March 2026 arXiv papers — page 98

Md Mahfuzur Rahman, Jareen Shuva, Nishith Tripathi, Jeffrey H. Reed

We propose a machine learning (ML)-based framework for downlink performance prediction in 5G networks using real-time measurements from commercial off-the-shelf (COTS) user equipment (UE). Our experimental platform integrates the srsRAN 5G New Radio (NR) stack deployed on a Dell desktop serving as the 5G next generation nodeB (gNB), operating at 3.4 GHz. Two

Nabiha Parvez, Tanvin Sarkar Pallab, Mia Mohammad Imran, Tarannum Shaila Zaman

Debugging consumes a substantial portion of the software development lifecycle, yet the effectiveness of Large Language Models(LLMs) in this task is not well understood. Competitive programming offers a rich benchmark for such evaluation, given its diverse problem domains and strict efficiency requirements. We present an empirical study of LLM-based debuggin

ALICE Collaboration

The first measurement of the elliptic flow coefficient of (anti)${}^3_{\Lambda}$H and the study of the $v_2$ of $^3\overline{\mathrm{He}}$ measured in Pb-Pb collisions at $\sqrt{s_{\rm NN}} = 5.36$ TeV with the ALICE detector are presented. Based on the large data sample of approximately five billion events collected in 2023 during the LHC Run 3 data taking,

Xueqiao Peng, Andrew Perrault

Non-pharmaceutical interventions (NPIs), such as diagnostic testing and quarantine, are crucial for controlling infectious disease outbreaks but are often constrained by limited resources, particularly in early outbreak stages. In real-world public health settings, resources must be allocated across multiple outbreak clusters that emerge asynchronously, vary

Giuseppe C. Calafiore

Scenario optimization and conformal prediction share a common goal, that is, turning finite samples into safety margins. Yet, different terminology often obscures the connection between their respective guarantees. This paper revisits that connection directly from a systems-and-control viewpoint. Building on the recent conformal/scenario bridge of \citet{OSu

Alyssa Taylor-LaPole, Uzochi Gideon, Beatrice Riviere, Duygu Vargun

A finite element solution coupled with an interior penalty discontinuous Galerkin solution are defined for the approximation of the coupled 3D-1D solute transport problem. Under sufficient regularity for the weak solutions, optimal error bounds are derived for the 3D concentration and 1D concentration, that are optimal with respect to the time step size and

Sujan Subedi, Wuzhang Fang, Fan Fei, Zixin Zhai

Nonlinear phononics provides a powerful ultrafast route to control lattice excitations, enabling access to hidden quantum orders, phononic computing, and quantum transduction. However, dynamic control of anharmonic phonon interactions remains limited, as these interactions are typically fixed by the equilibrium crystal lattice and lack external tunability. E

Omid Amini, Shu Kawaguchi

Given a divisor on a tropical curve, we associate to each point of the curve a Weierstrass gap sequence. We investigate structural properties of these gap sequences and explore their relationship with the Weierstrass gap sequences of line bundles on algebraic curves via the tropicalization process.

Lech Drewnowski, Alexandre Reggiolli Teixeira

It is shown that for any finite positive measure $\mu$ defined on a measure space $(S, \Sigma)$, and any Banach or Fr\'echet space $Z$, the control measure Theorem of Talagrand (T) is true for the case when the (stochastic) vector measure $\boldsymbol{m}:\mathcal{E} \to L_0(\mu,Z)$, defined on another measurable space $(E, \mathcal{E})$, takes values in $L_{

Nathan Reading, Salvatore Stella

We give an account of mutation of theta functions in cluster scattering diagrams, starting with a notion of mutation that is related to, but different from, the notion of mutation defined by Gross, Hacking, Keel, and Kontsevich. This different approach to mutation leads to several applications. Three of the applications simplify the process of computing stru

Laura Padilla-Angulo, Diego Jorrat, José-Ignacio Antón, Javier Sierra

This paper evaluates the effect of a short, interactive popularisation talk on upper-secondary students' interest in Economics. This contrasts with previous research, which has primarily examined impersonal interventions to boost interest in Economics. The intervention presents Economics as an empirical social science engaged with real-world social problems.

Govinda Kolli, Adinath Madhavrao Dukre, Behzad Bozorgtabar, Dwarikanath Mahapatra

Medical Vision-Language Models (VLMs) often hallucinate by generating responses based on language priors rather than visual evidence, posing risks in clinical applications. We propose Visual Grounding Score Guided Decoding (VGS-Decoding), a training-free method to mitigate hallucinations during inference. Our key insight is that hallucinated tokens maintain

J. A. Aguilar-Saavedra, J. A. Casas, J. M. Moreno

For decades, it has been known that local hidden variable theories cannot be disproved by collider experiments involving decaying particles. However, if these theories satisfy a small set of mild assumptions, they become testable. In particular, they can be disproved using Bell-like inequalities for $\mu^+ \mu^-$ and $\tau^+ \tau^-$ pairs.

ALICE Collaboration

The first measurements of the average transverse-momentum fraction ($\langle z \rangle$) as a function of transverse momentum ($p_{\rm T}$) for strange baryons ($\Lambda$ and $\bar{\Lambda}$) and strange mesons ($K_{\rm S}^0$), produced in mini-jets defined through angular correlations in pp collisions at $\sqrt{s} = 13$ TeV, are reported by the ALICE Collab

Atharva Rege, Adinath Madhavrao Dukre, Numan Balci, Dwarikanath Mahapatra

Contrast-enhanced magnetic resonance imaging (CE-MRI) plays a crucial role in brain tumor assessment; however, its acquisition requires gadolinium-based contrast agents (GBCAs), which increase costs and raise safety concerns. Consequently, synthesizing CE-MRI from non-contrast MRI (NC-MRI) has emerged as a promising alternative. Early Generative Adversarial

Zhenyu Wu, Sadegh Khochfar, Muhammad A. Latif, Ben Morton

We present a model for gas heating, driven by dynamical friction from orbiting subhalos within dark matter halos. Using data from the TNG50 simulation, we derive the subhalo mass function and calculate the dynamical friction heating rate for a wide range of halo masses and redshifts from $z = 15$ to 0. Our results show that, by converting gravitational poten

Ziyong Ma, Uksang Yoo, Jonathan Francis, Weiming Zhi

While soft robot manipulators offer compelling advantages over rigid counterparts, including inherent compliance, safe human-robot interaction, and the ability to conform to complex geometries, accurate forward modeling from low-dimensional actuation commands remains an open challenge due to nonlinear material phenomena such as hysteresis and manufacturing v

Faisal Karimi, Gérard M. T. Watts

The symplectic fermion is a much-studied non-unitary conformal field theory with $c=-2$ and is known to contain an infinite family of mutually commuting conserved charges. We derive expressions for the conserved charges on the cylinder and use these to construct Generalised Gibbs Ensembles (GGEs) in the particular case of the ${W}(1,2)$ triplet model. We der

Christian Kuehn, Fergal Murphy

We study adaptive network models in which coupling weights evolve on a fast time scale relative to the phase dynamics of the nodes. Using Geometric Singular Perturbation Theory (GSPT), we prove that, although the microscopic system is strictly pairwise, the effective slow dynamics on the invariant slow manifold can exhibit genuinely higher-order structure. M

Shang-Qiang Ning, Hiromi Ebisu, Ho Tat Lam

Modulated symmetries are internal symmetries that act in a spatially non-uniform manner. Consequently, when a modulated symmetry $G_{\text{int}}$ is combined with a spatial symmetry $G_{\text{sp}}$, the total symmetry group takes the form of a semidirect product $G=G_{\text{int}}\rtimes G_{\text{sp}}$. Using matrix product states, we classify topological pha

Harjot Singh Ranse

This study quantifies survivorship bias in India's NIFTY Smallcap 250 index using a dataset of 1,437 stocks over nine years (2016-2025). By reconstructing historical index composition through market capitalization ranking and comparing equal-weight portfolios of current constituents versus all historical members, I show that survivor-only backtesting oversta

Ali Khalesi

We study a tractable baseline for average-preserving broadcast gossip in decentralized drone swarms under a quasi-static planar Poisson model and a matching-based abstraction. With slotted Aloha, Rayleigh fading, and threshold decoding, we derive: 1) a closed-form SIR success law; 2) a mean-square contraction bound that separates ideal mixing from wireless s

Xiao-Ming Zhao, Cui-Xian Guo, Xin-Ran Ma, Xiao-Ran Wang

Altermagnets (AMs) exhibit vanishing net magnetization but strong momentum-dependent spin splitting enforced by crystal symmetry. Here, we explore the non-Hermitian effects in dissipative two-dimensional AMs. We show that symmetry-compliant dissipation naturally induces an imaginary staggered exchange field, driving a NH topological phase transition absent i

Dounia Nanadoumgar-Lacroze, Niccolò Muttoni, Michele Maggiore, Michele Mancarella

We perform forecasts for the Hubble parameter H_0 and for the parameter Xi_0 that describes modified gravitational-wave propagation, using information from the binary neutron star (BNS) mass function, for Einstein Telescope (ET), taken either in the triangle or in the ``2L'' configuration, as well as for detector network made by ET together with a 40-km Cosm

Sanjay Amrutiya, Sandipan Das

Let $X$ be a Riemann surface equipped with an anti-holomorphic involution $\sigma_X$. We show that this induces a natural anti-holomorphic involution on the space of parabolic $\mathrm{SL}(r,\mathbb{C})$-opers. The fixed-point locus of this involution is defined as real slice. We further study the induced involutions on different descriptions of parabolic $\

Sarat Mudunuri, Jian Wan, Ally Qin, Srinivasan Manoharan

Large Language Models (LLMs) with tool-calling capabilities have demonstrated remarkable potential in executing complex tasks through external tool integration. The Model Context Protocol (MCP) has emerged as a standardized framework for connecting LLMs to diverse toolsets, with individual MCP servers potentially exposing dozens to hundreds of tools. However

Toan Tran, Olivera Kotevska, Li Xiong

Membership inference attacks (MIAs), which enable adversaries to determine whether specific data points were part of a model's training dataset, have emerged as an important framework to understand, assess, and quantify the potential information leakage associated with machine learning systems. Designing effective MIAs is a challenging task that usually requ

Laura Russo, Caleb Allen, Cameron S. Jorgensen, Lizabeth Quigley

Scientists have long been fascinated by magnetoreception, the innate capacity of many animals to sense and use the Earth's magnetic field for navigation. In eusocial insects like honey bees, magnetoreception has been linked to communication and foraging. However, little is known about magnetoreception's phylogenetic patterns and relationship to species trait

ALICE Collaboration

This paper presents the first measurements of $\Xi$ and $\Omega$ hyperon yields at the highest multiplicities reached in pp collisions at $\sqrt{s} = 13$ TeV. This measurement exploits the high-multiplicity pp collisions collected by ALICE with dedicated triggers. The selected collisions are characterised by about 30 charged particles per unit of rapidity, o

Mayra Amezcua, Leigh Norris, Tom Gilliss, Ryan Sitler

Spatiotemporally correlated errors are widespread in quantum devices and are particularly adversarial to error correcting schemes. To characterize these errors, we propose and validate a nonparametric quantum noise spectroscopy (QNS) protocol to estimate both spectra and static errors associated with spatiotemporally correlated dephasing noise and fluctuatin

Annika Tjabben, Lea Bergkemper, Carolin Conrad, Michael Gundall

In-body communication is an upcoming field with significant implications for medical diagnostics and therapeutic interventions. Microbubbles have gained attention due to their distinct physical properties, making them promising candidates to facilitate communication within the human body. This work explores the use of microbubbles as communication carriers,

Rohit Jena, Pratik Chaudhari, James C. Gee

FireANTs introduced a novel Eulerian descent method for plug-and-play behavior with arbitrary optimizers adapted for diffeomorphic image registration as a test-time optimization problem, with a GPU-accelerated implementation. FireANTs uses Adam as its default optimizer for fast and more robust optimization. However, Adam requires storing state variables (i.e

Zirui Ge, Pengxiang Ding, Baohua Yin, Qishen Wang

Video action models are an appealing foundation for Vision--Language--Action systems because they can learn visual dynamics from large-scale video data and transfer this knowledge to downstream robot control. Yet current diffusion-based video predictors are trained with likelihood-surrogate objectives, which encourage globally plausible predictions without e

ATLAS Collaboration

This letter reports on the first evidence of Higgs-boson production at high transverse momentum in the $b\bar{b}$ final state, reconstructed in a single large-radius jet. The results are based on proton proton collision data recorded by the ATLAS detector at the Large Hadron Collider at a centre-of-mass energies of 13 TeV and 13.6 TeV, corresponding to a tot

Fereshteh Yousefirizi, Sara Harsini, Mobin Mohebi, Ian Alberts

In patients with biochemical recurrence of prostate cancer and negative PSMA PET/CT, radiomics features extracted from recurrence-prone organs can predict clinical progression and progression-free survival. In a cohort of 132 patients, combining PET/CT radiomics with clinical variables significantly improved prognostic performance (C-index 0.74 vs. 0.65). Mo

M. Lopez-Corredoira, W. Wu, H. -F. Wang, F. Garzon

CONTEXT. One of the most difficult and unexplored regions of the Milky Way is the highly extincted in-plane central region within the Galactic coordinates $10^\circ \lesssim |\ell |\lesssim 30^\circ $, $|b|\lesssim 3^\circ $, where we have the long-bar and 3 kpc arm with intermediate-age stellar population, whose morphological properties are still unclear. A

Fabrizio Camerin, Susana Marin-Aguilar, Anna Stradner, Peter Schurtenberger

Electrostatic interactions fundamentally govern the structure, stability, and dynamics of charged (bio)matter, yet the impact of heterogeneous and anisotropic charge distributions on the behavior of protein solutions remains elusive. Here, we introduce a versatile multiscale framework that directly connects molecular-level electrostatics to collective proper

André Belotto da Silva, François Bernard, Edward Bierstone

We show that stack-theoretic resolution of singularities preserving normal crossings (partial desingularization) by weighted blowings-up, can be obtained in a simple direct way from a splitting theorem of the first and third authors, using the algorithm of Abramovich, Temkin and W{\l}odarczyk for resolution of singularities by weighted blowings-up.

Ufaq Khan, L. D. M. S. Sai Teja, Ayuba Shakiru, Mai A. Shaaban

Ultrasound images vary widely across scanners, operators, and anatomical targets, which often causes models trained in one setting to generalize poorly to new hospitals and clinical conditions. The Foundation Model Challenge for Ultrasound Image Analysis (FMC-UIA) reflects this difficulty by requiring a single model to handle multiple tasks, including segmen

David Klemmer, Moritz S. Fischer, Kimberly K. Boddy, Manoj Kaplinghat

Self-interacting dark matter (SIDM) models feature short-range interactions between dark matter (DM) particles that lead to larger diversity in the inner parts of galactic rotation curves and potentially unique gravitational lensing signatures. Satellite galaxies and dark subhalos provide a valuable testing ground for such models. We develop a simulation fra

Danilo Saccani, Haoming Shen, Luca Furieri, Giancarlo Ferrari-Trecate

We study a control architecture for nonlinear constrained systems that integrates a performance-boosting (PB) controller with a scheduled Predictive Safety Filter (PSF). The PSF acts as a pre-stabilizing base controller that enforces state and input constraints. The PB controller, parameterized as a causal operator, influences the PSF in two ways: it propose

Serhii Donetskyi, Aleksandr Shvets

We investigate the long-term dynamics of a five-dimensional nonlinear system describing the non-ideal excitation of a spherical pendulum coupled to a limited-power electric motor. By analyzing the phase trajectories y(t) = (y1, y2, y3, y4, y5), we prove several structural theorems regarding the system's limit sets. First, we show that the bilinear combinatio

Minyoung Kim

Current auto-regressive (AR) LLMs, diffusion-based text/image generative models, and recent flow matching (FM) algorithms are capable of generating premium quality text/image samples. However, the inference or sample generation in these models is often very time-consuming and computationally demanding, mainly due to large numbers of function evaluations corr

Hugo A. Camargo, Yichao Fu, Keun-Young Kim, Yeong Han Park

We propose and test logarithmic Krylov (logK) complexity, an operator growth measure akin to Krylov complexity defined through a replica approach, as a viable probe of early-time operator scrambling without false positives. In finite-dimensional quantum systems, such as the Lipkin--Meshkov--Glick (LMG) model and the mixed-field Ising model at the chaotic poi

Hao-Lin Li, Yu-Han Ni, Ming-Lei Xiao, Jiang-Hao Yu

We present a systematic classification of tree-level ultraviolet (UV) completions for dimension-nine SMEFT operators relevant to short-range neutrinoless double beta decay. Using the SMEFT J-basis framework, we categorize distinct UV completions, including both all-boson and boson-fermion-boson topologies. A primary objective is the identification of minimal

Charlie Cresswell-Hogg, Daniel F. Litim

We demonstrate the renormalisability of quantum field theories in four dimensions with elementary self-interacting Dirac fermions and to leading order in the limit of many fermion flavours $N_{\rm f}$. Starting from the underlying divergence structure and using Gross-Neveu-type interactions as a template, we explain why extended four-fermion theories includi

S. Novell-Masot, H. Gil-Marín, L. Verde, J. Aguilar

We derive cosmological parameter constraints from the Dark Energy Spectroscopic Instrument (DESI) Data Release 1 (DR1) galaxy clustering data, based on a joint full-shape analysis of the power spectrum multipoles and the bispectrum monopole using the ShapeFit framework. This is the follow-up of our previous work, in which we obtained for the first time const

Haolin Li, Álvaro Pastor-Gutiérrez, Shahram Vatani

The infrared structure of gauge theories with chiral fermions remains largely unexplored. In this work we investigate the Bars-Yankielowicz class using the functional renormalisation group, building on recent developments in gauge-fermion systems that provide clear criteria for confinement and dynamical symmetry breaking. We show that two distinct phases ari

Duarte Feiteira, José P. S. Lemos, Oleg B. Zaslavskii

We study a recursive Penrose process and the energy extraction for the decay of electrically charged particles in a Reissner-Nordstr\"om black hole spacetime with anti-de Sitter (AdS) asymptotics, incorporating the backreaction on the black hole's mass and charge. A recursive process requires that the decay products are confined in a finite region so that th

Minsung Kho, Kimyeong Lee, Norton Lee, Rak-Kyeong Seong

The interplay between toric Calabi-Yau 3-folds, dimer integrable systems, and 5-dimensional quantum field theories has proved fruitful. We extend this framework to generalized toric polygons (GTPs) and show that their integrable systems arise from refined birational transformations of known dimer integrable systems acting on the Casimirs and Hamiltonians as

Zhilin Guo, Boqiao Zhang, Hakan Aktas, Kyle Fogarty

The ability to render scenes at adjustable fidelity from a single model, known as level of detail (LoD), is crucial for practical deployment of 3D Gaussian Splatting (3DGS). Existing discrete LoD methods expose only a limited set of operating points, while concurrent continuous LoD approaches enable smoother scaling but often suffer noticeable quality degrad

Bryce Grant, Xijia Zhao, Peng Wang

Vision-Language-Action (VLA) models combine perception, language, and motor control in a single architecture, yet how they translate multimodal inputs into actions remains poorly understood. We apply activation injection, sparse autoencoders (SAEs), and linear probes to six models spanning 80M--7B parameters across 394,000+ rollout episodes on four benchmark

Yuqing Wang, Chuofan Ma, Zhijie Lin, Yao Teng

Visual generation with discrete tokens has gained significant attention as it enables a unified token prediction paradigm shared with language models, promising seamless multimodal architectures. However, current discrete generation methods remain limited to low-dimensional latent tokens (typically 8-32 dims), sacrificing the semantic richness essential for

Haitian Li, Haozhe Xie, Junxiang Xu, Beichen Wen

Reconstructing articulated 3D objects from a single image requires jointly inferring object geometry, part structure, and motion parameters from limited visual evidence. A key difficulty lies in the entanglement between motion cues and object structure, which makes direct articulation regression unstable. Existing methods address this challenge through multi

Furkan Akbaba, Danny Horta, Olcay Plevne

We present a comprehensive chemical and age orbital cartography of the Galactic high-$\alpha$ disc using subgiant stars with precise ages, element abundances, and full phase-space information from the \textsl{LAMOST--Gaia} data set. Specifically, we map how average [Fe/H], [$\alpha$/Fe], and age vary across present-day kinematic and orbital coordinates. We a

Xinyao Zhang, Wenkai Dong, Yuxin Song, Bo Fang

Current instruction-guided video editing models struggle to simultaneously balance precise semantic modifications with faithful motion preservation. While existing approaches rely on injecting explicit external priors (e.g., VLM features or structural conditions) to mitigate these issues, this reliance severely bottlenecks model robustness and generalization

Chenyang Gu, Mingyuan Zhang, Haozhe Xie, Zhongang Cai

Prior motion generation largely follows two paradigms: continuous diffusion models that excel at kinematic control, and discrete token-based generators that are effective for semantic conditioning. To combine their strengths, we propose a three-stage framework comprising condition feature extraction (Perception), discrete token generation (Planning), and dif

Yang Fu, Yike Zheng, Ziyun Dai, Henghui Ding

Video object removal aims to eliminate dynamic target objects and their visual effects, such as deformation, shadows, and reflections, while restoring seamless backgrounds. Recent diffusion-based video inpainting and object removal methods can remove the objects but often struggle to erase these effects and to synthesize coherent backgrounds. Beyond method l

Ziyin Zhang, Zihan Liao, Hang Yu, Peng Di

We present F2LLM-v2, a new family of general-purpose, multilingual embedding models in 8 distinct sizes ranging from 80M to 14B. Trained on a newly curated composite of 60 million publicly available high-quality data samples, F2LLM-v2 supports more than 200 languages, with a particular emphasis on previously underserved mid- and low-resource languages. By in

Carlos Esteves, Ameesh Makadia

Denoising diffusion models are widely used for high-quality image and video generation. Their performance depends on noise schedules, which define the distribution of noise levels applied during training and the sequence of noise levels traversed during sampling. Noise schedules are typically handcrafted and require manual tuning across different resolutions

Mingyang Liu, Yongshan Chen, Zhiyuan Fan, Gabriele Farina

Online learning in arbitrary, and possibly adversarial, environments has been extensively studied in sequential decision-making, and it is closely connected to equilibrium computation in game theory. Most existing online learning algorithms rely on \emph{numeric} utility feedback from the environment, which may be unavailable in human-in-the-loop application

Roman Kracht, Andrea Trombettoni, Ilaria Maccari, Nicolò Defenu

Motivated by the interplay between 2D and 3D scaling signatures observed in unconventional layered superconductors, we present a systematic Monte Carlo study of the three-dimensional classical XY model with anisotropic in-plane $J_\parallel$ and inter-plane $J_\perp$ couplings. Our study includes very small values of the system anisotropy $\Delta=J_\perp /J_

Zhuolin Yang, Zihan Liu, Yang Chen, Wenliang Dai

We introduce Nemotron-Cascade 2, an open 30B MoE model with 3B activated parameters that delivers best-in-class reasoning and strong agentic capabilities. Despite its compact size, its mathematical and coding reasoning performance approaches that of frontier open models. It is the second open-weight LLM, after DeepSeekV3.2-Speciale-671B-A37B, to achieve Gold

Dong Zhuo, Wenzhao Zheng, Sicheng Zuo, Siming Yan

With the growing adoption of vision-language-action models and world models in autonomous driving systems, scalable image tokenization becomes crucial as the interface for the visual modality. However, most existing tokenizers are designed for monocular and 2D scenes, leading to inefficiency and inter-view inconsistency when applied to high-resolution multi-

Chaoyang Wang, Yaobo Liang, Boci Peng, Fan Duan

Taming diffusion models for generative segmentation has attracted increasing attention. While existing approaches primarily focus on architectural tweaks or training heuristics, there remains a limited understanding of the intrinsic mismatch between continuous flow matching objectives and discrete perception tasks. In this work, we revisit diffusion segmenta

Keda Tao, Yuhua Zheng, Jia Xu, Wenjie Du

Recent advancements in omnimodal large language models (OmniLLMs) have significantly improved the comprehension of audio and video inputs. However, current evaluations primarily focus on short audio and video clips ranging from 10 seconds to 5 minutes, failing to reflect the demands of real-world applications, where videos typically run for tens of minutes.

Dimitri Kanevsky, Julian Salazar, Matt Harvey

Let $V$ be a smooth cubic surface over a $p$-adic field $k$ with good reduction. Swinnerton-Dyer (1981) proved that $R$-equivalence is trivial on $V(k)$ except perhaps if $V$ is one of three special types--those whose $R$-equivalence he could not bound by proving the universal (admissible) equivalence is trivial. We consider all surfaces $V$ currently known

Faicel Khennoufa, Khelil Abdellatif, Metin Ozturk, Halim Yanikomeroglu

Uncrewed aerial vehicles (UAVs) are expected to enhance connectivity, extend network coverage, and support advanced communication services in sixth-generation (6G) cellular networks, particularly in public and civil domains. Although multi-UAV systems enhance connectivity for IoT networks more than single-UAV systems, energy-efficient communication systems a

Kevin Baum, Johann Laux

As AI systems permeate high-stakes decision-making, the terminology of human involvement---Human-in-the-Loop (HITL), Human-on-the-Loop (HOTL), and Human Oversight---has become vexingly ambiguous. This complicates interdisciplinary collaboration between computer science, law, philosophy, psychology, and sociology and breeds regulatory uncertainty. We propose

Jeremy Schlitt

Let $Q$ be a set of primes with relative density $\delta$. We count integers in $[1,x]$ with prime factors all in $Q$ that also have a divisor in $(y,2y]$. We establish the order of magnitude for all $\delta \in (0,1]$. This generalizes the case $\delta = 1$ from the 2008 work of Ford. We also show that there is a phase transition at the critical point $\del

Robert Pickett, Jennifer Hill, Sarah Cowan

To estimate the causal effect of an intervention, researchers need to identify a control group that represents what might have happened to the treatment group in the absence of that intervention. This is challenging without a randomized experiment and further complicated when few units (possibly only one) are treated. Nevertheless, when data are available on

Paolo Braccia, N. L. Diaz, Martin Larocca, M. Cerezo

In this work, we characterize the $t$-th order commutants of fermionic Gaussian unitaries and of their particle-preserving subgroup acting on $n$ fermionic modes. These commutants govern Haar averages over the corresponding groups and therefore play a central role in fermionic randomized protocols, invariant theory, and resource quantification. Using Howe du

Shang-Jui Ray Kuo, Paola Cascante-Bonilla

Large vision--language models (VLMs) often use a frozen vision backbone, whose image features are mapped into a large language model through a lightweight connector. While transformer-based encoders are the standard visual backbone, we ask whether state space model (SSM) vision backbones can be a strong alternative. We systematically evaluate SSM vision back

Timothée Hoffreumon, Mischa P. Woods

Whether the complex numbers of standard quantum theory are experimentally indispensable has remained open for decades. Real quantum theory (RQT), obtained by replacing complex amplitudes with real ones while retaining the usual Kronecker-product composition rule, reproduces all single-party and bipartite Bell correlations of quantum theory (QT), but its lack

Susovan Giri, Arpana Saboo, Hari Sadhan Ghosh, Vipin

Interfacial hydrodynamic instabilities in multicomponent superfluids provide a versatile platform to explore nonequilibrium quantum dynamics beyond classical fluid analogues. We study dynamical interfacial instabilities in a quasi-two-dimensional three-component Bose-Einstein condensate confined in a harmonic trap, where rotation is applied selectively to th

Yue Gong, Hongyu Li, Shanyuan Liu, Bo Cheng

Diffusion models have become the dominant paradigm for image generation and editing, with latent diffusion models shifting denoising to a compact latent space for efficiency and scalability. Recent attempts to leverage pretrained visual representation models as tokenizer priors either align diffusion features to representation features or directly reuse repr

Tuong Le, Chayim Lowen

Hobby has recently shown that almost all finite hyperfields of even order fail to be the quotient of a field. Using a probabilistic argument, we extend this result to all orders: a finite hyperfield is almost always non-quotient. This confirms a conjecture of Baker--Jin. We show that in almost every finite hyperfield the sum of any four or more nonzero eleme

Julian Allagan, Mohamed Elbakary, Zohreh Safari, Weizheng Gao

Phishing detectors built on engineered website features attain near-perfect accuracy under i.i.d.\ evaluation, yet deployment security depends on robustness to post-deployment feature manipulation. We study this gap through a cost-aware evasion framework that models discrete, monotone feature edits under explicit attacker budgets. Three diagnostics are intro

Wan-Cyuan Fan, Jiayun Luo, Declan Kutscher, Leonid Sigal

Vision-Language Models (VLMs) have been shown to be blind, often underutilizing their visual inputs even on tasks that require visual reasoning. In this work, we demonstrate that VLMs are selectively blind. They modulate the amount of attention applied to visual inputs based on linguistic framing even when alternative framings demand identical visual reasoni

Rohan Siva, Kai Cheung, Lichi Li, Ganesh Sundaram

Modern machine learning systems rely on complex data engineering workflows to extract, transform, and load (ELT) data into production pipelines. However, constructing these pipelines remains time-consuming and requires substantial expertise in data infrastructure and orchestration frameworks. Recent advances in large language model (LLM) agents offer a poten

Soohyun Park

Using preservations of piecewise linear (PL) homeomorphism types under edge contractions (the link condition) as a topological proxy for flagness, we give a quantitative description of the effect flagness on on gamma positivity of simplicial spheres. In particular, we show that the link condition has a trivial effect on the $g$-vectors (and thus gamma vector

Yuhang Zheng, Songen Gu, Yupeng Zheng, Weize Li

Contact-rich manipulation tasks, such as wiping and assembly, require accurate perception of contact forces, friction changes, and state transitions that cannot be reliably inferred from vision alone. Despite growing interest in visuo-tactile manipulation, progress is constrained by two persistent limitations: existing datasets are small in scale and narrow

Paul Argyle, Djamil Lakhdar-Hamina, Sarah H. Miller, Victor Galitski

We introduce a measurement-induced quantum neural network (MINN), an adaptive monitored-circuit architecture in which mid-circuit measurement outcomes determine the entangling gates in subsequent layers. In contrast to standard monitored circuits where sites and gates are sampled randomly, the gates are parametrized and variational, producing correlated hist

Alexandre Bloch, Samuel N. Cohen, Terry Lyons, Joël Mouterde

The signature is a canonical representation of a multidimensional path over an interval. However, it treats all historical information uniformly, offering no intrinsic mechanism for contextualising the relevance of the past. To address this, we introduce the Exponentially Weighted Signature (EWS), generalising the Exponentially Fading Memory (EFM) signature

Ziyu Mu, Xiyu Shi, Safak Dogan

The increasing sophistication of cyber threats, especially zero-day attacks, poses a significant challenge to cybersecurity. Zero-day attacks exploit unknown vulnerabilities, making them difficult to detect and defend against. Existing approaches patch flaws and deploy an Intrusion Detection System (IDS). Using advanced Wasserstein GANs with Gradient Penalty

Antonio Masucci, Gioele Ferrante, Tiziano Ghisu, Andrea Giusti

Large Eddy Simulations with flamelet-based thermochemistry are used to investigate the behaviour of a premixed hydrogen-air flame stabilised by a bluff-body. Validation against experimental data is carried out first to demonstrate the model's ability to predict both velocity field and flame structure. The capability of the model in predicting differential di

Sérgio Alves, Carlos Duarte, Kyle Montague, Tiago Guerreiro

User interface personalization enhances digital efficiency, usability, and accessibility. However, in user-driven setups, limited support for identifying and evaluating worthwhile opportunities often leads to underuse. We explore a reflexive personalization approach where individuals engage with their digital interaction data to identify meaningful personali

Ke-Han Lu, Szu-Wei Fu, Chao-Han Huck Yang, Zhehuai Chen

Large language models (LLMs) have been widely used as knowledge backbones of Large Audio Language Models (LALMs), yet how much auditory knowledge they encode through text-only pre-training and how this affects downstream performance remains unclear. We study this gap by comparing different LLMs under two text-only and one audio-grounded setting: (1) direct p

Nicolas Avila, Luis Ferroni, Alejandro H. Morales

Motivated by the combinatorics of parking functions and their several generalizations, we study the Ehrhart theory of Pitman--Stanley polytopes. We prove a strong positivity phenomenon called \emph{magic positivity} for the Ehrhart polynomials of these polytopes, which in turn implies that their $h^*$-polynomials are real-rooted (and thus log-concave and uni

Yiren Lu, Xin Ye, Burhaneddin Yaman, Jingru Luo

Bird's-Eye-View (BEV) perception serves as a cornerstone for autonomous driving, offering a unified spatial representation that fuses surrounding-view images to enable reasoning for various downstream tasks, such as semantic segmentation, 3D object detection, and motion prediction. However, most existing BEV perception frameworks adopt an end-to-end training

Zehao Li, Zhenyu Wu, Yibo Zhao, Bowen Yang

Reinforcement Learning (RL) has the potential to improve the robustness of GUI agents in stochastic environments, yet training is highly sensitive to the quality of the reward function. Existing reward approaches struggle to achieve both scalability and performance. To address this, we propose OS-Themis, a scalable and accurate multi-agent critic framework.

Rafail V. Abramov

We numerically simulate the two-dimensional inertial flow with the van der Waals effect in a straight periodic channel around the Poiseuille and Couette stationary states. Even though the flow remains laminar macroscopically, we observe complex dynamics and power decay of the Fourier spectra of small fluctuations of the density, velocity divergence, vorticit

Huiwen Yan, Mushuang Liu

Autonomous driving (AD) requires safe and reliable decision-making among interacting agents, e.g., vehicles, bicycles, and pedestrians. Multi-agent reinforcement learning (MARL) modeled by Markov games (MGs) provides a suitable framework to characterize such agents' interactions during decision-making. Nash equilibria (NEs) are often the desired solution in

Harshana Weligampola, Joshua Peter Ebenezer, Weidi Liu, Abhinau K. Venkataramanan

Camera pipelines receive raw Bayer-format frames that need to be denoised, demosaiced, and often super-resolved. Multiple frames are captured to utilize natural hand tremors and enhance resolution. Multi-frame super-resolution is therefore a fundamental problem in camera pipelines. Existing adversarial methods are constrained by the quality of ground truth.

Serkan Hoşten, Vadym Kurylenko, Elke Neuhaus, Nikolas Rieke

We study the Euler characteristic of a hypersurface in $(\mathbb{C}^*)^2 \times (\mathbb{C}^*)^n$ defined by a polynomial whose monomial support corresponds to lattice points in $\Delta_1 \times \Delta_1 \times \Delta_n$ as the coefficients of the defining polynomial vary. Each member of this hypersurface family corresponds to a three-way independence model

Zou Qiang

Large language models (LLMs) demonstrate strong generative capabilities but remain vulnerable to hallucination and unreliable reasoning under adversarial prompting. Existing safety approaches -- such as reinforcement learning from human feedback (RLHF) and output filtering -- primarily operate at the behavioral level and may lack explicit architectural mecha

Mark Armah, O. L. Dors, Rogério Riffel, M. V. Cardaci

We present the first semi-empirical strong-line calibrations to determine metallicity in Active Galactic Nuclei (AGNs) that use the directly observable X-ray luminosity (Lx) instead of the dimensionless ionization parameter ($U$). The calibrations are derived from an extensive grid of photoionization models computed with the {\sc Cloudy} code, which are comp

Anikat Kankaria, Ritwik Mukherjee, Sugan Durai Murugan, Marco Edoardo Rosti

We investigate how the defining statistical features of three-dimensional turbulence respond to systematic reductions of the Fourier-space triadic interaction network. Using direct numerical simulations of both fractally and homogeneously decimated Navier-Stokes dynamics, we show that progressive thinning of the set of active modes leads to a systematic supp

Ilyas Noor Bhatti, Rachna Chaurasia, Kazi Rumanna Rahman, Sukhendu Sadhukhan

In this work, we investigate the structural, magnetic, and microwave magnetic dynamics of multilayered \([{\rm LSMO}/{\rm SRO}]_n\) heterostructures \((n = 1 \text{ and } 5)\) grown on SrTiO\(_3\) (001) substrates. X-ray diffraction confirms high crystallinity and atomically sharp interfaces. Magnetic measurements reveal strong interfacial magnetic coupling,

Javier Rubio

We review recent progress in the understanding of the preheating stage of Higgs inflation formulated within the Einstein-Cartan framework of gravity. This setup smoothly interpolates between the metric and Palatini formulations of the theory, leading to a distinctive phenomenology in an intermediate regime. Following the end of inflation, the Higgs field und