March 2026 arXiv papers — page 111

Julius B. Pagayon, Klarence Tomas R. Cervantes, Denny Lane B. Sombillo

We perform a data-driven study of the doubly charmed tetraquark candidate $T_{cc}^+$. An ensemble of deep neural network classifiers, trained on synthetic amplitudes with controlled analytic structures, identifies a dominant pole topology characterized by an isolated pole on the $[bt]$ Riemann sheet which is robust against left-hand cut effects. A subsequent

Weijie Xiong, Jingran Lin, Di Jiang, Cunhua Pan

Polarization diversity offers significant flexibility for enhancing integrated sensing and communications (ISAC). However, conventional dual-polarized arrays typically require dedicated radio-frequency (RF) chains for each polarization branch, leading to prohibitive hardware costs. To address this, polarization-reconfigurable (PR) antennas have emerged as a

Yuxin Liu, Fei Wang, Kun Li, Yiqi Nie

Image Deepfake Detection (IDD) separates manipulated images from authentic ones by spotting artifacts of synthesis or tampering. Although large vision-language models (LVLMs) offer strong image understanding, adapting them to IDD often demands costly fine-tuning and generalizes poorly to diverse, evolving manipulations. We propose the Semantic Consistent Evi

Ivan Zelich

In this paper we will prove a strong version of the celebrated purity of the ramification locus theorem in algebraic geometry. Our key input is a Tor-independence result for global sections of \'{e}tale schemes over excellent regular local rings, which we will prove by tilting to perfect rings.

Ahmed Sharshar, Hosam Elgendy, Saad El Dine Ahmed, Yasser Rohaim

Dark humor often relies on subtle cultural nuances and implicit cues that require contextual reasoning to interpret, posing safety challenges that current static benchmarks fail to capture. To address this, we introduce a novel multimodal, multilingual benchmark for detecting and understanding harmful and offensive humor. Our manually curated dataset compris

Gyuyoung Park, OukJae Lee, Jintao Shuai

We present a phonon-magnon extension for the mumax+ micromagnetic framework that implements three surface acoustic wave (SAW) coupling mechanisms: magnetoelastic strain coupling, magneto-rotation coupling arising from the antisymmetric displacement gradient, and spin-rotation (Barnett) coupling from the lattice angular velocity. Six benchmark simulations val

Annika Wilde, Samira Briongos, Claudio Soriente, Ghassan Karame

RISC-V-based Trusted Execution Environments (TEEs) are gaining traction in the automotive and IoT sectors as a foundation for protecting sensitive computations. However, the supporting infrastructure around these TEEs remains immature. In particular, mechanisms for secure enclave updates and migrations - essential for complete enclave lifecycle management -

Hanno von Bergen, Reinhard Diestel

Tangle structure trees, introduced in [3], offer a unified data structure that displays all the tangles of a graph or data set together with certificates for the non-existence of any other tangles, either locally or overall. In this paper we apply tangle structure trees to derive new versions of the two fundamental tangle theorems: the tree-of-tangles theore

Akbar Davoodi, Diana Piguet, Hanka Řada, Nicolás Sanhueza-Matamala

Klimo\v{s}ov\'a, Piguet, and Rozho\v{n} conjectured that any graph with minimum degree $k/2$ and sufficiently many vertices of degree $k$ should contain all trees with $k$ edges. We prove an asymptotic version of this conjecture for dense host graphs. We obtain interesting corollaries: the first is an asymptotic version of the Erd\H{o}s--S\'os conjecture for

Wenbin Tan, Jiawen Lin, Fangyong Wang, Yuan Xie

3D Visual Grounding (3DVG) aims to localize the referent of natural language referring expressions through two core tasks: Referring Expression Comprehension (3DREC) and Segmentation (3DRES). While existing methods achieve high accuracy in simple, single-object scenes, they suffer from severe performance degradation in complex, multi-object scenes that are c

K. E. Polczynska, S. Karouaz, W. Pacuski, L. Besombes

We investigate the optical properties of neutral excitons in CdTe/ZnTe quantum dots containing a single Ni2+ ion. We show that the photoluminescence spectra provide a direct spectroscopic signature of strain induced mixing of the Ni2+ spin states. A misalignment between the principal axis of the local strain tensor and the quantum dot growth direction reorie

Qi Liu, Laure Zanna, Joan Bruna

Recent advances in autoregressive neural surrogate models have enabled orders-of-magnitude speedups in simulating dynamical systems. However, autoregressive models are generally prone to distribution drift: compounding errors in autoregressive rollouts that severely degrade generation quality over long time horizons. Existing work attempts to address this is

Joseph McCusker, John Christopher Meyer, Mabel Lizzy Rajendran

We present a self-contained investigation on the local and global well-posedness for a system of nonlocal advection--diffusion equations for a heterogeneous population over $\mathbb{R}^d$, $d \in \mathbb{N}$. Each convolution kernel $K_{ij}$, which describes the nonlocal advection of species $i$ according to the distribution of species $j$, is assumed to hav

S. Zhang, I. Babushkin, U. Morgner, A. Demircan

We study photonic meta-atoms, a unique class of composite solitary wave, supported in nonlinear waveguides. We establish an analogy to one-dimensional soft-core atoms, allowing to describe the complex dynamics via concepts from atomic physics. Higher-order dispersive effects cause specific spectral resonances characteristic for the eigenspectrum of a meta-at

Elena Danesi

In this paper we consider a family of time-dependent 1-dimensional cubic Schr\"odinger equation (NLS) with periodic potential. Exploiting semiclassical scaling and multiscale analysis, we derive an effective nonlinear Dirac equation, which describes the dynamics of solutions to NLS spectrally localized around Dirac points.

Haoyun Chen, Fenghe Tang, Wenxin Ma, Shaohua Kevin Zhou

Universal medical image segmentation seeks to use a single foundational model to handle diverse tasks across multiple imaging modalities. However, existing approaches often rely heavily on manual visual prompts or retrieved reference images, which limits their automation and robustness. In addition, naive joint training across modalities often fails to addre

Sarah Hirthe, Leticia Tarruell

These lecture notes contain an introduction to quantum simulation of bosonic systems in the continuum, focusing on weakly interacting Bose-Bose mixtures with competing mean-field interactions. When the values of such interactions are fine-tuned to almost completely cancel the mean-field energy, quantum fluctuations become apparent and dominate the behavior o

Ling He, Vaibhav Kumar, Roberto Bomfin, Yingyang Chen

Integrated sensing and communication (ISAC) has emerged as a key paradigm for next-generation wireless systems, which allows wireless resources to be used for data transmission and target sensing simultaneously. In this paper, multi-user collaborative target detection in the uplink ISAC system is investigated. To incorporate the target sensing functionality,

Michael Doherty, Matteo Puviani, Jasmine Brewer, Gabriel Matos

We propose a general method for preparing stabilizer states with reduced two-qubit gate count and depth compared to the state of the art. The method starts from a graph state representation of the stabilizer state and iteratively reduces the number of edges in the graph using two-qubit Clifford gates to produce a unitary preparation circuit. We explore vario

Jonathan D. Slavin, Marc Kornbleuth, Merav Opher, Gabor Toth

Interstellar dust has been detected in situ flowing through the heliosphere. However, our ability to derive the density and size distribution of the interstellar dust in the local interstellar medium from this directly detected dust requires modeling the transport of the grains as they interact with the solar wind magnetic field. The magnetic field in the se

Myoungjean Bae, Ben Duan, Chunjing Xie

We prove the global uniqueness of multidimensional subsonic flows for the steady Euler--Poisson system in a bounded nozzle in the sense that uniqueness holds without restricting solutions to be small perturbations of a background state. The proof is based on a convexity property of the set of subsonic states and energy estimates.

Hefei Xu, Le Wu, Yu Wang, Min Hou

As large language models (LLMs) increasingly shape content generation, interaction, and decision-making across the Web, aligning them with human values has become a central objective in trustworthy AI. This challenge becomes even more pronounced when aligning multiple, potentially conflicting human values. Although recent approaches, such as reward reweighti

Yoshiyuki Y. Yamaguchi, Julien Barré

Finite size fluctuations are a crucial ingredient in kinetic theory of long-range interacting collisionless systems. In this Letter, we introduce a phenomenological theory which predicts an anomalous scaling close to marginal stability for these fluctuations. It also pinpoints the critical window inside which the fluctuations are anomalous, and outside which

Oksana Kolomenko, Ricardo Knauer, Erik Rodner

Embeddings are a powerful way to enrich data-driven machine learning models with the world knowledge of large language models (LLMs). Yet, there is limited evidence on how to design effective LLM-based embedding pipelines for tabular prediction. In this work, we systematically benchmark 256 pipeline configurations, covering 8 preprocessing strategies, 16 emb

Yiyi Cai

Experimental implementations of Hamiltonian dynamics are often affected by dissipative noise arising from interactions with the environment. This raises the question of whether one can detect the presence or absence of such dissipation using only access to the observed time evolution of the system. We consider the following decision problem: given black-box

Yan Zeng, Haoran Jiang, Kaixin Yao, Qixuan Zhang

Automatically generating photorealistic and self-consistent appearances for untextured 3D models is a critical challenge in digital content creation. The advancement of large-scale video generation models offers a natural approach: directly synthesizing 360-degree turntable videos (TTVs), which can serve not only as high-quality dynamic previews but also as

Jared Marx-Kuo

We compute the p-widths, $\{\omega_p\}$, for the hemisphere with the standard round metric. This provides the first example of a manifold with boundary for which the $p$-widths are known for all $p$.

Kunjakanan Nath, Habibur Rahaman

Given any irrational number $\alpha$, we show that for any $0<\theta<6/17$, there are infinitely many $y$-smooth (friable) numbers $n$ such that $$\|n\alpha\| < n^{-\theta},$$ where $(\log n)^C\leq y\leq n$ for some large constant $C>0$. This improves the previous work of Baker, who obtained the exponent $1/3-2/(3C)+o(1)$ in the case of $y\geq (\log n)^C$, a

Dang H. Dang, Jelena Mitrovi, Michael Granitzer

We study whether large-scale unlabelled web data and LLM-based synthetic annotations can improve multilingual hate speech detection. Starting from texts crawled via OpenWebSearch.eu~(OWS) in four languages (English, German, Spanish, Vietnamese), we pursue two complementary strategies. First, we apply continued pre-training to BERT models by continuing masked

Sahil Tyagi, Feiyi Wang

Distributed training increases the number of batches processed per iteration either by scaling-out (adding more nodes) or scaling-up (increasing the batch-size). However, the largest configuration does not necessarily yield the best performance. Horizontal scaling introduces additional communication overhead, while vertical scaling is constrained by computat

W-E. Khatla, L. Duchemin, A. Eddi, E. Reyssat

We study the coalescence of identical viscous blisters beneath an elastic sheet both experimentally and numerically. Using a time-resolved synthetic schlieren technique, we measure the evolution of the thickness field of the merging blisters and more specifically the dynamics of the coalescence region. To explain this dynamics, we develop a one-dimensional m

Abhishek Dhawan

In recent work, Martinsson and Steiner proved that triangle-free $d$-degenerate graphs have fractional chromatic number $χ_f(G) = O\left(\frac{d}{\log d}\right)$. Here, we introduce an alternate proof of the bound rooted in the analysis of the entropy of certain random variables. Beyond simplifying the original argument, our technique naturally generalizes t

Xiancheng Wang, Lin Wang, Zhibo Zhang, Rui Wang

Contrastive learning methods for time series anomaly detection (TSAD) heavily depend on the quality of negative sample construction. However, existing strategies based on random perturbations or pseudo-anomaly injection often struggle to simultaneously preserve temporal semantic consistency and provide effective decision-boundary supervision. Most existing m

Jingxiao Yang, DaLin He, Miao Pan, Kaixiang Yao

Recent advances in Large Vision-Language Models (LVLMs) have enabled training-free Fine-Grained Visual Recognition (FGVR). However, effectively exploiting LVLMs for FGVR remains challenging due to the inherent visual ambiguity of subordinate-level categories. Existing methods predominantly adopt either retrieval-oriented or reasoning-oriented paradigms to ta

Bakhtinur Juraev, Arman Tursunov, Zdeněk Stuchlík, Martin Kološ

We investigate the orbital evolution of a classical charged particle around a Schwarzschild black hole immersed in an external, uniform magnetic field, taking into full account both local radiation-reaction and the nonlocal tail self-force arising in curved spacetime. Starting from the DeWitt-Brehme equation and its Landau-Lifshitz reduction, we derive analy

Shintaro Akamine, Wonjoo Lee, Seong-Deog Yang

We establish Bernstein-type theorems for entire constant mean curvature graphs in the three-dimensional light cone $\mathbb{Q}^3_+$ over the horosphere under the assumption that the Gaussian curvature $K$ is bounded below, by showing that such graphs are horospheres or spheres of $\mathbb{Q}^3_+$.

Amal Raj, Vivek Balachandran

Quantum computing often requires classical data to be supplied to execution environments that may not be fully trusted or isolated. While encryption protects data at rest and in transit, it provides limited protection once computation begins, when classical values are encoded into quantum registers. This paper explores data obfuscation for protecting classic

Zalán Gyenis, Zalán Molnár, Övge Öztürk

We study the relation between additivity and deduction theorems in the algebraic semantics of congruential modal logic. Additivity of the modal operator is well-known to imply the local deduction-detachment theorem. Our main theme is that deduction properties of modal logic persist far beyond the additive setting. We introduce the notion of a strongly non-ad

Jing Wang, Jie Shen, Yiming Luo, Amar Sra

Early prediction of Post-Acute Sequelae of SARS-CoV-2 severity is a critical challenge for women's health, particularly given the diagnostic overlap between PASC and common hormonal transitions such as menopause. Identifying and accounting for these confounding factors is essential for accurate long-term trajectory prediction. We conducted a retrospective st

J. Lopez-Gomez, A. Sahuquillo

In this paper we characterize the limiting behavior of the principal eigenvalue, $\s_1[-\D,\b,\O]$, of the boundary value problem \eqref{1.1} as the Lebesgue measure of the underlying domain, $\O$, tends to zero. Naturally, the domains $\O$ are assumed to be included on a fixed open set $D$ such that $\b\in\mc{C}(D)$, and they satisfy $\bar\O\subset D$. Our

Priyanka Mukhopadhyay, Alexandru Gheorghiu, Hari Krovi

Arithmetic operations are an important component of many quantum algorithms. Optimizing quantum circuits for these operations therefore leads to more efficient implementations of the corresponding algorithms. In this paper, we develop new fault-tolerant quantum circuits for various integer division algorithms (both reversible and non-reversible). These circu

Yuhe Tian, Kun Zhang, Haoran Ma, Rui Yan

While large language models (LLMs) have advanced CT report generation, existing methods typically encode 3D volumes holistically, failing to distinguish informative cues from redundant anatomical background. Inspired by radiological cognitive subtraction, we propose Differential Visual Prompting (DiffVP), which conditions report generation on explicit, high-

Santosh Arron

We report the discovery of bimodal structure in the drift rate distribution of upward-drifting burst clusters from the hyperactive repeating fast radio burst FRB 20240114A. Using unsupervised machine learning (UMAP dimensionality reduction combined with HDBSCAN density-based clustering) applied to 233 upward-drifting burst clusters from the FAST telescope da

Pedro Ornelas, Tatjana Kleine, André G. de Oliveira, Carmelo Rosales-Guzmán

Structured light in the quantum regime has garnered considerable attention due to the opportunities it offers when mixing light's internal degrees of freedom, for high-dimensional and multi-dimensional quantum states of light. A popular example is to harness polarisation and spatial entangled photons with a shared topological invariant that is robust against

Diederick C. Niehorster, Marcus Nyström

Previous work has reported that vision foundation models show promising zero-shot performance in eye image segmentation. Here we examine whether the latest iteration of the Segment Anything Model, SAM3, offers better eye image segmentation performance than SAM2, and explore the performance of its new concept (text) prompting mode. Eye image segmentation perf

Xiao-Chang Liu, Johan Wagemans

Image aesthetic assessment (IAA) aims to predict the aesthetic quality of images as perceived by humans. While recent IAA models achieve strong predictive performance, they offer little insight into the factors driving their predictions. Yet for users, understanding why an image is considered pleasing or not is as valuable as the score itself, motivating gro

A. Humnabadkar, A. Sikdar, B. Cave, H. Zhang

Autonomous driving technologies have achieved significant advances in recent years, yet their real-world deployment remains constrained by data scarcity, safety requirements, and the need for generalization across diverse environments. In response, synthetic data and virtual environments have emerged as powerful enablers, offering scalable, controllable, and

Jingzhi Huang, Junkai Huang, Haoyang Yang, Haoang Li

Zero-Shot Object Navigation (ZSON) in unknown multi-floor environments presents a significant challenge. Recent methods, mostly based on semantic value greedy waypoint selection, spatial topology-enhanced memory, and Multimodal Large Language Model (MLLM) as a decision-making framework, have led to improvements. However, these architectures struggle to balan

Motoi Kimata, Jun Ishizuka, Freya Husstedt, Yusei Shimizu

We present a combined experimental and theoretical study of the detailed Fermi-surface (FS) geometry of UTe2, a heavy-fermion superconductor that has recently attracted considerable attention as a promising candidate for spin-triplet pairing. Using angle-dependent magnetoresistance oscillations, a bulk- and low-energy-sensitive transport probe for quasi-two-

Mohamed Rissal Hedna, Jan Strich, Martin Semmann, Chris Biemann

Large language models (LLMs) are systematically overconfident: they routinely express high certainty on questions they often answer incorrectly. Existing calibration methods either require labeled validation data, degrade under distribution shifts, or incur substantial inference costs. Recent work has shown that LLMs already contain a better-calibrated signa

Andrei Kulikovsky

We have developed a non-isothermal analytical model for the impedance of the cathode catalyst layer (CCL) in a PEM fuel cell. In-phase harmonic perturbations to the current density and temperature reduce the impedance and the static polarisation resistivity of the CCL due to lowering proton transport losses. A special selection of the current and temperature

Armand Brumer, Kenneth Kramer

We extend methods of Fontaine, Abrashkin and Schoof to obtain criteria determining number fields K over which no non-zero abelian variety with everywhere good reduction exists. As an application, under the GRH, we find 24744 such fields of various degrees up to 16.

Haocheng Li, Juepeng Zheng, Shuangxi Miao, Ruibo Lu

Multimodal remote sensing semantic segmentation enhances scene interpretation by exploiting complementary physical cues from heterogeneous data. Although pretrained Vision Foundation Models (VFMs) provide strong general-purpose representations, adapting them to multimodal tasks often incurs substantial computational overhead and is prone to modality imbalanc

Haocheng Yuan, Adrien Bousseau, Hao Pan, Lei Zhong

Creating compelling 3D character animations typically requires either expert use of professional software or expensive motion capture systems operated by skilled actors. We present DancingBox, a lightweight, vision-based system that makes motion capture accessible to novices by reimagining the process as digital puppetry. Instead of tracking precise human mo

Anna Tegon, Nicholas Lehmann, Yawei Li, Andrea Cossettini

Objective: To enable continuous, long-term neuro-monitoring on wearable devices by overcoming the computational bottlenecks of Transformer-based Electroencephalography (EEG) foundation models and the quantization challenges inherent to State-Space Models (SSMs). Methods: We present FEMBA, a bidirectional Mamba architecture pre-trained on over 21,000 hours of

Aygul Azatovna Galimova

We introduce six independent trivariate bicycle (ITB) codes, which extend the bivariate bicycle framework of Bravyi et al.\ to three cyclic dimensions. Using asymmetric polynomial pairs on three-dimensional tori, we construct four codes including a $[[140,6,14]]$ code with $kd^2/n = 8.40$. In the code-capacity setting, the $[[140,6,14]]$ code achieves a pseu

Shunpu Tang, Qianqian Yang, Jihong Park, Zhaoyang Zhang

Learning-based semantic communication (SemCom) has recently emerged as a promising paradigm for improving the transmission efficiency of wireless networks. However, existing methods typically rely on extensive end-to-end training, which is both inflexible and computationally expensive in dynamic wireless environments. Moreover, they fail to exploit redundanc

Guoxin Chen, Wenjie Wang, Haiyang Lu, Jinxin Chen

Three-dimensional seismic full-waveform inversion (FWI) provides high-fidelity subsurface velocity models but is restricted by high computational cost, strong nonlinearity, cycle-skipping, and heavy dependence on initial models. Although data-driven deep learning mitigates these issues, it still produces over-smoothed results with limited physical interpreta

Xiangfei Zhu, Xiaofei Ma, Chenxin Gao, Tao Wang

The development of high-power single crystal fiber (SCF) lasers is critically hindered by the lack of a reliable cladding scheme to confine the optical mode and ensure beam quality. Here, we propose and demonstrate a two-step tapering-collapse method for the first time to fabricate a high-quality cladding on Yb:YAG SCFs based on elemental interdiffusion. Thi

Chengliang Lin, Yong Hou, Jianmin Yuan, Yong Wu

The dielectric response and structural properties of finite-temperature electron liquids are central to accurately describing the physical behavior of electronic systems. This study presents a robust analytical model for the static structure factor of the uniform electron gas, combining physically motivated form for the static structure factor with constrain

Fangzhou Guo, Jibo He

Gravitational wave astronomy is rapidly advancing with the development of new observatories, leading to an increasing volume and complexity of data. This trend places growing pressure on classical data analysis methods and motivates the exploration of quantum approaches. In this work, we introduce a quantum matched filtering framework for gravitational-wave

Klara Theophilo, Scott J Thomas, Georgina Croft, Yashna N D Lekhai

Robust laser delivery and stabilization are key components in atom-based quantum technologies, such as quantum computing. Moving these technologies towards product-like deployment requires scalable, compact, cost-effective, and upgradable modules. Here we describe laser systems consisting of application-flexible modules and demonstrate their performance by c

S. S. Jensen, S. Spezzano, P. Caselli, T. Grassi

This work explores the differences between static and dynamically evolving physico-chemical models of pre-stellar cores. A 3D MHD model of a pre-stellar core embedded in a dynamic star-forming cloud is post-processed using sequentially dust radiative transfer, a gas-grain chemical model, and a non-LTE line-radiative transfer model. The chemical evolution is

Jia-Yin Shen

A booklet is a geometric structure formed by gluing multiple bulk spacetimes along a common interface and imposing gravitational consistency conditions at the junction. We have systematically investigated the properties of booklet structures, constructed the booklet geometry, and derived the multiway junction conditions applicable at the interface. In this w

Yusen Wu, Yiran Liu, Xiaotie Deng

In the real economy, modern decision-making is fundamentally challenged by high-dimensional, multimodal environments, which are further complicated by agent heterogeneity and combinatorial data sparsity. This paper introduces a Multi-Agent Large Language Model-based Economic Sandbox (MALLES), leveraging the inherent generalization capabilities of large-sacle

Songtao Jiang, Sibo Song, Chenyi Zhou, Yuan Wang

The transition from image to video understanding requires vision-language models (VLMs) to shift from recognizing static patterns to reasoning over temporal dynamics such as motion trajectories, speed changes, and state transitions. Yet current post-training methods fall short due to two critical limitations: (1) existing datasets often lack temporal-centric

Joohyoung Jeon, Hongchul Lee

For LLM trading agents to be genuinely trustworthy, they must demonstrate understanding of market dynamics rather than exploitation of memorized ticker associations. Building responsible multi-agent systems demands rigorous signal validation: proving that predictions reflect legitimate patterns, not pre-trained recall. We address two sources of spurious perf

Tommaso Giovannelli, Jingfu Tan, Luis Nunes Vicente

In this paper, we develop a stochastic set-valued optimization (SVO) framework tailored for robust machine learning. In the SVO setting, each decision variable is mapped to a set of objective values, and optimality is defined via set relations. We focus on SVO problems with hyperbox sets, which can be reformulated as multi-objective optimization (MOO) proble

Florian Raßhofer, Erwin Frey

Chemical activity is known to affect phase coexistence and coarsening in liquid mixtures, most commonly through reaction-induced changes of intermolecular interactions. Here, we analyze a scenario in which chemical reactions regulate particle transport while leaving thermodynamic interactions unchanged. We study an incompressible mixture of thermodynamically

Chinenye Omejieke, Shuyao Chen, Xia Cui

We present a practical, reproducible framework for identifying undervalued football players grounded in objective mispricing. Instead of relying on subjective expert labels, we estimate an expected market value from structured data (historical market dynamics, biographical and contract features, transfer history) and compare it to the observed valuation to d

Bogdan Gheorghe, Daniel Ioan, Cristian Flutur, Ionela Prodan

The maximal positively invariant (MPI) set is obtained through a backward reachability procedure involving the iterative computation and intersection of predecessor sets under state and input constraints. However, standard static feedback synthesis may place some of the closed-loop eigenvalues at zero, leading to rank-deficient dynamics. This affects the MPI

Rahul D Ray

Deep learning models in quantitative finance often operate as black boxes, lacking interpretability and failing to incorporate fundamental economic principles such as no-arbitrage constraints. This paper introduces ARTEMIS (Arbitrage-free Representation Through Economic Models and Interpretable Symbolics), a novel neuro-symbolic framework combining a continu

Xingxing Xie, Jiahua Dong, Junwei Han, Gong Cheng

YOLO detectors are known for their fast inference speed, yet training them remains unexpectedly time-consuming due to their exhaustive pipeline that processes every training image in every epoch, even when many images have already been sufficiently learned. This stands in clear contrast to the efficiency suggested by the ``You Only Look Once'' philosophy. Th

Mohsen Arjmandi

Large language model (LLM) agents deployed in unknown environments must learn task structure at test time, but current approaches require thousands of interactions to form useful hypotheses. We present Sensi, an LLM agent architecture for the ARC-AGI-3 game-playing challenge that introduces structured test-time learning through three mechanisms: (1) a two-pl

Johann Verwee

In previous joint work with Tenenbaum, the truncation step $f \mapsto f_R$ in the conditional effective Erdos-Wintner theorem on the fibre $\omega(n)=k$ yields, in the continuous case for real strongly additive $f$, a remainder of size $\eta_f(R)^{r/(r+1)}$, where $R$ is the truncation level and $r=k/\log\log x$. We prove an effective linear truncation lemma

Joanna Bieri, Edgar Costa, Alyson Deines, Kyu-Hwan Lee

We apply one-dimensional convolutional neural networks to the Frobenius traces of elliptic curves over $\mathbb{Q}$ and evaluate and interpret their predictive capacity. In keeping with similar experiments by Kazalicki--Vlah, Bujanovi\'{c}--Kazalicki--Novak, and Pozdnyakov, we observe high accuracy predictions for the analytic rank across a range of conducto

Roja Sahoo, Anoop Namboodiri

Contactless fingerprint recognition enables hygienic and convenient biometric authentication but poses new challenges for spoof detection due to the absence of physical contact and traditional liveness cues. Most existing methods rely on single-image acquisition and appearance-based features, which often generalize poorly across devices, capture conditions,

Taieesa Peshkovsky, Stefan Andreas Schmid, David Taylor, Robert W. Style

Concentration gradients at the microscale play a central role in many physical, chemical, and biological systems, yet their quantitative visualization remains challenging due to the limited optical contrast associated with changes in concentration. Here, we present RIO (the Refractive Index Observer), a label-free interferometric tool for quantitative imagin

Alcides Garat

The discovery of the SU(3) symmetry was fundamental as to establishing an ordering principle in particle physics. We already studied how to couple the SU(3) symmetry to the gravitational field in four-dimensional curved Lorentzian spacetimes. The multiplets of equal quantum numbers are translated through natural elements in Riemannian geometry into local mul

Jaemin Kim, Jong Chul Ye

Retrieval-Augmented Generation (RAG) improves factual grounding by incorporating external knowledge into language model generation. However, when retrieved context is noisy, unreliable, or inconsistent with the model's parametric knowledge, it introduces retrieval-prior conflicts that can degrade generation quality. While this problem has been studied in aut

Roy Henha Eyono, Daniel Levenstein, Arna Ghosh, Jonathan Cornford

Normalization is a critical operation in neural circuits. In the brain, there is evidence that normalization is implemented via inhibitory interneurons and allows neural populations to adjust to changes in the distribution of their inputs. In artificial neural networks (ANNs), normalization is used to improve learning in tasks that involve complex input dist

Sarra Harrabi, Yichen Wu, Geoffrey H. Tison, Minhaj Ansari

Coronary angiography is the reference standard for evaluating coronary artery disease, yet visual interpretation remains variable between readers. Existing artificial intelligence methods typically analyze single frames or projections and focus mainly on stenosis, limiting comprehensive coronary assessment. We present DeepCORO-CLIP, a multi-view foundation m

Ilya Liubimov, Alexander Gorsky

We consider the Bethe ansatz integrable Russian Doll (RD) model of superconductivity with time-reversal symmetry breaking, which exhibits a cyclic renormalization group. By obtaining an exact solution for the renormalization group flows, we investigate the phase structure in the one-pair sector, which includes localized, fractal, and delocalized phases. We s

Xingyu Chen, Yi Xiong, Li Wen

In this paper, we introduce a consistency-driven dual LSTM framework for accurately learning both the forward and inverse kinematics of a pneumatically actuated soft robotic arm integrated into a wearable device. This approach effectively captures the nonlinear and hysteretic behaviors of soft pneumatic actuators while addressing the one-to-many mapping chal

Liangyu Yuan, Ruoyu Wang, Tong Zhao, Dingwen Fu

Diffusion and flow matching models generate high-fidelity data by simulating paths defined by Ordinary or Stochastic Differential Equations (ODEs/SDEs), starting from a tractable prior distribution. The probability flow ODE formulation enables the use of advanced numerical solvers to accelerate sampling. Orthogonal yet vital to solver design is the discretiz

Zihao Xin, Wentong Li, Yixuan Jiang, Ziyuan Huang

Vision-and-Language Navigation (VLN) requires an embodied agent to ground complex natural-language instructions into long-horizon navigation in unseen environments. While Vision-Language Models (VLMs) offer strong 2D semantic understanding, current VLN systems remain constrained by limited spatial perception, 2D-3D representation mismatch, and monocular scal

Aadi Joshi, Kavya Bhand

Digital image steganography requires a careful trade-off among payload capacity, visual fidelity, and statistical undetectability. Fixed-depth least significant bit embedding remains attractive because of its simplicity and high capacity, but it modifies smooth and textured regions uniformly, thereby increasing distortion and detectability in statistically s

Manuel Gonzalez-Espinoza, Y. Gómez-Leyton, Z. Stuchlik, Francisco Tello-Ortiz

In a scalar-vector-gravity theory with the vector sector described by nonlinear electrodynamics, the field equations are integrated using the well-known gravitational decoupling method. The resulting spacetime corresponds to a spherically symmetric and static non-Schwarzschild black hole. Employing the master equations for both even and odd parity modes, it

Pramod Chunduri, Francisco Romero, Ali Payani, Kexin Rong

Long-context question answering (QA) over lengthy documents is critical for applications such as financial analysis, legal review, and scientific research. Current approaches, such as processing entire documents via a single LLM call or retrieving relevant chunks via RAG have two drawbacks: First, as context size increases, response quality can degrade, impa

Jorryt Matthee, Alberto Torralba, Gabriele Pezzulli, Rohan P. Naidu

JWST data have enabled the abundant identification of compact broad Balmer line sources nicknamed the Little Red Dots. While they share broad lines with active galactic nuclei, they are unusually X-ray and infrared weak. We investigate the origin of the Balmer line profiles based on an empirical analysis of 18 broad H$\alpha$-selected sources with high quali

Víctor Fonoll

In the forthcoming years, the study of the fundamental interactions between gravitational waves (GWs) and matter will be crucial in order to understand what the new generations of GWs detectors will tell us. We present the inverse bremsstrahlung (IB) absorption of GWs as a novel approach to GWs physics that can help set constraints on different physical mode

Tijana Devaja, Milica Petkovic, Sokol Kosta, Dejan Vukobratovic

This paper analyzes the physical layer security performance of massive uplink Internet of Things (IoT) networks operating under the finite blocklength (FBL) regime. IoT devices and base stations (BS) are modeled using a stochastic geometry approach, while an eavesdropper is placed at a random location around the transmitting device. This system model capture

Benoît Groz, Jan Hidders, Nina Pardal, Jan Van den Bussche

We consider database schemas consisting of a single binary relation, with key constraints and inclusion dependencies. Over this space of 20 schemas, we completely characterize when one schema is generically dominated by another schema. Generic dominance, a classical notion for measuring information capacity, expresses that every instance of a schema can be u

Rui Xiao, Sanghwan Kim, Yongqin Xian, Zeynep Akata

Multimodal large language models (MLLMs) struggle with hallucinations, particularly with fine-grained queries, a challenge underrepresented by existing benchmarks that focus on coarse image-related questions. We introduce FIne-grained NEgative queRies (FINER), alongside two benchmarks: FINER-CompreCap and FINER-DOCCI. Using FINER, we analyze hallucinations a

Pavan Adroja, Sanjay Amrutiya

We study finite and semi-finite vector bundles on complex tori. We give an explicit decomposition of such bundles in terms of torsion and unipotent factors. As a consequence, we prove that the extended Nori fundamental group scheme of a complex torus decomposes as the product of its etale fundamental group scheme and its unipotent fundamental group scheme.

Milica Jovanović, Vuk Ovaskainen, Branislav I. Prvulović, Antonije Subotić

We calculate the heights of Stiefel--Whitney classes of the canonical vector bundle over the oriented Grassmannians $\widetilde G_{n,4}\cong SO(n)/(SO(4)\times SO(n-4))$ in the cases $n\in\{2^t-2,2^t-1,2^t,2^t+1\}$, $t\ge4$. Using some additional computations in modulo $2$ cohomology of $\widetilde G_{n,4}$ and the well-known connection between topological c

José Palazzo Moreira de Oliveira

The advent of Large Language Models (LLMs) represents a turning point in the theoretical foundations of Information Systems Engineering. Beyond their technical significance, LLMs challenge the ontological, epistemological, and semiotic assumptions that have long structured our understanding of in-formation, representation, and knowledge. This article propose

Tianrui Qiao, Shanpu Shen, Yijun Chen, Ross Murch

This work investigates antenna coding optimization to enhance the channel capacity of single-input single-output orthogonal frequency division multiplexing (SISO-OFDM) systems empowered by highly reconfigurable pixel antennas. We first introduce the model for pixel antenna empowered SISO-OFDM systems using a beamspace channel representation. We next formulat

Hiroya Sakurai, Yoshihiko Takano

In 2023, superconductivity in La$_3$Ni$_2$O$_7$ was discovered under high pressures above approximately 14 GPa. In addition to its high transition temperature ($T_{\mathrm{c}} \simeq 80$ K), the structural resemblance to high-$T_{\mathrm{c}}$ cuprates has strongly stimulated research, soon followed by the discovery of superconductivity in La$_4$Ni$_3$O$_{10}

Junhao Wei, Yanxiao Li, Seyedali Mirjalili, Dexing Yao

The Whale Optimization Algorithm (WOA) has shown strong optimization ability but still suffers from premature convergence and weak search diversity. To address these issues, this paper proposes an enhanced WOA variant called CICDWOA. The proposed algorithm introduces a Good Nodes Set (GNS) method for uniform population initialization, a Collective Cognitive