October 2025 arXiv papers — page 76

Damian Sobota, Tomasz Żuchowski

For an ideal $\mathcal{I}$ in a $\sigma$-complete Boolean algebra $\mathcal{A}$, we show that if the Boolean algebra $\mathcal{A}\langle\mathcal{I}\rangle$ generated by $\mathcal{I}$ does not have the Nikodym property, then it does not have the Grothendieck property either. The converse however does not hold -- we construct a family of $\mathfrak{c}$ many pa

Michelle J. Cummings-Koether, Franziska Durner, Theophile Shyiramunda, Matthias Huemmer

The swift diffusion of artificial intelligence (AI) raises critical questions about how cultural contexts shape adoption patterns and their consequences for human daily life. This study investigates the cultural dimensions of AI adoption and their influence on cognitive strategies across nine national contexts in Europe, Africa, Asia, and South America. Draw

Felix A. Palm, Alexander Impertro, Monika Aidelsburger, Nathan Goldman

Transport properties are central to characterizing quantum matter, yet their extraction typically requires external forcing and time-resolved measurements. In this work, we propose a scheme to access transport coefficients directly from measurements of local static ground-state currents -- quantities readily accessible in quantum-engineered platforms. By exp

Paul Ducarme, Bart Weber, Martin van Hecke, Johannes T. B. Overvelde

Nonlinearities and instabilities in mechanical structures have shown great promise for embedding advanced functionalities. However, simulating structures subject to nonlinearities can be challenging due to the complexity of their behavior, such as large shape changes, effect of pre-tension, negative stiffness and instabilities. While traditional finite eleme

Madhuparna Das, Nicolas Robles

We define an $f$-restricted partition $p_f(n,k)$ of fixed length $k$ given by the bivariate generating series \begin{align*} Q_f(z,u) \coloneqq 1+\sum_{n=1}^{\infty}\sum_{k=1}^{\infty} p_f(n,k) u^kz^n =\prod_{k=1}^{\infty}(1+uz^k)^{\Delta_f(k)}, \end{align*} where $\Delta_f(n)=f(n+1)-f(n)$. In this article, we establish a central limit theorem for the number

Yurii G. Arapov, Svetlana V. Gudina, Vladimir N. Neverov, Nikita S. Sandakov

After a brief survey of theoretical concepts for the two-parameter scaling theory in the integer quantum Hall effect regime, a comprehensive set of early, recent and new experimental results on constructing scaling diagrams for conductance in 2D semiconductor structures, as well as in graphene is displayed. A comparative analysis of scaling diagrams obtained

Rustem Turtayev, Natalia Fedorova, Oleg Serikov, Sergey Koldyba

Advanced AI systems sometimes act in ways that differ from human intent. To gather clear, reproducible examples, we ran the Misalignment Bounty: a crowdsourced project that collected cases of agents pursuing unintended or unsafe goals. The bounty received 295 submissions, of which nine were awarded. This report explains the program's motivation and evaluatio

Zijian Meng, Karim Zongo, Matthew Thoms, Ryan Eric Grant

Moment Tensor Potentials (MTPs) are machine-learning interatomic potentials whose basis functions are typically selected using a level-based scheme that is data-agnostic. We introduce a post-training, cost-aware pruning strategy that removes expensive basis functions with minimal loss of accuracy. Applied to nickel and silicon-oxygen systems, it yields model

Che-hsuan Ku, Omargeldi Atanov, King Yau Yip, Wenyan Wang

Superconductors that can be mechanically exfoliated are an interesting platform for exploring superconducting properties tuned by layer thickness. These layered superconductors are also expected to exhibit sensitivity to applied pressure. While pressure has been demonstrated to be an effective way of tuning bulk superconductors, analogous studies on supercon

NOvA, T2K Collaborations, :, K. Abe

The landmark discovery that neutrinos have mass and can change type (or "flavor") as they propagate -- a process called neutrino oscillation -- has opened up a rich array of theoretical and experimental questions being actively pursued today. Neutrino oscillation remains the most powerful experimental tool for addressing many of these questions, including wh

Bhavya Agrawalla, Krishnakumar Balasubramanian, Promit Ghosal

Stochastic Gradient Descent (SGD) has become a cornerstone method in modern data science. However, deploying SGD in high-stakes applications necessitates rigorous quantification of its inherent uncertainty. In this work, we establish \emph{non-asymptotic Berry--Esseen bounds} for linear functionals of online least-squares SGD, thereby providing a Gaussian Ce

M. H. I. Abdalla, Zhipin Wang, Christian Frey, Steffen Eger

Large Language Model (LLM) conditioning refers to instructing an LLM to generate content in accordance with the norms and values of a specific culture, beliefs of a particular political orientation, or any desired text-specified semantic conditioning. Unfortunately, prompt engineering does not ensure that LLMs behave in accordance with a desired conditioning

Gunshi Gupta, Karmesh Yadav, Zsolt Kira, Yarin Gal

To enable embodied agents to operate effectively over extended timeframes, it is crucial to develop models that form and access memories to stay contextualized in their environment. In the current paradigm of training transformer-based policies for embodied sequential decision-making tasks, visual inputs often overwhelm the context limits of transformers, wh

Uchenna Chukwu, Mohammad-Ali Miri, Nicholas Chancellor

We introduce an encoding of information in the relative displacement or photon number of different optical modes. Since the loss rate to interference is insensitive to squeezing and many non-Gaussian fluctuations, such a space is relatively protected from imperfections. We show that photon subtraction protocols can be used to create high-quality quantum supe

Télio Cropsal, Rocío Mercado

High-throughput phenotypic screens generate vast microscopy image datasets that push the limits of generative models due to their large dimensionality. Despite the growing popularity of general-purpose models trained on natural images for microscopy data analysis, their suitability in this domain has not been quantitatively demonstrated. We present the first

Kaiyi Xu, Junchao Gong, Wenlong Zhang, Ben Fei

Precipitation nowcasting based on radar echoes plays a crucial role in monitoring extreme weather and supporting disaster prevention. Although deep learning approaches have achieved significant progress, they still face notable limitations. For example, deterministic models tend to produce over-smoothed predictions, which struggle to capture extreme events a

Yongchao Huang, Pengfei Zhang, Shahzad Mumtaz

Membership inference attacks (MIAs) test whether a data point was part of a model's training set, posing serious privacy risks. Existing methods often depend on shadow models or heavy query access, which limits their practicality. We propose GP-MIA, an efficient and interpretable approach based on Gaussian process (GP) meta-modeling. Using post-hoc metrics s

Jingfan Meng, Tianji Yang, Jun Xu

In the set reconciliation (\textsf{SetR}) problem, two parties Alice and Bob, holding sets $\mathsf{A}$ and $\mathsf{B}$, communicate to learn the symmetric difference $\mathsf{A} \Delta \mathsf{B}$. In this work, we study a related but under-explored problem: set intersection (\textsf{SetX})~\cite{Ozisik2019}, where both parties learn $\mathsf{A} \cap \math

Claudio Campagnari, Sungwoong Cho, Suyong Choi, Seokju Chung

The SUB-Millicharge ExperimenT (SUBMET) investigates an unexplored parameter space of millicharged particles with mass $m_\chi < $ 1.6 GeV/c$^2$ and charge $Q_\chi < 10^{-3}e$. The detector consists of an Eljen-200 plastic scintillator coupled to a Hamamatsu Photonics R7725 photomultiplier tube (PMT). PMT afterpulses, delayed pulses produced after an energet

Ashish Chouhan, Michael Gertz

The accessibility of legal information remains a constant challenge, particularly for laypersons seeking to understand and apply complex institutional texts. While the European Union provides open access to legislation, parliamentary responses, and regulatory documents, these resources can be challenging for laypeople to explore. In this paper, we introduce

Sébastien Garneau, Carlos T. P. Zanini, Alexandra M. Schmidt

We propose correlated semi-implicit variational inference (Co-SIVI), a scalable approach for full posterior approximation in large spatial models with exponential-family likelihoods. Co-SIVI incorporates dependence directly into the conditional variational distribution of spatial random effects through an iterative weighted least squares algorithm that accom

Mengqing Liu, Dongwen Pang, Remi Abgrall, Kailiang Wu

We develop a positivity-preserving (PP) PAMPA (Point-Average-Moment PolynomiAl-interpreted) scheme that enforces a discrete divergence-free (DDF) magnetic field for ideal MHD on Cartesian grids. Extending our 1D invariant-domain-preserving (IDP) PAMPA framework (Abgrall, Jiao, Liu, Wu, SIAM J. Sci. Comput., to appear) to multidimensional, multiwave MHD, the

Y. Alipour Fakhri

We present a rigorous generalization of the classical Ginzburg--Landau model to smooth, compact Finsler manifolds without boundary. This framework provides a natural analytic setting for describing anisotropic superconductivity within Finsler geometry. The model is constructed via the Finsler--Laplacian, defined through the Legendre transform associated with

Zion Hefty, Paul Horn, Dylan King, Florian Pfender

We present a flexible random construction which, for certain graphs $H$, is able to produce $H$-free graphs with edge density strictly larger than that of the $H$-free process, while simultaneously preserving pseudorandom properties and allowing a much easier analysis. As our main application, we use this construction to show that the off-diagonal Ramsey num

Tania Le Pivert-Jolivet, Julia de León, Javier Licandro, Bryan Holler

The analysis of the composition of primitive C$-$complex asteroids is essential to understand the distribution of volatiles in the Solar System since its formation. Primitive low-albedo families within the inner main asteroid belt are of particular interest because they are a significant source of carbonaceous near-Earth asteroids, such as Ryugu and Bennu. T

Kuai Yu, Crystal Su, Xiang Liu, Judah Goldfeder

Extracting the true dynamical variables of a system from high-dimensional video is challenging due to distracting visual factors such as background motion, occlusions, and texture changes. We propose LyTimeT, a two-phase framework for interpretable variable extraction that learns robust and stable latent representations of dynamical systems. In Phase 1, LyTi

Sergei Khlebnikov

When a metastable state decays into radiation, there must be entanglement between the radiation and the decaying system, as well as between radiation collected at late and early times. We study the interplay between these two types of entanglement in simple Gaussian models in the Markov approximation. We define, via a windowed Fourier transform, multimode qu

Zehra Akbulut, Ilmar Gahramanov, Anıl Kahraman, Mustafa Mullahasanoglu

We study a two-dimensional $\mathcal{N}=(0,2)$ supersymmetric duality and construct novel Bailey pairs for the associated elliptic genera. This framework provides a systematic method to establish the equivalence of the elliptic genera of quiver gauge theories generated via iterative applications of the seed duality.

Hui He, Kun Yi, Yuanchi Ma, Qi Zhang

The recent boom of large pre-trained models witnesses remarkable success in developing foundation models (FMs) for time series forecasting. Despite impressive performance across diverse downstream forecasting tasks, existing time series FMs possess massive network architectures and require substantial pre-training on large-scale datasets, which significantly

Jasmin Wyss, Rebekah Overdorf

Authorship attribution techniques are increasingly being used in online contexts such as sock puppet detection, malicious account linking, and cross-platform account linking. Yet, it is unknown whether these models perform equitably across different demographic groups. Bias in such techniques could lead to false accusations, account banning, and privacy viol

Jounglag Lim, James Gossell, Keri Ann Sather-Wagstaff

We introduce and investigate the open neighborhood ideal $\mathcal{N}(G)$ of a finite simple graph $G$. We describe the minimal primary decomposition of $\mathcal{N}(G)$ in terms of the minimal total dominating sets (TDSs) of $G$. Then we prove that the open neighborhood ideal of a tree is Cohen-Macaulay if and only if the tree is well-totally dominated (WTD

Jefferson Baudin

We prove that the Albanese morphism of any normal proper variety $X$ in positive characteristic satisfying $S^0(X, \omega_X) \neq 0$ and $P_2(X) = 1$ is surjective with connected fibers, adn that $\mathrm{Alb}(X)$ is ordinary. We obtain from a variant of the above a purely positive characteristic proof of Chen and Hacon's effective birational characterizatio

Artur Donaldson, Bharathan Balaji, Cajetan Oriekezie, Manish Kumar

Purpose: Artificial intelligence (AI), and in particular large language models (LLMs), are increasingly being explored as tools to support life cycle assessment (LCA). While demonstrations exist across environmental and social domains, systematic evidence on their reliability, robustness, and usability remains limited. This study provides the first expert-gr

Clara Mohri, Haim Kaplan, Tal Schuster, Yishay Mansour

Transformer language models generate text autoregressively, making inference latency proportional to the number of tokens generated. Speculative decoding reduces this latency without sacrificing output quality, by leveraging a small draft model to propose tokens that the larger target model verifies in parallel. In practice, however, there may exist a set of

Markus Bläser, Sagnik Dutta, Gorav Jindal

Efficient algorithms for many problems in optimization and computational algebra often arise from casting them as systems of polynomial equations. Blum, Shub, and Smale formalized this as Hilbert's Nullstellensatz Problem $HN_R$: given multivariate polynomials over a ring $R$, decide whether they have a common solution in $R$. We can also view $HN_R$ as a co

Kai Neergård

A novel method of determining which Dynkin diagrams represent simple finite-dimensional Lie algebras over $\mathbb{C}$ is presented. It is based on a condition that is both necessary and sufficient for a suitably defined Cartan matrix to be expressible by scalar products in a Euclidean vector space. The sufficiency of this condition makes unnecessary subsequ

James Kim

Mathematically constructed S-boxes arise from algebraic structures and finite field theory to ensure strong, provable cryptographic properties. These mathematically grounded constructions allow for generation of thousands of S-Boxes with high nonlinearity, APN properties, and balanced avalanche characteristics, unlike fully random methods, which lack such th

Laura Montes-Montañez, Fernando Nuñez-Galvez, Melania Sanchez-Villa, Luis A. Angurel

This work addresses the fabrication of transparent glass surfaces with superior water-repellence (i.e., superhydrophobicity), and related functional properties such as omniphobicity, anti-fogging and anti-icing responses. Surfaces have been processed by means of mild femtosecond laser patterning combined with the grafting of fluorinated tethered molecules. C

Daniela Calvetti, Erkki Somersalo

A central goal in many brain studies is the identification of those brain regions that are activated during an observation window that may correspond to a motor task, a stimulus, or simply a resting state. While functional MRI is currently the most commonly employed modality for such task, methods based on the electromagnetic activity of the brain are valuab

Lizuo Liu, Tongtong Li, Anne Gelb

Data assimilation (DA) methods combine model predictions with observational data to improve state estimation in dynamical systems, inspiring their increasingly prominent role in geophysical and climate applications. Classical DA methods assume that the governing equations modeling the dynamics are known, which is unlikely for most real world applications. Ma

Jorge L. Rodríguez-Monteverde, Santiago Jaraba, Juan García-Bellido

Dynamical captures of black holes are unique events that provide an exceptional opportunity to probe the strong-field regime of gravitational physics. In this article, we perform numerical relativity simulations to study the events of dynamical capture of two equal-mass nonspinning black holes. We consider a suite of scenarios within a range of initial linea

Changhao Li, Vitaly Z. Zubialevich, Peter J. Parbrook, Brian Corbett

The development of uniform GaN micro-pyramids and platelets via selective area growth is a critical step toward advancing III-nitride device technologies, particularly for micro-light-emitting diode applications. This work investigates the origins of morphological non-uniformity in micro-pyramids and micro-platelets grown by metal-organic chemical vapor depo

Qingjun Jin, Ke Ren, Gang Yang, Rui Yu

Composite local operators are central to effective field theories (EFTs), as they define interaction vertices in effective Lagrangians and play a fundamental role in investigating the structure of quantum field theories. The contribution of high-dimensional operators in the Standard Model Effective Field Theory (SMEFT) grows increasingly important as experim

Rashik Shadman, M G Sarwar Murshed, Faraz Hussain

Presentation attacks represent a critical security threat where adversaries use fake biometric data, such as face, fingerprint, or iris images, to gain unauthorized access to protected systems. Various presentation attack detection (PAD) systems have been designed leveraging deep learning (DL) models to mitigate this type of threat. Despite their effectivene

Cesar Gonzalez-Gutierrez, Dirk Hovy

Prompting is a common approach for leveraging LMs in zero-shot settings. However, the underlying mechanisms that enable LMs to perform diverse tasks without task-specific supervision remain poorly understood. Studying the relationship between prompting and the quality of internal representations can shed light on how pre-trained embeddings may support in-con

Azree Nazri

Large Language Models (LLMs) like ChatGPT and LLaMA drive rapid progress in generative AI, yet their huge parameter scales create severe computational and environmental burdens. High training costs, energy use, and limited device deployment hinder accessibility. Existing compression - pruning, distillation, low-rank, and quantization - reduces size but ignor

Rashina Hoda

Agentic AI is poised to usher in a seismic paradigm shift in Software Engineering (SE). As technologists rush head-along to make agentic AI a reality, SE researchers are driven to establish agentic SE as a research area. While early visions of agentic SE are primarily focused on code-related activities, early empirical evidence calls for a consideration of a

R. González-Ibáñez, J. Macías-Cáceres, M. Villalta-Paucar

Technological advancements have made video games a central part of the digital lives of nearly 3 billion people worldwide. Although games can address various social, physical, and psychological needs, their potential to support human development and well-being remains underutilized. Research highlights both negative effects, such as addiction and isolation,

Ivan Calvo, Felix I. Parra, Hanne Thienpondt, Jose Manuel Garcia-Regaña

Recent nonlinear gyrokinetic simulations have shown that impurities can strongly modify the turbulent heat flux in stellarator plasmas. Here, the ion-temperature-gradient (ITG) dispersion relation in a plasma containing impurities is analytically solved in certain limits and an expression for the modification of the ITG growth rate by impurities is derived.

Guilin Zhang, Wulan Guo, Ziqi Tan, Srinivas Vippagunta

Industrial and government organizations increasingly depend on data-driven analytics for workforce, finance, and regulated decision processes, where timeliness, cost efficiency, and compliance are critical. Distributed frameworks such as Spark and Flink remain effective for massive-scale batch or streaming analytics but introduce coordination complexity and

Giorgio Cialdea, Steve Shkoller, Vlad Vicol

We prove that Guderley's self-similar imploding shock solution for the compressible Euler equations with ideal--gas law ($\gamma>1$) arises from classical, radially symmetric, shock--free data. For such data prescribed at initial time $\mathrm{T_{in}} < 0$, we prove that the flow remains smooth up to a first singular time $t=\mathrm{T}_* \in (\mathrm{T_{in}}

Addison J. Wu, Ryan Liu, Kerem Oktar, Theodore R. Sumers

Human communication is motivated: people speak, write, and create content with a particular communicative intent in mind. As a result, information that large language models (LLMs) and AI agents process is inherently framed by humans' intentions and incentives. People are adept at navigating such nuanced information: we routinely identify benevolent or self-

Francesca Tonolo

This perspective offers a viewpoint on how the challenges of molecular scattering investigations of astrophysical interest have evolved in recent years. Computational progress has steadily expanded collisional databases and provided essential tools for modeling non-LTE astronomical regions. However, the observational leap enabled by the JWST and new observat

Omar Alsaiari, Nilufar Baghaei, Jason M. Lodge, Omid Noroozi

Feedback is one of the most powerful influences on student learning, with extensive research examining how best to implement it in educational settings. Increasingly, feedback is being generated by artificial intelligence (AI), offering scalable and adaptive responses. Two widely studied approaches are directive feedback, which gives explicit explanations an

Yutian Wen, V. Ranjan, T. Lorriaux, D. Vion

Solid-state spin ensembles addressed via superconducting circuits are promising candidates for quantum memory applications, offering multimodal storage capability and second-long coherence times at their clock transition. Implementing practical memory schemes requires dynamic control over both the resonator frequency and bandwidth. In this letter, we report

Georgios Papas

In this third part in this series we continue from \cite{papaspadicpart1}, the study of relations among values of G-functions associated to a $1$-parameter family of principally polarized abelian surfaces. In particular, we establish relations among the values of these G-functions, in both the archimedean and $p$-adic setting, at points corresponding to abel

Daniele Ceppi, Guglielmo Lockhart

The dynamics of a stack of M5 branes probing a transverse multi-centered Taub-NUT space are described by a class of 6d $\mathcal{N}=(1,0)$ superconformal field theories known as the M-string orbifold SCFTs. We determine the equivariant partition functions for this class of theories on a geometric background of type $T^2\times\mathbb{C}^2/\Gamma$, where $\Gam

Jianfeng Hou, Xizhi Liu, Yixiao Zhang

A classical theorem of Ahlswede and Katona determines the maximum density of the $2$-edge star in a graph with a given edge density. Motivated by its application in hypergraph Tur\'{a}n problems, we establish a refinement of their result under the additional assumption that the graph contains a large independent set in which every vertex has high degree.

Margaux Roulet, Hamza Kebiri, Busra Bulut, Vladyslav Zalevskyi

Low-field T2 mapping MRI can democratize neuropediatric imaging by improving accessibility and providing quantitative biomarkers of brain development. \textbf{Purpose:} To evaluate the feasibility of high-resolution T2 mapping using a single-shot fast spin-echo (SS-FSE) sequence at 0.55~T in a healthy control cohort. \textbf{Study Type:} Prospective single-c

HM Shadman Tabib, Md. Hasnaen Adil, Ayesha Rahman, Ahmmad Nur Swapnil

Access to clinical multi-channel EEG remains limited in many regions worldwide. We present NEUROSKY-EPI, the first open dataset of single-channel, consumer-grade EEG for epilepsy, collected in a South Asian clinical setting along with rich contextual metadata. To explore its utility, we introduce EmbedCluster, a patient-stratification pipeline that transfers

Zihao Chen, Yi Zhou, Xudong Jiang, Li Chen

Unpaired image-to-image translation has emerged as a crucial technique in medical imaging, enabling cross-modality synthesis, domain adaptation, and data augmentation without costly paired datasets. Yet, existing approaches often distort fine curvilinear structures, such as microvasculature, undermining both diagnostic reliability and quantitative analysis.

John Burden, Jonathan Prunty, Ben Slater, Matthieu Tehenan

Multimodal large language models (MLLMs) achieve strong performance on vision-language tasks, yet their visual processing is opaque. Most black-box evaluations measure task accuracy, but reveal little about underlying mechanisms. Drawing on cognitive psychology, we adapt classic visual search paradigms -- originally developed to study human perception -- to

Caio F. B. Macedo, João Luís Rosa, Diego Rubiera-Garcia, Alejandro Rueda

We consider the observational signatures of thin accretion disks around a reflection-asymmetric traversable thin-shell wormhole. This wormhole, built in the framework of Palatini $f(R)$ gravity coupled to a Maxwell field using a junction conditions formalism, lacks horizons but features photon spheres on each side of the throat, described by different effect

Nowfel Mashnoor, Mohammad Akyash, Hadi Kamali, Kimia Azar

Large Language Models (LLMs) have achieved remarkable success in generative tasks, including register-transfer level (RTL) hardware synthesis. However, their tendency to memorize training data poses critical risks when proprietary or security-sensitive designs are unintentionally exposed during inference. While prior work has examined memorization in natural

Aël Quélennec, Nour Hezbri, Pavlo Mozharovskyi, Van-Tam Nguyen

Memory-efficient training of deep neural networks has become increasingly important as models grow larger while deployment environments impose strict resource constraints. We propose TraDy, a novel transfer learning scheme leveraging two key insights: layer importance for updates is architecture-dependent and determinable a priori, while dynamic stochastic c

R. Sammani, E. H Saidi, R. Ahl Laamara, L. B Drissi

In this work, we investigate the AdS$_{3}$ gravitational bulk dual to an ensemble of Narain CFTs and their generalisations to establish bounds consistent with the Swampland program. Focusing on the AdS distance and finiteness conjectures, we show that the central charge of Narain CFTs forming the ensemble must be finite. Combining anomaly and unitary require

Ayush Sawarni, Jikai Jin, Justin Whitehouse, Vasilis Syrgkanis

Policy learning algorithms are widely used in areas such as personalized medicine and advertising to develop individualized treatment regimes. However, most methods force a decision even when predictions are uncertain, which is risky in high-stakes settings. We study policy learning with abstention, where a policy may defer to a safe default or an expert. Wh

Silvia García-Méndez, Francisco de Arriba-Pérez

The increasing number of spectators and players in e-sports, along with the development of optimized communication solutions and cloud computing technology, has motivated the constant growth of the online game industry. Even though Artificial Intelligence-based solutions for e-sports analytics are traditionally defined as extracting meaningful patterns from

Hasan Akgul, Mari Eplik, Javier Rojas, Aina Binti Abdullah

We present CoSense-LLM, an edge-first framework that turns continuous multimodal sensor streams (for example Wi-Fi CSI, IMU, audio, RFID, and lightweight vision) into compact, verifiable semantic tokens and coordinates with large language models under explicit latency, energy, bandwidth, and privacy constraints. CoSense-LLM has four parts: (i) SenseFusion, a

Xiang Liu, Xuming Hu, Xiaowen Chu, Eunsol Choi

Recent reasoning Large Language Models (LLMs) demonstrate remarkable problem-solving abilities but often generate long thinking traces whose utility is unclear. Our work aims to improve their efficiency, enabling them to reach high performance without overthinking. First, we analyze the entropy of token probabilities in reasoning traces. Across three models,

Alejandro Pajón-Sanmartín, Francisco De Arriba-Pérez, Silvia García-Méndez, Fátima Leal

Transformer models have significantly advanced the field of emotion recognition. However, there are still open challenges when exploring open-ended queries for Large Language Models (LLMs). Although current models offer good results, automatic emotion analysis in open texts presents significant challenges, such as contextual ambiguity, linguistic variability

Yuxing Fei, Matthew J. McDermott, Christopher L. Rom, Shilong Wang

Powder X-ray diffraction (XRD) is a foundational technique for characterizing crystalline materials. However, the reliable interpretation of XRD patterns, particularly in multiphase systems, remains a manual and expertise-demanding task. As a characterization method that only provides structural information, multiple reference phases can often be fit to a si

Matthew Keating, Michael Casey

We present a graph-based engine for computing chord tone soloing suggestions for guitar students. Chord tone soloing is a fundamental practice for improvising over a chord progression, where the instrumentalist uses only the notes contained in the current chord. This practice is a building block for all advanced jazz guitar theory but is difficult to learn a

Maryanthe Malliaris

In this brief note, we first give a counterexample to a theorem in Chernikov and Towsner, arXiv:2510.02420(1). In arXiv:2510.02420(2), the theorem has changed but as we explain the proof has a mistake. The change in the statement, due to changes in the underlying definition, affects the paper's claims. Since that theorem had been relevant to connecting the w

Manuel M. Reyna, Alexandre M. Tartakovsky

We propose a framework for parameter estimation in river transport models using breakthrough curve data, which we refer to as Dimensionless Synthetic Transport Estimation (DSTE). We utilize this framework to parameterize the one-dimensional advection-dispersion equation model, incorporating immobile phase exchange through a memory function. We solve the gove

Vojtěch Vrba, Viktor Walter, Petr Štěpán, Martin Saska

A novel approach for the fast onboard detection of isolated markers for visual relative localisation of multiple teammates in agile UAV swarms is introduced in this paper. As the detection forms a key component of real-time localisation systems, a three-fold innovation is presented, consisting of an optimised procedure for CPUs, a GPU shader program, and a f

Qi-Rui Yang, Xiao-Bin Chen, Ruo-Yu Liu, Xiang-Yu Wang

The origin of TeV-PeV neutrinos detected by IceCube remains largely unknown. The most significant individual neutrino source is the close-by Seyfert galaxy NGC 1068 at 4.2$\sigma$ level with a soft spectral index. Another notable candidate is the Seyfert galaxy NGC 7469, which has been recently proposed as a potential neutrino emitter. The likelihood fit of

Xusen Guo, Mingxing Peng, Xixuan Hao, Xingchen Zou

Web-based participatory urban sensing has emerged as a vital approach for modern urban management by leveraging mobile individuals as distributed sensors. However, existing urban sensing systems struggle with limited generalization across diverse urban scenarios and poor interpretability in decision-making. In this work, we introduce AgentSense, a hybrid, tr

Andreas Mershin, Nikolas Stefanou, Adan Rotteveel, Matthew Kung

Machine olfaction is rapidly emerging as a transformative capability, with applications spanning non-invasive medical diagnostics, industrial monitoring, agriculture, and security and defense. Recent advances in stabilizing mammalian olfactory receptors and integrating them into biophotonic and bioelectronic systems have enabled detection at near single-mole

Mathias Stout, Floris Vermeulen

We develop a framework of motivic integration in the style of Hrushovski--Kazhdan in arbitrary Hensel minimal fields of equicharacteristic zero. Hence our work generalizes that of Hrushovski--Kazhdan and Yin, but applies more broadly to discretely valued fields, almost real closed fields with analytic structure, pseudo-local fields, and coarsenings. In more

S M Rakib Ul Karim, Rownak Ara Rasul, Tunazzina Sultana

In the era of rapid technological advancement, social media platforms such as Twitter (X) have emerged as indispensable tools for gathering consumer insights, capturing diverse opinions, and understanding public attitudes. This research applies advanced machine learning methods for sentiment analysis on Twitter data, with a focus on predicting consumer trend

Hongyu Ding, Ziming Xu, Yudong Fang, You Wu

LaViRA: Zero-shot Vision-and-Language Navigation in Continuous Environments (VLN-CE) requires an agent to navigate unseen environments based on natural language instructions without any prior training. Current methods face a critical trade-off: either rely on environment-specific waypoint predictors that limit scene generalization, or underutilize the reason

Zhida Zhao, Talas Fu, Yifan Wang, Lijun Wang

Despite remarkable progress in driving world models, their potential for autonomous systems remains largely untapped: the world models are mostly learned for world simulation and decoupled from trajectory planning. While recent efforts aim to unify world modeling and planning in a single framework, the synergistic facilitation mechanism of world modeling for

Dionysios Anninos, Chiara Baracco, Vasileios A. Letsios, Guillermo A. Silva

We consider fermionic fields of higher spin on a four-dimensional de Sitter background. A particular emphasis is placed on the Rarita-Schwinger spin-$\tfrac{3}{2}$ case. Both massive fields and gauge fields are considered, and their relation to the representation theory of $SO(4,1)$ is discussed. In Lorentzian signature, we study properties of the Bunch-Davi

Yukun Zhang, Yusen Wu, Xiao Yuan

Estimating the eigenvalues of non-normal matrices is a foundational problem with far-reaching implications, from modeling non-Hermitian quantum systems to analyzing complex fluid dynamics. Yet, this task remains beyond the reach of standard quantum algorithms, which are predominantly tailored for Hermitian matrices. Here we introduce a new class of quantum a

Tara Abrishami, Marcin Briański, James Davies, Xiying Du

A graph class is $\chi$-bounded if the only way to force large chromatic number in graphs from the class is by forming a large clique. In the 1970s, Erd\H{o}s conjectured that intersection graphs of straight-line segments in the plane are $\chi$-bounded, but this was disproved by Pawlik et al. (2014), who showed another way to force large chromatic number in

Shahar Monsa, Michael Shulinder, Shmuel Sternklar, Eliran Talker

We demonstrate detuning-tunable generation of orbital-angular-momentum (OAM) light using a double Lambda four-wave-mixing (FWM) process in Doppler broadened rubidium vapor. Two near-resonant pumps on the D1 line drive non degenerate FWM that produces bright probe conjugate beams whose transverse modes evolve with pump detuning. A paraxial density-matrix mode

Eleonora Raimondo, Andrea Grimaldi, Vasyl Tyberkevych, Riccardo Tomasello

We introduce a universal theory of phase auto-oscillators driven by a bi harmonic signal (having frequency components close to single and double of the free-running oscillator frequency) with noise. With it, we show how deterministic phase locking and stochastic phase slips can be continuously tuned by varying the relative amplitudes and frequencies of the d

Gaetan Bardy, Thomas Krajewski, Thomas Muller, Adrian Tanasa

We study a sextic tensor model where the interaction terms are given by all $O(N)^3$-invariant bubbles. The class of invariants studied here is thus a larger one that the class of the $U(N)^3$-invariant sextic tensor model. We implement the large $N$ limit mechanism for this general model and we explicitly identify the dominant graphs in the $1/N$ expansion.

STAR Collaboration

The observation of collective flow phenomena in small collision systems challenges our understanding of quark-gluon plasma (QGP) formation and evolution. This complexity lies in the initial geometries, which are influenced by both nucleon configuration and subnucleonic fluctuations, introducing uncertainties in interpreting flow patterns. We disentangle thes

Daria Cherniuk, Nikita Sukhorukov, Danil Gusak, Nikita Sushko

Retrieval-augmented generation has emerged as one of the most effective approaches for code completion enhancement, especially when repository-level context is important. However, adding this extra retrieved context significantly increases sequence length, raises prefill cost, and degrades time-to-first-token (TTFT), which slows down inference -- a critical

Anand Bhardwaj, Samer Faraj

In high stakes organizational contexts like healthcare, artificial intelligence (AI) systems are increasingly being designed to augment complex coordination tasks. This paper investigates how the ethical stakes of such systems are shaped by their epistemic framings: what aspects of work they represent, and what they exclude. Drawing on an embedded study of A

Konstantin Hess, Dennis Frauen, Mihaela van der Schaar, Stefan Feuerriegel

Estimating heterogeneous treatment effects (HTEs) in time-varying settings is particularly challenging, as the probability of observing certain treatment sequences decreases exponentially with longer prediction horizons. Thus, the observed data contain little support for many plausible treatment sequences, which creates severe overlap problems. Existing meta

Sathish Krishna Anumula

In the rapidly evolving landscape of global supply chains, where digital disruptions and sustainability imperatives converge, traditional operational frameworks often struggle to adapt. This paper introduces the Design-Based Supply Chain Operations Research Model, a novel extension of the Design SCOR framework, which embeds operational research techniques to

Alain M. Dikande

A rescaled Manning potential is obtained in the analysis of scatterings of small- amplitude excitations with a kink defect. The generic model is a nonlinear Klein- Gordon Hamiltonian describing a one-dimensional chain of identical molecules, sub- jected to an hyperbolic single-particle substrate potential. To account for isotope effects that are likely to af

Yangshijie Zhang, Xinda Wang, Jialin Liu, Wenqiang Wang

With social media growth, users employ stylistic fonts and font-like emoji to express individuality, creating visually appealing text that remains human-readable. However, these fonts introduce hidden vulnerabilities in NLP models: while humans easily read stylistic text, models process these characters as distinct tokens, causing interference. We identify t

Michael Aerni, Joshua Swanson, Kristina Nikolić, Florian Tramèr

We present modal aphasia, a systematic dissociation in which current unified multimodal models accurately memorize concepts visually but fail to articulate them in writing, despite being trained on images and text simultaneously. For one, we show that leading frontier models can generate near-perfect reproductions of iconic movie artwork, but confuse crucial

Shashi Kumar, Yacouba Kaloga, John Mitros, Petr Motlicek

Low-rank adaptation (LoRA) is a widely used method for parameter-efficient finetuning. However, existing LoRA variants lack mechanisms to explicitly disambiguate task-relevant information within the learned low-rank subspace, potentially limiting downstream performance. We propose Factorized Variational Autoencoder LoRA (FVAE-LoRA), which leverages a VAE to

Chenxing Tan, Yuguan Hou, Hao Wang, Zhonghao Yuan

This paper presents a novel micro-Doppler energy-based framework for robust multi-target vital signs monitoring using 77-GHz Frequency-Modulated Continuous-Wave (FMCW) radar. Unlike conventional phase-based methods that are susceptible to environmental noise, random body movements, and stringent calibration requirements, our approach exploits the energy vari

Vaibhav Vasudevan, Thomas Schuler, Pascal Bellon, Robert Averback

This research establishes a systematic, high-throughput computational framework for designing radiation-resistant, dilute ternary copper-based alloys by addition of solutes that bind to vacancies and reduce their mobility, thus promoting interstitial-vacancy recombination. The first challenge in developing alloys by this method is mitigating the vacancy-medi