October 2025 arXiv papers — page 171

Augustin Delecluse, Pierre Schaus, Pascal Van Hentenryck

Constraint Programming (CP) offers an intuitive, declarative framework for modeling Vehicle Routing Problems (VRP), yet classical CP models based on successor variables cannot always deal with optional visits or insertion based heuristics. To address these limitations, this paper formalizes sequence variables within CP. Unlike the classical successor models,

Križan Jurinović, Merry Mitra, Rakesh Mukherjee, Thomas E. Ouldridge

DNA strand displacement (SD) reactions are central to the operation of many synthetic nucleic acid systems, including molecular circuits, sensors, and machines. Over the years, a broad set of design frameworks has emerged to accommodate various functional goals, initial configurations, and environmental conditions. Nevertheless, key challenges persist, parti

Nitish K. Panigrahy, Leonardo Bacciottini, C. V. Hollot, Emily A. Van Milligen

We introduce a distributed resource allocation framework for the Quantum Internet that relies on feedback-based, fully decentralized coordination to serve multiple co-existing applications. We develop quantum network control algorithms under the mathematical framework of Quantum Network Utility Maximization (QNUM), where utility functions quantify network pe

Joseph Bernstein, Pritam Ganguly, Bernhard Krötz, Job Kuit

The Casselman-Wallach theorem is a foundational result in the theory of representations of real reductive groups connecting algebraic representations to topological representations. We provide a quantitative version of this theorem. For that we introduce the notion of {\it Sobolev gap} for a Harish-Chandra module. This is a new invariant whose finiteness is

Xingyu Lin, Yilin Wen, En Wang, Du Su

Group Relative Policy Optimization (GRPO) has significantly advanced the reasoning ability of large language models (LLMs), particularly by boosting their mathematical performance. However, GRPO and related entropy-regularization methods still face challenges rooted in the sparse token rewards inherent to chain-of-thought (CoT). Current approaches often rely

Sotiris Armeniakos, Aris Daniilidis

We study maximal monotone operators $A : X \rightrightarrows X^*$ whose Fitzpatrick family reduces to a singleton; such operators will be called uniquely representable. We show that every such operator is cyclically monotone (hence, $A=\partial f$ for some convex function $f$) if and only if it is 3-monotone. In Radon-Nikod\'{y}m spaces, under mild condition

Jinxiang Tu, Dayong Ren, Fei Shi, Zhenhong Jia

Accurate forest biomass quantification is vital for carbon cycle monitoring. While airborne LiDAR excels at capturing 3D forest structure, directly estimating woody volume and Aboveground Biomass (AGB) from point clouds is challenging due to difficulties in modeling long-range dependencies needed to distinguish trees.We propose Minkowski-MambaNet, a novel de

Peng Yang, Marta Perez-Gussinye, Shaowen Liu, Javier Garcia-Pintado

Intermediate rifted margins exhibit neither seaward dipping reflectors nor exhumed mantle at the continent-ocean transition (COT). Instead, they transition into normal-thickness, magmatic Penrose-type oceanic crust, and thus diverge from the classic magma-rich and magma-poor end-member models. However, several intermediate margins, such as the South China Se

Valentin Biller, Lucas Zimmer, Ayhan Can Erdur, Sandeep Nagar

Magnetic resonance imaging (MRI) inpainting supports numerous clinical and research applications. We introduce the first generative model that conditions on voxel-level, continuous tumor concentrations to synthesize high-fidelity brain tumor MRIs. For the BraTS 2025 Inpainting Challenge, we adapt this architecture to the complementary task of healthy tissue

Franco T. Lisandrini, Edmond Orignac, Roberta Citro, Ameneh Sheikhan

We study the effect of density-assisted hopping on different dimerized lattice geometries, such as bilayers and ladder structures. We show analytically that the density-assisted hopping induces an attractive interaction in the lower (bonding) band of the dimer structure and a repulsion in the upper (anti-bonding) band. Overcoming the onsite repulsion, this c

Razi Iqbal

One of the main problems for farmers is the protection of their crops, before and after harvesting, from animals and birds. To overcome this problem, this paper proposes a model of safe farming in which the crops will be protected from vertebrates attack through a prevention system that is based on Wirelesses Sensors Networks. Different sensor nodes are plac

Sankalp Gilda

In the era of exploding survey volumes, traditional methods of spectroscopic analysis are being pushed to their limits. In response, we develop deep-REMAP, a novel deep learning framework that utilizes a regularized, multi-task approach to predict stellar atmospheric parameters from observed spectra. We train a deep convolutional neural network on the PHOENI

Junyan Ye, Dongzhi Jiang, Jun He, Baichuan Zhou

Recently, Multimodal Large Language Models (MLLMs) have made rapid progress, particularly in enhancing their reasoning capabilities. However, existing reasoning benchmarks still primarily assess language-based reasoning, often treating visual input as replaceable context. To address this gap, we introduce BLINK-Twice, a vision-centric reasoning benchmark gro

Alwell Nwachukwu, Muhammad Garba, Jamel Ali, Theo Siegrist

We report experiments on the magnetophoresis of paramagnetic (MnCl2) and diamagnetic (ZnCl2) metal ions in porous media under the influence of a non-uniform magnetic field generated by a permanent magnet. Experiments were carried out in a range of initial ion concentrations (1-100 mM), porous media particle sizes (63 um and 500 um), and varying mixture ratio

Eshaan Tanwar, Deepak Nathani, William Yang Wang, Tanmoy Chakraborty

Large Language Models (LLMs) fine-tuned for specific domains exhibit strong performance; however, the underlying mechanisms by which this fine-tuning reshapes their parametric space are not well understood. Prior works primarily focus on auto-regressive or general-purpose instruct models, leaving domain-specialised LLMs under-explored. We present the first s

Qihang Ma, Shengyu Li, Jie Tang, Dingkang Yang

Multi-modal keyphrase prediction (MMKP) aims to advance beyond text-only methods by incorporating multiple modalities of input information to produce a set of conclusive phrases. Traditional multi-modal approaches have been proven to have significant limitations in handling the challenging absence and unseen scenarios. Additionally, we identify shortcomings

P. van Oerle, R. H. Bemthuis, F. A. Bukhsh

Large Language Models (LLMs) are increasingly used to generate textual explanations of process models discovered from event logs. Producing explanations from large behavioral abstractions (e.g., directly-follows graphs or Petri nets) can be computationally expensive. This paper reports an exploratory evaluation of explanation quality under progressive behavi

Luca Santosuosso, Bettina Klinz, Sonja Wogrin

Generation expansion planning (GEP) is a prominent example of capacity expansion problems in operations research. Being generally NP-hard, GEP optimization models can become intractable when nonconvex dynamics, time-coupling constraints, and complex asset interactions are involved. Time series aggregation (TSA) tackles this by reducing temporal complexity vi

Marc Masdeu, Eloi Torrents

Let $B$ be a totally-definite quaternion algebra over a totally real field $F$, let $\mathfrak{p}$ be a prime ideal of $F$, and let $\Gamma$ be the group of reduced norm-$1$ elements of an Eichler $\mathcal{O}_F[1/\mathfrak{p}]$-order $R$ inside $B$. We give an algorithm to compute the fundamental domain for the action of $\Gamma$ on the Bruhat-Tits tree of

Fang Yuan, Junjie Zeng, Yue Hu, Zhengqiu Zhu

SOAR, a classic symbol-based cognitive architecture, has been fostering the development of general, human-like intelligent agents. Nevertheless, its practical adoption is hindered by the laborious manual rule coding. Emerging Large Language Models (LLMs) present the immense potential for efficient rules generation. However, there is a critical gap that curre

Yunxiang Zhang, Muhammad Khalifa, Lechen Zhang, Xin Liu

Large reasoning models exhibit long chain-of-thought reasoning with complex strategies such as backtracking and self-verification. Yet, these capabilities typically require resource-intensive post-training. We investigate whether such behaviors can be elicited in large models without any gradient updates. To this end, we propose a decoding-time approach, Thi

Emerson de Melo, Júlia Kato

Let $G$ be a group. The orbits of the natural action of $Aut(G)$ on $G$ are called the automorphism orbits of $G$, and their number is denoted by $\omega(G)$. Let $\mathbb{F}$ be an infinite field, and let $UT_n(\mathbb{F})$ denote the group of unitriangular matrices over $\mathbb{F}$. We show that $\omega(UT_n(\mathbb{F}))$ is finite for $n \leq 5$ and infi

Francesco Maria Molfese, Luca Moroni, Ciro Porcaro, Simone Conia

While Small Language Models (SLMs) have demonstrated promising performance on an increasingly wide array of commonsense reasoning benchmarks, current evaluation practices rely almost exclusively on the accuracy of their final answers, neglecting the validity of the reasoning processes that lead to those answers. To address this issue, we present ReTraceQA, a

Vu Duc Anh Nguyen, Ziyue Li

The operational efficiency of railway networks, a cornerstone of modern economies, is persistently undermined by the cascading effects of train delays. Accurately forecasting this delay propagation is a critical challenge for real-time traffic management. While recent research has leveraged Graph Neural Networks (GNNs) to model the network structure of railw

Yeomoon Kim, Minsoo Kim, Jip Kim

Ensuring both feasibility and efficiency in optimal power flow (OPF) operations has become increasingly important in modern power systems with high penetrations of renewable energy and energy storage. While deep neural networks (DNNs) have emerged as promising fast surrogates for OPF solvers, they often fail to satisfy critical operational constraints, espec

Magdalena Furman, Marcin Muszyński, Przemysław Oliwa, Łukasz Zinkiewicz

On-chip optical architectures that enable angle-resolved spectroscopy are essential for advancing photonic platforms towards low-volume, scalable, and cryo-compatible devices. Here, we introduce spatially resolved momentum-space imaging using arrays of 3D-printed microlenses directly integrated onto semiconductor optical microcavities. Each microlens functio

Hairu Wang, Sheng You, Qiheng Zhang, Xike Xie

Unlike Business-to-Consumer e-commerce platforms (e.g., Amazon), inexperienced individual sellers on Consumer-to-Consumer platforms (e.g., eBay) often face significant challenges in setting prices for their second-hand products efficiently. Therefore, numerous studies have been proposed for automating price prediction. However, most of them are based on stat

Gianmaria Verzini

We consider a shape optimization problem for the persistence threshold of a biological species dispersing in a periodically fragmented environment, the unknown shape corresponding to the portion of the habitat which is favorable to the population. Analytically, this translates in the minimization of a weighted eigenvalue of the periodic Laplacian, with respe

Subaru Nomoto

We introduced generalized Bishop frames on curves in 4-dimensional Euclidean space $\mathbb{E}^{4}$, which are orthonormal frames such that the derivatives of the vectors of the frames along the curve can be expressed, via a certain matrix, as a linear combination of the vectors of the frame. In relation to that, we study generalized Bishop frames of regular

Jinyuan Liu, Zihang Chen, Zhu Liu, Zhiying Jiang

We engage in the relatively underexplored task named thermal infrared image enhancement. Existing infrared image enhancement methods primarily focus on tackling individual degradations, such as noise, contrast, and blurring, making it difficult to handle coupled degradations. Meanwhile, all-in-one enhancement methods, commonly applied to RGB sensors, often d

Ashutosh Kumar, Adrien Moll, Jitendra Kumar, Diana Dragoe

High-entropy oxides (HEOs) offer a unique platform for exploring the thermodynamic interaction between configurational entropy and enthalpy in stabilizing complex solid solutions. In this study, a series of rock-salt structured oxides with varying configurational entropy, ranging from binary to multi-cation systems, to elucidate the competing roles of enthal

Hanlin Sun, Filippo Radicchi, Ginestra Bianconi

Triadic interactions are special types of higher-order interactions that occur when regulator nodes modulate the interactions between other two or more nodes. In presence of triadic interactions, a percolation process occurring on a single-layer network becomes a fully-fledged dynamical system, characterized by period-doubling and a route to chaos. Here, we

Sebastian Magierowski, Zhongpan Wu, Abel Beyene, Karim Hammad

Miniature DNA sequencing hardware has begun to succeed in mobile contexts, driving demand for efficient machine learning at the edge. This domain leverages deep learning techniques familiar from speech and time-series analysis for both low-level signal processing and high-level genomic interpretation. Unlike audio, however, nanopore sequencing presents raw d

Joachim Diederich

We present a novel framework for training large language models with continuously adjustable internal representations that span the full spectrum from localist (interpretable, rule-based) to distributed (generalizable, efficient) encodings. The key innovation is a locality dial, a tunable parameter that dynamically controls the degree of localization during

Manzi Nan, Pengcheng Li, Guojun Wei, Xilong Xiang

The nucleon-nucleon ($NN$) inelastic cross section plays an important role in constraining the nuclear equation of state at high baryon density and in describing the formation and evolution of compact astrophysical objects. In this study, the temperature $T$ dependence of the $Δ^{++}$ and $Δ^{-}$ production cross sections in the isospin-symmetric and -asymme

Çetin Dişibüyük

In order to construct quantum trigonometric B\'ezier curves with shape parameter, one parameter family of trigonometric Bernstein basis functions are introduced. We study the total positivity of the basis functions to analyze the shape preserving properties of the quantum trigonometric B\'ezier curves. We also showed that quantum trigonometric B\'ezier curve

Shaoyun Bai, Jae Hee Lee

We define quantum deformations of Adams operations in $K$-theory, in the framework of quasimap quantum $K$-theory. They provide $K$-theoretic analogs of the quantum Steenrod operations from equivariant symplectic Gromov--Witten theory. We verify the compatibility of these operations with the Kahler and equivariant $q$-difference module structures, provide sa

Peteris Daugulis

Efficient and equitable access to municipal services hinges on well-designed administrative divisions. It requires ongoing adaptation to changing demographics, infrastructure, and economic factors. This article proposes a novel transparent data-driven method for territorial division based on the Voronoi partition of edge-weighted road graphs and the vertex $

Till Aczel, Lucas Theis, Roger Wattenhofer

Evaluating generative models is challenging because standard metrics often fail to reflect human preferences. Human evaluations are more reliable but costly and noisy, as participants vary in expertise, attention, and diligence. Pairwise comparisons improve consistency, yet aggregating them into overall quality scores requires careful modeling. Bradley-Terry

Grégory Berhuy

In this paper, we study the problem of decomposability of bilinear spaces of dimension four without symmetry, as well as the problem of decomposability of split central simple algebras of degree four with an anti-automorphism. In particular, we show that, contrary to the case of symmetric or skew-symmetric bilinear spaces, these two problems are not equivale

Yu-Chen Lu, Chong-Yan Chen, Chi-Chih Chang, Yu-Fang Hu

Although large language models (LLM) have achieved remarkable performance, their enormous parameter counts hinder deployment on resource-constrained hardware. Low-rank compression can reduce both memory usage and computational demand, but applying a uniform compression ratio across all layers often leads to significant performance degradation, and previous m

Rostyslav O. Serha, Carsten Dubs, Andrii V. Chumak

Quantum magnonics studies the quantum properties of magnons, the quanta of spin waves, and their application in quantum information processing. Progress in this field depends on identifying magnetic materials with characteristics tailored to the diverse requirements of magnonics and quantum magnonics. For single-magnon excitation, its control, hybrid couplin

Zenan Lin, Wei Li, Jintao Chen, Zihao Wu

Nuclei instance segmentation in pathological images is crucial for downstream tasks such as tumor microenvironment analysis. However, the high cost and scarcity of annotated data limit the applicability of fully supervised methods, while existing semi-supervised methods fail to adequately regularize consistency at the instance level, lack leverage of the inh

Aniss Aiman Medbouhi, Alejandro García-Castellanos, Giovanni Luca Marchetti, Daniel Pelt

We study the problem of constructing Steiner Minimal Trees (SMTs) in hyperbolic space. Exact SMT computation is NP-hard, and existing hyperbolic heuristics such as HyperSteiner are deterministic and often get trapped in locally suboptimal configurations. We introduce Randomized HyperSteiner (RHS), a stochastic Delaunay triangulation heuristic that incorporat

Jianyuan Qi, Shijie Xiong, Beining Ma, Xinghai Shen

The design and fabrication of room-temperature ferrotoroidic materials and magnetic semiconductors are recognized worldwide as a great challenge, and of both theoretical and practical importance in the field of condensed matter physics and information storage. Reported herein are ferrotoroidic crystal powder and film formed by supramolecular self-assembly ba

Romario Zarik, Nahum Kiryati, Michael Green, Liran Domachevsky

PET/CT imaging is the gold standard for tumor detection, offering high accuracy in identifying local and metastatic lesions. Radiologists often begin assessment with rotational Multi-Angle Maximum Intensity Projections (MIPs) from PET, confirming findings with volumetric slices. This workflow is time-consuming, especially in metastatic cases. Despite their c

Till Freihaut, Luca Viano, Emanuele Nevali, Volkan Cevher

We close open theoretical gaps in Multi-Agent Imitation Learning (MAIL) by characterizing the limits of non-interactive MAIL and presenting the first interactive algorithm with near-optimal sample complexity. In the non-interactive setting, we prove a statistical lower bound that identifies the all-policy deviation concentrability coefficient as the fundamen

Yishai Lavi, Ori Parzanchevski

We study the simplicial order complexes obtained from free modules over finite local rings. These complexes arise naturally as geodesic spheres in Bruhat-Tits buildings over non-archimedean local fields. We establish two forms of rigidity, showing that their automorphism groups arise from the underlying algebraic group, and that they are determined by sparse

Gopal Chandra Dutta, Amit Kumar Paul, Subhankar Sau

We study a generalized motion planning problem involving multiple autonomous robots navigating in a $d$-dimensional Euclidean space in the presence of a set of obstacles whose positions are unknown a priori. Each robot is required to visit sequentially a prescribed set of target states, with the number of targets varying between robots. This heterogeneous se

Gianluca Giacchi

Time-frequency representations stemmed in 1932 with the introduction of the Wigner distribution. For most of the 20th century, research in this area primarily focused on defining joint probability distributions for position and momentum in quantum mechanics. Applications to electrical engineering were soon established with the seminal works of Gabor and the

T. Cridge, L. A. Harland-Lang, R. S. Thorne

We present updates to the MSHT approximate N3LO PDFs focusing upon recent developments, examining the impacts of newly determined splitting function and transition matrix element calculations, performed since the public MSHT20aN3LO PDF set was released. We observe only small changes to the output PDFs, at most of similar size to their quoted uncertainties an

Wenyao Zhang, Hongsi Liu, Bohan Li, Jiawei He

Current self-supervised monocular depth estimation (MDE) approaches encounter performance limitations due to insufficient semantic-spatial knowledge extraction. To address this challenge, we propose Hybrid-depth, a novel framework that systematically integrates foundation models (e.g., CLIP and DINO) to extract visual priors and acquire sufficient contextual

Manisha Kumari, Dinesh Kumar

We have introduced the notion of the bungee set and the filled Julia set of a transcendental semigroup using Fatou-Julia theory. Numerous results of the bungee set of a single transcendental entire function have been generalized to a transcendental semigroup. For a transcendental semigroup having no oscillatory wandering domain, we provide some conditions fo

Matthias Sroczinski

This paper establishes global existence and asymptotic decay for small solutions to quasilinear systems of hyperbolic balance laws, where, generalizing previous works, the hyperbolic operator does not need to admit an entropy nor does the source term need to satisfy any symmetry assumptions. Dissipative properties are characterized by three conditions corres

Felix Brandt, Andreas Heuermann, Philip Hannebohm, Bernhard Bachmann

This paper presents a residual-informed machine learning approach for replacing algebraic loops in equation-based Modelica models with neural network surrogates. A feedforward neural network is trained using the residual (error) of the algebraic loop directly in its loss function, eliminating the need for a supervised dataset. This training strategy also res

Yuanming Zhang, Yan Lin, Arijit Khan, Huaiyu Wan

We compile 129 public LLM prompt datasets with more than 1.22TB and more than 673M instances and organize them into a unified taxonomy. We use seven datasets for detailed analysis and identify lexical, syntactic, and semantic patterns that distinguish prompts from general text. We evaluate these features in prompt filtering, source domain routing, and elicit

Siu-Kui Au, Zi-Jun Cao

Engineering risk is concerned with the likelihood of failure and the scenarios when it occurs. The sensitivity of failure probability to change in system parameters is relevant to risk-informed decision making. Computing sensitivity is at least one level more difficult than the probability itself, which is already challenged by a large number of input random

Haozhe Jia, Wenshuo Chen, Xiucheng Wang, Nan Cheng

Accurate and real-time radio map (RM) generation is crucial for next-generation wireless systems, yet diffusion-based approaches often suffer from large model sizes, slow iterative denoising, and high inference latency, which hinder practical deployment. To overcome these limitations, we propose \textbf{RadioFlow}, a novel flow-matching-based generative fram

Minsik Choi, Hyegang Son, Changhoon Kim, Young Geun Kim

Transformer-based models have achieved remarkable performance in NLP tasks. However, their structural characteristics-multiple layers and attention heads-introduce efficiency challenges in inference and deployment. To address these challenges, various pruning methods have recently been proposed. Notably, gradient-based methods using Head Importance Scores (H

Roman Prosanov

We first prove that given a Fuchsian representation $\rho_\circ: \pi_1S \ra {\rm PSL}(2,\R)$, where $S$ is a closed oriented surface of genus $\geq 2$, any hyperbolic cone-metric on $S$ with cone-angles $>2\pi$ isometrically embeds as a future-convex bent Cauchy surface in a globally hyperbolic maximal Cauchy compact (GHMC) anti-de Sitter (2+1)-spacetime who

Zheng Zhao, Yeskendir Koishekenov, Xianjun Yang, Naila Murray

Current Chain-of-Thought (CoT) verification methods predict reasoning correctness based on outputs (black-box) or activations (gray-box), but offer limited insight into why a computation fails. We introduce a white-box method: Circuit-based Reasoning Verification (CRV). We hypothesize that attribution graphs of correct CoT steps, viewed as execution traces o

Alexander Karlberg, Paolo Nason, Gavin Salam, Giulia Zanderighi

We document the three main new features in the v2 release series of the HOPPET parton distribution function evolution code, specifically support for N$^3$LO QCD evolution in the variable flavour number scheme, for the determination of hadronic structure functions for massless quarks up to N$^3$LO, and for QED evolution to an accuracy phenomenologically equiv

Jianuo Huang, Yaojie Zhang, Yicun Yang, Benhao Huang

Diffusion large language models (dLLMs) present a promising alternative to dominant autoregressive models (ARMs) by the ability of parallel decoding at the expense of substantial computation and memory costs. Specifically, the cache mechanism for bidirectional attention in dLLMs demands large memory footprint, restricting their ability to handle long context

Mira Raheem, Amal Elgammal, Michael Papazoglou, Bernd Krämer

Artificial intelligence (AI) has the potential to transform healthcare by supporting more accurate diagnoses and personalized treatments. However, its adoption in practice remains constrained by fragmented data sources, strict privacy rules, and the technical complexity of building reliable clinical systems. To address these challenges, we introduce a model

Natalia Tomashenko, Junichi Yamagishi, Xin Wang, Yun Liu

Most of the existing speaker anonymization research has focused on single-speaker audio, leading to the development of techniques and evaluation metrics optimized for such condition. This study addresses the significant challenge of speaker anonymization within multi-speaker conversational audio, specifically when only a single target speaker needs to be ano

Alemu Sisay Nigru, Michele Svanera, Austin Dibble, Connor Dalby

Accurate segmentation of infant brain MRI is critical for studying early neurodevelopment and diagnosing neurological disorders. Yet, it remains a fundamental challenge due to continuously evolving anatomy of the subjects, motion artifacts, and the scarcity of high-quality labeled data. In this work, we present LODi, a novel framework that utilizes prior kno

Monica Rainer, Evandro Balbi, Francesco Borsa, Paola Cianfarra

One of the frontier research fields of exoplanetary science is the study of the composition and variability of exoplanetary atmospheres. This field is now moving from the gas giant planets towards the smaller and colder telluric planets, and future instruments like ANDES will focus on the observations of the atmosphere of telluric planets in the habitable zo

Marwan Soliman, Pauline Kergus, Diego Regruto, Luiz Villa

The fundamental role of power converters is to efficiently manage and control the flow of electrical energy, ensuring compatibility between power sources and loads. All these applications of power converters need the design of an appropriate control law. Control of power converters is a challenging problem due to the presence of switching devices which are d

Sami Raatikainen, Syksy Rasanen, Eemeli Tomberg

We study stochastic effects in viable ultra-slow-roll inflation models that produce primordial black holes. We consider asteroid, solar, and supermassive black hole seed masses. In each case, we simulate $10^8$ patches of the universe that may collapse into PBHs. In every patch, we follow $4\times10^4$ momentum shells to construct its spherically symmetric p

Yuying Li, Siyi Qian, Hao Liang, Leqi Zheng

Geometric reasoning remains a core challenge for Multimodal Large Language Models (MLLMs). Even the most advanced closed-source systems, such as GPT-O3 and Gemini-2.5-Pro, still struggle to solve geometry problems reliably, despite exhibiting strong textual reasoning abilities on tasks like the International Mathematical Olympiad (IMO). This gap suggests tha

Ekaterina Borisova, Anastasiya Ponosova, Natalia Arutyunyan, Alexey Shilko

We experimentally demonstrate a power limiter based on single-walled carbon nanotubes dispersed in a polymer matrix. This simple fiber-optic device permanently increases its attenuation when subjected to 50-mW or higher cw illumination at 1550 nm and initiates a fiber-fuse effect at 1 to 5 W. It may be used for protecting quantum key distribution equipment f

Yuhua Ren, Hui Pan, Jian-Sheng Wang

Floquet engineering offers a powerful route to enhance emission in time-modulated media. Here, we investigate the influence of time-modulated permittivity in silicon carbide on its intensity spectrum. We consider both the nonequilibrium Green's function approach and the macroscopic quantum electrodynamics approach, and establish their formal compatibility by

Tejaswi V. Panchagnula

Animals often forage via Levy walks stochastic trajectories with heavy tailed step lengths optimized for sparse resource environments. We show that human visual gaze follows similar dynamics when scanning images. While traditional models emphasize image based saliency, the underlying spatiotemporal statistics of eye movements remain underexplored. Understand

Muhammad Munsif, Waqas Ahmad, Amjid Ali, Mohib Ullah

Connected Vision Systems (CVS) are transforming a variety of applications, including autonomous vehicles, smart cities, surveillance, and human-robot interaction. These systems harness multi-view multi-camera (MVMC) data to provide enhanced situational awareness through the integration of MVMC tracking, re-identification (Re-ID), and action understanding (AU

Prerna Paliwal, Jutta Toscano, Stefan Willitsch

Over the past years, radiofrequency ion traps have become an attractive platform for studying chemical reactions as they enable a high degree of control over ion-molecule dynamics. In this review, we summarize techniques for the trapping and cooling of atomic and molecular ions in radiofrequency traps including Doppler and resolved-sideband laser cooling, sy

Zhitian Hou, Kun Zeng

Criminal Court View Generation (CVG) is a fundamental task in legal artificial intelligence, aiming to automatically generate the "Court View" section of a legal case document. Generating court views is challenging due to the diversity and complexity of case facts, and directly generating from raw facts may limit performance. In this paper, we present ShiZhi

Philyoung Jeong, Ivann Schlosser, Alexei Poliakov, Elsa Arcaute

Healthy and liveable neighbourhoods have increasingly been recognised as essential components of sustainable urban development. Yet, ambiguity surrounding their definition and constituent elements presents challenges in understanding and evaluating neighbourhood profiles, highlighting the need for a more detailed and systematic assessment. This research deve

Jiapeng Wang, Changxin Tian, Kunlong Chen, Ziqi Liu

Reliable evaluation is fundamental to the progress of Large Language Models (LLMs), yet the evaluation process during pre-training is plagued by significant instability that obscures true learning dynamics. In this work, we systematically diagnose this instability, attributing it to two distinct sources: \textit{Parameter Instability} from training stochasti

Ilyas Varshavskiy, Bonu Boboeva, Shuhrat Khalilbekov, Azizjon Azimi

Machine Learning models in finance are highly susceptible to model drift, where predictive performance declines as data distributions shift. This issue is especially acute in developing economies such as those in Central Asia and the Caucasus - including Tajikistan, Uzbekistan, Kazakhstan, and Azerbaijan - where frequent and unpredictable macroeconomics shoc

Kohei Oda, Po-Min Chuang, Kiyoaki Shirai, Natthawut Kertkeidkachorn

Sentence embedding methods have made remarkable progress, yet they still struggle to capture the implicit semantics within sentences. This can be attributed to the inherent limitations of conventional sentence embedding methods that assign only a single vector per sentence. To overcome this limitation, we propose DualCSE, a sentence embedding method that ass

Flavio Ascari, Roberto Bruni, Roberta Gori, Azalea Raad

O'Hearn's Incorrectness Logic (IL) has sparked renewed interest in static analyses that aim to detect program errors rather than prove their absence, thereby avoiding false alarms -- a critical factor for practical adoption in industrial settings. As new incorrectness logics emerge to capture diverse error-related properties, a key question arises: can the c

Manuel R. Arahal, Manuel G. Satué, Kumars Rouzbehi, Francisco Colodro

Predictive Stator Current Control (PSCC) has been proposed for control of multi-phase drives. The flexibility offered by the use of a Cost Function has been used to deal with the increased number of phases. However, tuning of the Weighting Factors constitutes a problem. Intensive trial and error tests are usual in this context. Existing on-line selection met

Salah Mecheri

Let $B(X)$ be the Banach algebra of all bounded linear operators acting on a Banach space $X$. Are sums and products of commuting decomposable operators on Banach spaces decomposable? This is one of the most important open problems in the local spectral theory of operators on Banach spaces. Similarly, it is not known if local spectral properties such as the

Matthias Sroczinski, Kevin Zumbrun

We give the first proof of nonlinear stability for smooth shock profiles of second-order dissipative hyperbolic-hyperbolic systems under the assumption of spectral stability, showing stability of smooth small-amplitude profiles in dimensions greater than or equal to two. This class of systems notably includes the two types of causal viscous relativistic gas

Antoine Amarilli, Mikaël Monet, Rémi De Pretto

In this note, we study two rewrite rules on hypergraphs, called edge-domination and node-domination, and show that they are confluent. These rules are rather natural and commonly used before computing the minimum hitting sets of a hypergraph. Intuitively, edge-domination allows us to remove hyperedges that are supersets of another hyperedge, and node-dominat

Kentaro Imafuku

We prove that an effective temperature naturally emerges from the algorithmic structure of a regular universal Turing machine (UTM), without introducing any external physical parameter. In particular, the redundancy growth of the machine's wrapper language induces a Boltzmann--like exponential weighting over program lengths, yielding a canonical ensemble int

Siyuan Huang, Xiaoye Qu, Yafu Li, Yun Luo

While Reinforcement Learning with Verifiable Rewards (RLVR) has advanced the reasoning capabilities of Large Vision-Language Models (LVLMs), most existing methods in multimodal reasoning neglect the critical role of visual perception within the RLVR optimization process. In this paper, we undertake a pioneering exploration of multimodal RLVR through the nove

M. N. Sergeenko

Inclusive processes at high energies are studied in a non-perturbative approach in QCD using a modified Quark-Gluon String Model. Theoretical and experimental aspects of diffraction dissociation are discussed. In the calculations of cross sections, the parameters of complex nonlinear trajectories of Pomeranchuk and Reggeons are used. Particular attention is

Mariat James Elizebeth, Shufeng Chen, Halima El Badaoui, Siddartha Khastgir

Electric Vertical Take-Off and Landing (eVTOL) aircraft are expected to be quieter and more cost-effective than helicopters, offering major economic and social benefits through improved connectivity. Their adoption will require new ground infrastructure and airspace redesign, introducing risks involving multiple stakeholders (Regulators, eVTOL operators, Air

Manuel R. Arahal, Manuel G. Satué, Kumars Rouzbehi, Juana M. Martínez-Heredia

The field of Finite State Model Predictive Control for multiphase drives has produced many contributions. Many variants of FSMPC exist, each aiming at some aspect such as complexity of the cost function, switching frequency, etc. Despite past efforts to compare different techniques, the field is still out of consensus regarding the relative merits of each on

Salah Mecheri

In this paper we show that quasisimilar $n$-tuples of tensor products of $m$-isometric operators have the same spectra, essential spectra and indices. The properties of single Fredholm operators possess \cite{4} is related to an important property which has a leading role on the theory of Fredholm operators: Fredholm n-tuples of operators. It is well known t

Camille M. Montalcini, Peter J. Rousseeuw

Boxplots and related visualization methods are widely used exploratory tools for taking a first look at collections of univariate variables. In this note an extension is provided that is specifically designed to detect and display bimodality and multimodality when the data warrant it. For this purpose a univariate clustering method is constructed that ensure

Xiangxu Zhang, Lei Li, Yanyun Zhou, Xiao Zhou

Medical diagnostics is a high-stakes and complex domain that is critical to patient care. However, current evaluations of large language models (LLMs) remain limited in capturing key challenges of clinical diagnostic scenarios. Most rely on benchmarks derived from public exams, raising contamination bias that can inflate performance, and they overlook the co

Ming Dai, Sen Yang, Boqiang Duan, Wankou Yang

Referring Video Object Segmentation (RefVOS) seeks to segment target objects in videos guided by natural language descriptions, demanding both temporal reasoning and fine-grained visual comprehension. Existing sampling strategies for LLM-based approaches typically rely on either handcrafted heuristics or external keyframe models. The former often overlooks e

Wallace Jaffray, Sven Stengel, Farhan Ali, Mustafa Goksu Ozlu

When coherent light interacts with an ordered lattice whose periodicity is comparable to its wavelength, constructive interference produces a diffraction pattern as in crystallography, where x-rays are employed to reveal atomic structures. By asking 'when' the diffractive object exist, rather than 'where', we implicitly introduce time as a design parameter,

Moritz Steffin, Jiska Classen

The XNU kernel is the basis of Apple's operating systems. Although labeled as a hybrid kernel, it is found to generally operate in a monolithic manner by defining a single privileged trust zone in which all system functionality resides. This has security implications, as a kernel compromise has immediate and significant effects on the entire system. Over the

Alison Gonçalves Schemitt, Henrique Fan da Silva, Roben Castagna Lunardi, Diego Kreutz

The advent of quantum computing threatens the security of traditional encryption algorithms, motivating the development of post-quantum cryptography (PQC). In 2024, the National Institute of Standards and Technology (NIST) standardized several PQC algorithms, marking an important milestone in the transition toward quantum-resistant security. Blockchain syste

Zirun Zhou, Zhengyang Xiao, Haochuan Xu, Jing Sun

Recent advances in vision-language-action (VLA) models have greatly improved embodied AI, enabling robots to follow natural language instructions and perform diverse tasks. However, their reliance on uncurated training datasets raises serious security concerns. Existing backdoor attacks on VLAs mostly assume white-box access and result in task failures inste

G. Gödecke, A. O. Leonov, J. Grefe, S. Süllow

The metallic systems MnSi and Fe$_{1-x}$Co$_{x}$Si are known to feature a generic magnetic phase diagram primarily determined by the isotropic exchange and Dzyaloshinskii-Moriya interactions. However, additional weaker anisotropies, lowest in the hierarchy of energy scales, play a crucial role: they determine the relative order of phases in the phase diagram

Amina Ferrad, Johann Huber, François Hélénon, Julien Gleyze

Robotics research has made significant strides in learning, yet mastering basic skills like object placement remains a fundamental challenge. A key bottleneck is the acquisition of large-scale, high-quality data, which is often a manual and laborious process. Inspired by Graspit!, a foundational work that used simulation to automatically generate dexterous g