with a HII region or a G-type T Tauri star with a photo-evaporating disc, embedded in a molecular envelope. We obtained a map of the OI line

Kay Liu, Yuwei Han, Haoyan Xu, Henry Peng Zou

Large Language Models (LLMs) have recently revolutionized machine learning on text-attributed graphs, but the application of LLMs to graph outlier detection, particularly in the context of fake news detection, remains significantly underexplored. One of the key challenges is the scarcity of large-scale, realistic, and well-annotated datasets that can serve a

Joseph Abdou

This research aims at providing a mathematical model of the organization of the polity and its transformation. For that purpose we construct two categories named respectively Political Configuration and Political Foundation. Our construction depends on a couple of variables called the foundational pair. One variable, called the Base, consists of a finite num

Hans Gundlach, Alex Fogelson, Jayson Lynch, Ana Trisovic

Algorithms have been estimated to increase AI training FLOP efficiency by a factor of 22,000 between 2012 and 2023 [Ho et al., 2024]. Running small-scale ablation experiments on key innovations from this time period, we are able to account for less than 10x of these gains. Surveying the broader literature, we estimate that additional innovations not included

Linan Chen, Florence Clerc, Prakash Panangaden

Bisimulation is a concept that captures behavioural equivalence of states in a variety of types of transition systems. It has been widely studied in discrete-time settings where a key notion is the bisimulation metric which quantifies "how similar two states are". In [ 11], we generalized the concept of bisimulation metric in order to metrize the behaviour o

Jina Yang, Ky Quan Tran

This paper investigates the mean-square exponential stability of neutral stochastic differential delay equations (NSDDEs) with Markovian switching. The analysis addresses the complexities arising from the interaction between the neutral term, time-varying delays, and structural changes governed by a continuous-time Markov chain. We establish novel and practi

Lorenzo Nespoli, Vasco Medici

As the cost of batteries lowers, sizing and control methods that are both fast and can achieve their promised performances when deployed are becoming more important. In this paper, we show how stochastically tuned rule based controllers (RBCs) can be effectively used to achieve both these goals, providing more realistic estimates in terms of achievable level

Th. A. Rijken, J. W. Wagenaar

This paper presents the quantization of massive and massless spin-2 particles, using the auxiliary field method. The issue, the so-called vDVZ-discontinuity, whether the perihelion precessions for a massive graviton are in agreement with the data, is studied in the context of this spin-2 theory in tree-approximation. In the context of this setting, it is fou

Katherine Panebianco, Minghao Yue, Anna-Christina Eilers, Xiaohui Fan

We present JWST/NIRCam F070W and F480M imaging for a quasar at $z = 5.07$, J0025-0145, which is magnified by a foreground lensing galaxy. Existing Hubble Space Telescope (HST) imaging does not have sufficient spatial resolution to determine whether the background quasar is multiply imaged. Exploiting the sharp PSF of the F070W band, we confirm that the backg

Ethan Hsu, Harry Chen, Chudi Zhong, Lesia Semenova

Real-world machine learning (ML) pipelines rarely produce a single model; instead, they produce a Rashomon set of many near-optimal ones. We show that this multiplicity reshapes key aspects of trustworthiness. At the individual-model level, sparse interpretable models tend to preserve privacy but are fragile to adversarial attacks. In contrast, the diversity

Paul Boisseau

We construct a regularization of the Rankin-Selberg period on general linear groups for non-tempered automorphic representations using residues of Zeta integrals. We prove that it satisfies the global non-tempered Gan-Gross-Prasad conjecture and its Ichino-Ikeda refinement. We also build a local version of our regularization and show that it defines a non-ze

Umberto Pappalettera, Francesco Triggiano

The purpose of this work is twofold. First, we construct probabilistically strong solutions to the three-dimensional Euler equations perturbed by additive noise that are $\mathbb{P}$-almost surely continuous in time, H\"older in space, and satisfy the local energy inequality up to an arbitrarily large stopping time. Second, we prove several non-unique ergodi

Henrique L. Senger, Gustavo P. Gonçalves, Bruno S. Chang, Hyeon Seok Rou

The signal-to-interference ratio (SIR) of the Affine Filter Bank Modulation (AFBM) waveform is analyzed under minimum mean square error (MMSE) equalization in two domains; namely, the affine domain and the filtered time-domain (TD). Due to the incorporation of the discrete affine Fourier transform (DAFT) and despreading/mapping, an interesting and counter-in

Ahmad Shamail, Claire D. McWhite

We present an automated approach for identifying and annotating motifs and domains in protein sequences, using pretrained Protein Language Models (PLMs) and Concept Activation Vectors (CAVs), adapted from interpretability research in computer vision. We treat motifs as conceptual entities and represent them through learned CAVs in PLM embedding space by trai

Dongyang Fan, Diba Hashemi, Sai Praneeth Karimireddy, Martin Jaggi

Incorporating metadata in Large Language Models (LLMs) pretraining has recently emerged as a promising approach to accelerate training. However prior work highlighted only one useful signal-URLs, leaving open the question of whether other forms of metadata could yield greater benefits. In this study, we investigate a wider range of metadata types and find ot

Shahir Abdullah, Syed Rohit Zaman

Modern cloud databases present scaling as a binary decision: scale-out by adding nodes or scale-up by increasing per-node resources. This one-dimensional view is limiting because database performance, cost, and coordination overhead emerge from the joint interaction of horizontal elasticity and per-node CPU, memory, network bandwidth, and storage IOPS. As a

Eli A. Gendreau-Distler, Kate B. Bostow, Kishore C. Patra, Efrain Alvarado

We present new transit-timing measurements for the white dwarf-cold Jupiter system WD 1856+534, extending the baseline of observations from 311 epochs to 1498 epochs. The planet is unlikely to have survived the host star&#39;s red-giant phase at its present location and is likely too small for common envelope evolution to take place. As such, a plausible exp

Yixiu Zhao, Xiaozhi Wang, Zijun Yao, Lei Hou

Large language models (LLMs) exhibit remarkable capabilities across a wide range of tasks, yet their internal mechanisms remain largely opaque. In this paper, we introduce a simple, lightweight, and broadly applicable method with a focus on isolating neurons that encode specific skills. Building upon prior work that identified "skill neurons" via soft prompt

Priyanka Das, Tim Classen, Mathias Wien

Recent video codecs such as VVC and AV1 apply a Non-rectangular (NR) partitioning to combine prediction signals using a smooth blending around the boundary, followed by a rectangular transform on the whole block. The NR signal transformation is not yet supported. A transformation technique that applies the same partitioning to the 2D Discrete Cosine Transfor

Steph Foulds, Viv Kendon

Quantum walks, the quantum analogue of the classical random walk, have been shown to underpin quantum algorithms for fluid dynamics. We propose the quantum half-adder gate method for quantum walks as a good benchmark algorithm, specifically to compare native two-qubit gate and native multiqubit gate implementations. Neutral atom hardware is a promising choic

M. Naseer Subhani

Interactive segmentation models such as the Segment Anything Model (SAM) have demonstrated remarkable generalization on natural images, but they perform suboptimally on remote sensing imagery (RSI) due to severe domain shifts and the scarcity of dense annotations. To address this limitation, we propose a point-supervised, self-prompting framework that adapts

Chandrasekhar Gokavarapu

This paper develops the homological backbone of the theory of non-commutative $n$-ary $\Gamma$-semirings. Starting from an $n$-ary $\Gamma$-semiring $(T,+,\tilde{\mu})$ and its $\Gamma$-ideals, we work in the slot-sensitive categories of left, right, and bi-$\Gamma$-modules, and endow the bi-module category with a Quillen exact structure compatible with the

Alejandro Tabas, Heike Sönnichsen, Sandeep Kaur, Marco Meixner

How does the brain know what is out there and what is not? Living organisms cannot rely solely on sensory signals for perception because they are noisy and ambiguous. To transform sensory signals into stable percepts, the brain uses its prior knowledge or beliefs. Current theories describe perceptual beliefs as probability distributions over the features of

Marcos V. de S. Silva, T. M. Crispim, R. R. Landim, Gonzalo J. Olmo

In this work, we investigate relativistic tidal forces around a black hole sourced by a cloud of strings, described by the generalized Letelier-Alencar solution. We first review the original Letelier spacetime and its recent generalization, computing the Kretschmann scalar and showing that the generalized model exhibits a stronger curvature divergence at $r

Shah Zayed Riam, Zhenlin Pei, Kyle Mooney, Chenyun Pan

Memristor crossbar arrays have emerged as a key component for next-generation non-volatile memories, artificial neural networks, and analog in-memory computing (IMC) systems. By minimizing data transfer between the processor and memory, they offer substantial energy savings. However, a major design challenge in memristor crossbar arrays is the presence of sn

Chenxu Feng, Chenxu Hao

For a one-dimensional simple random walk, let $g_1(n)$ denote the number of sites visited exactly once at time $n$. Major (1988) proved that \begin{equation*} \limsup_{n\to\infty}\frac{g_1(n)}{\log^2 n}=C\qquad a.s. \end{equation*} where $C$ is a positive and finite constant. While this result settled the question of existence, the exact value of $C$ remaine

A. P. Meilakhs

We present a new way of deriving classical mechanics from quantum mechanics. A key feature of the method is its compatibility with the standard approach used to derive transition rates between quantum states due to interactions. We apply the developed method to derive the main formulas of physical kinetics. We observe that, through the Liouville equation, we

Baptiste Bermond, Lucila Peralta Gavensky, Anaïs Defossez, Nathan Goldman

We introduce a unified framework that relates orbital and heat magnetizations to experimentally accessible excitation spectra, through thermoelectric probes and generalized sum rules. By analyzing zero-temperature transport coefficients and applying Kramers-Kronig relations, we derive spectral representations of magnetization densities from thermoelectric co

Sylvain Massabeau, Amr Abdelsamie, Florian Godel, Filip Miljevic

Collective excitations such as magnons and polar phonons provide natural access to the terahertz (THz) regime, but efficient generation and tunability remain elusive. Multiferroic BiFeO3 combines both orders at room temperature, offering a unique platform for narrowband THz emission. Here, we achieve efficient sub-bandgap optical rectification of coupled pho

Mauro Bernardi, Antonio Canale, Marco Stefanucci

Model selection in penalized regression critically depends on an accurate assessment of model complexity, commonly quantified through the effective degrees of freedom. While the Lasso admits a simple and unbiased characterization, given by the size of the active set, this property does not extend to adaptive penalization methods, despite the widespread use o

Akash Vyas, Shreyas Kumar, Jayant Kumar Mohanta, Ravi Prakash

Designing optimal controllers for nonlinear dynamical systems often relies on reinforcement learning and adaptive dynamic programming (ADP) to approximate solutions of the Hamilton Jacobi Bellman (HJB) equation. However, these methods require iterative training and depend on an initially admissible policy. This work introduces a new analytical framework that

Aamiruddin Syed, Mohammed Ilyas Ahmad

Cloud security is an important concern. To identify and stop cyber threats, efficient data collection methods are necessary. This research presents an innovative method to cloud security by integrating numerous data sources and modalities with multi-modal deep learning autoencoders. The Multi-Modal Deep Learning Ensemble Architecture (MMDLEA), a unique appro

Haotian Xue, Qi Chen, Zhonghao Wang, Xun Huang

Video diffusion models achieve strong frame-level fidelity but still struggle with motion coherence, dynamics and realism, often producing jitter, ghosting, or implausible dynamics. A key limitation is that the standard denoising MSE objective provides no direct supervision on temporal consistency, allowing models to achieve low loss while still generating p

Charles Schepanowski, Charles Ling

Large language models (LLMs) achieve impressive results on many benchmarks, yet their capacity for planning and stateful reasoning remains unclear. We study these abilities directly, without code execution or other tools, using the 8-puzzle: a classic task that requires state tracking and goal-directed planning while allowing precise, step-by-step evaluation

Muhammad Siddique, Sohaib Zafar

Evolving smart grids require flexible and adaptive control methods. A harmonized hybrid cyber-physical framework, which considers both physical and cyber layers and ensures adaptability, is one of the critical challenges to enable sustainable and scalable smart grids. This paper proposes a three-layer (physical, cyber, control) architecture, with an energy m

O. G. Benvenuto, M. A. De Vito, M. Echeveste, M. L. Novarino

Spider systems are a class of close binaries in which a neutron star first accretes from a normal companion, and later ablates it in some cases. New observations have expanded this category, with the addition of a Huntsman group, tentatively linked to a short donor phase along the red bump in the secondary evolutionary track. We present explicit evolutionary

Soufiane El Amine El Alami, Abderazzak Mouiha, Abdelatif Hafid, Ahmed El Hilali Alaoui

This systematic review examines how machine learning (ML) and deep learning (DL) have transformed forecasting, decision-making, and financial modelling, promoting innovation and efficiency in financial systems. Following PRISMA 2020 guidelines, we analyze 22 peer-reviewed and open-access articles (2024 to 2026) indexed in Scopus, applying ML and DL models ac

Mario Castro

Mechanistic models are essential tools across ecology, epidemiology, and the life sciences, but parameter inference remains challenging when likelihood functions are intractable. Approximate Bayesian Computation with Sequential Monte Carlo (ABC-SMC) offers a powerful likelihood-free alternative that requires only the ability to simulate data from mechanistic

Markus Banagl, Eric Leichtnam, Paolo Piazza

Let G be a compact Lie group and let X be an oriented Witt G-pseudomanifold. Using intersection cohomology it is possible to define Sign(G,X) in R(G), the G-signature of X. Let g be an element in G. Assuming that the inclusion of the fixed point set associated to g is normally non-singular, we prove a formula for Sign(g,X), the G-signature of X computed at g

Paulo Cavalin, Cassia Sanctos, Marcelo Grave, Claudio Pinhanez

We introduce \textsc{CAT}, a framework designed to evaluate and visualize the \emph{interplay} of \emph{accuracy} and \emph{response consistency} of Large Language Models (LLMs) under controllable input variations, using multiple-choice (MC) benchmarks as a case study. Current evaluation practices primarily focus on model capabilities such as accuracy or ben

Francesco Sylos Labini, Tibor Antal

We quantify galaxy correlations using two distinct three-dimensional samples from the first data release of the Dark Energy Spectroscopic Instrument (DESI): the Bright Galaxy Sample (BGS) and the Luminous Red Galaxy Sample (LRGS). Specifically, we measure the conditional average density, defined as the average density of galaxies observed around a typical ga

Haohong Lin, Yunzhi Zhang, Wenhao Ding, Jiajun Wu

End-to-end (E2E) autonomous driving models have demonstrated strong performance in open-loop evaluations but often suffer from cascading errors and poor generalization in closed-loop settings. To address this gap, we propose Model-based Policy Adaptation (MPA), a general framework that enhances the robustness and safety of pretrained E2E driving agents durin

Dengjun Guo, Xiaoyutao Luo

We give an elementary proof of long time inviscid damping for Sobolev perturbations near the Couette flow $(y,0)$ for the 2D Euler equations on $\mathbb{T} \times \mathbb{R}$. For any $s>1$ and any initial vorticity perturbation of size $O(\epsilon)$ in $H^s$, we obtain velocity damping estimates up to a time scale $ t = O(\epsilon^{-\delta_s} )$, where $\de

Edoardo Legnaro, Sabrina Guastavino, Francesco Marchetti

In this paper, we introduce a threshold-based framework for multiclass classification that generalizes the standard argmax rule. This is done by replacing the probabilistic interpretation of softmax outputs with a geometric one on the multidimensional simplex, where the classification depends on a multidimensional threshold. This change of perspective enable

Joy Naoum, Revana Salama, Ali Hamdi

Oral cancer is frequently diagnosed at later stages due to its similarity to other lesions. Existing research on computer aided diagnosis has made progress using deep learning; however, most approaches remain limited by small, imbalanced datasets and a dependence on single-modality features, which restricts model generalization in real-world clinical setting

Alex Ning, Yen-Ling Kuo, Gabe Gomes

Latent reasoning represents a new development in Transformer language models that has shown potential in compressing reasoning lengths compared to chain-of-thought reasoning. By directly passing the information-rich previous final latent state into the next sequence, latent reasoning removes the restriction to human language tokens as the medium for reasonin

Benoît Giniès, Xiaoyu Bie, Olivier Fercoq, Gaël Richard

Bandwidth extension, the task of reconstructing the high-frequency components of an audio signal from its low-pass counterpart, is a long-standing problem in audio processing. While traditional approaches have evolved alongside the broader trends in signal processing, recent advances in neural architectures have significantly improved performance across a wi

Karl Svozil

We propose a highly speculative phenomenological framework in which nuclear detonations and high-energy collisions serve as probes for hidden sectors with effective superluminal propagation. Motivated by analogies between acoustic and electromagnetic phenomena, we stratify the physical description into three layers: a fundamental ``substrate'' layer, hidden-

Teng Hu, Zhentao Yu, Guozhen Zhang, Zihan Su

The synthesis of synchronized audio-visual content is a key challenge in generative AI, with open-source models facing challenges in robust audio-video alignment. Our analysis reveals that this issue is rooted in three fundamental challenges of the joint diffusion process: (1) Correspondence Drift, where concurrently evolving noisy latents impede stable lear

Ulrich Doraszelski, Lixiong Li

We develop a generalized control function approach to production function estimation. Our approach accommodates settings in which productivity evolves jointly with other unobservable factors such as latent demand shocks and the invertibility assumption underpinning the traditional proxy variable approach fails. We provide conditions under which the output el

Chou Mo, Yehyun Suh, J. Ryan Martin, Daniel Moyer

Automated landmark detection offers an efficient approach for medical professionals to understand patient anatomic structure and positioning using intra-operative imaging. While current detection methods for pelvic fluoroscopy demonstrate promising accuracy, most assume a fixed Antero-Posterior view of the pelvis. However, orientation often deviates from thi

Xiang Gu, Liming Lu, Xu Zheng, Anan Du

Adversarial attacks pose a significant threat to learning-based 3D point cloud models, critically undermining their reliability in security-sensitive applications. Existing defense methods often suffer from (1) high computational overhead and (2) poor generalization ability across diverse attack types. To bridge these gaps, we propose a novel yet efficient t

Gabriela Conde-Saavedra, Odylio Denys Aguiar, Henrique P. de Oliveira, Maximiliano Ujevic

The detection of the gravitational waves produced after the coalescence of two neutron stars is greatly anticipated because it will be able to provide information about matter in extreme conditions, especially if the remnant turns out to go through a hypermassive or a supermassive neutron star state before collapsing into a black hole. Next-generation gravit

Liming Yang, Junyu Luo, Xuanzhe Liu, Yiling Lou

Large language model (LLM)-based multi-agent systems have emerged as a powerful paradigm for enabling autonomous agents to solve complex tasks. As these systems scale in complexity, cost becomes an important consideration for practical deployment. However, existing work rarely addresses how to structure multi-agent systems under explicit budget constraints.

Freddie Illingworth, Arjun Ranganathan, Leo Versteegen, Ella Williams

Reiher, R\"odl, Sales, and Schacht initiated the study of relative Tur\'an densities of ordered graphs and showed that it is more subtle and interesting than the unordered case. For an ordered graph $F$, its relative Tur\'an density, $\rho_{<}(F)$, is the greatest $\alpha$ such that every ordered graph $G$ has an $F$-free subgraph with at least $\alpha e(G)$

Rémi Marsal, Stéphanie Chaillat

The boundary element method (BEM) provides an efficient numerical framework for solving multiple scattering problems in unbounded homogeneous domains, since it reduces the discretization to the domain boundaries, thereby condensing the computational complexity. The procedure first consists in determining the solution trace on the boundaries of the domain by

Maria Perez-Ortiz

In an era marked by rapid technological advancements and complex global challenges, responsible foresight has emerged as an essential framework for policymakers aiming to navigate future uncertainties and shape the future. Responsible foresight entails the ethical anticipation of emerging opportunities and risks, with a focus on fostering proactive, sustaina

Alex Diep

When language models are assigned professional personas, maintaining the persona can conflict with disclosing their AI nature. This behavior matters in deployments where AI identity disclosure is a requirement: a model that describes nonexistent medical training or board certification presents professional experience it does not possess. We use AI identity d

Minjoon Choi

Large Language Models (LLMs) often exhibit inconsistent behavior when answering paraphrased questions, suggesting a reliance on surface-level patterns rather than true semantic understanding. To address this limitation, we introduce RoParQ, a benchmark specifically constructed to evaluate cross-paraphrase consistency in closed-book multiple-choice QA. This b

Ali Amirahmadi, Gökçe Geylan, Leonardo De Maria, Farzaneh Etminani

Cyclic peptides are promising modalities for targeting intracellular sites; however, cell-membrane permeability remains a key bottleneck, exacerbated by limited public data and the need for well-calibrated uncertainty. Instead of relying on data-eager complex deep learning architecture, we propose a monomer-aware decoupled global alignment kernel (MD-GAK), w

Kang Du, Xue Liao, Junpeng Xia, Chaozheng Guo

Illumination inconsistency is a fundamental challenge in multi-view 3D reconstruction. Variations in sunlight direction, cloud cover, and shadows break the constant-lighting assumption underlying both classical multi-view stereo (MVS) and structure from motion (SfM) pipelines and recent neural rendering methods, leading to geometry drift, color inconsistency

Adrian Nachman, Peter Perry, Daniel Tataru

The modified Novikov-Veselov system (mNV) is a cubic third order dispersive evolution in two space dimensions. It is also completely integrable, belonging to the same hierarchy as the defocusing Davey-Stewartson II (DS II) system. The mNV system is $L^2$ critical. Some time ago, Schottdorf proved that for small $L^2$ initial data, the mNV equation is globall

Sam Power, Giorgos Vasdekis

Markov Chain Monte Carlo (MCMC) is a flexible approach to approximate sampling from intractable probability distributions, with a rich theoretical foundation and comprising a wealth of exemplar algorithms. While the qualitative correctness of MCMC algorithms is often easy to ensure, their practical efficiency is contingent on the `target' distribution being

Sang Kyu Lee, Tongwu Zhang, Hyokyoung G. Hong, Haolei Weng

Quantifying how genomic features influence different parts of an outcome distribution requires statistical tools that go beyond mean regression, especially in ultrahigh-dimensional settings. Motivated by the study of LINE-1 activity in cancer, we propose StaRQR-K, a stabilized regional quantile regression framework with model-X knockoffs for false discovery

Wei-Chen Chang, Lu Dai, Ting Xu

This study proposes a risk prediction method based on a Multi-Scale Temporal Alignment Network (MSTAN) to address the challenges of temporal irregularity, sampling interval differences, and multi-scale dynamic dependencies in Electronic Health Records (EHR). The method focuses on temporal feature modeling by introducing a learnable temporal alignment mechani

Jack Geary, Boyan Gao, Henry Gouk

We consider the problem of strategic classification, where the act of deploying a classifier leads to strategic behaviour that induces a distribution shift on subsequent observations. Current approaches to learning classifiers in strategic settings are focused primarily on the linear setting, but in many cases non-linear classifiers are more suitable. A cent

A. R. Balasubramanian, Matthew Hague, Rupak Majumdar, Ramanathan S. Thinniyam

Counters that hold natural numbers are ubiquitous in modeling and verifying software systems; for example, they model dynamic creation and use of resources in concurrent programs. Unfortunately, such discrete counters often lead to extremely high complexity. Continuous counters are an efficient over-approximation of discrete counters. They are obtained by re

Lev Astrakhantsev, Anastasia A. Golubtsova, Mikhail A. Podoinitsyn

In this paper we construct solutions with AdS/dS asymptotics for $D=3$ truncated gauged supergravity with a periodic scalar potential. In a holographic perspective, assuming Dirichlet boundary conditions, the solutions can be interpreted as deformations of 2d dual CFTs triggered by non-zero vacuum expectation values of irrelevant operators. In addition to th

Hui Zhou, Siyuan Huang, Minxing Li, Hao Zhang

Vision Language Action models have significantly advanced general purpose robotic manipulation by harnessing large scale pretrained vision and language representations. Among existing approaches, a majority of current VLA systems employ parallel two finger grippers as their default end effectors. However, such grippers face inherent limitations in handling c

Michele Bonollo, Martino Grasselli, Gianmarco Mori, Havva Nilsu Oz

Despite decades of research in risk management, most of the literature has focused on scalar risk measures (like e.g. Value-at-Risk and Expected Shortfall). While such scalar measures provide compact and tractable summaries, they provide a poor informative value as they miss the intrinsic multivariate nature of risk.To contribute to a paradigmatic enhancemen