October 2025 arXiv papers — page 198

Ali Naseh, Anshuman Suri, Yuefeng Peng, Harsh Chaudhari

Generative AI leaderboards are central to evaluating model capabilities, but remain vulnerable to manipulation. Among key adversarial objectives is rank manipulation, where an attacker must first deanonymize the models behind displayed outputs -- a threat previously demonstrated and explored for large language models (LLMs). We show that this problem can be

Walter Dempsey, Easton Huch

Recent work in dynamic causal inference introduced a class of discrete-time stochastic processes that generalize martingale difference sequences and arrays as follows: the random variates in each sequence have expectation zero given certain lagged filtrations but not given the natural filtration. We formalize this class of stochastic processes and prove stab

Kevin Smith

We study Dirichlet series arising as linear functionals on an inner product space of meromorphic functions and establish a relation between the discontinuities of the former on the boundary and the poles and zeros of the latter on the imaginary axis. As an example application of Delange's Tauberian theorem, it is shown that the conjectured asymptotic in the

Sabee Grewal, Dorian Rudolph

We prove several new results concerning the pure quantum polynomial hierarchy (pureQPH). First, we show that QMA(2) is contained in pureQSigma2, that is, two unentangled existential provers can be simulated by competing existential and universal provers. We further prove that pureQSigma2 is contained in QSigma3, which in turn is contained in NEXP. Second, we

Walaa Asakly, Noor Kezil

In this paper, we aim to derive an explicit formula for the total number of elements preceding records over all set partitions of $[n]$ with exactly $k$ blocks, as well as an asymptotic estimate for the total sum of elements preceding records in all set partitions of $[n]$, expressed in terms of Bell numbers. To achieve this, we analyze the generating functi

Pranabesh Das, J. C. Saunders

In 2014 Marques and Lengyel gave all of the solutions to the equation $T_n=m!$, where $T_n$ is the $n$th term of the Tribonacci sequence $0,1,1,2,4,7,13,24,\ldots$. In 2023 Alahmadi and Luca generalized their result to the equation $T_n=m_1!m_2!\cdots m_k!$ for every $k\in\mathbb{N}$, where $m_1\leq m_2\leq\ldots\leq m_k$ listing all the solutions to this eq

Utkarsh Singh, Leif Bauer, Angshuman Deka, Mohamed Mousa

The use of probabilistic spintronic devices for infrared radiation detection has introduced a shift in approach to thermal imaging. The integration of probabilistic magnetic tunnel junctions with infrared plasmonic nano-antennas achieves high-sensitivity digital-mode infrared sensors at room temperature. Here, we present a scalable approach towards multipixe

Malakhi Hopkins, Varun Murali, Vijay Kumar, Camillo J Taylor

Autonomous aerial robots are increasingly being deployed in real-world scenarios, where transparent obstacles present significant challenges to reliable navigation and mapping. These materials pose a unique problem for traditional perception systems because they lack discernible features and can cause conventional depth sensors to fail, leading to inaccurate

Xavier F. C. Sánchez-Díaz, Ole Jakob Mengshoel

This work walks through different visualization techniques for combinatorial search landscapes, focusing on multimodality. We discuss different techniques from the landscape analysis literature, and how they can be combined to provide a more comprehensive view of the search landscape. We also include examples and discuss relevant work to show how others have

Zhantao Deng, Mériem Er-Rafik, Anna Sushko, Cécile Hébert

Limited-angle electron tomography aims to reconstruct 3D shapes from 2D projections of Transmission Electron Microscopy (TEM) within a restricted range and number of tilting angles, but it suffers from the missing-wedge problem that causes severe reconstruction artifacts. Deep learning approaches have shown promising results in alleviating these artifacts, y

Chiara Mignacco, Matthieu Jonckheere, Gilles Stoltz

Online matching problems arise in many complex systems, from cloud services and online marketplaces to organ exchange networks, where timely, principled decisions are critical for maintaining high system performance. Traditional heuristics in these settings are simple and interpretable but typically tailored to specific operating regimes, which can lead to i

Harris Song, Long Le

We propose Avi, a novel 3D Vision-Language-Action (VLA) architecture that reframes robotic action generation as a problem of 3D perception and spatial reasoning, rather than low-level policy learning. While existing VLA models primarily operate on 2D visual inputs and are trained end-to-end on task-specific action policies, Avi leverages 3D point clouds and

Debendra Das Sharma, Swadesh Choudhary, Peter Onufryk, Rob Pelt

Emerging computing applications such as Artificial Intelligence (AI) are facing a memory wall with existing on-package memory solutions that are unable to meet the power-efficient bandwidth demands. We propose to enhance UCIe with memory semantics to deliver power-efficient bandwidth and cost-effective on-package memory solutions applicable across the entire

Avishree Khare, Hideki Okamoto, Bardh Hoxha, Georgios Fainekos

Neural models such as YOLO and HuBERT can be used to detect local properties such as objects ("car") and emotions ("angry") in individual frames of videos and audio clips respectively. The likelihood of these detections is indicated by scores in [0, 1]. Lifting these scores to temporal properties over sequences can be useful for several downstream applicatio

Aaron J. Moston-Duggan, Christopher J. Howls, Christopher J. Lustri

We study a discrete variant of the Airy equation, formulated as an advance-delay equation, to reveal that discretization induces the higher-order Stokes phenomenon, which is not present in the continuous Airy function and is typically only encountered in solutions to third-order or higher linear homogeneous, or nonlinear, differential equations. Using steepe

Konstantinos Horaites, Juan V. Rodriguez, Ying Liu

Solar sail technology is ready to be deployed in a satellite mission carrying a science-grade magnetometer. In preparation for such a mission, it is essential to characterize the interactions between the sail and the ambient plasma that could affect the magnetometer readings. The solar wind magnetic field is a key parameter in space weather prediction, becau

Shih-Yao Lin, Sibendu Paul, Caren Chen

Selecting informative keyframes is critical for efficient video understanding, yet existing approaches often rely on heuristics, ignore semantics, or produce redundant frames. We propose KeyScore, a caption-aware frame scoring method that combines three complementary signals: semantic similarity to captions, temporal representativeness, and contextual drop i

Yueqi Zhao, Michael M. Fogler, Yi-Zhuang You

We introduce RGFlow, a deep neural network-based real-space renormalization group (RG) framework tailored for continuum scalar field theories. Leveraging generative capabilities of flow-based neural networks, RGFlow autonomously learns real-space RG transformations from data without prior knowledge of the underlying model. In contrast to conventional approac

Yasuhiro Hasegawa, Courtney Dressing, Ludmila Carone

How do habitable environments arise and evolve within the context of their planetary systems? This is one fundamental question, and it can be addressed partly by identifying how planets in habitable zones obtain water. Historically, astronomers considered that water was delivered to the Earth via dynamical shake-up by Jupiter, which took place during the for

Ziming Wang, Yiqian Wu, Qingxiao Zheng, Shihan Zhang

What if future dining involved eating robots? We explore this question through a playful and poetic experiential dinner theater: a tangible design fiction staged as a 2052 Paris restaurant where diners consume a biohybrid flying robot in place of the banned delicacy of ortolan bunting. Moving beyond textual or visual speculation, our ``dinner-in-the-drama''

J. Galen Wang, Umesh Dhumal, Monica E. A. Zakhari, Roseanna N. Zia

Entropically driven fluid-solid transitions in monodisperse, purely repulsive hard spheres (MPRHS) are well established in theory, simulation, and experiment for atomic and colloidal systems. For MPRHS, however, coexistence is usually located via bulk free-energy calculations; the underlying microscopic balance between configurational and vibrational entropy

Qingxuan Wu, Zhiyang Dou, Chuan Guo, Yiming Huang

Modeling human-human interactions from text remains challenging because it requires not only realistic individual dynamics but also precise, text-consistent spatiotemporal coupling between agents. Currently, progress is hindered by 1) limited two-person training data, inadequate to capture the diverse intricacies of two-person interactions; and 2) insufficie

I-Hsi Kao, Kanji Uchino

Accurate time-series predictions in machine learning are heavily influenced by the selection of appropriate input time length and sampling rate. This paper introduces ATLO-ML, an adaptive time-length optimization system that automatically determines the optimal input time length and sampling rate based on user-defined output time length. The system provides

Khoa Trinh, Gaurav Menghani, Erik Vee

Algorithmic efficiency techniques such as distillation (\cite{hinton2015distillation}) are useful in improving model quality without increasing serving costs, provided a larger teacher model is available for a smaller student model to learn from during training. Standard distillation methods are limited to only forcing the student to match the teacher's outp

Seunghyun Lee, Rajarshi Mukherjee, Sumit Mukherjee

Gaussian mixture models are widely used to model data generated from multiple latent sources. Despite its popularity, most theoretical research assumes that the labels are either independent and identically distributed, or follows a Markov chain. It remains unclear how the fundamental limits of estimation change under more complex dependence. In this paper,

D. S. Akerib, A. K. Al Musalhi, F. Alder, B. J. Almquist

The LUX-ZEPLIN (LZ) experiment aims to detect rare interactions between dark matter particles and xenon. Although the detector is designed to be the most sensitive to GeV/$c^2$--TeV/$c^2$ Weakly Interacting Massive Particles (WIMPs), it is also capable of measuring low-energy ionization signals down to a single electron that may be produced by scatters of su

Zhepeng Cen, Haolin Chen, Shiyu Wang, Zuxin Liu

Large Language Models (LLMs) have achieved remarkable success through imitation learning on vast text corpora, but this paradigm creates a training-generation gap and limits robust reasoning. Reinforcement learning (RL) offers a more data-efficient solution capable of bridging this gap, yet its application has been constrained by a critical data bottleneck:

Colin Vendromin, Samuel E. Fontaine, J. E. Sipe

We develop a model for non-Gaussian state generation via spontaneous parametric down-conversion (SPDC) in InGaP microring resonators. The nonlinear Hamiltonian is written in terms of the asymptotic fields for the system, which includes a phantom channel to handle scattering loss. The full ket for the system is written as a Gaussian unitary acting on a residu

Rafael Fernandes, Brayan Ferreira

In this paper, we make use of elementary spectral invariants given by the max-min energy of pseudoholomorphic curves, recently defined by Michael Hutchings, to study periodic $3$-dimensional Reeb flows. We prove that Zoll contact forms on $S^3$ are characterized by $c_1 = c_2 = \mathcal{A}_{\min}$. This follows from the spectral gap closing bound property an

Daniel J. Spencer, Andrew Tanggara, Tobias Haug, Derek Khu

Qudits offer significant advantages over qubit-based architectures, including more efficient gate compilation, reduced resource requirements, improved error-correction primitives, and enhanced capabilities for quantum communication and cryptography. Yet, one of the most promising families of quantum error correction codes, namely quantum low-density parity-c

Anthony Noll, Sarbani Basu, Saskia Hekker

Red clump stars still pose open questions regarding several physical processes, such as the mixing around the core, or the nuclear reactions, which are ill-constrained by theory and experiments. The oscillations of red clump stars, which are of mixed gravito-acoustic nature, allow us to directly investigate the interior of these stars and thereby better unde

Johan Nilsson

We give exact formulas for the number of distinct triangular patterns (or subtriangles) of a given size that occur in the Sierpi\'{n}ski Triangle.

Danil Akhtiamov, Reza Ghane, Babak Hassibi

The Randomized Singular Value Decomposition (RSVD) is a widely used algorithm for efficiently computing low-rank approximations of large matrices, without the need to construct a full-blown SVD. Of interest, of course, is the approximation error of RSVD compared to the optimal low-rank approximation error obtained from the SVD. While the literature provides

Hadi Kharaghani, Vlad Zaitsev

This paper introduces and investigates a novel class of skew-regular Quaternary Hadamard matrices. For every odd prime power $p$, we establish the existence of these matrices for all orders $1+p^2$, each characterized by a constant row sum of $1-pi$. Motivated by the growing importance of large-excess matrices in the maximum determinant problem and quantum n

Peng Wang, Rana Saha, Holger L. Meyerheim, Ke Gu

A wide variety of chiral non-collinear spin textures have been discovered and have unique properties that make them highly interesting for technological applications. However, many of these are found in complex materials and only in a narrow window of temperature. Here, we show the formation of Neel-type skyrmions in thin layers of simple ferromagnetic alloy

Jack Roberts, Jeova Farias Sales Rocha Neto

Superpixels have long been used in image simplification to enable more efficient data processing and storage. However, despite their computational potential, their irregular spatial distribution has often forced deep learning approaches to rely on specialized training algorithms and architectures, undermining the original motivation for superpixelations. In

Eirik F. Kjønstad, Yann Damour, Sandeep Sharma, Garnet Kin-Lic Chan

We extend the use of coupled cluster (CC) trial states in the phaseless auxiliary-field quantum Monte Carlo (AFQMC) method beyond single and double excitations to include both triple and quadruple excitations. With this AFQMC/CC hierarchy, we are able to systematically benchmark the method's performance on molecular systems as the quality of the trial is imp

Shelley Hebert, Slawomir Klimek, Matt McBride

We explicitly construct Fredholm modules and spectral triples representing any element of $K$-homology groups of Hensel-Steinitz algebras.

W. E. Rogers, M. A. Janiga

We report the outcome of evaluations of the skill of long-range forecasts from the ocean wave model component of the Navy's global coupled modeling system. Specifically, the model output is taken from a single member of the ensemble system, and we evaluate the skill of predicting seven model "wave height" parameters, computed from: energy in four frequency b

Judith Michael, Lukas Netz, Bernhard Rumpe, Ingo Müller

Software applications often pose barriers for users with accessibility needs, e.g., visual impairments. Model-driven engineering (MDE), with its systematic nature of code derivation, offers systematic methods to integrate accessibility concerns into software development while reducing manual effort. This paper presents a systematic literature review on how M

Victor Dudarev, Alfred Ludwig

The digitalisation of research requires data management systems capable of supporting a broad spectrum of usage scenarios, ranging from document-oriented repositories to fully factographic environments. This paper introduces a methodological approach for the stepwise development of such systems, illustrated by the MatInf Research Data Management System (RDMS

Amirhossein Mollaei Khass, Guangyi Liu, Vivek Pandey, Wen Jiang

Safe navigation in uncertain environments requires planning methods that integrate risk aversion with active perception. In this work, we present a unified framework that refines a coarse reference path by constructing tail-sensitive risk maps from Average Value-at-Risk statistics on an online-updated 3D Gaussian-splat Radiance Field. These maps enable the g

Ziming Wang, Shiwei Yang, Rebecca Currano, Morten Fjeld

AI-powered road surveillance systems are increasingly proposed to monitor infractions such as speeding, phone use, and jaywalking. While these systems promise to enhance safety by discouraging dangerous behaviors, they also raise concerns about privacy, fairness, and potential misuse of personal data. Yet empirical research on how people perceive AI-enhanced

Sira Busch, Mark Pengitore, Jeroen Schillewaert, Hendrik Van Maldeghem

The Wiegold conjecture holds for the small Ree groups for $k$-tuples where $k \geq 5$.

Sanjeda Akter, Ibne Farabi Shihab, Anuj Sharma

We introduce Sequential-EDFL (Empirical Dynamic Formal Lift), which applies anytime-valid sequential testing to language model generation stopping. Our approach tracks information lift, defined as the log-likelihood ratio between the full model and deliberately weakened "skeleton" baselines, using self-normalized empirical-Bernstein e-processes that provide

Enrique Queipo-de-Llano, Álvaro Arroyo, Federico Barbero, Xiaowen Dong

Attention sinks and compression valleys have attracted significant attention as two puzzling phenomena in large language models, but have been studied in isolation. In this work, we present a surprising connection between attention sinks and compression valleys, tracing both to the formation of massive activations in the residual stream. We prove theoretical

Nishant Gadde, Yoshua Alexander, Sarvesh Parthasarthy, Arman Allidina

Load forecasting has always been a challenge for grid operators due to the growing complexity of power systems. The increase in extreme weather and the need for energy from customers has led to load forecasting sometimes failing. This research presents a Support Vector Regression (SVR) framework for electric load forecasting that outperforms the industry sta

Yitao Long, Yuru Jiang, Hongjun Liu, Yilun Zhao

This work investigates the reasoning and planning capabilities of foundation models and their scalability in complex, dynamic environments. We introduce PuzzlePlex, a benchmark designed to assess these capabilities through a diverse set of puzzles. PuzzlePlex consists of 15 types of puzzles, including deterministic and stochastic games of varying difficulty,

Omer Luria, Mor Elgarisi, Eytan Stibbe, Michael Lopez-Alegria

In-space manufacturing technologies are vital for enabling advanced space missions and addressing logistical limitations of space exploration. While additive manufacturing has progressed rapidly, it still falls short of delivering the ultra-smooth surfaces required for optical elements. Fluidic Shaping is a novel method that harnesses surface tension under m

Xiao Jin, Zhenhua Yu, Thrishantha Nanayakkara

This paper presents a bio-inspired underwater whisker sensor for robust hydrodynamic disturbance detection and efficient signal analysis based on Physical Reservoir Computing (PRC). The design uses a tapered nylon spring with embedded accelerometers to achieve spatially distributed vibration sensing and frequency separation along the whisker. Towing-tank exp

Ovishake Sen, Raghav Soni, Darpan Virmani, Akshar Parekh

Brain-computer interfaces (BCIs) offer a pathway to restore communication for individuals with severe motor or speech impairments. Imagined handwriting provides an intuitive paradigm for character-level neural decoding, bridging the gap between human intention and digital communication. While invasive approaches such as electrocorticography (ECoG) achieve hi

Richard Rhodes, Sandra Woolley

This forward-looking paper uses speculative design fiction to explore future museum scenarios where citizen curators design and share immersive virtual reality museums populated with tangible heritage artefacts, intangible virtual elements and interactive experiences. The work also explores takeaway 'asset packs' containing 3D artefact models, curation asset

Zihao Li, Shaoxiong Ji, Jörg Tiedemann

Test-time scaling (TTS) has enhanced the performance of Reasoning Models (RMs) on various tasks such as math and coding, yet its efficacy in machine translation (MT) remains underexplored. This paper investigates whether increased inference-time computation improves translation quality. We evaluate 12 RMs across a diverse suite of MT benchmarks spanning mult

Abdülbaki Şanlan, Fatih Erol, Murad Abu-Khalaf, Emre Koyuncu

We investigate the use of a point cloud measurement in terrain-aided navigation. Our goal is to aid an inertial navigation system, by exploring ways to generate a useful measurement innovation error for effective nonlinear state estimation. We compare two such measurement models that involve the scanning of a digital terrain elevation model: a) one that is b

Leela Krishna, Mengyang Zhao, Saicharithreddy Pasula, Harshit Rajgarhia

Training robust world models requires large-scale, precisely labeled multimodal datasets, a process historically bottlenecked by slow and expensive manual annotation. We present a production-tested GAZE pipeline that automates the conversion of raw, long-form video into rich, task-ready supervision for world-model training. Our system (i) normalizes propriet

Oindrila Saha, Vojtech Krs, Radomir Mech, Subhransu Maji

We present SIGMA-GEN, a unified framework for multi-identity preserving image generation. Unlike prior approaches, SIGMA-GEN is the first to enable single-pass multi-subject identity-preserved generation guided by both structural and spatial constraints. A key strength of our method is its ability to support user guidance at various levels of precision -- fr

Sergio Demian Lerner, Ariel Futoransky

We present BATTLE for Bitcoin, a DoS-resilient dispute layer that secures optimistic bridges between Bitcoin and rollups or sidechains. Our design adapts the BATTLE tournament protocol to Bitcoin's UTXO model using BitVM-style FLEX components and garbled circuits with on-demand L1 security bonds. Disputes are resolved in logarithmic rounds while recycling re

Marzia Bisi, Martina Conte, Maria Groppi

We propose a kinetic model to describe the dynamical evolution of wealth and knowledge in national and global markets, starting from a microscopic description of individual interactions. The model is built upon interaction rules that account for a strong interdependence between the microscopic variables, influencing agents' trading and saving propensities, k

Pei Xue, Yuanchun Ye

We develop a deep reinforcement learning framework for dynamic portfolio optimization that combines a Dirichlet policy with cross-sectional attention mechanisms. The Dirichlet formulation ensures that portfolio weights are always feasible, handles tradability constraints naturally, and provides a stable way to explore the allocation space. The model integrat

Philipp Sterzinger, Ioannis Kosmids, Irini Moustaki

Estimation in exploratory factor analysis often yields estimates on the boundary of the parameter space. Such occurrences, known as Heywood cases, are characterised by non-positive variance estimates and can cause issues in numerical optimisation procedures or convergence failures, which, in turn, can lead to misleading inferences, particularly regarding fac

M. C. Rodriguez, José C. Jiménez, Ignacio F. Ranea-Sandoval

We investigate the behavior of the non-radial gravity-pulsation discontinuity $g$-mode in neutron stars with a strong first-order phase transition which give rise to hybrid-star configurations. These modes are of utmost relevance since they can be potentially excited in isolated as well as binary neutron star systems in the inspiral phase, thus allowing us t

Sam Craig

A $(1,2t)$-Furstenberg set in $\mathbb{R}^3$ is naturally defined as a set containing a union of unit line segments forming a $2t$-dimensional subset of the affine Grassmannian in $\mathbb{R}^3$ and satisfying a suitable variant of the Frostman Convex Wolff Axiom. Some of these sets have a multi-scale self-similarity property called stickiness. We investigat

Massimo Daul, Alessio Tosolini, Claire Bowern

Automatic speech recognition (ASR) is a crucial tool for linguists aiming to perform a variety of language documentation tasks. However, modern ASR systems use data-hungry transformer architectures, rendering them generally unusable for underresourced languages. We fine-tune a wav2vec2 ASR model on Yan-nhangu, a dormant Indigenous Australian language, compar

Piyush Dashpute, Niki Nezakati, Wolfgang Heidrich, Vishwanath Saragadam

Thermal images from low-cost cameras often suffer from low resolution, fixed pattern noise, and other localized degradations. Available datasets for thermal imaging are also limited in both size and diversity. To address these challenges, we propose a patch-based diffusion framework (TDiff) that leverages the local nature of these distortions by training on

Bernardo Araneda, Maciej Dunajski

We disprove the Euclidean Einstein--Maxwell Black Hole Uniqueness Conjecture, and thus demonstrate that the semi-classical properties of coupled gravitational and electromagnetic fields are more subtle than expected from Lorentzian general relativity, where the Kerr-Newman family of metrics yields the most general stationary and asymptotically flat black hol

Hsin-Pei Yu, Si-Qin Lyu, Yi-Hsien Hsieh, Weichung Wang

Early recurrence (ER) prediction after curative-intent resection remains a critical challenge in the clinical management of hepatocellular carcinoma (HCC). Although contrast-enhanced computed tomography (CT) with full multi-phase acquisition is recommended in clinical guidelines and routinely performed in many tertiary centers, complete phase coverage is not

Lifei Wang, Natalie Friedman, Chengchao Zhu, Zeshu Zhu

As large language models (LLMs) become ubiquitous in workplace tools and decision-making processes, ensuring explainability and fostering user trust are critical. Although advancements in LLM engineering continue, human-centered design is still catching up, particularly when it comes to embedding transparency and trust into AI interfaces. This study evaluate

Shiv Shankar, Barak Ratzker, Claudio Pistidda, Dierk Raabe

Co-reduction of multicomponent oxides with hydrogen provides a carbon-neutral approach toward sustainable alloy design. Herein, we investigate the hydrogen-based direct reduction, using in-situ high-energy X-ray diffraction of two precursor variants: mechanically mixed powders and pre-sintered oxide mixtures, targeting an equiatomic CoFeMnNi alloy. We find d

David Rutherford, Marketa Šlapal Bařinková, Jaroslav Kuliček, Jelena Kozic

Carbon quantum dots (CQDs) are known for their antibacterial properties and ability to inhibit bacteria growth. In the current study, we observed a dopant and concentration-dependency on the antibacterial effect of CQDs. High concentrations of CQDs completely inhibited bacteria growth yet low concentrations enhanced growth. Unlike undoped CQDs, nitrogen-dope

Nader Nemati

Maritime object detection faces essential challenges due to the small target size and limitations of labeled real RGB data. This paper will present a real-time object detection system based on RT-DETR, enhanced by employing augmented synthetic images while strictly evaluating on real data. This study employs RT-DETR for the maritime environment by combining

Bedoor AlShebli, Bruno Gabriel Salvador Casara, Anne Maass

October 7, 2023 marked the start of a war against Gaza, one of the most devastating conflicts in modern history, which quickly produced a stark global attitudinal divide. To examine the role of media bias in shaping public understanding of this asymmetrical war, we analyzed more than 14,000 news articles published during its first year across three major Wes

Craig Belair

The Brownian web is a collection of coalescing Brownian motions started from every space-time point in R2. The Brownian web can be constructed as a scaling limit of coalescing one-dimensional simple random walks started at every point in a two-dimensional space-time lattice. Veto and Virag (2023) introduced a family of discrete random distance functions defi

Leandro Aurichi, Paulo Magalhães Júnior, Guilherme Eduardo Pinto

We prove that every 2k-edge-connected graph with countably many edge-ends admits a k-arc-connected orientation, extending the previous result by Assem, Koloschin and Pitz that also assumed the hypothesis of the graph being locally finite. We prove that, if every locally finite graph has a well-balanced orientation, so does every graph. Lastly, we explore an

Prabhant Singh, Sibylle Hess, Joaquin Vanschoren

Transferability estimation metrics are used to find a high-performing pre-trained model for a given target task without fine-tuning models and without access to the source dataset. Despite the growing interest in developing such metrics, the benchmarks used to measure their progress have gone largely unexamined. In this work, we empirically show the shortcom

Joseph E. Lawrence

A general semiclassical theory for the calculation of reaction rate constants is developed. The theory can be understood as a formal framework that encompasses existing semiclassical methods: instanton theory and semiclassical transition state theory (SCTST). Unlike SCTST, the present formalism does not start from the concept of "good" action-angle variables

Chetan Singh, Ava Goglia, Peter Mastracco, Michael Flickinger

Reactive aluminum (Al) alloy powders are promising for advanced manufacturing, joining, and energetic applications, yet scalable routes that couple controlled reactivity with safe handling remain limited. While nanoscale Al powders ignite readily, their agglomeration, handling, and safety limit broad deployment. Here, we manufacture micron-sized Al-based pow

Joel Pfeffer, J. M. Diederik Kruijssen, Clément Gossart, Mélanie Chevance

In decentralized learning networks, predictions from many participants are combined to generate a network inference. While many studies have demonstrated performance benefits of combining multiple model predictions, existing strategies using linear pooling methods (ranging from simple averaging to dynamic weight updates) face a key limitation. Dynamic predic

Danush Kumar Venkatesh, Adam Schmidt, Muhammad Abdullah Jamal, Omid Mohareri

Surgical video datasets are essential for scene understanding, enabling procedural modeling and intra-operative support. However, these datasets are often heavily imbalanced, with rare actions and tools under-represented, which limits the robustness of downstream models. We address this challenge with $SurgiFlowVid$, a sparse and controllable video diffusion

Xinnan Dai, Xianxuan Long, Chung-Hsiang Lo, Kai Guo

Large Language Models (LLMs) exhibit strong reasoning capabilities on structured tasks, yet the internal mechanisms underlying such behaviors remain poorly understood. Existing interpretation methods mainly focus on token-level attributions, which provide limited insight into multi-step reasoning inside the model. We propose GraphGhost, a graph-based framewo

Avi Shragai, Ezekiel Horsley, Subin Kim, Young-June Kim

The thermal Hall effect has been observed in a wide variety of magnetic insulators, yet its origins remains controversial. While some studies attribute the effect to intrinsic mechanism, such as heat carriers with Berry curvature, others propose extrinsic mechanisms, such as heat carriers scattering off crystal defects. Even the nature of the heat carriers i

Kai Jun Chen, Catherine Catrambone, Christopher Sowinski, Jacob Mukobi

Reprogrammable mechanical metamaterials, composed of a lattice of discretely adaptive elements, are emerging as a promising platform for mechanical intelligence. To operate in unknown environments, such structures must go beyond passive responsiveness and embody traits of mechanical intelligence: sensing, computing, adaptation, and memory. However, current a

Itai Benjamini, Guy Blachar, Ariel Yadin

We introduce and study a class of random walks on lamplighter groups $H\wr G$, where $H$ is a nontrivial finitely generated group and $G$ is an infinite finitely generated group, called \textbf{stationary random walks}. At each step, the walk switches the lamp at its current position, moves in the base group with a drift towards the identity, and switches th

Carly Sutter, Kara J. Sulia, Nick P. Bassill, Christopher D. Wirz

Transportation agencies make critical operational decisions during hazardous weather events, including assessment of road conditions and resource allocation. In this study, machine learning models are developed to provide additional support for the New York State Department of Transportation (NYSDOT) by automatically classifying current road conditions acros

Akash Yadav, Ruda Zhang

Hyperparameter tuning is a challenging problem especially when the system itself involves uncertainty. Due to noisy function evaluations, optimization under uncertainty can be computationally expensive. In this paper, we present a novel Bayesian optimization framework tailored for hyperparameter tuning under uncertainty, with a focus on optimizing a scale- o

Justin P. Bergfield

Molecules provide the smallest possible circuits in which quantum interference and electron correlation can be engineered to perform logical operations, including the universal NAND gate. We investigate a chemically encoded quantum NAND tree based on alkynyl-extended iso-polyacetylene backbones, where inputs are set by end-group substitution and outputs are

David Hernandez

In this lecture, we survey a number of recent results and developments regarding the representation theory of infinite-dimensional quantum groups (quantum affine algebras and related algebras), as well as their connections with cluster categorification and quantum integrable models. We will also give new examples and conjectures.

Thomas Marshall Vielmetti, Devansh R. Agrawal, Dimitra Panagou

Existing decentralized methods for multi-agent motion planning lack formal, infinite-horizon safety guarantees, especially for communication-constrained systems. We present R3R which, to our knowledge, is the first decentralized and asynchronous framework for multi-agent motion planning under range-limited communication constraints with infinite-horizon safe

Eliot Shekhtman, Yichen Zhou, Ingvar Ziemann, Nikolai Matni

Learning from temporally-correlated data is a core facet of modern machine learning. Yet our understanding of sequential learning remains incomplete, particularly in the multi-trajectory setting where data consists of many independent realizations of a time-indexed stochastic process. This important regime both reflects modern training pipelines such as for

Aryan Singh Dalal, Yinglun Zhang, Duru Doğan, Atalay Mert İleri

The focus on "food as medicine" is gaining traction in the field of health and several studies conducted in the past few years discussed this aspect of food in the literature. However, very little research has been done on representing the relationship between food and health in a standardized, machine-readable format using a semantic web that can help us le

Vipul Goyal, Justin Raizes

A central challenge in data security is not just preventing theft, but detecting whether it has occurred. Classically, this is impossible because a perfect copy leaves no evidence. Quantum mechanics, on the other hand, forbids general duplication, opening up new possibilities. We introduce Proofs of No Intrusion, which enable a classical client to remotely t

Naomi Tsuji, Yoshiyuki Inoue, Dmitry Khangulyan, Kaya Mori

We report on new Chandra ACIS-I observations of the X-ray knots located in the western and eastern lobes of W50 associated with the parsec-scale jets of the Galactic microquasar SS 433. These knots are likely counterparts of the recently detected very-high-energy ($E>100$ GeV) gamma-ray emission by HAWC and H.E.S.S. These findings, together with the ultra-hi

Neeraja Kirtane, Yuvraj Khanna, Peter Relan

Large language models excel on math benchmarks, but their math reasoning robustness to linguistic variation is underexplored. While recent work increasingly treats high-difficulty competitions like the IMO as the gold standard for evaluating reasoning, we believe in comprehensive benchmarking of high school-level math problems in real educational settings. W

Linlin Mao, Zeping Sui, Michail Matthaiou, Hongbin Li

A hybrid detector that fuses both quantized and full-precision observations is proposed for weak signal detection under additive and multiplicative Gaussian noise. We first derive a locally most powerful test (LMPT)--based hybrid detector from the composite probability distribution of the compound observations received by the fusion center, and then analyze

Eren Erberk Erkul

We propose that black holes are \emph{soliton-esque} objects, where gravitational collapse is balanced by quantum vacuum dispersion, modeled via \(R+\alpha R^{2}\) gravity. Classical singularities are replaced by oscillating, finite-radius cores, thereby evading static no-go theorems. The event horizon is replaced by the \textit{Lamarina}, a surface of maxim

Elena Chistova

We introduce UniRST, the first unified RST-style discourse parser capable of handling 18 treebanks in 11 languages without modifying their relation inventories. To overcome inventory incompatibilities, we propose and evaluate two training strategies: Multi-Head, which assigns separate relation classification layer per inventory, and Masked-Union, which enabl

Yitao Long, Tiansheng Hu, Yilun Zhao, Arman Cohan

Large Language Models (LLMs) frequently hallucinate to long-form questions, producing plausible yet factually incorrect answers. A common mitigation strategy is to provide attribution to LLM outputs. However, existing benchmarks primarily focus on simple attribution that retrieves supporting textual evidence as references. We argue that in real-world scenari

John E. Gough, Hideyasu Yamasita

We present a new technique for putting general boson fields into ant-Wick ordered form. The anti-Wick map associates an operator with a given function of complex variables, and we show that it may be realized as composition of a mapping to a commutative sub-algebra of a doubled-up boson algebra followed by a partial conditional expectation onto one of the fa

Ghazi Sarwat Syed, Philipp Schmidt, Frank Brückerhoff-Plückelmann, Jelle Dijkstra

Optimization problems are central to many important cross-disciplinary applications.In their conventional implementations, the sequential nature of operations imposes strict limitations on the computational efficiency. Here, we discuss how analog optical computing can overcome this fundamental bottleneck. We propose a photonic optimizer unit, together with s

Aidan Hennessey, Mathilde Kermorgant, Andy Zhu

For $J$ an abelian surface, the Galois representation $\varrho_{J, \ell} : {\rm Gal}(\overline{\mathbb{Q}}/\mathbb{Q}) \rightarrow {\rm Aut}(J[\ell]) \simeq {\rm GSp}_4(\mathbb{F}_\ell)$ is typically surjective, with smaller images indicating extra arithmetic structure. It is already known how to probabilistically compute whether $\rho_{J, \ell}$ is surjecti

Valeria Aravena, Samuel Cordova, Maximiliano Kairath, Matias Negrete-Pincetic

The increasing participation of renewable energy sources in power systems has entailed a series of challenges resulting from the replacement of conventional synchronous machines with carbon-free Inverter-Based-Resources (IBRs). In this context, the present work contributes to the existing literature on Frequency-Constrained Unit Commitment (FCUC) models by s