April 2026 arXiv papers — page 158

Oscar Stiffelman

In 1956 John Kelly wrote a paper at Bell Labs describing the relationship between gambling and Information Theory. What came to be known as the Kelly Criterion is both an objective and a closed-form solution to sizing wagers when odds and edge are known. Samuelson argued it was arbitrary and subjective, and successfully kept it out of mainstream economics. L

Matthew D. Kvalheim

We study whether second-order systems can be made to behave like prescribed first-order dynamical systems through feedback control. More precisely, we study whether prescribed vector fields on compact smooth manifolds, viewed geometrically as sections of the tangent bundle, can be asymptotically stabilized in a strong sense by second-order control systems on

Michal R Wrobel, Agnieszka Landowska, Karolina Makuch

The paper concerns affective information systems that represent and visualize human emotional states. The goal of the study was to find typical representations of discrete and dimensional emotion models in terms of color, size, speed, shape, and animation type. A total of 419 participants were asked about their preferences for emotion visualization. We found

Rongjun Ge, Chong Wang, Yuxin Liu, Chunqiang Lu

In the field of medical image segmentation, the scarcity of labeled data poses a major challenge for existing models to accurately perceive target regions. Compared with manual annotation, gaze data is easier and cheaper to obtain. As a classical semi-supervised learning framework, mean-teacher can effectively use a large number of unlabeled medical images f

Thibault D. Décoppet, Mateusz Stroiński

A finite pre-tensor category is a finite abelian category equipped with a right exact tensor product for which every projective object has duals. Finite tensor categories, for which every object has duals, are notable examples. More generally, the category of bimodules over an algebra in a finite tensor category is a finite pre-tensor category. In particular

Oleg Kiriukhin

I formulate an entropy-rate maximization problem at the observable level for stochastic processes observed through an information-reducing observation map. For a visible stationary law, the map determines an observational fiber of hidden stationary laws generating that law. In the finite-state finite-memory setting, retained visible constraints determine a f

Frans van der Sluis, Leif Azzopardi, Florian Meier

Millions of consumers search for products online each day, aiming to find items that meet their needs at an acceptable price. While price and quality are major factors in purchasing decisions, ethical considerations increasingly influence consumer behavior, giving rise to the socially responsible consumer. Insights from a recent survey of over 600 consumers

Soham Chatterjee, Aman Batra, Vivek Natarajan

Consider a non-uniform Euler-Bernoulli beam with a tip-mass at one end and a cantilever joint at the other end. The cantilever joint is not fixed and can itself be moved along an axis perpendicular to the beam. The position of the cantilever joint is the control input to the beam. The dynamics of the beam is governed by a coupled PDE-ODE model with boundary

Hendrik Baers, Angkana Rüland

We study the quantitative transfer of uniqueness from the classical to the fractional Calder\'on problem with exterior data. This allows us to deduce the first stability estimates for the principal part of the isotropic fractional Calder\'on problem with exterior data in the absence of Liouville transforms. Our argument relies on careful quantitative unique

Mehmet Can Şakiroğlu, H. Altay Güvenir, Kamer Kaya

Generating multiple-choice questions (MCQs) with difficulty estimation remains challenging in automated MCQ-generation systems used in adaptive, AI-assisted education. This study proposes a novel methodology for generating MCQs with difficulty estimation from the input documents by utilizing knowledge graphs (KGs) and large language models (LLMs). Our approa

Neha Rani, Jeevan Ram Munnangi, Austin Matthew Spangler, Donald Honeycutt

The evolution of LLM has resulted in coding-focused models that are able to produce code snippets with high accuracy. More and more AI coding assistant tools are now available, leading to greater integration of AI coding assistants into integrated development environments (IDEs). These tools introduce new possibilities for enhancing software development work

Shahid Sattar, Roman Stepanov, Alexander Tyner, M. F. Islam

The half-quantized anomalous Hall conductance (AHC) in topological materials is a condensed matter physics realization of the parity anomaly of (2+1) quantum field theory and an important challenge for both theoretical and experimental research. A possible realization of this phenomenon may be achieved by interfacing a two-dimensional (2D) ferromagnetic (FM)

Yunah Jang, Megha Sundriyal, Kyomin Jung, Meeyoung Cha

Adaptive Retrieval-Augmented Generation (RAG) promises accuracy and efficiency by dynamically triggering retrieval only when needed and is widely used in practice. However, real-world queries vary in surface form even with the same intent, and their impact on Adaptive RAG remains under-explored. We introduce the first large-scale benchmark of diverse yet sem

Moonmoon Mohanty, Gautham Bolar, Preetam Patil, Ayalvadi Ganesh

In modern data center networks, thousands of hosts contend for shared link capacity; the scale of these systems makes centralized scheduling impractical. This article models such scheduling as a bipartite matching problem under communication constraints: senders express interest in forming connections, and receivers respond using only locally available infor

Haotian Lu, Sheldon X. -D. Tan

This paper presents EMSpice~3, a full-chip multiphysics framework for coupled electromigration (EM), thermomigration (TM), and IR-drop analysis of practical power-grid (P/G) networks. The framework is, to our knowledge, the first EM-IR analysis flow that jointly incorporates Joule heating and practical spatial thermal profiles for full-chip P/G network desig

Minyu Feng, Yuhan Li, Jürgen Kurths

Epidemic modelling on complex networks has been studied intensively all the time. The majority of relative research assumes that the time scale of the underlying network evolution is much larger compared to the propagation dynamics on it, while the co-evolution of epidemics and networks needs exploring further. In this paper, we investigate how our recently

Fangda Ye, Zhifei Xie, Yuxin Hu, Yihang Yin

Recent agentic search frameworks enable deep research via iterative planning and retrieval, reducing hallucinations and enhancing factual grounding. However, they remain text-centric, overlooking the multimodal evidence that characterizes real-world expert reports. We introduce a pressing task: multimodal long-form generation. Accordingly, we propose Deep-Re

Shijia Xu, Yu Wang, Xiaolong Jia, Zhou Wu

Despite the widespread adoption of Large Language Models (LLMs) in Legal AI, their utility for automated contract revision remains impeded by hallucinated safety and a lack of rigorous behavioral constraints. To address these limitations, we propose the Risk-Constrained Bilevel Stackelberg Framework (RCBSF), which formulates revision as a non-cooperative Sta

Shu Zhou, Rui Ling, Junan Chen, Xin Wang

Scaling test-time compute through extended chains of thought has become a dominant paradigm for improving large language model reasoning. However, existing research implicitly assumes that longer thinking always yields better results. This assumption remains largely unexamined. We systematically investigate how the marginal utility of additional reasoning to

Chentao Tan, Zhun Lu

The longitudinal single-target spin asymmetry in exclusive $\pi^0$ production in $ep$ collisions is a sensitive probe of the imaginary part of the gluon generalized transverse momentum dependent distribution $F_{1,4}^g$. It appears as a characteristic $\sin(2\phi)$ azimuthal correlation between the transverse momenta of the scattered electron and the recoil

Rongjun Ge, Xin Li, Yuxing Liu, Chengliang Liu

The segmentation of 2D vascular structures via deep learning holds significant clinical value but is hindered by the scarcity of annotated data, severely limiting its widespread application. Developing a universal few-shot vascular segmentation model is highly desirable, yet remains challenging due to the need for extensive training and the inherent complexi

Jyoutir Raj, John Conway

Existing multilingual benchmarks include Irish among dozens of languages but apply no Irish-aware text normalisation, leaving reliable and reproducible ASR comparison impossible. We introduce BlasBench, an open evaluation harness that provides a standalone Irish-aware normaliser preserving fadas, lenition, and eclipsis; a reproducible scoring harness and per

Ashish Bawalia, Manil T. Mohan

We consider a nonlinear constrained heat flow evolving on the manifold $\mathcal{M}=\{v\in L^{2}:\|v\|_{L^{2}}=1\}$ over bounded smooth domains. It is known that the solution corresponding to any nonnegative initial datum remains on $\mathcal{M}$ and converges to the unique positive ground state of the associated stationary problem. In this work, we first es

Shijia Xu, Zhou Wu, Xiaolong Jia, Yu Wang

Retrieval-augmented generation (RAG) substantially extends the knowledge boundary of large language models. However, it still faces two major challenges when handling complex reasoning tasks: low context utilization and frequent hallucinations. To address these issues, we propose Self-Correcting RAG, a unified framework that reformulates retrieval and genera

Arya Shah, Deepali Mishra, Chaklam Silpasuwanchai

Large language models increasingly serve as conversational agents that adopt personas and role-play characters at user request. This capability, while valuable, raises concerns about sycophancy: the tendency to provide responses that validate users rather than prioritize factual accuracy. While prior work has established that sycophancy poses risks to AI saf

CMS Collaboration

A search for microscopic black holes, string balls, and electroweak sphalerons using proton-proton collisions at $\sqrt{s}$ = 13 TeV recorded with the CMS detector at the CERN LHC during the 2016$-$2018 data taking, and corresponding to an integrated luminosity of 138 fb$^{-1}$, is presented. Two search strategies based on control samples in data are used. M

Arpita Goswami, Pallabi Chatterjee, Ranjan Modak, Shaon Sahoo

We investigate a two leg ladder system subjected to an external magnetic field. In the absence of a magnetic field, the system is described by a clean tight binding model, with no disorder in either the onsite potential or the hopping amplitudes. The effect of magnetic field in this system is studied by introducing the Peierls phases in the hopping amplitude

Neha Rani, Erta Cenko, Laura Melissa Cruz Castro

The availability and increasing integration of generative AI tools have transformed computing education. While AI in education presents opportunities, it also raises new concerns about how these powerful know-it-all AI tools, which are becoming widespread, impact cognitive skill development among students. Cognitive skills are essential for academic success

CMS Collaboration

A first search at the LHC for a new heavy resonance decaying to a top quark and a neutral scalar boson $ϕ$ in the fully hadronic final state is presented, where the $ϕ$ boson is identified by its decay into a bottom quark-antiquark pair. The search is focused on final states in which the decay products of the highly Lorentz boosted top quark and $ϕ$ boson ar

Margarita Preobrazhenskaia, Makar Sidorov, Igor Preobrazhenskii, Eduard Gorbunov

We study the convergence of the last iterate (i.e., the $(N+1)$-th iterate) of the AdaGrad method. Although AdaGrad -- an adaptive subgradient method -- underpins a wide class of algorithms, most existing convergence analyses focus on averaged (or best) iterates. We derive worst-case upper bounds on the suboptimality of the final point and show that, with an

Zihan Wang

The topology of cosmic reionization, the sizes, shapes, and connectivity of ionized bubbles is a primary observable of next-generation 21\,cm experiments. We show that this topology is sensitive to the microphysics of dark matter. Self-interacting dark matter (SIDM), with cross-sections $\sigma/m\sim 1$--$10\;\mathrm{cm^2/g}$ motivated by small-scale structu

Subho Mitra, Arijit K. De

We simulate a third-order nonlinear signal in a pump-probe spectroscopy from the interference between first- and second-order wavepackets (WPs), as well as from a state-to-state transition for Stokes and coherent anti-Stokes pathways in the context of impulsive stimulated Raman scattering (ISRS) excitation. We present a detailed step-by-step description of b

Jessica Lin, Amir Zeldes

Previous work examining the Uniform Information Density (UID) hypothesis has shown that while information as measured by surprisal metrics is distributed more or less evenly across documents overall, local discrepancies can arise due to functional pressures corresponding to syntactic and discourse structural constraints. However, work thus far has largely di

Zhu-Yu Ren, Sheng-Quan Wang, Jian-Ming Shen, Xing-Gang Wu

The energy-energy correlation (EEC) in electron-positron annihilation plays a crucial role in precision tests of quantum chromodynamics (QCD) and measurements of the QCD coupling constant. In this paper, we provide a novel analysis for the EEC by using the Principle of Maximum Conformality (PMC), a systematic method for eliminating renormalization scheme-and

Kya C. Sorli, Paul O. Hayne, Lucas Lange, Sylvain Piqueux

Europa's double ridges often display lower albedo and redder color than their surroundings. Their unique topography may cause sublimation-driven darkening due to illumination and self-heating, a process known as thermal segregation. We apply an advanced 3D thermophysical model, including shadowing and self-heating through mutual exchange of radiation, to dig

Yuqi Chen, Xiaohan Zhang, Ahmad Arrabi, Waqas Sultani

Natural-language Guided Cross-view Geo-localization (NGCG) aims to retrieve geo-tagged satellite imagery using textual descriptions of ground scenes. While recent NGCG methods commonly rely on CLIP-style dual-encoder architectures, they often suffer from weak cross-modal generalization and require complex architectural designs. In contrast, Multimodal Large

Kenneth Goodenough, Paul E. Gunnells

Let G be a graph. The black-white polynomial W_G(t) enumerates colorings of the vertices of G with two colors (black and white), where the power of t keeps track of how many white vertices have an even number of black neighbors. Such polynomials appear in quantum information theory, where they are used to capture properties of the entanglement in certain qua

Udari Madhushani Sehwag, Elaine Lau, Haniyeh Ehsani Oskouie, Shayan Shabihi

Accelerating scientific discovery requires the identification of which experiments would yield the best outcomes before committing resources to costly physical validation. While existing benchmarks evaluate LLMs on scientific knowledge and reasoning, their ability to predict experimental outcomes - a task where AI could significantly exceed human capabilitie

Yuanbo Xie, Yingjie Zhang, Yulin Li, Shouyou Song

Retrieval-Augmented Generation (RAG) systems augment large language models with external knowledge, yet introduce a critical security vulnerability: RAG Knowledge Base Leakage, wherein adversarial prompts can induce the model to divulge retrieved proprietary content. Recent studies reveal that such leakage can be executed through adaptive and iterative attac

Wei Zhang, Xinyu Chang, Xiao Li, Yiming Zhu

Adversarial examples present significant challenges to the security of Deep Neural Network (DNN) applications. Specifically, there are patch-based and texture-based attacks that are usually used to craft physical-world adversarial examples, posing real threats to security-critical applications such as person detection in surveillance and autonomous systems,

Abdellatif Elgrou, Omar Oukdach, Abdelaziz Rhandi

We investigate the robust Stackelberg null controllability of a one-dimensional forward linear stochastic Kuramoto--Sivashinsky--Korteweg--de Vries (KS--KdV) equation. The control framework is formulated as a hierarchical Stackelberg game involving two leaders, one follower, and worst-case disturbances acting in both the drift and diffusion terms. The first

Mirna Džamonja

We give an exposition of an iteration theorem for iterating $(<\lambda)$-closed stationary $\lambda^+$-cc forcing with supports of size $<\lambda$ and preserving these two properties. We discuss the relation of this theorem with other iteration theorems and forcing axioms that have appeared in the literature, notably the one from \cite{Sh80}.

Yigit Yilmaz, Elena Petrova, Mehmet Kaya, Lucia Rossi

Invisible watermarking for autoregressive (AR) image generation has recently gained attention as a means of protecting image ownership and tracing AI-generated content. However, existing approaches suffer from three key limitations: (1) they embed only zero-bit watermarks for binary verification, lacking the ability to convey multi-bit messages; (2) they rel

Arkady Pikovsky

The transition to synchrony in the Kuramoto model of globally coupled phase oscillators with a uniform distribution of natural frequencies is discontinuous. We extend the theory of this transition to the Kuramoto-Sakaguchi model, taking into account a phase shift in coupling. In the thermodynamic limit, we derive dependencies of the order parameters on the c

Jiaqi Tong, Chao Cheng, Fan Li

Causal mediation analysis in cluster-randomized trials (CRTs) is complicated by the presence of multiple mediators, intracluster correlation, and within-cluster interference. Existing mediation methods often fall short in accommodating these features simultaneously, and semiparametric efficient estimators that fully address them remain unavailable. We develo

Kazuya U. Kobayash, Komei Jinbo, Riku Kodama, Masakazu Muto

Applying an impulsive force to a powder layer shaped with a concave surface generates a sharp powder jet. This phenomenon has been proposed as a method for evaluating the flowability of powders from small amount of samples. In this study, we systematically varied the radius of the initial concave shape as a controllable parameter and quantitatively examined

Zeyue Tian, Binxin Yang, Zhaoyang Liu, Jiexuan Zhang

Recent progress in multimodal models has spurred rapid advances in audio understanding, generation, and editing. However, these capabilities are typically addressed by specialized models, leaving the development of a truly unified framework that can seamlessly integrate all three tasks underexplored. While some pioneering works have explored unifying audio u

Burak Akgül, Erol Şahin, Sinan Kalkan

While appearance-based gaze estimation has achieved significant improvements in accuracy and domain adaptation, the fairness of these systems across different demographic groups remains largely unexplored. To date, there is no comprehensive benchmark quantifying algorithmic bias in gaze estimation. This paper presents the first extensive evaluation of fairne

Nrupen A. Bhavsar, Lingyu Zhou, Samuel I. Berchuck, Matthew L. Maciejewski

Missing values in electronic health record (EHR) data pose a significant challenge for epidemiologic research. Traditional methods for handling missing data, like mean imputation, may introduce bias. Multiple imputation (MI) offers a principled solution by generating multiple plausible values based on statistical models. However, MI requires careful model sp

Christian Houdré, Jorge Víquez

Motivated by extending the functional stochastic calculus, to important functionals to which it does not apply, a notion of functional derivative along a curve is introduced. This new setting is developed by incorporating path-dependent directional extensions. Our results then focus on a comprehensive exploration of these derivatives and the insights they pr

Peter Schurtenberger, Marco Polimeni, Sophia Marzouk, Robin Curtis

Colloid models have frequently been used to successfully describe the influence of protein-protein interactions on antibody solution properties, but they suffer from inherent problems due to the anisotropic shape of the particles. The net charge required to describe electrostatic interactions is an effective quantity that cannot directly be obtained from the

Giansalvo Cirrincione

Transformer architectures are designed by trial and error: the number of attention heads, the depth, and the head size are fixed before training begins, with no mathematical principle to guide the choice. The result is systematic structural redundancy -- between half and four-fifths of all heads in a trained model can be removed without measurable loss -- be

Zikang Shan, Han Zhong, Liwei Wang, Li Zhao

Credit assignment is a central challenge in reinforcement learning (RL). Classical actor-critic methods address this challenge through fine-grained advantage estimation based on a learned value function. However, learned value models are often avoided in modern large language model (LLM) RL because conventional discriminative critics are difficult to train r

Rongjun Ge, Weilong Mao, Jian Lu, Rong Yan

Digital Subtraction Angiography (DSA) is a clinically significant imaging technique for diagnosing cerebrovascular disease, as gold-standard. However, the artifacts caused by motion of high-attenuation tissues such as bones, teeth, and catheters, seriously reduce the visibility of blood vessels. This paper presents a novel Vascular Consistency Constrained DS

Eric Keto

Recent observations of hydrostatic structure and virial equilibrium in supersonically turbulent, self-gravitating molecular clouds imply a stability that contrasts with the transcience of turbulent structure. To investigate this contradiction, we model a molecular cloud as a turbulent eddy and study its evolution as a dynamical system. In a two-dimensional p

Amir Hasanzadeh, Anne-Marie Broomhall, Dmitrii Kolotkov, Tishtrya Mehta

Quasi-biennial oscillations (QBOs) are shorter-term periodic signals that occur alongside the dominant 11-year solar cycle. In this study, we examine the spatial and temporal evolution of QBOs using helioseismic p-mode frequency shifts from the Global Oscillation Network Group (GONG) across solar Cycles 23 and 24 and the ascending phase of Cycle 25. By apply

Bardia Nadimi, Hao Zheng

Understanding communication behavior in modern system-on-chip (SoC) designs is critical for functional verification, performance analysis, and post-silicon debugging. Communication traces capture message exchanges among system components and provide valuable insights into system behavior. However, deriving concise communication specifications from such trace

Yifan Gao, Haoyue Li, Feng Yuan, Xin Gao

We present Camyla, a system for fully autonomous research within the scientific domain of medical image segmentation. Camyla transforms raw datasets into literature-grounded research proposals, executable experiments, and complete manuscripts without human intervention. Autonomous experimentation over long horizons poses three interrelated challenges: search

Jiayu Zhang, Shuo Ye, Qilang Ye, Zihan Song

Recent Audio-Visual Question Answering (AVQA) methods have advanced significantly. However, most AVQA methods lack effective mechanisms for handling missing modalities, suffering from severe performance degradation in real-world scenarios with data interruptions. Furthermore, prevailing methods for handling missing modalities predominantly rely on generative

Erhard Reschenhofer

To answer the questions of whether global warming is accelerating and when the 1.5{\deg}C Paris Agreement target will be exceeded, the global mean surface temperature from 1880 to 2025 is first examined using a purely graphical approach and later, in a more conventional way, using various time-domain and frequency-domain methods. In an effort to reduce varia

Yuxi Sun, Aoqi Zuo, Haotian Xie, Wei Gao

Chain-of-Thought (CoT) prompting has improved LLM reasoning, but models often generate explanations that appear coherent while containing unfaithful intermediate steps. Existing self-evaluation approaches are prone to inherent biases: the model may confidently endorse coherence even when the step-to-step implication is not valid, leading to unreliable faithf

Wen Fan, Dandan Zhang

Elastomers are central to vision-based tactile sensors (VBTSs), where they transduce external contact into observable deformation. Different VBTS architectures, however, require distinct optical and mechanical properties, particularly transparency and hardness. Conventional elastomer design relies on a forward, trial-and-error optimisation process from mater

Rajneesh Joshi, Gyaprasad

We propose a theoretical framework to dynamically control the degree of polarization of light by using the superposition of incoherent orthogonally polarized beams in a Mach-Zehnder interferometer incorporating a twisted nematic liquid crystal cell in one of its arms. The liquid crystal acts as an elecro-optically controlled polarization rotator, where the a

Weijiang Li, Yilin Zhu, Rajarshi Das, Parijat Dube

Foundation models have shown remarkable performance across diverse tasks, yet their ability to construct internal spatial world models for reasoning and planning remains unclear. We systematically evaluate the spatial understanding of large language models through maze tasks, a controlled testing context requiring multi-step planning and spatial abstraction.

Xun Qian, Alexander Gaponov, Grigory Malinovsky, Peter Richtárik

Recent developments have shown that Muon-type optimizers based on linear minimization oracles (LMOs) over non-Euclidean norm balls have the potential to get superior practical performance than Adam-type methods in the training of large language models. Since large-scale neural networks are trained across massive machines, communication cost becomes the bottl

Vuk Đuranović, Marko Robnik Šikonja

Large language models (LLMs) demonstrate strong performance in text summarization, yet their effectiveness drops significantly across languages with restricted training resources. This work addresses the challenge of query-focused summarization (QFS) in less-resourced languages, where labeled datasets and evaluation tools are limited. We present a novel QFS

Elia Onofri, Andrea De Salve, Paolo Mori, Laura Emilia Maria Ricci

The Self-Sovereign Identity (SSI) paradigm is instrumental for decentralised identity management, allowing an entity to create, manage, and present their digital credentials without relying on centralised authorities. Credential selective disclosure is one of the most attractive privacy-preserving features of SSI, allowing users to reveal only the minimum ne

Pawan Kumar, Sadashiv, Sanjay Kumar, Sushree S. Nayak

In this paper, we identify the magnetic reconnections at the hyperbolic flux tube (HFT), aided by slipping reconnection at quasi-separatrix layers (QSLs), which are pivotal to the occurrence of a confined M2.1 class flare in NOAA active region 12268. The magnetic field topology before the flare's onset is obtained through a non-force-free-field extrapolation

Nan Chen, Jianhui Lian, Yuze Zhao

The vertical structure of galactic disks is an important probe of disk assembly history. We investigate a sample of 79 local disk galaxies within 50 Mpc using data from the DESI Legacy Imaging Surveys. Vertical luminosity profiles as a function of radius in the g, r, and z bands are extracted and fitted with a single-component sech^2 model to determine the s

Ke Chen, Ruilin Hu, Quoc-Hung Nguyen

We establish Schauder-type estimates for linear parabolic systems driven by variable-coefficient nonlocal pseudo-differential operators of order $s>0$. These estimates are formulated in critical time-weighted H\"older/Besov-type spaces and are tailored to quasilinear equations at scaling-critical regularity. A key ingredient is a kernel-adapted freezing-coef

Vu Tuan Truong, Long Bao Le

Large Language Models (LLMs), despite their impressive capabilities across domains, have been shown to be vulnerable to backdoor attacks. Prior backdoor strategies predominantly operate at the token level, where an injected trigger causes the model to generate a specific target word, choice, or class (depending on the task). Recent advances, however, exploit

Youssef Ait Si, Ratnangshu Das, Negar Monir, Sadegh Soudjani

In this paper, we consider the notions of effort and resilience of a dynamical control system defined by the maximum disturbance the system can withstand while satisfying given finite temporal logic specifications. Given a dynamical system and a specification, the objective is to synthesize the controller such that the system satisfies the specification whil

Hong-Bae Jeon, Kai-Kit Wong, Chan-Byoung Chae

This paper investigates a fluid reconfigurable intelligent surface (FRIS)-assisted Rydberg Atomic REceiver (RARE) architecture under magnitude-only heterodyne readout. We show that, unlike conventional coherent systems, the optimal propagation environment is fundamentally governed by the receiver's nonlinear measurement structure. In particular, under the st

Yingguang Yang, Hao Liu, Xin Zhang, Yunhui Liu

Social bot detection is critical to the stability and security of online social platforms. However, current state-of-the-art bot detection models are largely developed in isolation, overlooking the benefits of leveraging shared detection patterns across platforms to improve performance and promptly identify emerging bot variants. The heterogeneity of data di

Yifu Xu, Bokai Lin, Xinyu Zhan, Hongjie Fang

Scaling up robot learning is hindered by the scarcity of robotic demonstrations, whereas human videos offer a vast, untapped source of interaction data. However, bridging the embodiment gap between human hands and robot arms remains a critical challenge. Existing cross-embodiment transfer strategies typically rely on visual editing, but they often introduce

Hanwen Liu

We establish rigidity results for holomorphic mappings and plurisubharmonic functions in complex geometry. First, under mild conditions, we show that the gradient of a $\operatorname{U}(1)$-invariant strictly plurisubharmonic function in $\mathbb{C}^2$ possesses finite fibers and induces a analytic mapping of topological degree $1$ on the symplectic quotient

Marco Schouten, Ioannis Siglidis, Serge Belongie, Dim P. Papadopoulos

We propose a method to learn explicit, class-conditioned spatial priors for object placement in natural scenes by distilling the implicit placement knowledge encoded in text-conditioned diffusion models. Prior work relies either on manually annotated data, which is inherently limited in scale, or on inpainting-based object-removal pipelines, whose artifacts

Hao Wang, Guozhi Wang, Han Xiao, Yufeng Zhou

Reinforcement learning (RL) has been widely used to train LLM agents for multi-turn interactive tasks, but its sample efficiency is severely limited by sparse rewards and long horizons. On-policy self-distillation (OPSD) alleviates this by providing dense token-level supervision from a privileged teacher that has access to ground-truth answers. However, such

Behrooz Razeghi

AI alignment is often framed as the task of ensuring that an AI system follows a set of stated principles or human preferences, but general principles rarely determine their own application in concrete cases. When principles conflict, when they are too broad to settle a situation, or when the relevant facts are unclear, an additional act of judgment is requi

Wei Chen, Qibin Zhao, John Paisley, Junmei Yang

Density ratio estimation (DRE) is a useful tool for quantifying discrepancies between probability distributions, but existing approaches often involve a trade-off between estimation quality and computational efficiency. Classical direct DRE methods are usually efficient at inference time, yet their performance can seriously deteriorate when the discrepancy b

A. Ben-Yehuda, A. Rack, S. Shwartz, N. Viganò

X-ray fluorescence (XRF) enables element-specific, nondestructive imaging, but conventional raster scanning scales poorly with sample size, particularly for tomography, because measurements must be repeated at every projection angle and spatial position. We demonstrate direct volumetric XRF ghost tomography, which replaces point-by-point acquisition with com

Friedemann Schuricht

The paper, that continuous some previous work of Sch\"onherr & Schuricht, treats density measures on ${\mathbb R}^n$ that concentrate in any neighborhood of a Lebesgue null set. Such measures are typical for purely finitely additive measures. We study their basic properties and investigate related integrals. Measures taking only the values 0 and 1 are consid

Aaron W. Zeeb, John T. Allen, Matthew Van Den Broeke

Left-moving (LM) supercells, characterized by anticyclonically rotating updrafts in the Northern Hemisphere, are significant due to their propensity to produce large hail. Although less common than right-moving supercells, they present notable forecasting challenges and societal impacts. However, despite these impacts, the environments of LM supercells are p

Mohammad Albinhassan, Pranava Madhyastha, Mark Law, Alessandra Russo

Controlling the output of Large Language Models (LLMs) through context-sensitive constraints has emerged as a promising approach to overcome the limitations of Context-Free Grammars (CFGs) in guaranteeing generation validity. However, such constraints typically require manual specification -- a significant barrier demanding specialized expertise. We introduc

Yuxuan Gao, Xiaohao Liu, Xiaobo Xia, Tongliang Liu

Dataset distillation compresses large-scale datasets into compact synthetic sets while preserving training performance, but existing methods are largely restricted to single-modal or bimodal settings. Extending dataset distillation to scenarios involving more than two modalities, i.e., Omnimodal Dataset Distillation, remains underexplored and challenging due

Duaa Abdullah

The sigma index of a graph, defined as the population variance of its degree sequence, is a fundamental measure of structural irregularity. In this paper, we introduce and systematically investigate its natural extension to fuzzy graphs, termed the fuzzy sigma index $$ \sigma^*(\Gamma) = \frac{1}{n} \sum_{v \in V(\Gamma)} \left( d_\Gamma(v) - \frac{2\,\mathr

Jian Chen, Yuzhu Hu, Xiaoyan Yuan, Yuxuan Hu

Irregular Medical Time Series play a critical role in the clinical domain to better understand the patient's condition. However, inherent irregularity arising from heterogeneous sampling rates, asynchronous observations, and variable gaps poses key challenges for reliable modeling. Existing methods often distort temporal sampling irregularity and missingness

Senol Gulgonul

HeceTokenizer is a syllable-based tokenizer for Turkish that exploits the deterministic six-pattern phonological structure of the language to construct a closed, out-of-vocabulary (OOV)-free vocabulary of approximately 8,000 unique syllable types. A BERT-tiny encoder (1.5M parameters) is trained from scratch on a subset of Turkish Wikipedia using a masked la

Jiahuan Jin, Wenhao Zhao, Rong Qu, Jianfeng Ren

Multi-objective optimization (MOO) has been widely studied in literature because of its versatility in human-centered decision making in real-life applications. Recently, demand for dynamic MOO is fast-emerging due to tough market dynamics that require real-time re-adjustments of priorities for different objectives. However, most existing studies focus eithe

David Santiago Quevedo, Felipe Segundo Abril-Bermúdez, Cristiane Morais Smith

Many complex systems are described by Langevin-type equations in which the noise exhibits long-range correlations and couples to the system in a state-dependent, multiplicative manner, leading to heterogeneous non-Markovian diffusion. Here, we investigate the problem of diffusion driven by fractional Gaussian noise with a general multiplicative coefficient f

Wenhao Zhang, Lin Mu, Li Ni, Peiquan Jin

Low-rank adaptation (LoRA) is a widely used strategy for efficient fine-tuning of large language models (LLMs), but its strictly linear structure fundamentally limits expressive capacity. The bilinear formulation of weight updates captures only first-order dependencies between low-rank factors, restricting the modeling of nonlinear and higher-order parameter

Haihui Xie, Wenkun Wen, Shuwu Chen, Zhaogang Shu

Large-scale Internet of Things (IoT) networks enable intelligent services such as smart cities and autonomous driving, but often face resource constraints. Collecting heterogeneous sensory data, especially in small-scale datasets, is challenging, and independent edge nodes can lead to inefficient resource utilization and reduced learning performance. To addr

Houcine Abdelkader Cherief, Florent Avellaneda, Naouel Moha

Mobile apps have become essential of our daily lives, making code quality a critical concern for developers. Behavioural code smells are characteristics in the source code that induce inappropriate code behaviour during execution, which negatively impact software quality in terms of performance, energy consumption, and memory. Dynamics, the latest state-of-t

Nakyung Lee, Sangwoo Hong, Jungwoo Lee

Recent research has devoted considerable effort to verifying the intermediate reasoning steps of chain-of-thought (CoT) trajectories using process reward models (PRMs) and other verifier models. However, training a PRM typically requires human annotators to assign reward scores to each reasoning step, which is both costly and time-consuming. Existing automat

Jiahua Lu, Huangzhaoxiang Chen, Zhe Zhang, Xinyue Wang

RuO2, widely proposed as a prototypical altermagnet, remains intensely debated with regard to its magnetic nature. Here, we demonstrate that RuO2 is non-magnetic in the bulk, but possesses a spontaneous surface ferrimagnetic order. Using spin- and angle-resolved photoemission spectroscopy, we directly detect a narrow surface state with identical spin polariz

Mamadou Seck

Institutional decisions -- regulatory compliance, clinical triage, prior authorization appeal -- require a different AI architecture than general-purpose agents provide. Agent frameworks infer authority conversationally, reconstruct accountability from logs, and produce silent errors: incorrect determinations that execute without any human review signal. We

Zhenfeng Zou

This paper introduces the Lambda extension of the R\'{e}nyi entropic value-at-risk ($\Lambda$-EVaR), a novel family of risk measures that unifies the flexible confidence level structure of the $\Lambda$-framework with the higher-moment sensitivity of EVaR. We define $\Lambda$-EVaR, establish its foundational properties including monotonicity, cash subadditiv

Iain McDonald

A method of determining ages and masses of asymptotic giant branch (AGB) stars between 0.8 and $\sim$6 M$_\odot$ is demonstrated, based on comparing the star's position in the period--absolute-magnitude diagram to theoretical evolutionary models. For samples of Milky Way stars, the method provides errors (statistical and systematic, respectively) of order of

Chenghao Qian, Xin Li, Yeying Jin, Shangguan Sun

This paper presents a review of the LoViF 2026 Challenge on Weather Removal in Videos. The challenge encourages the development of methods for restoring clean videos from inputs degraded by adverse weather conditions such as rain and snow, with an emphasis on achieving visually plausible and temporally consistent results while preserving scene structure and

Zi-Han Zhang, Tan Liu, Shuai Zhang, Zong-Kuan Guo

We investigate the dissipative dynamics of charged compact binaries in Einstein-Maxwell theory. By evaluating the mass and electric multipole moments, we compute the gravitational and electromagnetic fluxes {through next-to-leading order in the post-Newtonian expansion}. Using the flux-balance equations, we derive the evolution of the orbital angular frequen