November 2025 arXiv papers — page 199

Mohsin Mahmud Topu, Mahfuz Ahmed Anik, Azmine Toushik Wasi, Md Manjurul Ahsan

Pavement infrastructure monitoring is challenged by complex spatial dependencies, changing environmental conditions, and non-linear deterioration across road networks. Traditional Pavement Management Systems (PMS) remain largely reactive, lacking real-time intelligence for failure prevention and optimal maintenance planning. To address this, we propose a uni

A. V. Rodina, M. A. Semina, E. L. Ivchenko

We present a review of experimental and theoretical studies of the spin response of charge carriers to an external magnetic field in bulk semiconductors and semiconductor nanostructures. The linear response is quantitatively characterized by the magnitude of the electron or hole g factor. Various experimental methods for measuring the electron g factor are c

Jialin Zhang

This article establishes necessary and sufficient conditions under which a finite set of Generalized Shannon's Entropy (GSE) characterizes a finite discrete distribution up to permutation. For an alphabet of cardinality K, it is shown that K-1 distinct positive real orders of GSE are sufficient (and necessary if no multiplicity) to identify the distribution

Ivor van der Hoog, Eva Rotenberg, Daniel Rutschmann

The element distinctness problem takes as input a list $I$ of $n$ values from a totally ordered universe and the goal is to decide whether $I$ contains any duplicates. It is a well-studied problem with a classical worst-case $\Omega(n \log n)$ comparison-based lower bound by Fredman. At first glance, this lower bound appears to rule out any algorithm more ef

Sadiq Layi Macaulay, Nimet Kaygusuz, Simon Hadfield

Event cameras, with their high dynamic range (HDR) and low latency, offer a promising alternative for robust depth estimation in challenging environments. However, many event-based depth estimation approaches are constrained by small-scale annotated datasets, limiting their generalizability to real-world scenarios. To bridge this gap, we introduce EvtSlowTV,

Zhenzhou Qi, Yuncheng Yao, Yiming Li, Chung-Hsuan Tung

Emerging virtualized radio access networks (vRANs) demand flexible and efficient baseband processing across heterogeneous compute substrates. In this paper, we present DecodeX, a unified benchmarking framework for evaluating low-density parity-check (LDPC) decoding acceleration across different hardware platforms. DecodeX integrates a comprehensive suite of

Sheida Rabeti, Hessam Mahdavifar

In this paper, we propose a new decoder, called the Multiple-Bases Belief-Propagation List Decoder (MBBP-LD), for Quantum Low-Density Parity-Check (QLDPC) codes. It extends the Multiple-Bases Belief-Propagation (MBBP) framework, originally developed for classical cyclic LDPC codes. The proposed method preserves the linear-time complexity of standard BP decod

Rohith Vaidyanathan, Srinath Srinivasa, Praseeda, Dev Shinde

Digital public infrastructures (DPIs) represent networks of open technology standards, applications, services, and digital assets made available for the public good. One of the key challenges in DPI design is to resolve complex issues of consent, scaled over large populations. While the primary objective of consent management is to empower the data owner, ow

Rachel Longjohn, Shang Wu, Saatvik Kher, Catarina Belém

It is increasingly important to evaluate how text generation systems based on large language models (LLMs) behave, such as their tendency to produce harmful output or their sensitivity to adversarial inputs. Such evaluations often rely on a curated benchmark set of input prompts provided to the LLM, where the output for each prompt may be assessed in a binar

Zhendong Wang, Chenyang Meng, Jun Yang, Jiayuan Wang

The 6G wireless networks impose extremely high requirements on physical layer secure communication. However, the existing solutions usually can only achieve one-dimensional physical layer security (PLS) in the angle dimension, and cannot achieve PLS in the range dimension. In this paper, we propose the NF-SecRIS system, the first range-angle-dependent (2D) P

Clare Burhenne, Kristen B. W. McQuinn, Roger E. Cohen, Claire E. Murray

We measure the star formation histories (SFHs) from the Scylla survey in approximately 98,000 pc^2 and 75,000 pc^2 of the SMC and LMC, respectively, using deep Hubble Space Telescope imaging (80% complete to more than 1 mag below the ancient main-sequence turnoff, 25.1 and 26.0 mag in F475W and F814W) from 74 pointings. We group the fields into eight sub-reg

Srikumar Sastry, Subash Khanal, Aayush Dhakal, Jiayu Lin

We introduce ProM3E, a probabilistic masked multimodal embedding model for any-to-any generation of multimodal representations for ecology. ProM3E is based on masked modality reconstruction in the embedding space, learning to infer missing modalities given a few context modalities. By design, our model supports modality inversion in the embedding space. The

Marek Górski, Grzegorz Pietrzyński, Paulina Karczmarek, Gergely Hajdu

We present precise J- and K-band photometric measurements for 128 near-infrared secondary standard stars, located in the 19 UKIRT/MKO primary faint standard fields. The data were collected over more than 50 nights, covering a decade of observations between 2008 and 2018 at the ESO La Silla Observatory, using the New Technology Telescope (NTT) equipped with t

Fengxu Li, Stephanie M. Carpenter, Matthew P. Buman, Yonatan Mintz

A common challenge for decision makers is selecting actions whose rewards are unknown and evolve over time based on prior policies. For instance, repeated use may reduce an action's effectiveness (habituation), while inactivity may restore it (recovery). These nonstationarities are captured by the Reducing or Gaining Unknown Efficacy (ROGUE) bandit framework

Henry Fleischmann, George Z. Li, Jason Li

We give faster algorithms for weak expander decompositions and approximate max flow on undirected graphs. First, we show that it is possible to "warm start" the cut-matching game when computing weak expander decompositions, avoiding the cost of the recursion depth. Our algorithm is also flexible enough to support weaker flow subroutines than previous algorit

Ekaterina Kubyshkina, Marcio Kléos Pereira, Mattia Petrolo

There is a lively debate in the current literature on epistemology on which type of ignorance may provide a moral excuse. A good candidate is the one in which an agent has never thought about or considered as true a proposition $p$. From a logical perspective, it is usual to model situations involving ignorance by means of epistemic logic. However, no formal

Stefan Teufel, Marius Wesle, Tom Wessel

We prove global existence and uniqueness of dynamics on the quasi-local algebra $\mathcal{A}$ of a quantum lattice system for spatially growing derivations $\mathcal{L}_\Phi = \sum_x [ \Phi_x , \cdot ]$. Existing results assume that the local terms $\Phi_x\in\mathcal{A}$ of the generator are uniformly bounded in space with respect to appropriate weighted nor

J. S. Bianco, A. Tenerani, C. Gonzalez, L. Matteini

We investigate the self-consistent formation and long-term evolution of proton beams in the expanding solar wind using an ensemble of one-dimensional hybrid expanding box simulations. Initial conditions are chosen to represent a range of plasma states observed by the Helios spacecraft at 0.3 AU, including an amplitude-modulated Alfv\'en wave that nonlinearly

M. Davis, G. Stage, A. Borjigin, S. Beringer

Low-Gain Avalanche Detectors (LGADs) are characterized by a fast rise time (500 ps) and extremely good time resolution (down to 17 ps). The intrinsic low granularity of LGADs and the large power consumption of readout chips for precise timing are problematic in near-future experiments such as e+e- Higgs factories (FCC-ee) and the ePIC detector at the Electro

Huseyin Goksu

Spectral Graph Neural Networks (GNNs) operating in the canonical [-1, 1] domain (like ChebyNet and its adaptive generalization, L-JacobiNet) face a fundamental Flexibility-Stability Trade-off. Our previous work revealed a critical puzzle: the 2-parameter adaptive L-JacobiNet often suffered from high variance and was surprisingly outperformed by the 0-paramet

Sepideh KhakzadGharamaleki, Hassan Rivaz, Brandon Helfield

Conventional pulse-echo ultrasound suffers when low-cost probes deliver only narrow fractional bandwidths, elongating pulses and erasing high-frequency detail. We address this limitation by learning a data-driven mapping from band-limited to broadband spectrogram of radio-frequency (RF) lines. To this end, a variation of Tiny Vision Transform (ViT) auto-enco

Neil Byers, Ali Zaidi, Valerie Skye, Chris Beecroft

Efforts have increased in recent years to identify associations between specific datasets and the scientific literature that incorporates them. Knowing that a given publication cites a given dataset, the next logical step is to explore how or why that data was used. Advances in recent years with pretrained, transformer-based large language models (LLMs) offe

Huseyin Goksu

Graph Neural Networks (GNNs) based on spectral filters, such as the Adaptive Orthogonal Polynomial Filter (AOPF) class (e.g., LaguerreNet), have shown promise in unifying the solutions for heterophily and over-smoothing. However, these single-filter models suffer from a "compromise" problem, as their single adaptive parameter (e.g., alpha) must learn a subop

Louis Deaett, Kevin Grace

The problem of finding the minimum rank of a matrix with a given zero-nonzero pattern has been generalized to a class of matroids associated to the pattern. The fundamental lower bound known as the triangle number still holds in this generalized setting. But the matroid minimum rank of a pattern need not match that of its transpose. We associate to each patt

Théophile Dolmaire, Alessia Nota

In this paper we provide a rigorous derivation of the inelastic linear Boltzmann equation, in the Boltzmann-Grad limit, from a dissipative, random, Lorentz gas in arbitrary dimensions d $\geq$ 2. Specifically, we consider a microscopic particle system where scatterers are randomly distributed according to a Poisson process, and a tagged light particle underg

Andy Dimnaku, Abdullah Yusuf Kavranoglu, Yaser Abu-Mostafa

Data augmentation is widely used in vision to introduce variation and mitigate overfitting, by enabling models to learn invariant properties. However, augmentation only indirectly captures these properties and does not explicitly constrain the learned function to satisfy them beyond the empirical training set. We propose generative hints, a training methodol

Teresia Ndungu, Jana Zecha, Lisa Cazares, Sonja Hess

In clinical proteomics, available input is often limited. In addition, phospho-proteomics is of particular interest since the dysregulation of these post-translational modifications (PTMs) has been implicated in various diseases such as cancer. We therefore assessed the feasibility of low input phospho-proteomics via phospho-bulk titration and low-input star

Aline Blankertz, Brianna Rock, Nicholas Shaxson

This paper presents striking new data about the scale of Google's involvement in the global digital and corporate landscape, head and shoulders above the other big tech firms. While public attention and some antitrust scrutiny has focused on these firms' mergers and acquisitions (M&A) activities, Google has also been amassing an empire of more than 6,000 com

Saumya Sinha, Alexandre Cortiella, Rawad El Kontar, Andrew Glaws

Understanding current energy consumption behavior in communities is critical for informing future energy use decisions and enabling efficient energy management. Urban energy models, which are used to simulate these energy use patterns, require large datasets with detailed building characteristics for accurate outcomes. However, such detailed characteristics

Zichu Wang, Esteban G. Tabak

A new pairwise cost function is proposed for the optimal transport barycenter problem, adopting the form of the minimal action between two points, with a Lagrangian that takes into account an underlying probability distribution. Under this notion of distance, two points can only be close if there exist paths joining them that do not traverse areas of small p

Ilerioluwakiiye Abolade, Aniekan Udo, Augustine Ojo, Abdulbasit Oyetunji

Glioma segmentation is critical for diagnosis and treatment planning, yet remains challenging in Sub-Saharan Africa due to limited MRI infrastructure and heterogeneous acquisition protocols that induce severe domain shift. We propose SegFormer3D-plus, a radiomics-guided transformer architecture designed for robust segmentation under domain variability. Our m

Rafael Baez, Alejandro Olivas, Nathan K. Diamond, Marcelo Frias

Differential testing is a highly effective technique for automatically detecting software bugs and vulnerabilities when the specifications involve an analysis over multiple executions simultaneously. Differential fuzzing, in particular, operates as a guided randomized search, aiming to find (similar) inputs that lead to a maximum difference in software outpu

Euclid Collaboration, N. Allen, P. A. Oesch, R. A. A. Bowler

The evolution of the rest-frame ultraviolet luminosity function (UV LF) is a powerful probe of early star formation and stellar mass build-up. At z > 6, its bright end (MUV < -21) remains poorly constrained due to the small volumes of existing near-infrared (NIR) space-based surveys. The Euclid Deep Fields (EDFs) will cover 53 deg^2 with NIR imaging down to

Yimeng Wang, Christiane P. Koch

Einstein, Podolsky, and Rosen discussed their paradox in terms of measuring the positions or momenta of two particles. These degrees of freedom can become entangled upon scattering, but how much entanglement can be created in this process? Here we address this question using fully coherent calculations of bipartite scattering in three-dimensional space, quan

Haranath Rakshit, Rajkumar Bhandari, Subhasis Banerjee

The proliferation of Internet of Things (IoT) networks demands security mechanisms that protect constrained devices without the computational cost of public-key cryptography. Conventional Pre-Shared Key (PSK) encryption, while efficient, remains vulnerable due to static key reuse, replay attacks, and the lack of key freshness. This paper presents the Dynamic

Ivan Zvonkov, Gabriel Tseng, Inbal Becker-Reshef, Hannah Kerner

Accurate and up-to-date land cover maps are essential for understanding land use change, a key driver of climate change. Geospatial embeddings offer a more efficient and accessible way to map landscape features, yet their use in real-world mapping applications remains underexplored. In this work, we evaluated the utility of geospatial embeddings for cropland

Yunhan Qiao, Md Istiak Hossain Shihab, Summit Haque, and Christopher Hundhausen

Teaching Computer Science (CS) students how to comprehend and maintain legacy code bases is a critical challenge in software engineering education. While Generative AI (GenAI) assistants like GitHub Copilot improve task completion speed and correctness, their impact on code understanding remains unclear. We conducted a within-subject study with 15 graduate C

Begoña Cano, Angel Durán, Melquíades Rodríguez

In this paper, the use of partitioned linear multistep methods (PLMM) as time integrators for the numerical approximation of some partial differential equations (pdes) is studied. We consider the periodic initial-value problem of two nonlinear dispersive wave models as case studies. From the spatial discretization with pseudospectral methods, the theory deve

James E. Owen, Douglas N. C. Lin

Active galactic nuclei (AGN) are important drivers of galactic evolution; however, the underlying physical processes governing their properties remain uncertain. In particular, the specific cause for the generation of the broad-line region is unclear. There is a region where the underlying accretion disc atmosphere becomes cool enough for dust condensation.

Shuhang Lin, Zhencan Peng, Lingyao Li, Xiao Lin

Recent advances in Large Language Model (LLM)-based agents have been propelled by Retrieval-Augmented Generation (RAG), which grants the models access to vast external knowledge bases. Despite RAG's success in improving agent performance, agent-level cache management, particularly constructing, maintaining, and updating a compact, relevant corpus dynamically

Juliana Carrasco, Suchita Kulkarni, Wei Liu, Joshua Lockyer

We propose a new class of dark-shower signatures in Standard Model extensions featuring Hidden Valleys or dark sectors coupled through an s-channel mediator. In this framework, unstable dark pions appear as long-lived particles (LLPs), with their lifetimes treated as free parameters. The resulting signatures, which we term semi-visible emerging jets (SVEJ),

Norbert Schartel, Maria Santos-Lleo

In December 2024, the European Space Agency's (ESA) XMM-Newton X-ray Observatory celebrated the 25th anniversary of its launch. The annual number of peer-reviewed articles utilising XMM-Newton data has exhibited a consistent upward trajectory over the past two and a half decades, attaining more than 400 in 2022. The annual call for observing time proposals c

Lin Chen, Yuchen Fu, Dennis Gaitsgory, David Yang

We show that the (2-)category of categorical representations of the loop group embeds fully faithfully into the (2-)category of factorization module categories with respect to the affine Grassmannian.

Francesco Crescimbeni, Gregorio Carullo, Emanuele Berti, Giada Caneva Santoro

The ''ringdown'' stage of gravitational-wave signals from binary black hole mergers, mainly consisting of a superposition of quasinormal modes emitted by the merger remnant, is a key tool to test fundamental physics and to probe black hole dynamics. However, ringdown models are known to be accurate only in the late-time, stationary regime. A key open problem

Kuntal Sengupta, Mirjam Weilenmann, Roger Colbeck

Correlation self-testing of quantum theory involves identifying a task or set of tasks whose optimal performance can be achieved only by theories that can realise the same set of correlations as quantum theory in every causal structure. Following this approach, previous work has ruled out various classes of generalised probabilistic theories whose joint stat

Ryan M. L. McFadden, Jonathan W. Angle, Eric M. Lechner, Michael J. Kelley

We report direct, simultaneous measurements of the London penetration depth ($\lambda_L$) and Bardeen-Cooper-Schrieffer (BCS) coherence length ($\xi_0$) in oxygen-doped niobium, with impurity concentrations spanning the "clean" to "dirty" limits. Two depth-resolved techniques - low-energy muon spin spectroscopy (LE-$\mu$SR) and secondary-ion mass spectrometr

Francesco Del Porro, Jacopo Mazza

We investigate the mechanics of stationary axisymmetric non-Killing horizons, which emerge in spacetimes that do not enjoy the symmetry known as circularity -- as is commonly the case for rotating black holes beyond general relativity. Specifically, we define and compute three notions of surface gravity: inaffinity, normal, and peeling; and find that the ina

Conny Aerts

Studies of the rotational velocities of intermediate-mass main-sequence stars are crucial for testing stellar evolution theory. They often rely on spectroscopic measurements of the projected rotation velocities. These not only suffer from the unknown projection factor but tend to ignore additional line-profile broadening mechanisms aside from rotation, such

Amanda Stoffers, Sandro Tacchella, Charlotte Simmonds, Benjamin D. Johnson

Direct observations of Lyman continuum (LyC) radiation from galaxies during the Epoch of Reionization (EoR) are impeded by absorption in the intergalactic medium, requiring indirect methods to infer the escape fraction of ionizing photons ($f_{\rm esc}^{\rm LyC}$). One approach is to develop and validate such methods on local analogues of the high-redshift g

Zhou-Quan Wan, Xu-Dong Dai, Guo-Yi Zhu

The quantum error correction threshold is closely related to the Nishimori physics of random statistical models. We extend quantum information measures such as coherent information beyond the Nishimori line and establish them as sharp indicators of phase transitions over the full $p$-$T$ plane. These generalized measures admit a natural operational interpret

Mark Dodici, Scott Tremaine, Yanqin Wu

Stellar binaries in galactic centers are relevant to several observable phenomena, including hypervelocity stars, X-ray binaries, and mergers of stars and compact objects; however, we know little about the properties of these binaries. Past works have suggested that a small fraction of them should contract to a few stellar radii or collide, due to the co-ope

Radouane Gannouji, Ayan Mukhopadhyay, Nicolas Pinochet

Using holographic realizations of the Araki-Lieb (AL) inequality, we show that typical pure states in large $N$ holographic CFTs possess two characteristic length scales determined solely by energy and conserved charges: a microscopic $L_{\mathrm{UV}}$ and an infrared $L_{\mathrm{IR}} > L_{\mathrm{UV}}$. Degrees of freedom between these scales effectively fa

Sarah E. I. Bosman, Javier Álvarez-Márquez, Frederick B. Davies, Klaudia Protušová

The presence of supermassive black holes (SMBHs, $M_\text{BH}\sim10^9 M_\odot$) at $z>7$ remains a puzzle. While their existence appears to require exotic formation or growth processes, it is possible that BH mass estimates are incorrect due to differences from the low-$z$ quasars where BH mass scaling relations are calibrated. In this work, we employ JWST M

Siyu Zhu, Arthur P. Ramirez, Sergey Syzranov

The entropy that an insulating magnetic material releases upon cooling can reveal important information about the properties of spin states in that material. In many geometrically frustrated (GF) magnetic compounds, the heat capacity exhibits a low-temperature peak that comes from the spin states continuously connected to the ground states of classical model

Niall T. Macpherson, Ricardo Stuardo

Bi-spinor and G-structure methods are used to classify the possible consistent truncations of type II supergravity to $d=6$ Einstein-Maxwell (gauged) supergravity, and its consistent sub-sectors. In the absence of R-symmetry gauging and a tensor multiplet we establish that every supersymmetric Mink$_6$ solution defines an embedding of the $d=6$ theory. Addin

Huawei Lin, Yunzhi Shi, Tong Geng, Weijie Zhao

Multimodal large language models (MLLMs) have shown strong capabilities but remain limited to fixed modality pairs and require costly fine-tuning with large aligned datasets. Building fully omni-capable models that can integrate text, images, audio, and video remains impractical and lacks robust reasoning support. In this paper, we propose an Agent-Omni fram

Abhishek Panigrahi, Bingbin Liu, Sadhika Malladi, Sham Kakade

Knowledge distillation is an efficient strategy to use data generated by large "teacher" language models to train smaller capable "student" models, but selecting the optimal teacher for a specific student-task combination requires expensive trial-and-error. We propose a lightweight score called GRACE to quantify how effective a teacher will be for post-train

Yanjie Ze, Siheng Zhao, Weizhuo Wang, Angjoo Kanazawa

Large-scale data has driven breakthroughs in robotics, from language models to vision-language-action models in bimanual manipulation. However, humanoid robotics lacks equally effective data collection frameworks. Existing humanoid teleoperation systems either use decoupled control or depend on expensive motion capture setups. We introduce TWIST2, a portable

Hakob Tamazyan, Ani Vanyan, Alvard Barseghyan, Anna Khosrovyan

The number and diversity of remote sensing satellites grows over time, while the vast majority of labeled data comes from older satellites. As the foundation models for Earth observation scale up, the cost of (re-)training to support new satellites grows too, so the generalization capabilities of the models towards new satellites become increasingly importan

Dmitrii Pozdeev, Alexey Artemov, Ananta R. Bhattarai, Artem Sevastopolsky

We propose DenseMarks - a new learned representation for human heads, enabling high-quality dense correspondences of human head images. For a 2D image of a human head, a Vision Transformer network predicts a 3D embedding for each pixel, which corresponds to a location in a 3D canonical unit cube. In order to train our network, we collect a dataset of pairwis

Weston Bondurant, Arkaprava Sinha, Hieu Le, Srijan Das

Diffusion-based approaches have recently achieved strong results in face swapping, offering improved visual quality over traditional GAN-based methods. However, even state-of-the-art models often suffer from fine-grained artifacts and poor identity preservation, particularly under challenging poses and expressions. A key limitation of existing approaches is

Alex Takeda

We establish that the dioperad $Y^{(n)}$, encoding bialgebras with a product of degree zero, a coproduct of degree $(1-n)$ and a rank three cyclic tensor, which satisfy a deformed version of the balanced infinitesimal bialgebra condition, is Koszul. This result is established by studying specific subcomplexes of the assocoipahedra of Poirier and Tradler. The

Charlotte Olsen, Eric Gawiser, Charlotte Welker, Harry Teplitz

Understanding the complicated processes that regulate star formation and cause a galaxy to become quiescent is key to our comprehension of galaxy evolution. We used eight well resolved star-forming z$<$ 0.3 galaxies from the UVCANDELS survey, where a total of 10 HST bands including UV follow up in UVIS/F275W allow us to reconstruct the star formation histori

Mohamed Almukhtar, Anwar Ghammam, Marouane Kessentini, Hua Ming

In an era shaped by Generative Artificial Intelligence for code generation and the rising adoption of Python-based Machine Learning systems (MLS), software quality has emerged as a major concern. As these systems grow in complexity and importance, a key obstacle lies in understanding exactly how specific code changes affect overall quality-a shortfall aggrav

Harshith Padigela, Shima Nofallah, Atchuth Naveen Chilaparasetti, Ryun Han

Foundation models trained on large-scale pathology image corpora have demonstrated strong transfer capabilities across diverse histopathology tasks. Building on this progress, we introduce PLUTO-4, our next generation of pathology foundation models that extend the Pathology-Universal Transformer (PLUTO) to frontier scale. We share two complementary Vision Tr

Artur d'Avila Garcez, Simon Odense

Artificial Intelligence (AI) is a powerful new language of science as evidenced by recent Nobel Prizes in chemistry and physics that recognized contributions to AI applied to those areas. Yet, this new language lacks semantics, which makes AI's scientific discoveries unsatisfactory at best. With the purpose of uncovering new facts but also improving our unde

Ludovico Mitchener, Angela Yiu, Benjamin Chang, Mathieu Bourdenx

Data-driven scientific discovery requires iterative cycles of literature search, hypothesis generation, and data analysis. Substantial progress has been made towards AI agents that can automate scientific research, but all such agents remain limited in the number of actions they can take before losing coherence, thus limiting the depth of their findings. Her

Atena Khoshkonesh, Mohsen Mohammadagha, Navid Ebrahimi

Persistent cost and schedule overruns in U.S. building projects expose limitations of conventional, document-based estimating and deterministic Critical Path Method (CPM) scheduling, which remain inflexible under uncertainty and lag dynamic field conditions. This study presents an integrated 4D/5D digital-twin framework unifying Building Information Modeling

Chloe Loughridge, Paul Colognese, Avery Griffin, Tyler Tracy

As AI deployments become more complex and high-stakes, it becomes increasingly important to be able to estimate their risk. AI control is one framework for doing so. However, good control evaluations require eliciting strong attack policies. This can be challenging in complex agentic environments where compute constraints leave us data-poor. In this work, we

Sami Aljhani

In this paper, based on Newton interpolation we have proposed a numerical scheme of predictor-corrector type in order to solve fractional differential equations with the fractional derivative involving the Mittag-Leffler function. We have added an auxiliary midpoint in each sub-interval, this allows us to use a piecewise quadratic Newton interpolation to der

Dan Garber

We develop new accelerated first-order algorithms in the Frank-Wolfe (FW) family for minimizing smooth convex functions over compact convex sets, with a focus on two prominent constraint classes: (1) polytopes and (2) matrix domains given by the spectrahedron and nuclear-norm balls. A key technical ingredient is a complementarity condition that captures solu

Pelin Keşrit, Bahar Çavdar, Joseph Geunes

We consider a distribution network for delivering a natural resource or physical good to a set of nodes, each of which serves a set of customers, in which disruptions may occur at one or more nodes. Each node receives flow through a path from a source node, implying that the service at a node is interrupted if one or more nodes on the path from a source node

Shamil Asgarli, Donald Falkenhagen, Kaya Hoshi

Computing the cardinality of a maximum induced acyclic vertex set in a digraph is NP-hard. Since finding an exact solution is computationally difficult, a fruitful approach is to establish high-quality lower bounds that are efficiently computable. We build on the Akbari--Ghodrati--Jabalameli--Saghafian (AGJS) bound for digraphs by adapting refinement techniq

Mohamed Bouadi, Pratinav Seth, Aditya Tanna, Vinay Kumar Sankarapu

Tabular data remain the predominant format for real-world applications. Yet, developing effective neural models for tabular data remains challenging due to heterogeneous feature types and complex interactions occurring at multiple scales. Recent advances in tabular in-context learning (ICL), such as TabPFN and TabICL, have achieved state-of-the-art performan

Amanda Bertsch, Adithya Pratapa, Teruko Mitamura, Graham Neubig

As model context lengths continue to grow, concerns about whether models effectively use the full context length have persisted. While several carefully designed long-context evaluations have recently been released, these evaluations tend to rely on retrieval from one or more sections of the context, which allows nearly all of the context tokens to be disreg

Sukgyu Shin

In this paper, we examine identification in dynamic panel logit models with state dependence, a first-order Markov feedback process, and individual unobserved heterogeneity by introducing sufficient statistics for the feedback process and the unobserved heterogeneity. If a sequentially exogenous discrete covariate follows a first-order Markov process, identi

Morgan Allen, Paul Savala

In Major League Baseball, strategy and planning are major factors in determining the outcome of a game. Previous studies have aided this by building machine learning models for predicting the winning team of any given game. We extend this work by training a comprehensive set of machine learning models using a common dataset. In addition, we relate the win pr

J. F. Parisi, A. Rutkowski, J. Harter, J. A. Schwartz

We show that transmutation driven by high-energy neutrons from deuterium-tritium (D-T) fusion reactions can produce many important medical radioisotopes - including $^{32}$P, $^{60}$Co, $^{64}$Cu, $^{89}$Sr, $^{90}$Y, $^{89}$Zr, $^{99}$Mo/$^{99\mathrm{m}}$Tc, $^{103}$Pd, $^{111}$In, $^{117}$In/$^{117\mathrm{m}1}$Sn, $^{123}$I, $^{125}$I, $^{131}$I, $^{133}$X

Alberto Cerezo

We construct an infinite family of non-planar free boundary disks of non-positive Gaussian curvature in the unit ball of $\mathbb{R}^3$.

Michael L. Weber, Eleftheria Sarafidou, Christian Rab, Oliver Gressel

Disc winds driven by thermal and magnetic processes are thought to play a critical role in protoplanetary disc evolution. However, the relative contribution of each mechanism remains uncertain, particularly in light of their observational signatures. We investigate whether spatially resolved emission and synthetic spectral line profiles can distinguish betwe

Suddhasvatta Das, Kevin Gary

Software developed using modern agile practices delivers a stream of software versions that require continuous regression testing rather than testing once close to the delivery or maintenance phase, as assumed by classical regression-testing theory. In this work, we formalize the phenomenon of continuous or near-continuous regression testing using successive

Diego González-Hernández, Molly Wolfson, Joseph F. Hennawi

We present an application of the Balanced Neural Ratio Estimation (BNRE) algorithm to improve the statistical validity of parameter estimates used to characterize the Epoch of Reionization, where the common assumption of a multivariate Gaussian likelihood leads to overconfident and biased posterior distributions. Using a two-parameter model of the Ly$\alpha$

You-Jin Kim, Misha Sra, Tobias Höllerer

Audience reactions can considerably enhance live experiences; conversely, in anytime-anywhere augmented reality (AR) experiences, large crowds of people might not always be available to congregate. To get closer to simulating live events with large audiences, we created a mobile AR experience where users can wander around naturally and engage in AR theater w

Ankitkumar Maisuriya, Siddhi Mali, Sunil Mittal

The interplay between nonlinear and topological physics has led to intriguing emergent phenomena, such as quantized and fractionally quantized Thouless pumping of solitons dictated by the topological invariants of the underlying band structure. Unlike linear Thouless pumping, which requires excitation of a Wannier function of a uniformly filled band, quantiz

Qianhao Yuan, Jie Lou, Zichao Li, Jiawei Chen

LLM-based search agents often concatenate the full interaction history into the context, producing long and noisy inputs, and increasing compute cost and GPU memory overhead. To address this issue, we propose MemSearcher, an agent framework that maintains a compact memory during multi-turn interactions, retaining only question-relevant information and thereb

Andrew D. Bond, Daniel F. Litim, Gabriel Picanço

We study fixed points and phase diagrams of semi-simple supersymmetric gauge theories coupled to chiral superfields and a superpotential. Particular emphasis is put on new phenomena which arise due to the semi-simple nature of gauge interactions and the constraints dictated by supersymmetry, unitarity, and the $a$-theorem. Using field multiplicities as free

Ismail Cosandal, Sennur Ulukus

We revisit the source coding problem for a Markov chain under the assumption that the transmission times and how fast the Markov chain transitions its state happen at the same time-scale. Specifically, we assume that the transmission of each bit takes a single time slot, and the Markov chain updates its state in the same time slot. Thus, the length of the co

Aditya Tanna, Pratinav Seth, Mohamed Bouadi, Utsav Avaiya

Tabular foundation models represent a growing paradigm in structured data learning, extending the benefits of large-scale pretraining to tabular domains. However, their adoption remains limited due to heterogeneous preprocessing pipelines, fragmented APIs, inconsistent fine-tuning procedures, and the absence of standardized evaluation for deployment-oriented

L. Manchon, M. Deal, J. P. C. Marques, Y. Lebreton

We present Cesam2k20, the latest version of the hydrostatic stellar evolution code CESAM originally developed by P. Morel and collaborators. Over the last three decades, it has undergone many improvements and has been extensively tested against other stellar evolution codes before being selected to compute the first-generation grid of stellar models for the

Vijay Ganesh Sadhasivam, Jan M. Rost, Stuart C. Althorpe

The question of thermalization in quantum many-body systems has long been studied through the properties of matrix elements of operators corresponding to local observables. More recently, the focus has shifted to the dynamics of operators, which lead to seminal works proposing universal bounds on the rate of operator growth. In this work, we unify these two

J. L. Roque, F. Da Silva Lopes, J. A. Giles, B. D. Gutknecht

Understanding regional hydroclimatic variability and its drivers is essential for anticipating the impacts of climate change on water resources and sustainability. Yet, considerable uncertainty remains in the simulation of the coupled land atmosphere water and energy cycles, largely due to structural model limitations, simplified process representations, and

Chris Gilly, Steven Cranmer

Standard visualizations of Extreme Ultraviolet (EUV) solar imagery often fail to convey the full complexity of the Sun's corona, especially in faint off-limb regions. This can leave the misleading impression of the Sun as a bright ball in a dark void, rather than revealing it as the dynamic, structured source of the solar wind and space weather. A variety of

Nicolas Riccieri Gardin Assumpcao, Leandro Villas

Federated Learning (FL) is a distributed training paradigm wherein participants collaborate to build a global model while ensuring the privacy of the involved data, which remains stored on participant devices. However, proposals aiming to ensure such privacy also make it challenging to protect against potential attackers seeking to compromise the training ou

Mayank Jobanputra, Nils Philipp Walter, Maitrey Mehta, Blerta Veseli

We present a systematic study of subtraction in large language models (LLMs). While prior benchmarks emphasize addition and multiplication, subtraction has received comparatively little attention despite being structurally distinct as a non-commutative operation. We evaluate eight pretrained LLMs spanning four families on addition and subtraction problems. O

Chenyu Zhang, Minsol Kim, Shohreh Ghorbani, Jingyao Wu

Despite rapid growth in multimodal large language models (MLLMs), their reasoning traces remain opaque: it is often unclear which modality drives a prediction, how conflicts are resolved, or when one stream dominates. In this paper, we introduce modality sabotage, a diagnostic failure mode in which a high-confidence unimodal error overrides other evidence an

Mika Yagoda, Shady Abu-Hussein, Raja Giryes

Diffusion models have gained significant attention for high-fidelity image generation. Our work investigates the potential of exploiting diffusion models for adversarial robustness in image classification and object detection. Adversarial attacks challenge standard models in these tasks by perturbing inputs to force incorrect predictions. To address this iss

Daniel Ting, Kenneth Hung

As we exhaust methods that reduces variance without introducing bias, reducing variance in experiments often requires accepting some bias, using methods like winsorization or surrogate metrics. While this bias-variance tradeoff can be optimized for individual experiments, bias may accumulate over time, raising concerns for long-term optimization. We analyze

Nusrat Tasnim, Kutub Uddin, Khalid Mahmood Malik

The threats posed by AI-generated media, particularly deepfakes, are now raising significant challenges for multimedia forensics, misinformation detection, and biometric system resulting in erosion of public trust in the legal system, significant increase in frauds, and social engineering attacks. Although several forensic methods have been proposed, they su

Kate Thomas

A base-$g$ Niven number is a natural number divisible by the sum of its base-$g$ digits. We show that, for any $g\geq 3$, all sufficiently large natural numbers can be written as the sum of three base-$g$ Niven numbers. We also give an asymptotic formula for the number of representations of a sufficiently large integer as the sum of three integers with fixed

Roberto Garrone

Micro and small enterprises (SMEs) remain structurally vulnerable to cyber threats while facing capacity constraints that make formal compliance burdensome. This article develops a governance design model for proportionate SME cybersecurity, grounded in an awareness-first logic and informed by the EU Squad 2025 experience. Using a qualitative policy-analysis