October 2025 arXiv papers — page 192

Leonardo Christov-Moore, Arthur Juliani, Alex Kiefer, Joel Lehman

As artificial agents enter open-ended physical environments -- eldercare, disaster response, and space missions -- they must persist under uncertainty while providing reliable care. Yet current systems struggle to generalize across distribution shifts and lack intrinsic motivation to preserve the well-being of others. Vulnerability and mortality are often se

Ruben Ruiz-Mateos Serrano, Joe G Troughton, Nima Mirkhani, Natalia Martinez

Neurotechnologies are transforming how we measure, interpret, and modulate brain-body interactions, integrating real-time sensing, computation, and stimulation to enable precise physiological control. They hold transformative potential across clinical and non-clinical domains, from treating disorders to enhancing cognition and performance. Realizing this pot

Rémi Kazmierczak, Steve Azzolin, Eloïse Berthier, Goran Frehse

This paper addresses explainable AI (XAI) through the lens of Concept Bottleneck Models (CBMs) that do not require explicit concept annotations, relying instead on concepts extracted using CLIP in a zero-shot manner. We show that CLIP, which is central in these techniques, is prone to concept hallucination, incorrectly predicting the presence or absence of c

Alexander Betz

This article develops the theory of fusion categories acting on algebras. We will demonstrate that weak Hopf algebra actions on algebras correspond to specific actions of fusion categories. As an application of this theory, we introduce a family of filtered actions of weak Hopf algebras on the path algebra, and for weak Hopf algebras whose representation cat

Nikolaus Würkner, Yevhenii Kuriatnikov, Karthikeyan Kumaran, M Venkat Ramana

Splitting a Bose--Einstein condensate (BEC) is a key operation in fundamental physics experiments and emerging quantum technologies, where precise preparation of well--defined initial states requires fast yet coherent control of the condensate's nonlinear dynamics. This work formulates the BEC splitting process as an optimal feedforward control problem based

Ethan Davies, Alastair Kay

A user, Alice, wants to get server Bob to implement a quantum computation for her. However, she wants to leave him blind to what she's doing. What are the minimal communication resources Alice must use in order to achieve information-theoretic security? In this paper, we consider a single step of the protocol, where Alice conveys to Bob whether or not he sho

Elia Fiammengo, Morten Lüders

We study the question of the existence of a decomposition of the diagonal for very general quartic and $(2,3)$-complete intersection $n$-folds. Using cycle-theoretic techniques of Lange, Pavic and Schreieder we reduce the question via a degeneration argument to the existence of such a decomposition for $n-1$-dimensional cubic hypersurfaces and their essentia

Tran Van Duy, Tuan Hai Vu, Vu Trung Duong Le, Hoai Luan Pham

In recent years, there has been a growing interest in the development of quantum emulation. However, existing studies often struggle to achieve broad applicability, high performance, and efficient resource and memory utilization. To address these challenges, we provide HPQEA, a quantum emulator based on the state-vector emulation approach. HPQEA includes thr

Guan-Yan Yang, Farn Wang, Kuo-Hui Yeh

Consumer electronics (CE) connected to the Internet of Things are susceptible to various attacks, including DDoS and web-based threats, which can compromise their functionality and facilitate remote hijacking. These vulnerabilities allow attackers to exploit CE for broader system attacks while enabling the propagation of malicious code across the CE network,

Lingyi Wang, Rashed Shelim, Walid Saad, Naren Ramakrishnan

The use of a learnable codebook provides an efficient way for semantic communications to map vector-based high-dimensional semantic features onto discrete symbol representations required in digital communication systems. In this paper, the problem of codebook-enabled quantization mapping for digital semantic communications is studied from the perspective of

Sin Kyu Kang, Raymundo Ramos

We propose an extension of the standard model (SM) by two SU(2) triplet scalars and an inert SU(2) doublet. We demonstrate that this setup can simultaneously produce an inflaton and baryon asymmetry in the early universe, provide a dark matter candidate and explain the smallness of neutrino masses. The two triplets are particularly important as they become m

Xuemin Liu, Tom Hickling, Jonathan F. MacArt

We train active neural-network flow controllers using a deep learning PDE augmentation method to optimize lift-to-drag ratios in turbulent airfoil flows at Reynolds number $5\times10^4$ and Mach number 0.4. Direct numerical simulation and large eddy simulation are employed to model compressible, unconfined flow over two- and three-dimensional semi-infinite N

Taylor Sorensen, Yejin Choi

Many natural language processing (NLP) tasks involve subjectivity, ambiguity, or legitimate disagreement between annotators. In this paper, we outline our system for modeling human variation. Our system leverages language models' (LLMs) in-context learning abilities, along with a two-step meta-learning training procedure for 1) post-training on many datasets

Johannes Bäumler, Tejas Iyer

Consider a model of $N$ independent, increasing $\mathbb{N}_0$-valued processes, with random, independent waiting times between jumps. It is known that there is either an emergent `leader', in which a single process possesses the maximal value for all sufficiently large times, or every pair of processes alternates leadership infinitely often. We show that in

Nihal Jalal Pullisseri, Sanil Unnikrishnan

We consider a model of non-canonical scalar-tensor theory in which the kinetic term in the Brans-Dicke action is replaced by a non-canonical scalar field Lagrangian $\mathcal{L}(X, \phi)= \lambda X^\alpha \phi^\beta - V(\phi)$ where $X = (1/2) \partial_{\mu} \phi \partial^{\mu} \phi$ and $\alpha$, $\beta$ and $\lambda$ are parameters of the model. This can b

Bart van der Holst, Thomas Swarts, Phuong Nguyen, Johan Morren

Redispatch markets are widely used by system operators to manage network congestion. A well-known drawback, however, is that Flexibility Service Providers (FSPs) may strategically adjust their baselines in anticipation of redispatch actions, thereby aggravating congestion and raising system costs. To address this increase-decrease gaming, Distribution System

Pasquale Casaburi, Giovanni Piccioli, Pierpaolo Vivo

Data is the central commodity of the digital economy. Unlike physical goods, it is non-rival, replicable at near-zero cost, and traded under heterogeneous licensing rules. These properties defy standard supply--demand theory and call for new pricing principles. We propose a game-theoretic approach in which the value of a data string emerges from strategic co

Himanshu Choudhary, Arishi Orra, Manoj Thakur

In the ever-changing and intricate landscape of financial markets, portfolio optimisation remains a formidable challenge for investors and asset managers. Conventional methods often struggle to capture the complex dynamics of market behaviour and align with diverse investor preferences. To address this, we propose an innovative framework, termed Diffusion-Au

Guo Yutong, Wanying Wang, Yue Wu, Zichen Miao

Table Visual Question Answering (Table VQA) is typically addressed by large vision-language models (VLMs). While such models can answer directly from images, they often miss fine-grained details unless scaled to very large sizes, which are computationally prohibitive, especially for mobile deployment. A lighter alternative is to have a small VLM perform OCR

Wei Zhou, Bolei Ma, Annemarie Friedrich, Mohsen Mesgar

Table Question Answering (TQA) aims to answer natural language questions about tabular data, often accompanied by additional contexts such as text passages. The task spans diverse settings, varying in table representation, question/answer complexity, modality involved, and domain. While recent advances in large language models (LLMs) have led to substantial

F. Liu, Z. Yang, Y. Luo, S. Guo

In complex oxides, charge carriers often couple strongly with lattice vibrations to form polarons-entangled electron-phonon quasiparticles whose transport properties remain difficult to characterize. Experimental access to intrinsic polaronic transport requires ultraclean samples, while theoretical descriptions demand methods beyond low-order perturbation th

Charles De Clercq, Evan Marth, Kirill Zainoulline

We show that the strong Rost nilpotence holds for motives of generic hyperplane sections of twisted Milnor hypersurfaces. Hence, we provide a new family of examples of smooth projective algebraic varieties which satisfy the strong Rost nilpotence principle. As an application, we compute the $p$-canonical dimension for such varieties.

Yunzhen Yao, Lie He, Michael Gastpar

Conformal prediction provides prediction sets with coverage guarantees. The informativeness of conformal prediction depends on its efficiency, typically quantified by the expected size of the prediction set. Prior work on the efficiency of conformalized regression commonly treats the miscoverage level $\alpha$ as a fixed constant. In this work, we establish

Riccardo Mereu, Aidan Scannell, Yuxin Hou, Yi Zhao

World models are a powerful paradigm in AI and robotics, enabling agents to reason about the future by predicting visual observations or compact latent states. The 1X World Model Challenge introduces an open-source benchmark of real-world humanoid interaction, with two complementary tracks: sampling, focused on forecasting future image frames, and compressio

Baixuan Xu, Tianshi Zheng, Zhaowei Wang, Hong Ting Tsang

Enabling LLMs to effectively operate long-horizon task which requires long-term planning and multiple interactions is essential for open-world autonomy. Conventional methods adopt planning with actions where a executable action list would be provided as reference. However, this action representation choice would be impractical when the environment action spa

Marcella Palese, Fabrizio Zanello

In the frame of the Lagrangian formalism on $r$-order prolongations of fibered manifolds and related structures such as (prolongation of) projectable vector fields, (sheaves of) differential forms and contact structures, we propose a Lagrangian two-field derivation of $2D$ modified Boussinesq equations, obtained as coupled systems of Euler--Lagrange (E-L) eq

Federico Gonzalez, Estefania Talavera, Petia Radeva

Unsupervised object discovery, the task of identifying and localizing objects in images without human-annotated labels, remains a significant challenge and a growing focus in computer vision. In this work, we introduce a novel model, DADO (Depth-Attention self-supervised technique for Discovering unseen Objects), which combines an attention mechanism and a d

Baptiste Ferrere, Nicolas Bousquet, Fabrice Gamboa, Jean-Michel Loubes

Fourier analysis on the Boolean hypercube is fundamentally defined as the orthogonal decomposition of the space of pseudo-Boolean functions with respect to the uniform probability measure. In this work, we propose an ANOVA-based generalization of the Fourier decomposition on the Boolean hypercube endowed with any arbitrary probability measure. We provide an

Cyril Lecuire

The bending map of a hyperbolic 3-manifold with boundary maps a geometrically hyperbolic metric to its bending measured geodesic lamination. We show that the bending map is proper. As a byproduct of the proof we show that the group of isotopy classes of homeomorphisms of M acts properly discontinuously on the set of doubly incompressible measured geodesic la

Tommaso Bertin, Paulin Huguet

We study integral functionals defined on scalar Sobolev spaces of the form $$E[f]:u\mapsto \int_\Omega f(x,u(x),\nabla u(x)) d x,$$ with an emphasis on the non-convex case, and the difficulties it involves to prevent the Lavrentiev phenomenon. We determine a formulation of the lower semicontinuous envelope of $E[f]$ with respect to various topologies and wit

Tan Wang, Yun Wei Dong, Qi Wang

Transformer-based methods have achieved impressive results in time series forecasting. However, existing Transformers still exhibit limitations in sequence modeling as they tend to overemphasize temporal dependencies. This incurs additional computational overhead without yielding corresponding performance gains. We find that the performance of Transformers i

Miriam Wanner, Leif Azzopardi, Paul Thomas, Soham Dan

Existing methods for evaluating the factuality of large language model (LLM) responses treat all claims as equally important. This results in misleading evaluations when vital information is missing or incorrect as it receives the same weight as peripheral details, raising the question: how can we reliably detect such differences when there are errors in key

Hanieh Shojaei Miandashti, Claus Brenner

In addition to accurate scene understanding through precise semantic segmentation of LiDAR point clouds, detecting out-of-distribution (OOD) objects, instances not encountered during training, is essential to prevent the incorrect assignment of unknown objects to known classes. While supervised OOD detection methods depend on auxiliary OOD datasets, unsuperv

Gülsüm Yaren Durdu, Azka Maula Iskandar Muda, Uğur Teğin

We explore rogue wave formation in multimode silicon nitride (Si$_3$N$_4$) waveguides with multimode nonlinear Schr\"odinger equation-based simulations. Pure fundamental-mode excitation produces smooth propagation without extreme events, whereas higher-order modes or multimode superpositions yield heavy-tailed statistics with bursts exceeding the $8\sigma$ t

Fanheng Kong, Jingyuan Zhang, Yahui Liu, Zirui Wu

Diffusion large language models (dLLMs) represent a significant advancement in text generation, offering parallel token decoding capabilities. However, existing open-source implementations suffer from quality-speed trade-offs that impede their practical deployment. Conservative sampling strategies typically decode only the most confident token per step to en

Maxime Reynouard

This study tackles the computational challenges of solving Markov Decision Processes (MDPs) for a restricted class of problems. It is motivated by the Last Revealer Attack (LRA), which undermines fairness in some Proof-of-Stake (PoS) blockchains such as Ethereum (\$400B market capitalization). We introduce pseudo-MDPs (pMDPs) a framework that naturally model

Stefano Markidis, Gilbert Netzer, Luca Pennati, Ivy Peng

We present a blueprint for a quantum middle layer that supports applications across various quantum technologies. Inspired by concepts and abstractions from HPC libraries and middleware, our design is backend-neutral and context-aware. A program only needs to specify its intent once as typed data and operator descriptors. It declares what the quantum registe

Kento Kawaharazuka, Jihoon Oh, Jun Yamada, Ingmar Posner

Amid growing efforts to leverage advances in large language models (LLMs) and vision-language models (VLMs) for robotics, Vision-Language-Action (VLA) models have recently gained significant attention. By unifying vision, language, and action data at scale, which have traditionally been studied separately, VLA models aim to learn policies that generalise acr

Paul N Zivich, Stephen R Cole, Noah Greifer, Lina M Montoya

In epidemiology, some have argued that multiple comparison corrections are not necessary as there is rarely interest in the universal null hypothesis. From a parameter estimation perspective, epidemiologists may still be interested in multiple parameters. In this context, standard confidence intervals are not guaranteed to provide simultaneous coverage of mo

Yu Li, Zhiqiang Zhao, Zhuhua Zhang, Yong-Wei Zhang

The design of strain-stable, or even strain-enhanced thermal transport materials is critical for stable operation of high-performance electronic devices. However, most nanomaterials suffer from strain-induced degradation, with even minor tensile strains markedly reducing thermal conductivity. Here, we demonstrate that screw-dislocated graphites (SDGs), recen

André Hottung, Federico Berto, Chuanbo Hua, Nayeli Gast Zepeda

Designing high-performing heuristics for vehicle routing problems (VRPs) is a complex task that requires both intuition and deep domain knowledge. Large language model (LLM)-based code generation has recently shown promise across many domains, but it still falls short of producing heuristics that rival those crafted by human experts. In this paper, we propos

A. Giammarini, A. Pandolfi

We introduce a new model of the human corneal stroma, regarded as a fluid-saturated continuum, able to describe surface flattening and thickness thinning observed in several pathological conditions. In contrast with more common approaches that describe the human cornea as a quasi-incompressible hyperelastic medium, eventually including micro-structured aniso

Zheng Xing, Junting Chen

Channel state information (CSI) acquisition is a challenging problem in massive multiple-input multiple-output (MIMO) networks. Radio maps provide a promising solution for radio resource management by reducing online CSI acquisition. However, conventional approaches for radio map construction require location-labeled CSI data, which is challenging in practic

Hongbo Hu, Yisong Wang, Yi Huang, Kewen Wang

Possibilistic logic programs (poss-programs) under stable models are a major variant of answer set programming (ASP). While its semantics (possibilistic stable models) and properties have been well investigated, the problem of inductive reasoning has not been investigated yet. This paper presents an approach to extracting poss-programs from a background prog

Feng Qiao, Zu-Jian Ying

We propose a scheme to implement Heisenberg-limited spin squeezing in a hybrid cavity optomechanical-spin system. In our system, $N$ two-level systems are coupled via Tavis-Cummings interactions to a mechanical resonator (MR) in a standard optomechanical setup. Within the dispersive coupling regime, adiabatic elimination of the optical mode induces a squeezi

Daria Pugacheva, Andrey Moskalenko, Denis Shepelev, Andrey Kuznetsov

Vision Language Action (VLA) models are widely used in Embodied AI, enabling robots to interpret and execute language instructions. However, their robustness to natural language variability in real-world scenarios has not been thoroughly investigated. In this work, we present a novel systematic study of the robustness of state-of-the-art VLA models under lin

Nathan Ilten, Francesco Meazzini, Andrea Petracci

We compute the completion of the local ring of the Hilbert scheme of degree $n+1$ subschemes of $\mathbb{A}^n$ at the point corresponding to the ideal $\langle x_1,\ldots,x_n\rangle^2$, and describe the completion of the universal family. For the purposes of comparison, we do this computation with both classical and DGLA methods. We use our explicit equation

Ajinkya Gaikwad

We study the parameterized and kernelization complexity of the \emph{\textsc{$s$-Club Cluster Edge Deletion}} problem, a distance-bounded generalization of \emph{\textsc{Cluster Edge Deletion}}. Given a graph $G=(V,E)$ and integers $k,s$, the goal is to delete at most $k$ edges so that every resulting connected component has diameter at most $s$. On the stru

Manh Hung Nguyen, Sebastian Tschiatschek, Adish Singla

The difficulty and expense of obtaining large-scale human responses make Large Language Models (LLMs) an attractive alternative and a promising proxy for human behavior. However, prior work shows that LLMs often produce homogeneous outputs that fail to capture the rich diversity of human perspectives and behaviors. Thus, rather than trying to capture this di

Eugene Neelou, Ivan Novikov, Max Moroz, Om Narayan

The A2AS framework is introduced as a security layer for AI agents and LLM-powered applications, similar to how HTTPS secures HTTP. A2AS enforces certified behavior, activates model self-defense, and ensures context window integrity. It defines security boundaries, authenticates prompts, applies security rules and custom policies, and controls agentic behavi

Hadar Rotschield, Liat Peterfreund

SQL/PGQ is the emerging ISO standard for querying property graphs defined as views over relational data. We formalize its expressive power across three fragments: the read-only core, the read-write extension, and an extended variant with richer view definitions. Our results show that graph creation plays a central role in determining the expressiveness. The

Abdessamad Ahouita, Rene Baltazar, M'hammed El Kahoui, Sergey Gaifullin

Given an algebraically closed field $k$ of characteristic zero, we consider in this paper $k$-algebras of the form $$A_{c,q}=k[x,y,z]/\big(c(x)z-q(x,y)\big),$$ where $c(x)\in k[x]$ is a polynomial of degree at least two and $q(x,y)\in k[x,y]$ is a quasi-monic polynomial of degree at least two with respect to $y$. We give a complete description of the $k$-aut

Ori Nizan, Oren Shrout, Ayellet Tal

A concept may reflect either a concrete or abstract idea. Given an input image, this paper seeks to retrieve other images that share its central concepts, capturing aspects of the underlying narrative. This goes beyond conventional retrieval or clustering methods, which emphasize visual or semantic similarity. We formally define the problem, outline key requ

Rahul Kumar Padhy, Krishnan Suresh, Aaditya Chandrasekhar

Latent heat thermal energy storage (LHTES) systems are compelling candidates for energy storage, primarily owing to their high storage density. Improving their performance is crucial for developing the next-generation efficient and cost effective devices. Topology optimization (TO) has emerged as a powerful computational tool to design LHTES systems by optim

Sanoli Gun, Sunil Naik

In this article, we estimate the density of the set of primes $p$ such that the $p$-th Hecke eigenvalue of an Ikeda lift is divisible by a fixed positive integer. One of the main ingredients involves the study of abelian subfields of fixed fields of the kernel of Galois representations attached to elliptic Hecke eigenforms. Further, we study the distribution

Takao Tomono, Kazuya Tsujimura

Maintenance of production equipment is critical in manufacturing. Typically, machine learning models are trained on sensor data closely attached to equipment. However, as the number of machines increases, computational cost grows rapidly. In practice, anomalies are often identified by human operators through auditory perception, relying heavily on experience

Timo Eikelmann, Mara Brinkmann, Leonie Eggers, Tuncay Ulas

Nanophotonic light-matter interfaces hold great promise for quantum technologies. Enhancing local electromagnetic fields, they enable highly efficient detectors, can help realize optically connected processors, or serve as quantum repeaters. In-situ fiber-coupling at sub-Kelvin temperatures, as required for test and development of new devices, proves challen

Manshika Charvi Bissessur, Efimia Panagiotaki, Daniele De Martini

This work investigates how semantics influence localisation performance and robustness in a learned self-supervised, contrastive semantic localisation framework. After training a localisation network on both original and perturbed maps, we conduct a thorough post-hoc introspection analysis to probe whether the model filters environmental noise and prioritise

Xinlun Cheng, Bingzhe Chen, Joseph Choi, Yen T. Nguyen

Modeling shock-to-detonation phenomena in energetic materials (EMs) requires capturing complex physical processes such as strong shocks, rapid changes in microstructural morphology, and nonlinear dynamics of chemical reaction fronts. These processes participate in energy localization at hotspots, which initiate chemical energy release leading to detonation.

Aryan Golbaghi, Shuo Zhou

We propose a workflow for speech emotion recognition (SER) that combines pre-trained representations with automated hyperparameter optimisation (HPO). Using SpeechBrain wav2vec2-base model fine-tuned on IEMOCAP as the encoder, we compare two HPO strategies, Gaussian Process Bayesian Optimisation (GP-BO) and Tree-structured Parzen Estimators (TPE), under an i

Nicola Rossberg, Bennett Kleinberg, Barry O'Sullivan, Luca Longo

As artificial intelligence becomes increasingly pervasive and powerful, the ability to audit AI-based systems is growing in importance. However, explainability for artificial intelligence systems is not a one-size-fits-all solution; different target audiences have varying requirements and expectations for explanations. While various approaches to explainabil

Xingran Xu, Chunyu Jia, Xin-Xin Yang

The growing interest in exciton-polaritons has driven the need to manipulate their motion and engineer their band structures to the forefront of contemporary research. This study explores the band structures that emerge from a spatially modulated potential, ingeniously realized through the use of an optical conveyor belt. By leveraging Bloch theory and condu

Yuntao Gui, James Cheng

Despite their remarkable natural language understanding capabilities, Large Language Models (LLMs) have been underutilized for retrieval tasks. We present Search-R3, a novel framework that addresses this limitation by adapting LLMs to generate search embeddings as a direct output of their reasoning process. Our approach exploits LLMs' chain-of-thought capabi

Ernest Górka, Dariusz Baran, Michał Ćwiąkała, Gabriela Wojak

This study explores how different managerial behaviors influence team effectiveness and organizational outcomes, using Kenneth Blanchard's situational leadership model as a diagnostic tool. Conducted across ten companies, the research evaluates leadership adaptability through a scenario-based questionnaire identifying instructional, teaching, supportive, and

M. Ram Murty, Sunil Naik

In a seminal paper of 1915, V. Brun introduced Brun's sieve, which is based on Brun's inequality for the M\"{o}bius function and is a very powerful tool in modern number theory. The importance of the M\"{o}bius function in enumeration problems led G.-C. Rota to introduce the concept of the M\"{o}bius function to partially ordered sets. In this article, we pr

Yannick Strocka, Mohamed Belhassen, Tim Schröder, Gregor Pieplow

In the rapidly evolving field of quantum technology, the precise and detailed description of quantum components is not just a necessity but the foundation for advancing research, development, and applications. Optically accessible quantum memories are key building blocks for devices such as quantum repeaters and two-factor authentication. The memory we descr

Hendrik Bernd Zarucha, Peter Jung, Giuseppe Caire

The first part of this work considers a general class of covariance estimators. Each estimator of that class is generated by a real-valued function $g$ and a set of model covariance matrices $H$. If $\bf{W}$ is a potentially perturbed observation of a searched covariance matrix, then the estimator is the minimizer of the sum of $g$ applied to each eigenvalue

Tian Qin, Felix Bai, Ting-Yao Hu, Raviteja Vemulapalli

Human decision-making often involves constrained optimization. As LLM agents are deployed to assist with real-world tasks like travel planning, shopping, and scheduling, they must mirror this capability. We introduce COMPASS, a benchmark that evaluates whether LLM agents can perform constrained optimization in realistic travel planning settings. To success i

D. Schmeling, M. Russo, B. T. Gebreamlak, T. J. Kiker

The Columbia Stellarator eXperiment (CSX) is an upgrade of the Columbia Non-neutral Torus (CNT) that aims to demonstrate a university-scale, quasi-axisymmetric stellarator using high-temperature superconducting (HTS) technology at an on-axis magnetic field target of 0.5 T. Due to the strain sensitivity of ReBCO (Rare-earth Barium Copper Oxides), adapting it

Fenghe Tang, Chengqi Dong, Wenxin Ma, Zikang Xu

Over the past decade, U-Net has been the dominant architecture in medical image segmentation, leading to the development of thousands of U-shaped variants. Despite its widespread adoption, there is still no comprehensive benchmark to systematically evaluate their performance and utility, largely because of insufficient statistical validation and limited cons

Sandra Mantovani, Mariano Messora

In this paper we extend several classical results on pointed torsion theories -- also known as torsion pairs -- to the setting of non-pointed torsion theories defined via kernels and cokernels relative to a fixed class of trivial objects (often referred to as pretorsion theories). Our results are developed in the recently introduced framework of (non-pointed

Shaunak Kulkarni, Rohan Ajay Dubey

Currency crises are frequently discussed retrospectively as a necessary and deterministic outcome of a finite sequence of fiscal decisions, monetary manoeuvres, and limited exogenous inputs. Parallelly, the Twin Deficits Hypothesis (TDH) posits that an increase in the budget deficit leads to a direct rise in the current account deficit; although analogous to

Wenxun Wu, Yuanyang Li, Guhan Chen, Linyue Wang

Recent advances in large language models (LLMs) have popularized test-time scaling, where models generate additional reasoning tokens before producing final answers. These approaches have demonstrated significant performance improvements on benchmarks involving mathematical reasoning. However, language models relying solely on direct inference still struggle

Rajvee Sheth, Samridhi Raj Sinha, Mahavir Patil, Himanshu Beniwal

Amidst the rapid advances of large language models (LLMs), most LLMs still struggle with mixed-language inputs, limited Codeswitching (CSW) datasets, and evaluation biases, which hinder their deployment in multilingual societies. This survey provides the first comprehensive analysis of CSW-aware LLM research, reviewing 327 studies spanning five research area

Timur Bakiev, Yulij S. Ilyashenko

A bifurcation that occurs in a multiparameter family is a Cartesian product if it splits into two factors in the sense that one bifurcation takes place in one part of the phase portrait, another one -- in another part, and they are in a sense independent, do not interact with each other. To understand how a family bifurcates, it is sufficient to study it in

Tengwei Song, Min Wu, Yuan Fang

Molecular representation learning plays a crucial role in advancing applications such as drug discovery and material design. Existing work leverages 2D and 3D modalities of molecular information for pre-training, aiming to capture comprehensive structural and geometric insights. However, these methods require paired 2D and 3D molecular data to train the mode

Anoop Gavankar, Tanish Mittal, Joe Ninan, Shravan Hanasoge

Extreme precision radial velocity (EPRV) surveys usually require extensive observational baselines to confirm planetary candidates, making them resource-intensive. Traditionally, periodograms are used to identify promising candidate signals before further observational investment, but their effectiveness is often limited for low-amplitude signals due to stel

Akira Kamimaki, Yuhei Sekiguchi, Daisuke Ito, Taichi Fujiwara

Efficient quantum devices across various physical systems have been rapidly developed for entanglement-based quantum repeaters and spin-photon conversion; however, far less attention has been paid to standardizing platforms through quantum memory optical interfaces. We present a modular interface for color centers in diamond that is structurally isolated fro

Lixin Cheng, Chunlan Jiang, Liping Yuan

It is well known that every convex body in a finite dimensional normed space can be uniformly approximated by strictly convex and smooth convex bodies. However, in the case of infinite dimensions, little progress has been made since Klee asked how it is in the case of infinite dimensions in 1959. In this paper, we show that for an infinite dimensional Banach

Abhinav Kumar, Fan Yang, Sergio Aguilera Marinovic, Soshi Iba

Multi-fingered hands are emerging as powerful platforms for performing fine manipulation tasks, including tool use. However, environmental perturbations or execution errors can impede task performance, motivating the use of recovery behaviors that enable normal task execution to resume. In this work, we take advantage of recent advances in diffusion models t

Sicong Pan, Luca Lobefaro, Moein Taherkhani, Xuying Huang

Repeated plant monitoring is essential for tracking crop growth, and 3D reconstruction enables consistent comparison across monitoring sessions. However, rebuilding a 3D model from scratch in every session is costly and overlooks informative geometry already observed previously. We propose efficient view planning guided by a previous-session reconstruction,

Saravana Prashanth Murali Babu, Aida Parvaresh, Ahmad Rafsanjani

Kirigami, the traditional paper-cutting craft, holds immense potential for revolutionizing robotics by providing multifunctional, lightweight, and adaptable solutions. Kirigami structures, characterized by their bending-dominated deformation, offer resilience to tensile forces and facilitate shape morphing under small actuation forces. Kirigami components su

Dávid Csercsik, Mihály András Vághy

In a recent publication, using a simple two-period model, which is already capable to capture essential non-convex multiperiod bids, Richstein et al. have shown that in the case of optimal bidding, multi-part bidding always ensures a higher expected profit for the bidder, compared to simple bidding and block-bidding. The model proposed in their analysis assu

Shrestha Ghosh, Luca Giordano, Yujia Hu, Tuan-Phong Nguyen

LLMs are remarkable artifacts that have revolutionized a range of NLP and AI tasks. A significant contributor is their factual knowledge, which, to date, remains poorly understood, and is usually analyzed from biased samples. In this paper, we take a deep tour into the factual knowledge (or beliefs) of a frontier LLM, based on GPTKB v1.5 (Hu et al., 2025a),

Adam Gammon-Smith, Michael Knap, Frank Pollmann

It is an ongoing quest to realize topologically ordered quantum states on different platforms including condensed matter systems, quantum simulators and digital quantum processors. Unlike conventional states characterized by their local order, these exotic states are characterized by their non-local entanglement. The consequences of topological order can be

ZiHeng Huang, Di Wu, Jun Bai, Jiale Zhang

Machine unlearning is critical for enforcing data deletion rights like the "right to be forgotten." As a decentralized paradigm, Federated Learning (FL) also requires unlearning, but realistic implementations face two major challenges. First, fairness in Federated Unlearning (FU) is often overlooked. Exact unlearning methods typically force all clients into

Daniel Marín Pina, Mark Gieles, Sara Rastello, Giuliano Iorio

The Gaia collaboration announced the discovery of a massive black hole (BH) with a low-mass giant star companion, Gaia BH3, located in the ED-2 stellar stream. The properties of Gaia BH3 bridge the gap between known Milky Way BHs and extragalactic BHs found with gravitational waves (GWs). We aim to determine the most likely formation scenario for Gaia BH3 in

Yogesh Kumar, Hurmal Saren, Pintu Das

Thermal gradient driven skyrmion dynamics offers a promising route toward green spintronics, enabling the utilization of waste heat for information transport and processing. Using micromagnetic simulations, we investigate Neel skyrmions in a Co-Pt bilayer nanoracetrack and demonstrate that stochastic torques induced by a thermal gradient drive skyrmion motio

Jusen Du, Jiaxi Hu, Tao Zhang, Weigao Sun

Transformers excel at sequence modeling but face quadratic complexity, while linear attention offers improved efficiency but often compromises recall accuracy over long contexts. In this work, we introduce Native Hybrid Attention (NHA), a novel hybrid architecture of linear and full attention that integrates both intra & inter-layer hybridization into a unif

Dung Hoang-Anh, Cuong Pham Trung Le, Jianfei Cai, Thanh-Toan Do

Zero-shot quantization aims to learn a quantized model from a pre-trained full-precision model with no access to original real training data. The common idea in zero-shot quantization approaches is to generate synthetic data for quantizing the full-precision model. While it is well-known that deep neural networks with low sharpness have better generalization

Hong Liu

We review recent developments in the use of von Neumann algebras to analyze the entanglement structure of quantum gravity and the emergence of spacetime in the semi-classical limit. Von Neumann algebras provide a natural framework for describing quantum subsystems when standard tensor factorizations are unavailable, capturing both kinematic and dynamical asp

Dariusz Baran, Ernest Górka, Michał Ćwiąkała, Gabriela Wojak

This paper examines the impact of internal communication on effective business management through a comparative analysis of two medium-sized car rental companies operating in Poland. Using a structured survey completed by 220 employees, the study evaluates 15 communication-related factors, including feedback culture, managerial accessibility, message clarity

Jonas G. Matt, Pengcheng Huang, Balz Maag

Our increasingly digital and connected world has led to the generation of unprecedented amounts of data. This data must be efficiently managed, transmitted, and stored to preserve resources and allow scalability. Data compression has therein been a key technology for a long time, resulting in a vast landscape of available techniques. This largest-to-date stu

Ryu Hayakawa, Casper Gyurik, Mahtab Yaghubi Rad, Vedran Dunjko

We show the existence of an MA-complete homology problem for a certain subclass of simplicial complexes. The problem is defined through a new concept of orientability of simplicial complexes that we call a "uniform orientable filtration", which is related to sign-problem freeness in homology. The containment in MA is achieved through the design of new, highe

Vilma Orgoványi, Alex Rutar

We introduce the notion of a two-scale branching function associated with an arbitrary metric space, which encodes the lower and upper box dimensions as well as the Assouad spectrum. If the metric space is quasi-doubling, this function is approximately Lipschitz. We fully classify the attainable Lipschitz two-scale branching functions, which gives a new proo

G. H. Bandeira, D. Bazeia, G. S. Santiago, Ya. Shnir

We investigate a class of models described by two real scalar fields in two-dimensional spacetime. The study focuses mainly on the presence of exact static solutions which satisfy the first-order formalism, in models constructed to engender Lorentz symmetry violation. We start by exploring a direct connection between Lorentz breaking and geometric constraint

Jayanth R. Banavar, Achille Giacometti, Trinh X. Hoang, Amos Maritan

Proteins are linear chain molecules that play a central role in life and health. Protein native state folds are modular assemblies of space-filling building blocks of {\alpha}-helices, \{beta}-sheets and tight turns. Here we deduce the structures of a countable set of space-filling helical forms of a uniform discrete thick string from first principles with n

Jacopo Lenti, Lorenzo Costantini, Ariadna Fosch, Anna Monticelli

It is increasingly important to generate synthetic populations with explicit coordinates rather than coarse geographic areas, yet no established methods exist to achieve this. One reason is that latitude and longitude differ from other continuous variables, exhibiting large empty spaces and highly uneven densities. To address this, we propose a population sy

Mrityunjay Kumar, Venkatesh Choppella

Working effectively with large, existing software systems requires strong comprehension skills, yet most graduates enter the industry with little preparation for this challenge. We report early results from a pilot intervention integrated into a SaaS company's onboarding program: a five-session course introducing systems thinking and Labelled Transition Syst

Takahiro Matsumoto, Takahiro Kusabuka, Hiroshi Chigira, Kazuhiko Murasaki

We present a low-latency tele-immersive entertainment system that streams 3D point clouds and performers' footstep vibrations, creating the sense that the stage is present. Moving performers and their surroundings are captured as dynamic point clouds under rapidly changing lighting, then processed, transmitted, and rendered within a total latency of less tha