October 2025 arXiv papers — page 201

Raju Dhakal, Prashant Shekhar, Laxima Niure Kandel

Radio Frequency Fingerprinting (RFF) has evolved as an effective solution for authenticating devices by leveraging the unique imperfections in hardware components involved in the signal generation process. In this work, we propose a Convolutional Neural Network (CNN) based framework for detecting rogue devices and identifying genuine ones using softmax proba

Renee Shelby, Fernando Diaz, Vinodkumar Prabhakaran

The growing ubiquity of conversational AI highlights the need for frameworks that capture not only users' instrumental goals but also the situated, adaptive, and social practices through which they achieve them. Existing taxonomies of conversational behavior either overgeneralize, remain domain-specific, or reduce interactions to narrow dialogue functions. T

Mosong Ma, Tania Stathaki, Michalis Lazarou

Deep learning in medical imaging is often limited by scarce and imbalanced annotated data. We present SSGNet, a unified framework that combines class specific generative modeling with iterative semisupervised pseudo labeling to enhance both classification and segmentation. Rather than functioning as a standalone model, SSGNet augments existing baselines by e

Markus Krimmel, Philip Hartout, Karsten Borgwardt, Dexiong Chen

Existing methods for evaluating graph generative models primarily rely on Maximum Mean Discrepancy (MMD) metrics based on graph descriptors. While these metrics can rank generative models, they do not provide an absolute measure of performance. Their values are also highly sensitive to extrinsic parameters, namely kernel and descriptor parametrization, makin

Adam Bloomston, Elizabeth Burke, Megan Cacace, Anne Diaz

In this paper, we first situate the challenges for measuring data quality under Project Lighthouse in the broader academic context. We then discuss in detail the three core data quality metrics we use for measurement--two of which extend prior academic work. Using those data quality metrics as examples, we propose a framework, based on machine learning class

Vincent Painchaud

The hard edge and bulk scaling limits of $\beta$-ensembles are described by the stochastic Bessel and sine operators, which are respectively a random Sturm-Liouville operator and a random Dirac operator. By representing both operators as canonical systems, we show that in a suitable high-energy scaling limit, the stochastic Bessel operator converges in law t

Neil Natarajan, Kadeem Noray

Organizations (e.g., talent investment programs, schools, firms) are perennially interested in selecting cohorts of talented people. And organizations are increasingly interested in selecting diverse cohorts. Except in trivial cases, measuring the tradeoff between cohort diversity and talent is computationally difficult. Thus, organizations are presently una

Duc-Trong Le, Tran-Binh Dang, Anh-Duc Hoang Gia, Duc-Hai Nguyen

This study presents a deep learning (DL) architecture based on residual convolutional neural networks (ResNet) to reconstruct the climatology of tropical cyclogenesis (TCG) in the Western North Pacific (WNP) basin from climate reanalysis datasets. Using different TCG data labeling strategies and data enrichment windows for the NASA Modern-Era Retrospective a

Jian Zheng, Mario Sznaier

This letter presents a robust data-driven receding-horizon control framework for the discrete time linear quadratic regulator (LQR) with input constraints. Unlike existing data-driven approaches that design a controller from initial data and apply it unchanged throughout the trajectory, our method exploits all available execution data in a receding-horizon m

Guilherme Perticarari, Dongjun Wu, Thierry Baasch

Actuating the acoustic resonance modes of a microfluidic device containing suspended particles (e.g., cells) allows for the manipulation of their individual positions. In this work, we investigate how the number of resonance modes $M$ chosen for actuation and the number of particles $P$ affect the probability of success $S$ of manipulation tasks, denoted Aco

Sander Gribling, Simon Apers, Harold Nieuwboer, Michael Walter

Spectral gaps play a fundamental role in many areas of mathematics, computer science, and physics. In quantum mechanics, the spectral gap of Schr\"odinger operators has a long history of study due to its physical relevance, while in quantum computing spectral gaps are an important proxy for efficiency, such as in the quantum adiabatic algorithm. Motivated by

Alexander Reeves, Simone Ferraro, Andrina Nicola, Alexandre Refregier

We perform a multiprobe analysis combining cosmic microwave background (CMB) data from Planck and the Atacama Cosmology Telescope (ACT), ACT CMB lensing, and large-scale structure (LSS) measurements from the Dark Energy Spectroscopic Instrument (DESI), including DESI Legacy Imaging Survey (LS) galaxies and baryon acoustic oscillations (BAOs). We present the

Shuo Jiang, Zhuwen Chen, Liaoman Xu, Yanming Zhu

Survival analysis plays a vital role in making clinical decisions. However, the models currently in use are often difficult to interpret, which reduces their usefulness in clinical settings. Prototype learning presents a potential solution, yet traditional methods focus on local similarities and static matching, neglecting the broader tumor context and lacki

Ali Jaberi, Amin Sadeghi, Runze Zhang, Zhaoyang Zhao

Electrochemical impedance spectroscopy (EIS) data is typically modeled using an equivalent circuit model (ECM), with parameters obtained by minimizing a loss function via nonlinear least squares fitting. This paper introduces two new loss functions, log-B and log-BW, derived from the Bode representation of EIS. Using a large dataset of generated EIS data, th

Adam Bloomston, Elizabeth Burke, Megan Cacace, Anne Diaz

We present Core Mondrian, a scalable extension of the Original Mondrian partition-based anonymization algorithm. A modular strategy layer supports k-anonymity, allowing new privacy models to be added easily. A hybrid recursive/queue execution engine exploits multi-core parallelism while maintaining deterministic output. Utility-preserving enhancements includ

Venkat Chandrasekaran, Timothy Duff, Jose Israel Rodriguez, Kevin Shu

Hidden convexity is a powerful idea in optimization: under the right transformations, nonconvex problems that are seemingly intractable can be solved efficiently using convex optimization. We introduce the notion of a Lagrangian dual section of a nonlinear program defined over a topological space, and we use it to give a sufficient condition for a nonconvex

Urvashi Verma, Kanishka Goyal, Chanaka Kottegoda, Rana D. Parshad

Additional food sources for an introduced predator are known to increase its efficiency on a target pest. In this context, inhibiting factors such as interference, predator competition, and the introduction of temporally dependent quantity and quality of additional food are all known to enable pest extinction. As climate change and habitat degradation have i

Sandip Roy, Debopriya Mukherjee, Manil Thankamani Mohan

The present paper focuses on the stochastic nonlinear Schrodinger equation with polynomial nonlinearity, and a zero-order (no derivatives involved) linear damping. Here, the random forcing term appears as a mix of a nonlinear noise in the Ito sense and a linear multiplicative noise in the Stratonovich sense. We prove the Laplace principle for the family of s

A. A. Mazanik, C. -H. Huang, Miguel A. Cazalilla, F. S. Bergeret

We study the Josephson effect in junctions composed of two ferromagnetic insulator/diffusive superconductor bilayers separated by an insulating barrier. By computing the free energy of the system, we identify two distinct contributions: (i) The work performed by a current source to create a supercurrent through the junction, and (ii) an antiferromagnetic cou

Prateek Humane, Paolo Cudrano, Daniel Z. Kaplan, Matteo Matteucci

Fine-tuning large language models (LLMs) on chain-of-thought (CoT) data shows that a small amount of high-quality data can outperform massive datasets. Yet, what constitutes "quality" remains ill-defined. Existing reasoning methods rely on indirect heuristics such as problem difficulty or trace length, while instruction-tuning has explored a broader range of

Gagan Bhatia, Somayajulu G Sripada, Kevin Allan, Jacobo Azcona

Hallucinations in large language models (LLMs) produce fluent continuations that are not supported by the prompt, especially under minimal contextual cues and ambiguity. We introduce Distributional Semantics Tracing (DST), a model-native method that builds layer-wise semantic maps at the answer position by decoding residual-stream states through the unembedd

Alessandro Favero

Deep neural networks have achieved remarkable success, yet our understanding of how they learn remains limited. These models can learn high-dimensional tasks, which is generally statistically intractable due to the curse of dimensionality. This apparent paradox suggests that learnable data must have an underlying latent structure. What is the nature of this

Batu El, James Zou

Large language models (LLMs) are increasingly shaping how information is created and disseminated, from companies using them to craft persuasive advertisements, to election campaigns optimizing messaging to gain votes, to social media influencers boosting engagement. These settings are inherently competitive, with sellers, candidates, and influencers vying f

Elijah Kayode Adejumo, Brittany Johnson

Open Source Software (OSS) has become a very important and crucial infrastructure worldwide because of the value it provides. OSS typically depends on contributions from developers across diverse backgrounds and levels of experience. Making safe changes, such as fixing a bug or implementing a new feature, can be challenging, especially in object-oriented sys

Xue-Qi Xi, Kai-Bao Chen, Xuan-Bo Tong, Shu-Yi Wei

We study the helicity correlation of two $\Lambda$ hyperons produced in unpolarized semi-inclusive deep inelastic scatterings (SIDIS), with one hyperon detected in the current fragmentation region and the other in the target fragmentation region. This observable provides direct access to the spin-dependent fragmentation function $G_{1Lq}$ and the spin-depend

Yashaswini Mathur, Prafullkumar Tale

We study the \textsc{Labeled Contractibility} problem, where the input consists of two vertex-labeled graphs $G$ and $H$, and the goal is to determine whether $H$ can be obtained from $G$ via a sequence of edge contractions. Lafond and Marchand~[WADS 2025] initiated the parameterized complexity study of this problem, showing it to be \(\W[1]\)-hard when para

Muyu He, Muhammad Ali Shafique, Anand Kumar, Tsach Mackey

Distilling the thinking traces of a Large Language Model (LLM) with reasoning capabilities into a smaller model has been proven effective. Yet, there is a scarcity of work done on how model performances scale with the quantity of distillation data. In this work, we study the scaling trend of distilling competitive coding skills on two small non-reasoning LLM

C. J. K. Larkin, C. Hawcroft, J. Mackey, R. R. Lefever

Feedback from Young Massive Clusters (YMCs) is an important driver of galaxy evolution. In the first few Myr, mechanical feedback is dominated by collective effects of the massive stellar winds in the YMC. The mass-loss rates and terminal wind velocities of these stars change by orders of magnitude over pre-SN timescales as the massive stars evolve, and mass

Tzula B. Propp, Jeroen Grimbergen, Emil R. Hellebek, Junior R. Gonzales-Ureta

Near-term quantum networks face a bottleneck due to low quantum communication rates. This degrades performance both by lowering operating speeds and increasing qubit storage time in noisy memories, making some quantum internet applications infeasible. One way to circumvent this bottleneck is multiplexing: combining multiple signals into a single signal to im

Yinjian Wang, Wei Li, Yuanyuan Gui, Gemine Vivone

Fusing a hyperspectral image with a multispectral image acquired over the same scene, \textit{i.e.}, hyperspectral image super-resolution, has become a popular computational way to access the latent high-spatial-spectral-resolution image. To date, a variety of fusion methods have been proposed, among which the tensor-based ones have testified that multiple p

André Chailloux, Paul Hermouet

Chen, Liu, and Zhandry [CLZ22] introduced the problems $S|LWE\rangle$ and $C|LWE\rangle$ as quantum analogues of the Learning with Errors problem, designed to construct quantum algorithms for the Inhomogeneous Short Integer Solution ($ISIS$) problem. Several later works have used this framework for constructing new quantum algorithms in specific cases. Howev

Althea Sterrett, Austin Adams

The programmable and composable nature of smart contract protocols has enabled the emergence of novel market structures and asset classes that are architecturally frictional to implement in traditional financial paradigms. This fluidity has produced an understudied class of market dynamics, particularly in coupled markets where one market serves as an oracle

Eric R. Bittner

We develop a stochastic framework for anyonic systems in which the exchange phase is promoted from a fixed parameter to a fluctuating quantity. Starting from the Stratonovich stochastic Liouville equation, we perform the Stratonovich--It\^o conversion to obtain a Lindblad master equation that ties the dissipator directly to the distorted anyon algebra. This

Mallika Mainali, Harsha Sureshbabu, Anik Sen, Christopher B. Rauch

As algorithmic decision-makers are increasingly applied to high-stakes domains, AI alignment research has evolved from a focus on universal value alignment to context-specific approaches that account for decision-maker attributes. Prior work on Decision-Maker Alignment (DMA) has explored two primary strategies: (1) classical AI methods integrating case-based

Nyal Patel, Matthieu Bou, Arjun Jagota, Satyapriya Krishna

Reinforcement Learning from Human Feedback (RLHF) aligns Large Language Models (LLMs) with human preferences, yet the underlying reward signals they internalize remain hidden, posing a critical challenge for interpretability and safety. Existing approaches attempt to extract these latent incentives using Inverse Reinforcement Learning (IRL), but treat all pr

Lulu Gong, Shreya Saxena

Mixtures of linear dynamical systems (MoLDS) provide a path to model time-series data that exhibit diverse temporal dynamics across trajectories. However, its application remains challenging in complex and noisy settings, limiting its effectiveness for neural data analysis. Tensor-based moment methods can provide global identifiability guarantees for MoLDS,

Bjoern Hansen, Jonas Pedersen, Klaus F. Kofoed, Oscar Camara

Despite the success of advanced segmentation frameworks such as TotalSegmentator (TS), accurate segmentations of the left atrial appendage (LAA), coronary arteries (CAs), and pulmonary veins (PVs) remain a significant challenge in medical imaging. In this work, we present the first open-source, anatomically coherent dataset of curated, high-resolution segmen

Adrien Segovia

Given any poset $P$ and chain $\phi$ in $P$, we define the $(P,\phi)$-Tamari lattice. We study in depth these lattices and prove in particular that they are join-semidistributive, join-congruence uniform and left modular. We prove that the lattices of higher torsion classes of the higher Auslander and Nakayama algebras of type $\mathbb{A}$ are examples of $(

Zijun Meng, Dong Zhang

We establish novel max-min and minimax characterizations of Cheeger $k$-constants in weighted forests, thereby providing the first combinatorial analogue of the Courant-Fischer-Weyl minimax principle. As for applications, we prove that the forest 1-Laplacian variational eigenvalues are independent of the choice of typical indexes; we propose a refined higher

Tanmoy Kumar, N. P. S. Mithun, Subhendra Mohanty, Sourov Roy

Axions and axion-like particles (ALPs) have gained immense attention in searches for beyond Standard Model (BSM) physics. Experiments searching for axions leverage their predicted couplings to Standard Model (SM) particles to look for observable signals. Though weak, these couplings allow axions to be produced abundantly in the interiors of stars such as the

Avijit Maity, Aman Kumar, Vikram Tripathi

The chiral central charge $c_-$ is a key topological invariant of the edge characterizing the bulk two-dimensional chiral topological order, but its direct evaluation in microscopic spin models has long been a challenge, especially for non-abelian topological order. Building on the recently developed modular commutator formalism, we numerically obtain $c_-$

Roman Ibrahimov, Jannik Matthias Heinen

This project proposes a bioinspired multi-robot system using Distributed Optimization for efficient exploration and mapping of unknown environments. Each robot explores its environment and creates a map, which is afterwards put together to form a global 2D map of the environment. Inspired by wall-following behaviors, each robot autonomously explores its neig

Taylor Sorensen, Benjamin Newman, Jared Moore, Chan Park

Language model post-training has enhanced instruction-following and performance on many downstream tasks, but also comes with an often-overlooked cost on tasks with many possible valid answers. On many tasks such as creative writing, synthetic data generation, or steering to diverse preferences, models must cover an entire distribution of outputs, rather tha

Nicolas Thomé, Yukiteru Murakami, Katharina Krischer

We study an intricate mechanism of pattern formation in globally coupled heterogeneous oscillatory media. In anodic electrochemical etching of silicon, the electrode surface splits into two amplitude-phase regions, while all oscillators remain frequency-locked. Additionally, the relative ratio of the pattern can be tuned via a coupling term. We introduce a h

Monika Yadav, Anuradha Sharma

Let $\mathscr{R}_{e,m}$ denote a finite commutative chain ring of even characteristic with maximal ideal $\langle u \rangle$ of nilpotency index $e \geq 3,$ Teichm$\ddot{u}$ller set $\mathcal{T}_{m},$ and residue field $\mathscr{R}_{e,m}/\langle u \rangle$ of order $2^m.$ Suppose that $2 \in \langle u^{\kappa}\rangle \setminus \langle u^{\kappa+1}\rangle$ fo

Luca Scimeca, Thomas Jiralerspong, Berton Earnshaw, Jason Hartford

Diffusion Probabilistic Models (DPMs) have achieved strong generative performance, yet their inductive biases remain largely implicit. In this work, we aim to build inductive biases into the training and sampling of diffusion models to better accommodate the target distribution of the data to model. We introduce an anisotropic noise operator that shapes thes

Gerald E. Marsh

The basic physics for plasma core rocket engines was already completed in 1985. At the time successful containment of a fissioning uranium hexafluoride U(93%)F6 plasma was achieved for ~120 seconds using an argon vortex. Unfortunately, hydrodynamic confinement of a fissioning fuel in a gas core nuclear rocket without uranium loss is probably unachievable. Th

Nikolaos D. Kouvakas, Fotis N. Koumboulis, Konstantinos G. Tzierakis, John Sigalas

The problem of regulation of the orientation angle of a remotely controlled differential-drive mobile robot with actuator dynamics and network-induced delays is studied. Using a preinstalled two-layer nonlinear control scheme that decouples linear and angular velocities and regulates heading, a third, delay-dependent layer that achieves exact model matching

Nga Nguyen, Olivier Bonnefon, René Gato, Luis Almeida

Biological control strategies against mosquito-borne diseases--such as the sterile insect technique (SIT), RIDL, and Wolbachia-based releases--require reliable estimates of dispersal and survival of released males. We propose a mechanistic--statistical framework for mark--release--recapture (MRR) data linking an individual-based 2D diffusion model with its r

Zilong Ye, Shiqian Ma, Junfeng Yang, Danqing Zhou

Consider composite nonconvex optimization problems where the objective function consists of a smooth nonconvex term (with Lipschitz-continuous gradient) and a convex (possibly nonsmooth) term. Existing parameter-free methods for such problems often rely on complex multi-loop structures, require line searches, or depend on restrictive assumptions (e.g., bound

Tao Zhe, Rui Liu, Fateme Memar, Xiao Luo

Route recommendation aims to provide users with optimal travel plans that satisfy diverse and complex requirements. Classical routing algorithms (e.g., shortest-path and constraint-aware search) are efficient but assume structured inputs and fixed objectives, limiting adaptability to natural-language queries. Recent LLM-based approaches enhance flexibility b

Mi Luo, Zihui Xue, Alex Dimakis, Kristen Grauman

Video reasoning, the task of enabling machines to infer from dynamic visual content through multi-step logic, is crucial for advanced AI. While the Chain-of-Thought (CoT) mechanism has enhanced reasoning in text-based tasks, its application to video understanding remains underexplored. This paper presents a systematic analysis revealing that CoT often degrad

Edgar E. Robles, Dikshant Sagar, Alejandro Yankelevich, Jianming Bian

NOvA is a long-baseline neutrino oscillation experiment that detects neutrino particles from the NuMI beam at Fermilab. Before data from this experiment can be used in analyses, raw hits in the detector must be matched to their source particles, and the type of each particle must be identified. This task has commonly been done using a mix of traditional clus

Luis Y. Meza-Pérez, Pedro L. del Ángel R., Carlos Pompeyo-Gutiérrez, Miguel Angel Dela-Rosa

In this paper, we study the subvarieties of a complex flag variety that are invariant under the action of a maximal torus. Using combinatorial techniques derived from matroid theory, we introduce a decomposition of this variety into affine, locally closed subsets, which we refer to as thin Schubert cells, each indexed by an element of a Cartesian product of

Alberto Enciso, Manuel Garzón, Daniel Peralta-Salas

We prove that, on each low energy level, the natural Hamiltonian system defined by a generic smooth potential on $\mathbf{T}^2$ exhibits an arbitrarily high number of hyperbolic periodic orbits and a positive-measure set of invariant tori. Hence, quasi-periodic motion and hyperbolic behavior typically coexist in the low-energy dynamics of natural Hamiltonian

Akshay Muppidi, Martin Radfar

Speech emotion recognition (SER) is pivotal for enhancing human-machine interactions. This paper introduces "EmoHRNet", a novel adaptation of High-Resolution Networks (HRNet) tailored for SER. The HRNet structure is designed to maintain high-resolution representations from the initial to the final layers. By transforming audio samples into spectrograms, EmoH

João Palmeiro, Diogo Duarte, Rita Costa, Pedro Bizarro

AI models are increasingly used for data analysis and visualization, yet benchmarks rarely address scatterplot-specific tasks, limiting insight into performance. To address this gap for one of the most common chart types, we introduce a synthetic, annotated dataset of over 18,000 scatterplots from six data generators and 17 chart designs, and a benchmark bas

Meghna P Ayyar, Jenny Benois-Pineau, Akka Zemmari

Explainable AI (XAI) has become increasingly important with the rise of large transformer models, yet many explanation methods designed for CNNs transfer poorly to Vision Transformers (ViTs). Existing ViT explanations often rely on attention weights, which tend to yield noisy maps as they capture token-to-token interactions within each layer.While attributio

Monika Yadav, Anuradha Sharma

Let $\mathcal{R}_{e,m}$ be a finite commutative chain ring of even characteristic with maximal ideal $\langle u \rangle$ of nilpotency index $e \geq 2,$ Teichm$\ddot{u}$ller set $\mathcal{T}_{m},$ and residue field $\mathcal{R}_{e,m}/\langle u \rangle$ of order $2^m.$ Suppose that $2 \in \langle u^{\kappa}\rangle \setminus \langle u^{\kappa+1}\rangle$ for so

Heng Zhang, Kevin Yuchen Ma, Mike Zheng Shou, Weisi Lin

Dexterous grasping with multi-fingered hands remains challenging due to high-dimensional articulations and the cost of optimization-based pipelines. Existing end-to-end methods require training on large-scale datasets for specific hands, limiting their ability to generalize across different embodiments. We propose MachaGrasp, an eigengrasp-based, end-to-end

Python Song, Luke Tenyi Chang, Yun-Yun Tsai, Penghui Li

CAPTCHA, originally designed to distinguish humans from robots, has evolved into a real-world benchmark for assessing the spatial reasoning capabilities of vision-language models. In this work, we first show that step-by-step reasoning is crucial for vision-language models (VLMs) to solve CAPTCHAs, which represent high-difficulty spatial reasoning tasks, and

Dimitrios Kelesis, Dimitris Fotakis, Georgios Paliouras

In this paper, we study the factors that contribute to the effect of oversmoothing in deep Graph Neural Networks (GNNs). Specifically, our analysis is based on a new metric (Mean Average Squared Distance - $MASED$) to quantify the extent of oversmoothing. We derive layer-wise bounds on $MASED$, which aggregate to yield global upper and lower distance bounds.

Jingdong Shao, Mei Huang

A false vacuum could be a profound ingredient of fundamental physics, yet its direct detection in laboratories is hindered when the lifetime is exponentially long. Conventional static phase diagrams often discard metastable false vacuum, we show that, however, in a dynamical treatment of a first-order QCD phase transition at large quark chemical potential th

Akshay Muppidi, Martin Radfar

Vision-based Proximal Policy Optimization (PPO) struggles with visual observation-based robotic laparoscopic surgical tasks due to the high-dimensional nature of visual input, the sparsity of rewards in surgical environments, and the difficulty of extracting task-relevant features from raw visual data. We introduce a simple approach integrating MedFlamingo,

Santanu Kumar Das, Sarika Shivaji Bangar, Amaresh Dalal, Gaurav Tomar

The impact of viscoelasticity on drop deformation in the presence of an electric field is investigated using both analytical and numerical methods. The study focuses on two configurations: a viscoelastic drop suspended in a Newtonian fluid and a Newtonian drop suspended in a viscoelastic medium. Oldroyd-B constitutive equation is employed to model constant v

Christian Marinoni, Riccardo Fosco Gramaccioni, Eleonora Grassucci, Danilo Comminiello

The generation of sounding videos has seen significant advancements with the advent of diffusion models. However, existing methods often lack the fine-grained control needed to generate viewpoint-specific content from larger, immersive 360-degree environments. This limitation restricts the creation of audio-visual experiences that are aware of off-camera eve

Huai-Dong Cao

In this survey paper, we analyse and compare the recent curvature estimates for three types of $4$-dimensional gradient Ricci solitons, especially between Ricci shrinkers [58] and expanders [17]. In addition, we provide some new curvature estimates for $4$-dimensional gradient steady Ricci solitons, including the sharp curvature estimate $|Rm|\le C R$ for gr

Przemysław Grabowski

We build a purely inseparable Galois theory using non-derived commutative algebra. Our theory works on fields and on normal varieties. It says that a purely inseparable morphism corresponds to a finite (saturated) subalgebra of differential operators. Our approach unifies most of the literature about purely inseparable morphisms and shows new research direct

Ferdinand Wagner

Hodge-filtered derived de Rham cohomology of a ring $R$ can be described (up to completion and shift) as the graded pieces of the even filtration on $\mathrm{HC}^-(R)$. In this paper we show a deformation of this result: If $R$ admits a spherical $\mathbb{E}_2$-lift, then the graded pieces of the even filtration on $\mathrm{TC}^-(\mathrm{ku}\otimes\mathbb{S}

Gang Liu, Yihan Zhu, Jie Chen, Meng Jiang

Large language models hold promise as scientific assistants, yet existing agents either rely solely on algorithm evolution or on deep research in isolation, both of which face critical limitations. Pure algorithm evolution, as in AlphaEvolve, depends only on the internal knowledge of LLMs and quickly plateaus in complex domains, while pure deep research prop

Andreas Anastasiou, Christophe Ley, Sophia Loizidou

In this paper, we develop optimal tests for symmetry on the hyper-dimensional torus, leveraging Le Cam's methodology. We address both scenarios where the center of symmetry is known and where it is unknown. These tests are not only valid under a given parametric hypothesis but also under a very broad class of symmetric distributions. The asymptotic behavior

Farhad de Sousa, François Ribalet, Jacob Bien

Phytoplankton are microscopic algae responsible for roughly half of the world's photosynthesis that play a critical role in global carbon cycles and oxygen production, and measuring the abundance of their subtypes across a wide range of spatiotemporal scales is of great relevance to oceanography. High-frequency flow cytometry is a powerful technique in which

David Lüdke, Marten Lienen, Marcel Kollovieh, Stephan Günnemann

Temporal point processes (TPPs) are a fundamental tool for modeling event sequences in continuous time, but most existing approaches rely on autoregressive parameterizations that are limited by their sequential sampling. Recent non-autoregressive, diffusion-style models mitigate these issues by jointly interpolating between noise and data through event inser

A. Circiello, A. McDaniel, M. Di Mauro, C. Karwin

We evaluate the contribution to the Isotropic Gamma-Ray Background (IGRB) coming from Radio Galaxies (RGs), the subclass of radio-loud Active Galactic Nuclei (AGN) with the highest misalignment from the line of sight (l.o.s.). Since only a small number of RGs are detected in gamma rays compared to the largest known radio population, the correlation between r

Pol Caselles Rico, Francesc Moreno Noguer

Existing 3D face modeling methods usually depend on 3D Morphable Models, which inherently constrain the representation capacity to fixed shape priors. Optimization-based approaches offer high-quality reconstructions but tend to be computationally expensive. In this work, we introduce GLVD, a hybrid method for 3D face reconstruction from few-shot images that

David Cortes, Jean Leneutre, Vadim Malvone, James Ortiz

Real-time cybersecurity and privacy applications require reliable verification methods and system design tools to ensure their correctness. Many of these reactive real-time applications embedded in various infrastructures, such as airports, hospitals, and oil pipelines, are potentially vulnerable to malicious cyber-attacks. Recently, a growing literature has

Harrison Agrusa, Patrick Michel

We model the outcomes of catastrophic disruptions on small, gravity-dominated natural satellites, accounting for the tidal potential of the central body, which is neglected in classical disruption scaling laws. We introduce the concept of $Q^\star_\text{TD}$, the specific energy required to disperse half of the total mass involved in a collision, accounting

Claudinei Daitx, Haitham Amar

Large language models (LLMs) are increasingly used to answer high-stakes study-abroad questions about admissions, visas, scholarships, and eligibility. Yet it remains unclear how reliably they advise students, and how often otherwise helpful answers drift into unsupported claims (``hallucinations''). This work provides a clear, domain-grounded overview of ho

Renyu Luo, Chunhua Zhu, Guoliang Lü, Helei Liu

During the late stages of massive stellar evolution, failed supernovae (FSN) may form through core-collapse processes. The traditional evaluation criterion $\xi_{2.5}$ $=$ 0.45, primarily established using non-rotating progenitor models, suffers from significant inaccuracies when applied to rotating pre-supernova systems. The effects of metallicity and rotat

Shang Ma, Xusheng Xiao, Yanfang Ye

Large Language Model (LLM)-based UI agents show great promise for UI automation but often hallucinate in long-horizon tasks due to their lack of understanding of the global UI transition structure. To address this, we introduce AGENT+P, a novel framework that leverages symbolic planning to guide LLM-based UI agents. Specifically, we model an app's UI transit

Imane Boucenna, Florent Carn, Ahmed Mourchid

Nanoplatelets open up a wide range of possibilities for building materials with novel properties linked to their shape anisotropy. A challenge consists of controlling dynamically the order of positioning and orientation in three dimensions by assembly to exploit the collective properties at the macroscale. While most studies to date have focused on hard plat

Xinye Cao, Hongcan Guo, Jiawen Qian, Guoshun Nan

Understanding hour-long videos with multi-modal large language models (MM-LLMs) enriches the landscape of human-centered AI applications. However, for end-to-end video understanding with LLMs, uniformly sampling video frames results in LLMs being overwhelmed by a vast amount of irrelevant information as video length increases. Existing hierarchical key frame

Chengwei Wu, Xingrui Zhuo, Mingyang Gao, Xinghe Cheng

Reliable evaluation of knowledge-grounded Large Language Models (LLMs) in Chinese requires resources that explicitly align Chinese-language text with verifiable Knowledge Graph (KG) facts. Yet existing Chinese benchmarks primarily assess general language understanding and offer limited support for structured reasoning under Chinese-specific linguistic phenom

Li Zeqiao, Wang Yijing, Wang Haoyu, Li Zheng

Autonomous driving with reinforcement learning (RL) has significant potential. However, applying RL in real-world settings remains challenging due to the need for safe, efficient, and robust learning. Incorporating human expertise into the learning process can help overcome these challenges by reducing risky exploration and improving sample efficiency. In th

Luis Enrique Coronas, Stepan Timr, Fabio Sterpone, Giancarlo Franzese

Biological processes like the sequestration of Superoxide Dismutase 1 (SOD1) into biomolecular condensates such as FUS and stress granules are essential to understanding disease mechanisms, including amyotrophic lateral sclerosis (ALS). Our study demonstrates that the hydration environment is crucial in these processes. Using the advanced CVF water model, wh

Qingyu Yin, Chak Tou Leong, Linyi Yang, Wenxuan Huang

Large reasoning models (LRMs) with multi-step reasoning capabilities have shown remarkable problem-solving abilities, yet they exhibit concerning safety vulnerabilities that remain poorly understood. In this work, we investigate why safety alignment fails in reasoning models through a mechanistic interpretability lens. Using a linear probing approach to trac

Ondřej Týbl, Lukáš Neumann

We introduce Universal Neural Architecture Space (UniNAS), a generic search space for neural architecture search (NAS) which unifies convolutional networks, transformers, and their hybrid architectures under a single, flexible framework. Our approach enables discovery of novel architectures as well as analyzing existing architectures in a common framework. W

Marta Catalano, Hugo Lavenant

Measuring dependence between random variables is a fundamental problem in Statistics, with applications across diverse fields. While classical measures such as Pearson's correlation have been widely used for over a century, they have notable limitations, particularly in capturing nonlinear relationships and extending to general metric spaces. In recent years

Jim Dai, Manxi Wu, Zhanhao Zhang

Stochastic processing networks (SPNs) have broad applications in healthcare, transportation, and communication networks. The control of SPN is to dynamically assign servers in batches under uncertainty to optimize long-run performance. This problem is challenging as the policy dimension grows exponentially with the number of servers, making standard reinforc

Zijie Gu

This paper investigates the Erd\H{o}s distinct subset sums problem in $\mathbb{Z}^k$. Beyond the classical variance method, using alternative statistical quantities like $\mathbb{E}[\|X\|_1]$ and $\mathbb{E}[\|X\|_3^3]$ can yield better bounds in certain dimensions. This innovation improves previous low-dimensional results and provides a framework for choosi

Andrew J. Larkoski

Non-global logarithms are low energy correlations between the substructure of a jet and the event in which it is immersed. We study the leading non-global logarithms that arise from soft quark--anti-quark emission and calculate their coefficient as a series in the jet radius, $R$, in arbitrary processes. We calculate the exact coefficient through quadratic o

Rohit Goswami, Hannes Jónsson

Gaussian process (GP) regression provides a strategy for accelerating saddle point searches on high-dimensional energy surfaces by reducing the number of times the energy and its derivatives with respect to atomic coordinates need to be evaluated. The computational overhead in the hyperparameter optimization can, however, be large and make the approach ineff

Guillaume Godin

We introduce molFTP (molecular fragment-target prevalence), a compact representation that delivers strong predictive performance. To prevent feature leakage across cross-validation folds, we implement a dummy-masking procedure that removes information about fragments present in the held-out molecules. We further show that key leave-one-out (key-loo) closely

Muhammad Zia, Moritz Cygorek, Erik M. Gauger, Brendon W. Lovett

We investigate how fermionic anticommutation shapes transport in a noninteracting resonant-level model where a single central site is coupled to an environment. To this end, we compare a fermionic reservoir with a bath of spin-half modes using exact diagonalization and a perturbative expansion of the master equation to identify the differences. Notably, we f

An Thi Nguyen, Radina Stoykova, Eric Arazo

Generic instance search models can dramatically reduce the manual effort required to analyze vast surveillance footage during criminal investigations by retrieving specific objects of interest to law enforcement. However, our research reveals an unintended emergent capability: through overlearning, these models can single out specific individuals even when t

Kevin Raina, Tanya Schmah

Out-of-Distribution (OOD) detection is critical to AI reliability and safety, yet in many practical settings, only a limited amount of training data is available. Bayesian Neural Networks (BNNs) are a promising class of model on which to base OOD detection, because they explicitly represent epistemic (i.e. model) uncertainty. In the small training data regim

Yi Feng, Weihua Wang

In this paper, we study the problem of energy equality for weak solutions of the 3D incompressible fractional Navier-Stokes / MHD equations. With the help of the technique of symmetrization and interpolation method, we obtain some new sufficient conditions including the Sobolev multiplier spaces, which insures the validity of the energy equality of the weak

Yuval Efron, Joachim Neu, Ling Ren, Ertem Nusret Tas

In the context of Byzantine consensus problems such as Byzantine broadcast (BB) and Byzantine agreement (BA), the good-case setting aims to study the minimal possible latency of a BB or BA protocol under certain favorable conditions, namely the designated leader being correct (for BB), or all parties having the same input value (for BA). We provide a full ch

Albert Jang, Hyungseok Jang, Nian Wang, Alexey Samsonov

We propose a signal acquisition and modeling framework for multi-component tissue quantification that encompasses transmit field inhomogeneity, multi-component relaxation and magnetization transfer (MT) effects. By applying off-resonance irradiation between excitation and acquisition within an RF-spoiled gradient-echo scheme, in combination with multiple ech

Jan Dobrowolski, Francesco Gallinaro, Rosario Mennuni

We study valued fields equipped with an automorphism. We prove that all of them have an extension admitting an equivariant cross-section of the valuation. In residual characteristic zero, and in the presence of such a cross-section, we show that amalgamation problems are solvable precisely when the induced residual problem is, characterise the existentially