October 2025 arXiv papers — page 176

X. Jiang, J. Qiu, K. Gustavsson, B. Mehlig

Artificial gliders are designed to disperse as they settle through a fluid, requiring precise navigation to reach target locations. We show that a compact glider settling in a viscous fluid can navigate by dynamically adjusting its centre-of-mass. Using fully resolved direct numerical simulations (DNS) and reinforcement learning, we find two optimal navigati

Torunn Jensen

We prove unique continuation properties related to the Hardy uncertainty principle for solutions of the hyperbolic nonlinear Schrödinger equation and the hyperbolic Schrödinger equation with potential. Under suitable conditions on the nonlinearity, or the potential, we show that if $u$ is a solution with Gaussian decay at two different times, then $u\equiv 0

Hong Wu, Xue-Min Yang, Hui Liu

Topological edge states in gaps of non-Hermitian systems are robust due to topological protection. Using the non-Hermitian Floquet Su-Schrieffer-Heeger model, we show that this robustness can break down: edge states may be suppressed by infinitesimal perturbations that preserve sublattice symmetry. We identify this fragility to the instability of the quasien

Oliver Josephs, Barbara Dymerska, Nadine N. Graedel, Yael Balbastre

Parallel imaging is ubiquitous in MRI, enabling diverse applications such as ultra-high-resolution functional and quantitative imaging with greater temporal resolution or reduced scan times respectively. Successful unfolding is contingent on robust and accurate estimation of the relative coil sensitivities, which often involves computation times that preclud

Samuel Schindler

Philosophers have spilled much ink over the discovery of ideas in the classical 'context of discovery'. However, there has been little engagement with the question of what constitutes a discovery of 'things in the world'. A much-overlooked answer to this question is provided by T.S. Kuhn. In this paper, I show that discoveries awarded with a

Huu Tuong Tu, Huan Vu, cuong tien nguyen, Dien Hy Ngo

Traditional voice conversion (VC) methods typically attempt to separate speaker identity and linguistic information into distinct representations, which are then combined to reconstruct the audio. However, effectively disentangling these factors remains challenging, often leading to information loss during training. In this paper, we propose a new approach t

Shobhit Gupta, Nikolay Sheshko, Daniel J. Dilley, Alvin Gonzales

Distributing circuits across quantum processor modules will enable the execution of circuits larger than the qubit count limitations of monolithic processors. While distributed quantum computation has primarily utilized circuit cutting, it incurs an exponential growth of sub-circuit sampling and classical post-processing overhead with an increasing number of

Tyla R. Holoman, B. P. Prajwal, Glen M. Hocky, Thomas M. Truskett

Dynamic bonding is an essential feature of many soft materials. Molecular simulations have proven to be a powerful tool for modeling bonding kinetics and thermodynamics in these materials, providing insights into their properties that cannot be obtained by experiments alone. Here, we review recent advances in modeling dynamic bonding in soft matter via molec

Jan-Niklas Heidkamp, Johannes Schwandt-Krause, Swarup Deb, Kenji Watanabe

In recent years, sliding ferroelectricity has emerged as a topic of significant interest due to its possible application in non-volatile, reconfigurable storage devices. This phenomenon is unique to two-dimensional van der Waals materials, where out-of-plane ferroelectric polarization switching is induced by relative in-plane sliding of adjacent layers. The

Sahil Sethi, David Chen, Michael C. Burkhart, Nipun Bhandari

Prototype-based neural networks offer interpretable predictions by comparing inputs to learned, representative signal patterns anchored in training data. While such models have shown promise in the classification of physiological data, it remains unclear whether their prototypes capture an underlying structure that aligns with broader clinical phenotypes. We

Hong Wu, Jia-Ji Zhu, Jian Li, Xue-Min Yang

Dirac semimetals can be classified into types I, II, and III based on the topological charge of their Dirac points. If a three-dimensional (3D) system can be sliced into a family of kz-dependent normal and topological insulators, type I Dirac points separate a 2D normal insulator from a 2D first-order topological insulator, while type II (III) Dirac points s

Takehito Yokoyama

We show that Hamiltonian for nonunitary mixed parity superconductivity can be recast into that for magnetism with spin-orbit coupling by the Schrieffer-Wolff transformation, indicating that nonunitary mixed parity superconductivity and magnetism with spin-orbit coupling can share the same physics. As demonstrations, we discuss the Dzyaloshinskii-Moriya type

Joseph Ho, Jonathan W. Webb, Russell M. J. Brooks, Federico Grasselli

Quantum networks can enhance both security and privacy conditions for multi-user communication, delegated computation, and distributed sensing tasks. An example quantum protocol is private parameter estimation (PPE) where only the aggregate information is accessible while individual sensor data remain confidential. Specifically, the protocol enables the esti

Huixi Li, Biao Wang, Chunlin Wang, Shaoyun Yi

In 2022, Bergelson and Richter gave a new dynamical generalization of the prime number theorem by establishing an ergodic theorem along the number of prime factors of integers. They also showed that this generalization holds as well if the integers are restricted to be squarefree. In this paper, we present the concept of invariant averages under multiplicati

Umur Hasturk, Albert H. Schrotenboer, Kees Jan Roodbergen, Evrim Ursavas

Hydrogen is produced from water using renewable electricity. Unlike electricity, hydrogen can be stored in large quantities for long periods. This storage ability acts as a green battery, allowing solar and wind energy to be generated and used at different times. As a result, green hydrogen plays a central role in facilitating a climate-neutral economy. Howe

Simone Castellan

We develop the theory of $\hbar$-vertex algebras, algebraic structures closely related to vertex algebras but with a deformed translation covariance axiom. We establish their structure theory, including analogues of Goddard's Uniqueness Theorem, the Reconstruction Theorem, Borcherds Identity, and the OPE Expansion Formula, and introduce the associated no

Patrick Mesana, Gilles Caporossi, Sebastien Gambs

Data valuation methods assign marginal utility to each data point that has contributed to the training of a machine learning model. If used directly as a payout mechanism, this creates a hidden cost of valuation, in which contributors with near-zero marginal value would receive nothing, even though their data had to be collected and assessed. To better forma

Yu-Heng Shen, Kai-Xing Lu, Wei-Jian Guo, Sha-Sha Li

The changing-look active galactic nucleus (CL-AGN), an extraordinary subpopulation of supermassive black holes, has attracted growing attention for understanding its nature. We present an analysis of the spectral properties of 203 low-redshift CL-AGNs ($z<0.35$) using two-epoch spectra from SDSS DR16 and DESI DR1 with time baseline ranging from $\sim$1000 to

Samira Falahatdoost, Yair D. J. Prawer, Danli Peng, Andre Chambers

This study demonstrates the control of neuronal survival and development using nitrogen-doped ultrananocrystalline diamond (N-UNCD). We highlight the role of N-UNCD in regulating neuronal activity via near-infrared illumination, demonstrating the generation of stable photocurrents that enhance neuronal survival and neurite outgrowth and foster a more active,

Varun Rajesh, Om Jodhpurkar, Pooja Anbuselvan, Mantinder Singh

We present a systematic, empirical evaluation of five local large language model (LLM) runtimes on Apple Silicon: MLX, MLC-LLM, llama.cpp, Ollama, and PyTorch MPS. Experiments were conducted on a Mac Studio equipped with an M2 Ultra processor and 192 GB of unified memory. Using the Qwen-2.5 model family across prompts ranging from a few hundred to 100,000 to

Gaurav Sahu, Hugo Larochelle, Laurent Charlin, Christopher Pal

Peer review is the cornerstone of scientific publishing, yet it suffers from inconsistencies, reviewer subjectivity, and scalability challenges. We introduce ReviewerToo, a modular framework for studying and deploying AI-assisted peer review to complement human judgment with systematic and consistent assessments. ReviewerToo supports systematic experiments w

Maria Gordina, Rohan Sarkar

We consider non-local perturbations $\Delta^\psi_G$ of sub-Laplacians on a step $2$ Carnot group $G$. The perturbations are by translation-invariant non-local operators acting along the vertical directions in $G$. We use harmonic analysis on $G$ to obtain intertwining relationship between the semigroups generated by $\Delta^\psi_G$ and some strongly continuo

Sushil Khairnar, Gaurav Bansod, Vijay Dahiphale

Lightweight cryptography is an emerging field in the field of research, which endorses algorithms which are best suited for constrained environment. Design metrics like Gate Equivalence (GE), Memory Requirement, Power Consumption, and Throughput play a vital role in the applications like IoT. This paper presents the 6LoWPAN Protocol Stack which is a popular

Murray Cutforth, Yiming Yang, Tiffany Fan, Serge Guillas

Many science and engineering problems rely on expensive computational simulations, where a multi-fidelity approach can accelerate the exploration of a parameter space. We study efficient allocation of a simulation budget using a Gaussian Process (GP) model in the binary simulation output case. This paper introduces Bernoulli Parameter Mutual Information (BPM

Oleg Udalov

The VQE algorithm is applied to the problem of finding the ground state of a lattice model with on-site Coulomb repulsion, nearest-neighbor hopping, and on-site sd-interaction. We compare the performance of several ansatze, including cluster and generic forms. Several modifications of the standard cluster ansatz implementation are proposed, which significant

Junyao Huang, Rumin Situ

Traditional pet emotion recognition from vocalizations, based on discrete classification, struggles with ambiguity and capturing intensity variations. We propose a continuous Valence-Arousal (VA) model that represents emotions in a two-dimensional space. Our method uses an automatic VA label generation algorithm, enabling large-scale annotation of 42,553 pet

Deep Bodra, Sushil Khairnar

The rise of distributed applications and cloud computing has created a demand for scalable, high-performance key-value storage systems. This paper presents a performance evaluation of three prominent NoSQL key-value stores: Redis, Aerospike, and Dragonfly, using the Yahoo! Cloud Serving Benchmark (YCSB) framework. We conducted extensive experiments across th

Ernie Croot, Junzhe Mao, Chi Hoi Yip

In this paper, we prove several results on the structure of maximal sets $S \subseteq [N]$ such that $S$ mod $p$ is contained in a short arithmetic progression, or the union of short progressions, where $p$ ranges over a subset of primes in an interval $[y,2y]$ with $(\log N)^{O(1)} < y \leq N$. We also provide several constructions demonstrating the sharpne

Kevin Carlson, Evan Patterson

We introduce a notion of (co)presheaf on a lax double functor $X$, which we generally call an instance. In the terminology of double-categorical logic, a lax double functor valued in sets, possibly preserving finite products, is called a model of a double (Lawvere) theory. By varying the double theory, we uniformly define a well-behaved notion of instances o

Fanxin Wu

We consider the two-cardinal Kurepa Hypothesis $\mathsf{KH}(\kappa,\lambda)$. We observe that if $\kappa\leq\lambda<\mu$ are infinite cardinals then $\lnot\mathsf{KH}(\kappa,\lambda)\land\mathsf{KH}(\kappa,\mu)\rightarrow\mathsf{KH}(\lambda^+,\mu)$, and show that in some sense this is the only $\mathsf{ZFC}$ constraint. The case of singular $\lambda$ and its

Ragib Amin Nihal, Rui Wen, Kazuhiro Nakadai, Jun Sakuma

Large language models (LLMs) remain vulnerable to multi-turn jailbreaking attacks that exploit conversational context to bypass safety constraints gradually. These attacks target different harm categories through distinct conversational approaches. Existing multi-turn methods often rely on heuristic or ad hoc exploration strategies, providing limited insight

Ammar I Marvi, Nancy G Kanwisher, Meenakshi Khosla

The ventral, dorsal, and lateral streams in high-level human visual cortex are implicated in distinct functional processes. Yet, deep neural networks (DNNs) trained on a single task model the entire visual system surprisingly well, hinting at common computational principles across these pathways. To explore this inconsistency, we applied a novel sparse decom

Manik Dhar, Sammy Luo

Given a set $A \subseteq \mathbb{F}_p^n$, what conditions does one need to guarantee that iterated sumsets of the form $A+\cdots+A$ expand quickly (say, within $O(p)$ terms) to the whole space? When only the size of $A$ is known, such expansion results are only possible when $|A|>\frac{1}{p}|\mathbb{F}_p^n|$. However, heuristic considerations suggest that ex

Jheelum Sarkar

Catastrophic floods directly risk 1.8 billion lives worldwide, most of whom are from East and South Asia. How do extreme floods reshape paid labor outcomes? To answer this, I focus on a 1-in-100 year flood event in India. I first combine Sentinel-1 SAR with JRC Global Surface Water dataset to generate flood map. Using information from this map in various rou

T. Ed Li, Junyu Ren

Understanding the internal representations of large language models is crucial for ensuring their reliability and safety, with sparse autoencoders (SAEs) emerging as a promising interpretability approach. However, current SAE training methods face feature absorption, where features (or neurons) are absorbed into each other to minimize $L_1$ penalty, making i

Abhijeet, Mohamed Naveed Gul Mohamed, Aayushman Sharma, Suman Chakravorty

This paper investigates the infinite horizon optimal control problem (OCP) for space applications characterized by nonlinear dynamics. The proposed approach divides the problem into a finite horizon OCP with a regularized terminal cost, guiding the system towards a terminal set, and an infinite horizon linear regulation phase within this set. This strategy g

Caitlin H. Daly, Chloe Tan, Audrey Béliveau

In recent years, there has been growing research interest in addressing treatment hierarchy questions within network meta-analysis (NMA). In NMAs involving many treatments, the number of possible hierarchy questions becomes prohibitively large. To manage this complexity, previous work has recommended pre-selecting specific hierarchy questions of interest (e.

Achleshwar Luthra, Priyadarsi Mishra, Tomer Galanti

Self-supervised contrastive learning (CL) has achieved remarkable empirical success, often producing representations that rival supervised pre-training on downstream tasks. Recent theory explains this by showing that the CL loss closely approximates a supervised surrogate, Negatives-Only Supervised Contrastive Learning (NSCL) loss, as the number of classes g

Le Mao, Andrew H. Liu, Renos Zabounidis, Yanan Niu

Intelligent exploration remains a critical challenge in reinforcement learning (RL), especially in visual control tasks. Unlike low-dimensional state-based RL, visual RL must extract task-relevant structure from raw pixels, making exploration inefficient. We propose Concept-Driven Exploration (CDE), which leverages a pre-trained vision-language model (VLM) t

Vasudha Yanuganti, Ishaan Puri, Swapnil Chhatre, Mantinder Singh

Modern codebases make it hard for developers and AI coding assistants to find the right source files when answering questions like "How does this feature work?" or "Where was the bug introduced?" Traditional code search (keyword or IR based) often misses semantic context and cross file links, while large language models (LLMs) understand natural language but

Sudip Khadka, L. S. Paudel

Static feature exclusion strategies often fail to prevent bias when hidden dependencies influence the model predictions. To address this issue, we explore a reinforcement learning (RL) framework that integrates bias mitigation and automated feature selection within a single learning process. Unlike traditional heuristic-driven filter or wrapper approaches, o

Hongrui Wu, Zhicheng Gao, Jin Cao, Kelu Yao

Open-vocabulary 3D instance segmentation seeks to segment and classify instances beyond the annotated label space. Existing methods typically map 3D instances to 2D RGB-D images, and then employ vision-language models (VLMs) for classification. However, such a mapping strategy usually introduces noise from 2D occlusions and incurs substantial computational a

Nicholas S. Conroy, Michi Bauböck, Vedant Dhruv, Daeyoung Lee

The Event Horizon Telescope is preparing to produce time sequences of black hole images, or movies. In anticipation, we developed an autocorrelation technique to measure apparent rotational motion using the image-domain pattern speed $\Omega_p$. Here, we extend this technique to the visibility domain and introduce the visibility amplitude pattern speed $\Ome

Allison Sihan Jia, Daniel Huang, Nikhil Vytla, Seung Won Wilson Yoo

We introduce the Agent GPA (Goal-Plan-Action) framework, driven by the fundamental insight that critical agent failures emerge at the intersections of setting goals, devising plans, and executing actions. We operationalize the framework with a factorized suite of LLM judges designed to measure distinct elements of Goal-Plan-Act alignment. To make this method

Ettore Lo Giudice

We study the positive Hermitian curvature flow for left-invariant metrics on $2$-step nilpotent Lie groups with a left-invariant complex structure $J$. We describe the long-time behavior of the flow under the assumption that $J[\mathfrak{g}, \mathfrak{g}]$ is contained in the center of $\mathfrak{g}$. We show that under our assumption the flow $g_{t}$ exists

Lin Tang, Hui-Yu Xing, Minghui Ding, Craig D. Roberts

A reaction model for $\gamma + p \to V + p$, $V=\rho^0, \phi, J/\psi, \Upsilon$, which exposes the quark-antiquark content of the photon in making the transition $\gamma\to {q} \bar{q} + \mathbb P \to V$, where ${q}$ depends on $V$, and couples the intermediate ${q} \bar{q}$ system to the proton's valence quarks via Pomeron ($\mathbb P$) exchange, is used to

Vinay Bamil

Recent advances in large language models have enabled developers to generate software by conversing with artificial intelligence systems rather than writing code directly. This paper introduces vibe coding, an emerging AI-native programming paradigm in which a developer specifies high-level functional intent along with qualitative descriptors of the desired

Tim Lichtenberg, Oliver Shorttle, Johanna Teske, Eliza M. -R. Kempton

Astronomical surveys have identified numerous exoplanets with bulk compositions that are unlike the planets of the Solar System, including rocky super-Earths and gas-enveloped sub-Neptunes. Observing the atmospheres of these objects provides information on the geological processes that influence their climates and surfaces. In this Review, we summarize the c

Rajarshi Ghosh, Abhay Gupta, Hudson McBride, Anurag Vaidya

Large language models (LLMs) are increasingly deployed in clinical decision support, yet subtle demographic cues can influence their reasoning. Prior work has documented disparities in outputs across patient groups, but little is known about how internal reasoning shifts under controlled demographic changes. We introduce MEDEQUALQA, a counterfactual benchmar

Noam Goldberg, Michael Poss, Shimrit Shtern

We propose a novel polyhedral uncertainty set for robust optimization, termed the smooth uncertainty set, which captures dependencies of uncertain parameters by constraining their pairwise differences. The bounds on these differences may be dictated by the underlying physics of the problem and may be expressed by domain experts. When correlations are availab

Molang Wu, Zhao Zhang

Training deep learning (DL) models across Graphics Processing Unit (GPU) clusters is technically challenging. One aspect is that users have to compose command lines to adapt to the heterogeneous launchers, schedulers, affinity options, DL framework arguments, and environment variables. Composing correct command lines is error-prone and can easily frustrate u

Sufiyan Mallu

Let \( D \) be a strongly connected digraph. The average distance of a vertex \( v \) in \( D \) is defined as the arithmetic mean of the distances from \( v \) to all other vertices in \( D \). The remoteness \( \rho(D) \) of \( D \) is the maximum of the average distances of the vertices in \( D \). In this paper, we provide a sharp upper bound on the remo

Thai-Hoang Pham, Jiayuan Chen, Seungyeon Lee, Yuanlong Wang

As machine learning (ML) algorithms are increasingly used in medical image analysis, concerns have emerged about their potential biases against certain social groups. Although many approaches have been proposed to ensure the fairness of ML models, most existing works focus only on medical image diagnosis tasks, such as image classification and segmentation,

Motahare Mounesan, Sourya Saha, Houchao Gan, Md. Nurul Absur

Real-time multi-view 3D reconstruction is a mission-critical application for key edge-native use cases, such as fire rescue, where timely and accurate 3D scene modeling enables situational awareness and informed decision-making. However, the dynamic and unpredictable nature of edge resource availability introduces disruptions, such as degraded image quality,

Ziyi Song, Federico Camerlenghi, Weining Shen, Michele Guindani

In many scientific domains, clustering aims to reveal interpretable latent structure that reflects relevant subpopulations or processes. Widely used Bayesian mixture models for model-based clustering often produce overlapping or redundant components because priors on cluster locations are specified independently, hindering interpretability. To mitigate this,

Benjamin J. Vaughan, Jack Sayers, Locke Spencer, Nicholas Swidinsky

The conversion of gravitational potential to kinetic energy results in an intracluster medium (ICM) gas with a characteristic temperature near 10 keV in the most massive galaxy clusters. X-ray observations, primarily from Chandra and XMM-Newton, have revealed a wealth of information about the thermodynamics of this gas. However, two regimes remain difficult

Fudong Lin, Xu Yuan

The imbalance (or long-tail) is the nature of many real-world data distributions, which often induces the undesirable bias of deep classification models toward frequent classes, resulting in poor performance for tail classes. In this paper, we propose a novel two-stage learning approach to mitigate such a majority-biased tendency while preserving valuable in

Ian Kolaja, Lee Bernstein, Ludovic Jantzen, Eleanor Tubman

Burnup measurement is essential for monitoring and controlling pebble bed reactors (PBRs), where fuel pebbles circulate rapidly through the core. However, conventional gamma spectroscopy using high purity germanium (HPGe) detectors is difficult due to high activity levels in discharge pebbles, leading to excessive dead time and Compton scattering. This study

Carlos Pinto Gomez, Fabio Petrillo

Conventional debugging techniques used in traditional software are similarly used when debugging video games. However, the reality of video games require its own set of unique debugging techniques such as On-Screen Console, Debug Draws, Debug Camera, Cheats and In-Game Menus, and Data Scrubbing. In this article, we provide insights from a video game studio o

Simon A. Levin, Ted Loch-Temzelides

We employ an n-player coordination game to model mutualism emergence and abandonment. We illustrate our findings in the context of the host--host interactions among plants in plant-mycorrhizal fungi (MF) mutualisms. The coordination game payoff structure captures the insight that mutualistic strategies lead to robust advantages only after such "biological ma

Michael Müller, Reed Essick

We study the ability of tidal signatures within the inspiral of compact binaries observed through gravitational waves (GWs) to distinguish between neutron stars (NSs) and black holes (BHs). After quantifying how hard this measurement is on a single-event basis, we investigate the ability of a large catalog of GW detections to constrain the fraction of NS in

Wouter Haverals, Meredith Martin

As AI writing tools become widespread, we need to understand how both humans and machines evaluate literary style, a domain where objective standards are elusive and judgments are inherently subjective. We conducted controlled experiments using Raymond Queneau&#39;s Exercises in Style (1947) to measure attribution bias across evaluators. Study 1 compared hum

Shuzhi Xu, Yifan Guo, Hiroki Kawabe, Kentaro Yaji

Multiscale topology optimization is crucial for designing porous infill structures with high stiffness-to-weight ratios and excellent energy absorption. Although gradient-based methods provide a rigorous framework, they are computationally expensive and struggle to capture cross-scale sensitivities in nonlinear settings. Moreover, the resulting hierarchical

Debeshee Das, Luca Beurer-Kellner, Marc Fischer, Maximilian Baader

The increasing adoption of LLM agents with access to numerous tools and sensitive data significantly widens the attack surface for indirect prompt injections. Due to the context-dependent nature of attacks, however, current defenses are often ill-calibrated as they cannot reliably differentiate malicious and benign instructions, leading to high false positiv

Iarley P. Lobo, Kelvin Sampaio, Gislaine Varão, Moises Rojas

Inspired by approaches based on the stochastic generalized uncertainty principle, we propose a Lindblad equation derived from the quantization of a stochastic modified dispersion relation in a Lorentz Invariance Violation (LIV) scenario. This framework enables us to investigate decoherence effects in a system of particles exhibiting gravitationally induced e

Erfan Al-Hossami, Razvan Bunescu

When learning to code, students often develop misconceptions about various programming language concepts. These can not only lead to bugs or inefficient code, but also slow down the learning of related concepts. In this paper, we introduce McMining, the task of mining programming misconceptions from samples of code from a student. To enable the training and

Georg Lehner

We present an approach to measure theory using the theory of locales. This includes concrete constructions of measure algebras associated to Radon measures, such as the Lebesgue measure on $\mathbb{R}^n$, via Grothendieck topologies constructed from valuations, that circumvent the classical approach via $\sigma$-algebras. As an application we obtain a functo

Shizhen Jia, Mingjun Ying, Marco Mezzavilla, Doru Calin

The upper mid-band FR3 spectrum (7-24 GHz) has garnered significant interest for future cellular services. However, utilizing a large portion of this band requires careful interference coordination with incumbent satellite systems. This paper investigates interference from high-power terrestrial base stations (TN-BSs) to satellite downlink receivers. A centr

Peng-Cheng Hang, Alexey Kuznetsov

We compute Fourier transforms of functions expressed as a ratio of one of the Jacobi elliptic functions divided by $\sinh(\pi x)$ or $\cosh(\pi x)$. In many cases, the resulting Fourier transform remains within the same class of functions. Applying the Mellin transform, we obtain sixteen Eisenstein-type series $\zeta_{j,l}(s,\tau)$, for which we establish se

Romain Speciel

The Laplacian $\Delta_{\mathbb{S}^{n-1}}$ on the unit sphere $\mathbb{S}^{n-1}\subset \mathbb{R}^n$ has the property that it can explicitly be expressed in terms of $\Lambda$, the Dirichlet-to-Neumann map of the unit ball, as $\Delta_{\mathbb{S}^{n-1}}=\Lambda^2+(n-2)\Lambda$. In this paper, we seek to characterize those manifolds for which such an exact rel

Andrew Kobin, David Zureick-Brown

We extend work of Voight and the second author to compute the log canonical ring of a wild stacky curve over a field of characteristic $p > 0$, which allows us to compute rings of mod $p$ modular forms of level $\Gamma_{0}(N)$. Our approach also reveals that in characteristics $2$ and $3$, there are infinitely many levels $N$ for which there are weight $2$ m

Samuel Alperin

We consider the dynamics of the quantum Rabi model driven parametrically by a periodic modulation of a complex coupling. We show both analytically and numerically that instead of Rabi oscillations, this nonunitary coherent driving leads to a unidirectional instanton solution which mediates the rapid and deterministic one-way tunneling of any initial coherent

Matthew Schlegel, Matthew E. Taylor, Mostafa Farrokhabadi

Time domain simulation, i.e., modeling the system's evolution over time, is a crucial tool for studying and enhancing power system stability and dynamic performance. However, these simulations become computationally intractable for renewable-penetrated grids, due to the small simulation time step required to capture renewable energy resources' ultra-fast dyn

Dali Cheng, Heming Wang, Janet Zhong, Eran Lustig

Non-Hermiticity naturally arises in many physical systems that exchange energy with their environment. The presence of non-Hermiticity leads to many novel topological physics phenomena and device applications. In the non-Hermitian energy band theory, the foundation of these physics and applications, both energies and wavevectors can take complex values. The

Yiyang Huang, Yizhou Wang, Yun Fu

Video large language models (Vid-LLMs), which excel in diverse video-language tasks, can be effectively constructed by adapting image-pretrained vision-language models (VLMs). However, this adaptation remains challenging, as it requires processing dense and temporally extended visual inputs that exceed the capacity of image-based models. This paper identifie

F. Dellmann, J. Glorius, Yu. A. Litvinov, R. Reifarth

We present the first nuclear cross-section measurements of (p,{\gamma}) and (p,n) reactions on 118Te at energies relevant for the {\gamma}-process nucleosynthesis. Absolute cross-section values for center-of-mass energies of 6, 7 and 10 MeV are provided, together with a theoretical extrapolation to the Gamow window. This experiment marks the first time that

Juan José Burred, Carmine-Emanuele Cella

We propose the use of Non-Negative Autoencoders (NAEs) for sound deconstruction and user-guided manipulation of sounds for creative purposes. NAEs offer a versatile and scalable extension of traditional Non-Negative Matrix Factorization (NMF)-based approaches for interpretable audio decomposition. By enforcing non-negativity constraints through projected gra

Andrew B. Newman, Gwen C. Rudie, Guillermo A. Blanc, Daniel D. Kelson

We present the data release of the Ly$\alpha$ Tomography IMACS Survey (LATIS), one of the largest optical spectroscopic surveys of faint high-redshift galaxies. The survey provides 7408 optical spectra of candidate $z \sim 2-3$ galaxies and QSOs in the Canada-France-Hawaii Telescope Legacy Survey D1, D2 (COSMOS), and D4 fields. The $R \sim 1000$ spectra were

Ben Goertzel

We present a proof architecture for \(P \neq NP\) based on an upper--lower clash in polytime-capped conditional description length. We construct an efficiently samplable family of SAT instances \(Y\) such that every satisfying witness for \(Y\) yields the same global message \(M(Y)\). If \(P=NP\), then a standard polynomial-time SAT self-reduction recovers \

Abhishek K. Mishra, Antoine Boutet, Lucas Magnana

Large Language Models (LLMs) are increasingly deployed across multilingual applications that handle sensitive data, yet their scale and linguistic variability introduce major privacy risks. Mostly evaluated for English, this paper investigates how language structure affects privacy leakage in LLMs trained on English, Spanish, French, and Italian medical corp

Grace Ra Kim, Hailey Warner, Duncan Eddy, Evan Astle

Deep space missions face extreme communication delays and environmental uncertainty that prevent real-time ground operations. To support autonomous science operations in communication-constrained environments, we present a partially observable Markov decision process (POMDP) framework that adaptively sequences spacecraft science instruments. We integrate a B

Jiurun Song, Xiao Liang, Minghui Zheng

Human-robot collaboration (HRC) requires robots to adapt their motions to human intent to ensure safe and efficient cooperation in shared spaces. Although large language models (LLMs) provide high-level reasoning for inferring human intent, their application to reliable motion planning in HRC remains challenging. Physical human-robot interaction (pHRI) is in

Mohayeminul Islam, Ajay Kumar Jha, May Mahmoud, Sarah Nadi

Library migration is the process of replacing a library with a similar one in a software project. Manual library migration is time consuming and error prone, as it requires developers to understand the Application Programming Interfaces (API) of both libraries, map equivalent APIs, and perform the necessary code transformations. Due to the difficulty of the

Tarique Anwar, Diana DallAglio, Milad Sabzehparvar, Giulia Tagliabue

Light and heat drive interfacial chemistry at solid-liquid interfaces, underpinning processes central to sustainable energy conversion, including photoelectrochemical and hydrovoltaic systems. Yet, non-invasive probing of light-induced interfacial dynamics remains challenging due to the weak and spatially complex nature of optical signals. Here, we introduce

Michal Podstawski

Graphs provide a universal framework for representing complex relational systems, and inferring their structural properties is a core challenge in graph analysis and reasoning. While large language models have recently demonstrated emerging abilities to perform symbolic and numerical reasoning, the potential of smaller, resource-efficient models in this cont

Souvik Das, Subhrakanti Dey

Compression techniques are essential in distributed optimization and learning algorithms with high-dimensional model parameters, particularly in scenarios with tight communication constraints such as limited bandwidth. This article presents a communication-efficient second-order distributed optimization algorithm, termed as CoNet-GIANT, equipped with a compr

Nicola F. Zaugg, Lech A. Grzelak

The lifted Heston model is a stochastic volatility model emerging as a Markovian lift of the rough Heston model and the class of rough volatility processes. The model encodes the path dependency of volatility on a set of N square-root state processes driven by a common stochastic factor. While the system is Markovian, simulation schemes such as the Euler sch

Siddeshwar Raghavan, Tanwi Mallick

We present MOSAIC, a multi-agent Large Language Model (LLM) framework for solving challenging scientific coding tasks. Unlike general-purpose coding, scientific workflows require algorithms that are rigorous, interconnected with deep domain knowledge, and incorporate domain-specific reasoning, as well as algorithm iteration without requiring I/O test cases.

Rohit Dwivedula, Divyanshu Saxena, Aditya Akella, Swarat Chaudhuri

Policy design for various systems controllers has conventionally been a manual process, with domain experts carefully tailoring heuristics for the specific instance in which the policy will be deployed. In this paper, we re-imagine policy design via a novel automated search technique fueled by recent advances in generative models, specifically Large Language

S M Rafiuddin

Understanding learner emotions in online education is critical for improving engagement and personalized instruction. While prior work in emotion recognition has explored multimodal fusion and temporal modeling, existing methods often rely on static fusion strategies and assume that modality inputs are consistently reliable, which is rarely the case in real-

Rachel Honor, Seth Cohen, Timothy Carleton, Steven Willner

El Gordo (ACT-CL J0102$-$4915) is a massive galaxy cluster with two major mass components at redshift $z=0.87$. Using SED fitting results from JWST/NIRCam photometry, the fraction of quenched galaxies in this cluster was measured in two bins of stellar mass: $9<\log{({M_*}/\mathrm{M}_{\odot})}<10$ and $10\leq\log{({M_*}/\mathrm{M}_{\odot})}<12$. While there

Wangjie You, Xusheng Wang, Xing Wang, Wenxiang Jiao

While Large Language Models (LLMs) have demonstrated advanced reasoning capabilities, their comprehensive evaluation in general Chinese-language contexts remains understudied. To bridge this gap, we propose Chinese Commonsense Multi-hop Reasoning (CCMOR), a novel benchmark designed to evaluate LLMs' ability to integrate Chinese-specific factual knowledge wit

Rohan Choudhury, Shanchuan Lin, Jianyi Wang, Hao Chen

Diffusion-based super-resolution (SR) is a key component in video generation and video restoration, but is slow and expensive, limiting scalability to higher resolutions and longer videos. Our key insight is that many regions in video are inherently low-detail and gain little from refinement, yet current methods process all pixels uniformly. To take advantag

S M Rafiuddin, Muntaha Nujat Khan

Transformer attention scales quadratically with sequence length O(n^2), limiting long-context use. We propose Adaptive Retention, a probabilistic, layer-wise token selection mechanism that learns which representations to keep under a strict global budget M. Retention is modeled with Bernoulli gates trained via a Hard-Concrete/variational relaxation and enfor

Guangya Wan, Zixin Stephen Xu, Sasa Zorc, Manel Baucells

Sampling multiple responses is a common way to improve LLM output quality, but it comes at the cost of additional computation. The key challenge is deciding when to stop generating new samples to balance accuracy gains against efficiency. To address this, we introduce BEACON (Bayesian Efficient Adaptive Criterion for Optimal N-stopping), a principled adaptiv

Eshika Saxena, Alberto Alfarano, François Charton, Emily Wenger

AI-powered attacks on Learning with Errors (LWE), an important hard math problem in post-quantum cryptography, rival or outperform "classical" attacks on LWE under certain parameter settings. Despite the promise of this approach, a dearth of accessible data limits AI practitioners' ability to study and improve these attacks. Creating LWE data for AI model tr

G. Oliveira-Neto, Y. Soncco Apaza

Understanding the early evolution of the universe requires models that incorporate possible quantum and anisotropic effects in its dynamics. In this work, we analyze the dynamical evolution of locally rotationally symmetric anisotropic cosmological models of Bianchi type I (flat curvature) and Bianchi type III (open curvature) within a noncommutative phase s

Junyi Wu, Guang Lin

We propose PO-CKAN, a physics-informed deep operator framework based on Chunkwise Rational Kolmogorov--Arnold Networks (KANs), for approximating the solution operators of partial differential equations. This framework leverages a Deep Operator Network (DeepONet) architecture that incorporates Chunkwise Rational Kolmogorov-Arnold Network (CKAN) sub-networks f

I. Arda Vurankaya, Mustafa O. Karabag, Wesley A. Suttle, Jesse Milzman

We consider a multi-armed bandit setting in which each arm has a public and a private reward distribution. An observer expects an agent to follow Thompson Sampling according to the public rewards, however, the deceptive agent aims to quickly identify the best private arm without being noticed. The observer can observe the public rewards and the pulled arms,

Ataher Sams, Simone Di Bari, Besma Smida, Natasha Devroye

This paper explores the fundamental limits of Integrated Sensing and Communication (ISAC) in a more realistic setting compared to previous literature when the Base Staion (BS) has only statistical CSI of the communication user rather than full CSI. We analyze a monostatic setting where the BS performs multi-target Angle of Arrival (AoA) estimation while simu