April 2026 arXiv papers — page 106

Yang Shanglin

Training-free token reduction methods for Vision Transformers (ToMe, ToFu, PiToMe, and MCTF) employ different scoring mechanisms, yet they share a closely matched cliff-like collapse at high compression. This paper explains \emph{why}. We develop a diagnostic framework with two tools, ranking consistency $\rho_s$ and off-diagonal correlation $\rho_\text{off}

J. Staforelli-Vivanco, R. Jofré, B. Muñoz, V. Salamanca

Traditional melissopalynology is a time-consuming and subjective process, often taking 4-6 hours per sample. We present an automated, high-throughput microscopy system that integrates $H\infty$ robust mechanical control with advanced deep learning pipelines for the precise counting, classification, and morphological analysis of pollen grains from Bio Bio reg

Allan Wang, Aaron Steinfeld

Robot navigation in crowded pedestrian environments is a well-known challenge and we explore the practical deployment of group-based representations in this setting. Pedestrian groups have been empirically shown to enable a mobile robot's navigation behavior to be safer and more social. However, existing approaches either explored groups only in limited scen

Jing Tang, Runlu Yan, Donglan Zhang, Ronald Schnitzer

Grain growth fundamentally shapes the microstructure of crystalline materials upon annealing, affecting their overall mechanical and functional properties. Recently, it has been rationalized that grain growth in polycrystals does not result solely from weighted curvature flow, but elastic effects (intrinsic stress) arised from shear coupling also need to be

Daisuke Kishimoto, Donald Stanley, Carlos Gabriel Valenzuela Ruiz

For a field $\mathbb{F}$ and a triangulated compact $\mathbb{F}$-orientable manifold, consider the homology of the associated Moment-Angle ccomplex $H_*(\mathcal{Z}_{\mathcal{K}})$. We show the total homology rank $\beta(\mathcal{Z}_{\mathcal{K}})$ satisfies the inequality $\beta(\mathcal{Z}_{\mathcal{K}};\mathbb{F})\geq 2^{m-1}(\beta(\mathcal{K};\mathbb{F})

Alex Liu, Min Sun, Lief Esbenshade, Victor Tian

GenAI has rapidly entered instructional and learning settings as a teaching assistant or AI tutor. However, less is known about how pedagogical intent connects to the learning generated within these systems, especially when student-facing AI dialogues are fine-tuned through teacher orchestration in live classrooms. This study examines a classroom deployment

Andrew Kiruluta

Classical representation systems such as Fourier series, wavelets, and fixed dictionaries provide analytically tractable basis expansions, but they are not intrinsically adapted to the empirical structure of modern high-dimensional data. Neural networks overcome this limitation by learning features from data, yet they do so through layered nonlinear paramete

Shuo Fan, Fredrik Viklund, Yilin Wang

To any Jordan curve one may associate a circle homeomorphism $\varphi : \mathbb S^1 \to \mathbb S^1$ via conformal welding. Through this correspondence, the Loewner energy $I^L$, also known as the universal Liouville action, is a K\"ahler potential for the unique homogeneous K\"ahler metric on the universal Teichm\"uller space. Despite this, explicit express

Aaryan Patel

We present KinetiDiff, a structure-based framework for de novo kinase inhibitor design that integrates a Geometry-Complete Diffusion Model with real-time AutoDock Vina gradient guidance. By injecting physics-based docking gradients into the diffusion denoising loop, KinetiDiff steers molecule generation toward high-affinity conformations for ACVR1 (ALK2), th

Justice Owusu Agyemang, Michael Agyare, Miriam Kobbinah, Nathaniel Agbugblah

LLM-powered coding agents suffer from a poorly understood failure mode we term output stalling: the agent silently produces empty responses when attempting to generate large, format-heavy documents. We present a theoretical framework that explains and prevents this failure through three contributions. (1) We introduce Output Generation Capacity (OGC), a form

David Avis, Luc Devroye, Antoine Deza

In this paper, we investigate the relationships between the volumes of four convex bodies: the cut polytope, metric polytope, rooted metric polytope, and elliptope, defined on graphs with n vertices. After an affine change of coordinates for the elliptope, the cut polytope is contained in each of the other three, which, for optimization purposes, provide pol

Junwan Kim, Hyunkyung Bae

Multimodal large language models (MLLMs) have recently demonstrated strong capabilities in understanding and generating responses from diverse visual inputs, including high-resolution images and long video sequences. As these models scale to richer visual representations, inference increasingly relies on storing large numbers of vision tokens in the key-valu

Elaheh Vaezpour, Amirhosein Javadi, Tara Javidi

Physical awareness, especially in a large and dynamic environment, is shaped by sensing decisions that determine observability across space, time, and scale, while observations impact the quality of sensing decisions. This loopy information structure makes physical awareness a fundamentally challenging decision problem with partial observations. While in the

W. Wills, D. Blume, Q. Guan

We develop a coupled-channel framework to describe the dynamics of spinor Bose-Einstein condensates (BECs), with particular emphasis on the behavior near resonances between spin dynamics and spatial excitations. Taking advantage of the disparity between the spin-dependent and spin-independent scattering lengths in typical spinor BECs, the Bogoliubov modes of

Marcilio Ferreira dos Santos, Cleiton de Lima Ricardo

Understanding the evolution of connectivity in spatiotemporal systems requires mathematical frameworks capable of encoding not only instantaneous interactions but also their cumulative causal structure. In this work, we introduce the \emph{Causal Edge Rees Algebra} (CERA), a new algebraic construction associated with causal spatiotemporal graphs. Given a tem

Kevin J Napier, Matthew J Holman, Hsing-Wen Lin, David W Gerdes

The study of faint solar system objects is a promising avenue for understanding the origin and evolution of planetary systems. However, such objects are difficult to detect in conventional surveys. Here we introduce heliostack, an algorithm for nonlinear shift-and-stack searches for solar system objects, which enables us to combine images taken over longer t

Leonardo F. Toso, Kasra Fallah, Charis Stamouli, George J. Pappas

We study multitask learning for stochastic and partially observed control systems, focusing on the linear quadratic Gaussian (LQG) problem. Our goal is to learn a common stabilizing controller that generalizes across a distribution of systems and objectives. To this end, we leverage a history-dependent lifting that recasts the multitask LQG problem into an e

Ayhan Can Erdur, Daniel Scholz, Jiazhen Pan, Benedikt Wiestler

State-of-the-art large language models (LLMs) show high performance in general visual question answering. However, a fundamental limitation remains: current architectures lack the native 3D spatial reasoning required to directly analyze volumetric medical imaging, such as CT or MRI. Emerging agentic AI offers a new solution, eliminating the need for intrinsi

Junyao Zhang, Jonathan Ku, Zhiding Liang, Hai Li

Quantum teleportation is a cornerstone of quantum information processing, enabling the nonlocal transmission of quantum states across arbitrary distances using shared entanglement and classical communication. While the standard protocol typically employs Z-basis Bell-state measurements, this fixed-basis approach limits flexibility in practical quantum networ

Felipe Almeida-Fernandes, Guilherme Limberg, Hélio D. Perottoni, João A. S. Amarante

We study the metallicity distribution along the Sagittarius (Sgr) stream using photometric metallicities from S-PLUS DR4, combined with Gaia DR3 kinematics and APOGEE DR17 spectroscopy. Our analysis confirms that the leading arm (Galactic latitude $b > 0$) is systematically more metal-poor than the trailing arm ($b < 0$) by 0.15--0.20 dex, and reveals a clea

Jose M. Saavedra, Crhistopher Stears, Marcelo Pizarro, Cristóbal Loyola

Considering the imminent massification of digital books, it has become critical to facilitate searching collections through graphical patterns. Current strategies for document retrieval and pattern spotting in historical documents still need to be improved. State-of-the-art strategies achieve an overall precision of $0.494$ for pattern spotting, where the pr

Rosina Kharal, Trevor Brown, Justus Henneberg, Felix Schuhknecht

GPU-based concurrent data structures (CDSs) achieve high throughput for read-only queries, but efficient support for dynamic updates on fully GPU-resident data remains challenging. Ordered CDSs (e.g., B-trees and LSM-trees) maintain an index layer that directs operations to a data layer (buckets or leaves), while hash tables avoid the cost of maintaining ord

Moein Salimi, Babak Hosseini Mohtasham, Amin Aghakasiri, Mahdi Naieni

Large Language Models (LLMs) have demonstrated potential in automating scientific ideation, yet current approaches relying on iterative prompting or complex multi-agent architectures often suffer from hallucination or computational inefficiency. A critical bottleneck in applying Reinforcement Learning (RL) to this open-ended domain is reward hacking -- where

William Howes, Farid Ahmed, Kazuma Kobayashi, Souvik Chakraborty

Predicting full-field physics through the real-time virtual sensing of engineering systems can enhance limited physical sensors but often requires sparse-to-dense reconstruction, complex multiphysics, and highly irregular geometries as well as strict latency and energy constraints for edge-deployability. Neural operators have been presented as a potential ca

Eva van Tegelen, Taniya Kapoor, George A. K. van Voorn, Peter van Heijster

Developing neural operators that accurately predict the behavior of systems governed by partial differential equations (PDEs) across unseen parameter regimes is crucial for robust generalization in scientific and engineering applications. In practical applications, variations in physical parameters induce distribution shifts between training and prediction r

Urban Duh, Marko Žnidarič

Dynamical properties of classical chaotic systems, for instance relaxation, can be understood as emerging from the time evolution of initially smooth long-wavelength densities to ever finer short-wavelength densities with fractal structure. Whether there is any analogous fractality by which one could characterize quantum many-body chaos is not known. By stud

Xan Carey, Yash Deshmukh, Aileen Huang, Sunit Jadhav

Time-series forecasting is central to many scientific and industrial domains, such as energy systems, climate modeling, finance, and retail. While forecasting methods have evolved from classical statistical models to automated, and neural approaches, the surrounding software ecosystem remains anchored to the traditional Python numerical stack. Existing libra

Ayush Nadiger, Adriana Caraeni, Katie Schouten

We investigate the potential of the Quantum Approximate Optimization Algorithm (QAOA) for reducing energy consumption in route planning, a key challenge in logistics due to the NP-hard nature of the Traveling Salesman and Vehicle Routing Problems. By encoding route optimization as a Quadratic Unconstrained Binary Optimization (QUBO) problem and implementing

Christopher Ormerod, Gitit Kehat

This paper addresses a critical safety gap in the use Automated Verbal Response Scoring (AVRS). We present a novel hybrid framework for troubled student detection that combines a text classifier, trained to detect responses based on their content, and an audio classifier, trained to detect responses using prosodic markers. This approach overcomes key limitat

Satya Narayana Panda, Aishworzo Saha

This paper develops a geospatial framework for climate risk stress testing in California with applications to banking and climate-exposed sectors such as agriculture, real estate, and tourism. The study integrates physical hazard mapping, sector-specific exposure analysis, and scenario-based financial risk assessment to evaluate how wildfires, drought, flood

Jun-Liang Lin, Kamesh Madduri, Mahmut Taylan Kandemir

Graph foundation models have demonstrated remarkable adaptability across diverse downstream tasks through large-scale pretraining on graphs. However, existing implementations of the backbone model, graph transformers, are typically limited to single-GPU systems, leading to long training times or out-of-memory issues on large graphs. Moreover, parallelizing g

Lena Zellinger, Nicola Branchini, Lennert De Smet, Víctor Elvira

Classical mixture models (MMs) are widely used tractable proposals for approximate inference settings such as variational inference (VI) and importance sampling (IS). Recently, mixture models with negative coefficients, called subtractive mixture models (SMMs), have been proposed as a potentially more expressive alternative. However, how to effectively use S

N. Tobias Jacobson, Natalie D. Foster, Ryan M. Jock, Martin Rudolph

While bulk silicon has long been understood to exhibit relatively weak spin-orbit coupling (SOC), confinement of electrons to quantum dots (QDs) at a silicon heterointerface results in significantly larger SOC. This is a concern for electron spin qubit performance, as intravalley and intervalley SOC can significantly perturb the operation of electron spin qu

Pierre Amenoagbadji, Michael I. Weinstein

We study wave propagation in 2D honeycomb structures with a non-commensurate or ``irrational&#39;&#39; line defect or edge. Our model is a Schrödinger operator which interpolates, across the edge, between two distinct bulk (asymptotic) Hamiltonians with a common spectral gap about the ``Dirac point&#39;&#39; of an unperturbed honeycomb operator. We seek edge

Nehad AttaElmanan AbdElrahim Mabrouk, Barry C Sanders

Claims of successful quantum teleportation are backed up by showing that fidelity exceeds some specified threshold, but whether fidelity is the performance metric and what the threshold should be has been a subject of vigorous debate. We construct adversarial models for quantum teleportation, i.e., involving cheating parties, and show that fidelity threshold

James Mckenna, Christos Iliadis, Vassilis Glenis

Due to the increasing frequency and severity of storm events, driven by the escalation of anthropogenic climate change and urban expansion, there is a requirement for increasingly efficient flood risk management strategies. While Blue-Green Infrastructure (BGI) offers a sustainable solution for managing flood risk, optimal implementation is challenging. To h

William Retnaraj, Simone Betteti, Alexander Davydov, Francesco Bullo

Linear-threshold networks (LTNs) capture the mesoscale behavior of interacting populations of neurons and are of particular interest to control theorists due to their dynamical richness and relative ease of analysis. The aim of this paper is to advance the study of global asymptotic stability in LTNs with asymmetric neural interactions and heterogeneous diss

Marwan Jalaleddine, Jiajie Li, Syed Mohsin Abbas, Warren J. Gross

The high computational cost of approaching the performance of Maximum-likelihood (ML) decoding has limited its practical use for decades. Because the complexity grows exponentially with the message length, researchers have spent years developing algorithms like Ordered Statistics Decoding (OSD), Partial Ordered Statistics Decoding (POSD) and Guessing Random

Mengyuan Ma, Isuri Welgamage, Ahmed Alkhateeb, A. Lee Swindlehurst

Beam training and prediction in real-world millimeter-wave (mmWave) communications systems are challenging due to rapidly time-varying channels and strong interference from surrounding objects. In this context, widely available sensors, such as cameras and radars, can capture rich environmental information, enabling efficient beam management. This paper prop

Bhaskar Gurram

Automated evaluation of tool-using large language model (LLM) agents is widely assumed to be reliable, yet this assumption is rarely validated against human annotation. We present AgentProp-Bench, a diagnostic benchmark of 14,750 execution traces from thirteen LLM agents (nine proprietary, four open-weight) across four domains, and use it to audit three ques

Cong Bai, Salish Maharjan, Yunyi Li, Wenlong Shi

Prolonged blackouts in distribution systems (DSs) with high penetration of distributed energy resources (DERs) necessitate novel restoration strategies to rapidly restore loads. However, the resulting complex optimization problem significantly limits scalability. This paper proposes a synchronization-safe dynamic microgrid (MG) formation (SSDMGF)-enabled res

Nasim Al-wagieh, Mohammed Q. Shormani

This study examines the role of artificial intelligence in translation, focusing on ChatGPT, specifically ChatGPT-4, and the extent to which human postediting is required in literary translation. A mixed-method approach was adopted, involving 30 professional translators who evaluated and postedited AI-generated translations of selected Arabic and English lit

Glenn Barnich, Ali Seraj

The memory effect for Robinson-Trautman waves is explicitly worked out. In a first step, we construct the combined frame rotation and coordinate transformation in which Robinson-Trautman waves are manifestly locally asymptotically flat at future null infinity. This allows us to apply well-established results on how to derive the memory effect in this context

Shathushan Sivashangaran, Vihaan Dutta, Apoorva Khairnar, Sepideh Gohari

Road traffic accidents are a leading cause of fatalities worldwide. In the US, human error causes 94% of crashes, resulting in excess of 7,000 pedestrian fatalities and $500 billion in costs annually. Autonomous Vehicles (AVs) with emergency collision avoidance systems that operate at the limits of vehicle dynamics at a high frequency, a dual constraint of n

Luca Cuccovillo, Xin Wang, Milica Gerhardt, Patrick Aichroth

This paper reviews the current state and emerging trends in synthetic speech detection. It outlines the main data-driven approaches, discusses the advantages and drawbacks of focusing future research solely on neural encoding detection, and offers recommendations for promising research directions. Unlike works that introduce new detection methods or datasets

Yun-Ping Hsiao, Yanda Li, Youssef Gamal, Halima Bouzidi

As Cyber-Physical Systems (CPS) become increasingly pervasive and autonomous, ensuring the resilience of their embedded logic is critical to maintaining safety and integrity. Among the most stealthy and damaging threats are non-invasive fault injection attacks, where hardware-level disturbances propagate into software execution and compromise control logic.

Simon Lapointe, Mykola Matviichuk, Brent Pym, Boris Zupancic

We establish existence of functorial orbifold reductions of singularities for Poisson subvarieties in smooth Poisson threefolds. Namely, we show that with enough weighted blowups, one can reduce the singularities of such Poisson subvarieties to certain simple, explicit, local normal forms: Du Val surface singularities where the Poisson structure is locally J

Gustavo Sandoval, Brendan Dolan-Gavitt, Siddharth Garg

Large language models write production code, and yet they routinely introduce well-known vulnerabilities. We show that this is not a knowledge deficit: the same models that generate insecure code, correctly identify and explain the vulnerability when asked directly, this is a gap we call the Format-Reliability Gap. Mechanistic analysis reveals the cause: sec

Mustaqeem Khan, Aidana Nurakhmetova, Wail Gueaieb, Abdulmotaleb El Saddik

3D object detection in point cloud data remains a challenging task due to the sparsity and lack of global structure inherent in the input. In this work, we propose a novel Multi-Scale Attention (MSA) mechanism integrated into the 3DETR architecture to better capture both local geometry and global context. Our method introduces an upsampling operation that ge

Jiayi Tian, Yupeng Su, Ryan Solgi, Souvik Kundu

Large reasoning models (LRMs) enhance problem-solving capabilities by generating explicit multi-step chains of thought (CoT) reasoning; however, they incur substantial inference latency and computational overhead. To mitigate this issue, recent works have explored model collaboration paradigms, where small reasoning models (SRMs) generate intermediate reason

Goren Gordon

The interaction of quantum fields with fractal and self-similar geometries encompasses multiple distinct physical regimes, including spectral geometry on intrinsic fractals, macroscopic self-similar Casimir configurations, and bounded Euclidean cavities with fractal boundaries. While the thermal equations of state and spectral asymptotics for these systems a

Pierre-Luc Thériault, Emna Azek, Gabriel Juteau, Anagh Mukherjee

The performance of organic optoelectronic devices is critically dependent on how molecules orient within organic thin films. Yet, standard characterization techniques only reveal the first and second moments of the molecular orientation distribution. This limitation obscures the true molecular arrangement, as diverse distributions can yield identical low-ord

Joachim Brod, Emmanuel Stamou, Tom Steudtner

We calculate the anomalous dimension of the $|\Delta S| = 1$ current-current operators of the weak effective Lagrangian at next-to-next-to-next-to-leading order (NNNLO) in QCD. This constitutes the first step towards a full four-loop calculation of the QCD correction to $\epsilon_K$, the measure for indirect CP violation in the neutral kaon system. We presen

Reca Sarfati, Vod Vilfort

Empirical researchers often use diagnostic checks to assess the plausibility of their modeling assumptions, such as testing for covariate balance in RCTs, pre-trends in event studies, or instrument validity in IV designs. While these checks are traditionally treated as external hurdles to estimation, we argue they should be integrated into the estimation pro

Megan Masterson, Erin Kara, William N. Alston, Riccardo Arcodia

1ES 1927+654 is an extreme active galactic nucleus (AGN) that has defied our canonical expectations for how AGN appear across the electromagnetic spectrum and how they vary on short timescales. In 2022, this source began showing a X-ray quasi-periodic oscillation (QPO) at mHz frequencies, along with a newly launched radio jet. Unlike the handful of other kno

Varun Kumar, George Em Karniadakis

This paper introduces a multi-agent framework guided by Large Language Models (LLMs) to assist in the early stages of engineering design, a phase often characterized by vast parameter spaces and inherent uncertainty. Operating under a human-in-the-loop paradigm and demonstrated on the canonical problem of aerodynamic airfoil design, the framework employs a t

Yufei Tao, Ameeta Agrawal

Large language models (LLMs) can answer questions and summarize documents when conditioned on external contexts (e.g., retrieved evidence), yet context use remains unreliable: models may overwrite an already-correct output (neutral regression) even when the context is non-informative. We formalize neutral regression as a do-no-harm requirement and quantify i

Koushik Howlader, Md Tauhidul Islam, Wei Le

Accurate prediction of cancer progression remains a challenge due to the high heterogeneity of molecular omics data across patients. While biologically informed models have improved the interpretability of these predictions, a persistent limitation lies in how they encode individual genes to construct pathway representations. Existing hierarchical models typ

Argyrios Gerogiannis, Yu-Han Huang, Venugopal V. Veeravalli

We study model-free reinforcement learning (RL) in non-stationary finite-horizon episodic Markov decision processes (MDPs) without prior knowledge of the non-stationarity. We focus on the piecewise stationary (PS) setting, where both rewards and transition dynamics can change at unknown times. We first revisit existing state-of-the-art approaches and identif

Gehan Zheng, Sanjay Seenivasan, Matthew Johnson-Roberson, Weiming Zhi

Imitation learning has enabled robots to acquire complex visuomotor manipulation skills from demonstrations, but deployment failures remain a major obstacle, especially for long-horizon action-chunked policies. Once execution drifts off the demonstration manifold, these policies often continue producing locally plausible actions without recovering from the f

Yichao Yuan, Mosharaf Chowdhury, Nishil Talati

Power has become a central bottleneck for AI inference. This problem is becoming more urgent as agentic AI emerges as a major workload class, yet prior power-management techniques focus almost entirely on single-turn LLM serving. Our analysis shows that agentic serving behaves fundamentally differently: each request carries long-lived context that evolves ac

Alejandro Gil-García, Giovanni Russo

We study the existence of left-invariant harmonic spinors on three-dimensional Lie groups equipped with a left-invariant pseudo-Riemannian metric. An existing formula for the spin Dirac operator acting on left-invariant spinors in the Riemannian setting is revised and specialised to our cases, in particular to almost Abelian Lie algebras. Focussing on dimens

Yuval Haitman, Amit Efraim, Joseph M. Francos

We introduce C-GenReg, a training-free framework for 3D point cloud registration that leverages the complementary strengths of world-scale generative priors and registration-oriented Vision Foundation Models (VFMs). Current learning-based 3D point cloud registration methods struggle to generalize across sensing modalities, sampling differences, and environme

M. L. Fernández-Pérez, S. Rosado-Navarro, A. Rosado

We present a comparative collider study of three flavor-violating Higgs signatures in the Type-III Two-Higgs-Doublet Model (\ddHmIII) at \(\sqrt{s}=14\)~TeV: \(pp \to H \to t\bar{c} \ (\bar{t}c)\), \(pp \to H^\pm \to c\bar{b} \ (\bar{c}b)\), and \(pp \to H^\pm \to t\bar{b} \ (\bar{t}b)\). Using a common cut-based analysis and realistic detector simulation, w

Hangke Sui, Yuqing Wang, Minh N Do

Contrastive objectives power state-of-the-art multimodal models, but their training remains slow, relying on long stochastic optimization. We propose a Unified Framework for Efficient Contrastive Alignment via Kernels (UniCon), which spans linear and nonlinear encoders as well as one-to-one and many-to-many alignments. At its core, UniCon introduces the cont

Lingling Chen, Zongyao Lyu, William J. Beksi

Vision-language-action (VLA) models have emerged as generalist robotic controllers capable of mapping visual observations and natural language instructions to continuous action sequences. However, VLAs provide no calibrated measure of confidence in their action predictions, thus limiting their reliability in real-world settings where uncertainty and failures

Alain Couvreur, Rati Ludhani

We study the classification of minimal codewords of projective Reed-Muller codes of order $2$. This problem is equivalent to identifying quadrics over finite fields whose set of rational points is maximal with respect to the inclusion. We prove that except one particular case over $\mathbb{F}_2$, any two absolutely irreducible quadrics whose sets of rational

Prerana Kumar, Martin A. Giese

Action recognition is a fundamental ability for social species. Yet, its underlying computations are not well understood. Classical psychophysical studies using simplified stimuli have shown that humans can perceive body motion even under degradation of relevant shape cues. Recent work using real-world action videos and their appearance-free counterparts (th

Cristiano Fanelli, James Giroux, Cole Granger, Justin Stevens

We present a Mixture-of-Experts-based foundation model applied to the GlueX DIRC detector at Jefferson Lab, demonstrating its utility as a unified framework for fast simulation, particle identification, and hit-level noise filtering of Cherenkov photons. By leveraging a single shared transformer backbone across all tasks, the approach eliminates the fragment

Patricia R. Gargiulo, Caracé Gutiérrez, Juan P. Tarigo, Cecilia Stari

An experimental study of a periodically forced Duffing--Holmes-type oscillator with a double-well potential, emulated by a piecewise-linear analog electronic circuit, is presented. By systematically varying the forcing amplitude and frequency, the full dynamical landscape of the system is characterized through bifurcation diagrams, Poincaré maps, and largest

P. E. Mogaddam, S. S. Gousheh

Fermion bound states in the background of the &#39;t Hooft-Polyakov SU(2) monopole are investigated for various values of gauge coupling constant $g$, the Higgs self-coupling constant $λ$, and the Yukawa coupling constant $y_q$. Numerical solutions to the set of coupled differential equations for various selected points in the parameter space reveal only a z

Haoruo Zhao, Wenshuo Tang, Duncan Guthrie, Michele Sevegnani

In active learning, membership queries (MQs) allow a learner to pose questions to a teacher, such as ''Is every apple a fruit?'', to which the teacher responds correctly with yes or no. These MQs can be viewed as subsumption tests with respect to the target ontology. Inspired by the standard reduction of subsumption to satisfiability in description logics, w

Reza Hosseini

Online controlled experiments face growing challenges from overlapping tests on shared traffic, where interactions between concurrent experiments obscure insights into feature combinations and produce effect estimates that do not correspond to any actionable launch scenario. While traffic splitting, layering, and sequential execution (non-concurrent) mitigat

Jushan Chen, Jonathan Fried, Santiago Paternain

We present a framework leveraging a novel variant of the model-based diffusion algorithm to minimize the time required for a redundant dual-arm robot configuration to follow a desired relative Cartesian path. Our prior work proposed a bi-level optimization approach for the dual-arm problem, where we derived the analytical solution to the lower-level convex s

Victor Kebande

The modern cryptographic primitives are known to generate large volumes of sequential data like keystreams, ciphertext blocks, and hash outputs. Traditional cryptgraphic evaluation methods rely primarily on statistical randomness tests and algebraic cryptanalysis techniques. This paper introduces the concept of Stringology-Based Cryptology (SBC), which appli

Samuli Hynninen, Ville Kyrki

Manipulating open liquid containers is challenging because liquids are highly sensitive to vessel accelerations and jerks. Although spill-free liquid manipulation has been widely studied, emergency stopping under unexpected hazards has received little attention, despite the fact that abrupt braking may cause hazardous spills. This letter presents an emergenc

D. J. McLeod, J. S. Dunlop, R. J. McLure, C. T. Donnan

We present a new determination of the evolving galaxy UV luminosity function (LF) over the extreme redshift range $12.5<z<18.5$, based on a wide-area search of $>$0.6 deg$^2$ of JWST NIRCam imaging containing $>150$ independent sight-lines. We find evidence for an accelerated decline in the UV LF, and hence inferred star-formation rate density ($\rho_{\rm SF

Anik Saha, Mst. Fahmida Sultana Naznin, Zia Ul Hassan Abdullah, Anisa Binte Asad

Urgent blood donation seeking posts and messages on social media often go unnoticed due to the overwhelming volume of daily communications. Traditional app-based systems, reliant on manual input, struggle to reach users in low-resource settings, delaying critical responses. To address this, we introduce the Cognitive Blood Request System (CBRS), a multi-plat

Jeongwoo Nam, William Anderson, Youngsoo Choi, Hai P. Le

Non-local thermodynamic equilibrium (NLTE) calculations remain a major computational bottleneck in radiation--hydrodynamics, while most existing machine-learning surrogates treat NLTE as a static input--output mapping rather than a kinetic evolution problem. Here, we present a physics-informed Latent Space Dynamics Identification (pLaSDI) framework specifica

Md Kowsher, Weiwei Zhan, Chen Chen

Landslide detection from high resolution satellite imagery is a critical task for disaster response and risk assessment, yet the relative effectiveness of modern segmentation architectures and finetuning strategies for this problem remains insufficiently understood. In this work, we present a systematic benchmarking study of convolutional neural networks, tr

Haowei Shi, Visuttha Manthamkarn, Christopher M. Jones, Zheshen Zhang

Quantum sensing can enhance imaging performance by reducing measurement noise below the classical limit, thereby improving the signal-to-noise ratio (SNR) of acquired data. In conventional quantum imaging schemes, squeezing is applied independently to each pixel or spatial mode, leading to a quantum resource cost that scales linearly with image dimension. Th

Percy S. Zhai, Veronika Ročková

Predictive inference in the sparse Gaussian sequence model has received considerably less attention than its non-sparse, finite-sample counterpart. Existing work has largely been confined to discrete mixture priors. In this paper, we study predictive inference under a widely used continuous mixture prior, the Horseshoe. We provide new theoretical results est

Benjamin Grant

We define several topological spaces whose points are quivers with a given infinite vertex set $X$. In the special case when $X$ is countably infinite, we show that two of the spaces of interest are homeomorphic to the Baire space $\mathbb{N}^\mathbb{N}$. We study properties of countably infinite quivers as subspaces of these topological spaces and prove a `

Jaechul Roh, Amir Houmansadr

Prior work shows that fine-tuning aligned models on benign data degrades safety in text and vision modalities, and that proximity to harmful content in representation space predicts which samples cause the most damage. However, existing analyses operate within a single, undifferentiated embedding space -- leaving open whether distinct input properties drive

Ignasi Sole

Empirical studies of recorded performance have conventionally modelled tempo change as a unidirectional historical process, fitting linear regression lines to tempo data plotted against recording year. This paper argues that such approaches impose a false narrative of uniform stylistic evolution on what is, in fact, a plurality of coexisting interpretive tra

Habibeh Naderi, Behrouz Haji Soleimani, Stan Matwin

Large pre-trained language models are increasingly adapted to downstream tasks using parameter-efficient fine-tuning (PEFT), but existing PEFT methods are typically deterministic and unimodal, making them poorly suited for low-resource multimodal settings where predictive uncertainty and cross-modal reliability both matter. We introduce CALIBER (Context-Awar

Maitrey Mehta, Nishant Subramani, Zhichao Xu, Ashim Gupta

All languages are equal; when it comes to tokenization, some are more equal than others. Tokens are the hidden currency that dictate the cost and latency of access to contemporary LLMs. However, many languages written in non-Latin scripts observe a poor exchange rate: LLMs take several multiples of tokens to encode the same information in many languages as t

Dingyi Zhang, Ruiying Liu, Yun Wang

The accurate quantification of brain age from MRI has emerged as an important biomarker of brain health. However, existing approaches are often restricted to narrow age ranges and single-modality MRI data, limiting their capacity to capture the coordinated macro- and microstructural changes that unfold across the human lifespan. To address these limitations,

Christina Chance, Rebecca Pattichis, Arjun Subramonian, James He

Reclaimed slur usage is a common and meaningful practice online for many marginalized communities. It serves as a source of solidarity, identity, and shared experience. However, contemporary automated and AI-based moderation tools for online content largely fail to distinguish between reclaimed and hateful uses of slurs, resulting in the suppression of margi

Hugo Lavenant, Giuseppe Savaré

Given a function $F$ transforming a probability measure $\mu$ into another one $F(\mu)$, we study the existence and regularity of a transport representation of it. That is, we ask whether we can represent the image $F(\mu)$ of the input probability measure $\mu$ as the push-forward of $\mu$ by a map $f(\cdot,\mu)$ which may depend on $\mu$; and furthermore,

Alexander Migala, Kate Scholberg

We explore what may be deduced about the neutrino mass ordering problem from the observation of core-collapse supernova burst neutrinos in modern terrestrial detectors. We employ ternary plots in a novel way to visualize the time evolution of the flavor composition of various supernova neutrino flux models from the SNEWPY software package. Through our analys

David Alonso del Barrio, Paula Dolores Rescala, Victor Bros, Daniel Gatica-Perez

Research shows news consumption differs across demographics, yet little is known about non-mainstream audiences, especially in relation to local media. Our study addresses this gap by examining how French-speaking migrants in a mid-size European city engage with local news, and whether their needs are reflected in coverage. Eight community members participat

Kittipong Thiamchaiboonthawee, Ghadi Nehme, Ram Mohan Telikicherla, Jiawei Tian

Directed energy deposition (DED) produces complex thermo-mechanical responses that can lead to distortion and reduced dimensional accuracy of a manufactured part. Thermo-mechanical finite element simulations are widely used to estimate these effects, but their computational cost and the complexity of accurately capturing DED physics limit their use in design

Luca Chirolli, Alessandro Braggio, Michele Governale

Cooper quartets are aggregates of four electrons that generalize the concept of Cooper pairs, and their study can unfold unexplored perspectives in correlated matter and many-body physics. We propose a method to isolate them in a double-quantum-dot system coupled to conventional superconducting and normal leads. By driving the system out of equilibrium, we s

Zeeshan Rasheed, Abdul Malik Sami, Muhammad Waseem, Kai-Kristian Kemell

Recent advances in agentic frameworks have enabled AI agents to perform complex reasoning and decision-making. However, evidence comparing their reasoning performance, efficiency, and practical suitability remains limited. To address this gap, we empirically evaluate 22 widely used agentic frameworks across three reasoning benchmarks: BBH, GSM8K, and ARC. Th

Predrag Pilipović, Adeline Samson, Susanne Ditlevsen

Multivariate Pearson diffusions are characterized by a linear drift and a diffusion matrix that is quadratic in the state variables. We derive closed-form expressions for the mean and covariance matrix of this class using matrix exponential integrals, and extend this framework to a broader class of nonlinear diffusions with Pearson-type multiplicative noise.

Alexander F. Goncharov, Elena Bykova, Iskander Batyrev, Maxim Bykov

Molecular nitrogen exhibits remarkable structural diversity near the polymeric transition, where multiple phases are metastable. Here, we report two new molecular phases. The first, $t\zeta$-N$_2$, is a polytype of monoclinic $C2/c$ $\zeta$-N$_2$, characterized by a tripled $c$ axis and 96 atoms per unit cell. The second, $\xi$-N$_2$, is a previously unrepor

Zhentian Zhang, Hao Jiang, Kai-Kit Wong, Hyundong Shin

Fluid antenna systems (FAS) enable unprecedented spatial diversity within a compact form factor by flexibly switching among high-density antenna ports. To activate this capability, channel state information (CSI) over the ports is required, which implies high estimation overhead because the number of ports is usually very large. Conventional estimation schem

Dimitrios Tyrovolas, Sotiris A. Tegos, Kunrui Cao, Yue Xiao

Zero-energy reconfigurable intelligent surfaces (zeRISs) have recently emerged as a promising solution for enabling energy-efficient and scalable programmable wireless environments (PWEs) by harvesting their operational energy from impinging radio-frequency signals. However, the operation of zeRIS-assisted systems is inherently constrained by the coupling be

Neel Singh, Audrey A. Watkins, Giovanni Bordiga, Vincent Tournat

Flexible mechanical structures can undergo large deformations under small loads, enabling large, complex, and nonlinear wave responses under finite-frequency driving. Here, we study a dynamically driven canonical flexible mechanical metamaterial composed of rigid squares connected at their corners by flexible hinges. This metamaterial supports a uniform dila