December 2025 arXiv papers — page 74

Sophia N. Fricke, Haiyan Mao, Manas Sajjan, Ashok Ajoy

The out-of-time-order correlator (OTOC) has emerged as a central tool for quantifying decoherence across wide-ranging physical platforms. Here we demonstrate its direct measurement in a classical ensemble using nuclear magnetic resonance (NMR) with a modulated gradient spin echo (MGSE) sequence and extend the method into a multidimensional correlation to tra

Maria Daghofer, Krzysztof Wohlfeld, Jeroen van den Brink

While a spin-dependent band splitting is one of the characteristic features of altermagnets, the conventional band picture itself breaks down in the many altermagnets that are correlated Mott materials. We employ two numerical many-body methods, the self-consistent Born approximation and variational cluster approach, to explore this strongly correlated regim

Petru Milev, Blanca Mellado-Pinto, Muralidhar Nalabothula, Ali Esquembre Kucukalic

We analyze the performance of two strategies in solving the structured eigenvalue problem deriving from the Bethe-Salpeter equation (BSE) in condensed matter physics. The BSE matrix is constructed with the Yambo code, and the two strategies are implemented by interfacing Yambo with the ScaLAPACK and ELPA libraries for direct diagonalization, and with the SLE

Boyang Xia, David Moxey

High-order discontinuous Galerkin spectral element methods (DGSEM) have received growing attention and development, especially in the regime of computational fluid dynamics in recent years. The inherent flexibility of the discontinuous Galerkin approach in handling non-conforming interfaces, such as those encountered in moving geometries or hp-refinement, pr

David Rodriguez, William Seymour, Jose M. Del Alamo, Jose Such

User-configured chatbots built on top of large language models are increasingly available through centralized marketplaces such as OpenAI's GPT Store. While these platforms enforce usage policies intended to prevent harmful or inappropriate behavior, the scale and opacity of customized chatbots make systematic policy enforcement challenging. As a result,

Zaira Manigrasso, Matteo Dunnhofer, Antonino Furnari, Moritz Nottebaum

Episodic memory retrieval enables wearable cameras to recall objects or events previously observed in video. However, existing formulations assume an "offline" setting with full video access at query time, limiting their applicability in real-world scenarios with power and storage-constrained wearable devices. Towards more application-ready episodic

Daisy Q. Wang, Zeb Krix, Olga A. Tkachenko, Vitaly A. Tkachenko

The electronic properties of solids are determined by the crystal structure and interactions between electrons, giving rise to a variety of collective phenomena including superconductivity, strange metals and correlated insulators. The mechanisms underpinning many of these collective phenomena remain unknown, driving interest in creating artificial crystals

Da-Young Lee, Dong-Yeop Na

Light interacting with plant leaves undergoes reflection, transmission, scattering, and absorption, which together determine leaf optical properties. Changes in leaf architecture disrupt internal light scattering dynamics and consequently affect photosynthetic performance. Previous studies on internal leaf light scattering have primarily relied on ray-tracin

Aaron Defazio, Konstantin Mishchenko, Parameswaran Raman, Hao-Jun Michael Shi

We propose Generalized Primal Averaging (GPA), an extension of Nesterov's method that unifies and generalizes recent averaging-based optimizers like single-worker DiLoCo and Schedule-Free, within a non-distributed setting. While DiLoCo relies on a memory-intensive two-loop structure to periodically aggregate pseudo-gradients using Nesterov momentum, GPA elim

Akhil Shajan, Danil Kaliakin, Fangchun Liang, Thaddeus Pellegrini

This work presents the implementation of a fragment-based, quantum-centric supercomputing workflow for computing molecular electronic structure using quantum hardware. The workflow is applied to predict the relative energies of two conformers of the 300-atom Trp-cage miniprotein. The methodology employs wave function-based embedding (EWF) as the underlying f

Seong Ho Pahng, Guoye Guan, Benjamin Fefferman, Sahand Hormoz

Biological systems can form complex three-dimensional structures through the collective behavior of agents that share a common update rule and operate without central control. How such distributed control gives rise to precise global patterns remains a central question not only in developmental biology but also in distributed robotics, programmable matter, a

Muhammad Ibtsaam Qadir, Duane Schonlau, Ulrike Dydak, Fiona R. Kolbinger

This study quantitatively evaluates the impact of MRI scanner magnetic field strength on the performance and generalizability of deep learning-based segmentation algorithms. Three publicly available MRI datasets (breast tumor, pancreas, and cervical spine) were stratified by scanner field strength (1.5T vs. 3.0T). For each segmentation task, three nnU-Net-ba

Lin Sok, Martianus Frederic Ezerman, Ling San

Interest in the hulls of linear codes has been growing rapidly. More is known when the inner product is Euclidean than Hermitian. A shift to the latter is gaining traction. The focus is on a code whose Hermitian hull dimension and dual distance can be systematically determined. Such a code can serve as an ingredient in designing the parameters of entanglemen

Benjamin S. H. Lyo, Eero P. Simoncelli, Cristina Savin

We present the Score-based Autoencoder for Multiscale Inference (SAMI), a method for unsupervised representation learning that combines the theoretical frameworks of diffusion models and VAEs. By unifying their respective evidence lower bounds, SAMI formulates a principled objective that learns representations through score-based guidance of the underlying d

Xiaoxiao Zhou, Zihan Wang, Jingbo Shang, Yang E. Li

DNA language models have advanced genomics, but their downstream performance varies widely due to differences in tokenization, pretraining data, and architecture. We argue that a major bottleneck lies in tokenizing sparse and unevenly distributed DNA sequence motifs, which are critical for accurate and interpretable models. To investigate, we systematically

Talha Akyildiz, Hessam Mahdavifar

Ambient backscatter communication (AmBC) enables battery-free connectivity by letting passive tags modulate existing RF signals, but reliable detection of multiple tags is challenging due to strong direct link interference, very weak backscatter signals, and an exponentially large joint state space. Classical multi-hypothesis likelihood ratio tests (LRTs) ar

Alexandra Langford-Avelar, Delphine Bosson-Rieutort

The rapid expansion of health data has led to unprecedented information availability within healthcare systems. Health information systems (HIS) play a central role in managing this data and enabling improvements in care delivery, system performance, and population health monitoring. Maximizing the value of HIS, however, requires effective information exchan

Richard Dawid, Guilherme Franzmann

Dualities in physics have challenged traditional forms of scientific realism by undermining the idea that theories describe a unique underlying ontology. In this paper, we develop a new perspective on scientific realism that responds to this challenge. We argue that while realist commitment remains appropriate at the level of a theory's full formal structure

Giuliana Caramella Hofheins, Aleksandra B. Biedron, Elaine M Petro

The operational lifetime of ionic liquid electrospray propulsion systems is limited by plume-extractor electrode interactions. Over time, propellant accumulation, surface erosion, and electrical shorts degrade the extractor and therefore restrict the total impulse throughput. Characterizing the secondary species generated by plume impacts with deposited ioni

Jasmine Vu, Shivanand Sheshappanavar

Large vision-language models like CLIP are increasingly used in medical imaging tasks due to their ability to align images and text without the need for extensive labeled data. This makes them particularly useful for applications like image retrieval, report generation, and classification in clinical settings. A potential issue to this approach is that CLIP-

Kingsley Yeon, Mihai Anitescu

Low-rank plus diagonal (LRPD) decompositions provide a powerful structural model for large covariance matrices, simultaneously capturing global shared factors and localized corrections that arise in covariance estimation, factor analysis, and large-scale kernel learning. We introduce an alternating low-rank then diagonal (Alt) algorithm that provably reduces

Laura Pardo, Juan Sosa, Juan Pablo Torres-Clavijo, Andrés Felipe Arévalo-Arévalo

This article develops a Bayesian hierarchical framework to analyze academic performance in the 2022 second semester Saber 11 examination in Colombia. Our approach combines multilevel regression with municipal and departmental spatial random effects, and it incorporates Ridge and Lasso regularization priors to compare the contribution of sociodemographic cova

Saheb Mohapatra

We prove that there is no extension of the abelian groups appearing in the Bloch-Suslin complex to strongly $\mathbb{A}^{1}-$invariant sheaves on $Sm_{k}$ (char($k$)=0) that also extend the canonical symbol maps from the respective $\mathbb{G}_{m}^{\wedge n}$ (i.e., from $\mathbb{G}_{m}$ and $\mathbb{G}_{m}^{\wedge2}$).

Halfdan Nordahl Fundal, Johannes Eide Rambøll, Karsten Olsen

Human-AI interactions are increasingly part of everyday life, yet the interpersonal dynamics that unfold during such exchanges remain underexplored. This study investigates how emotional alignment, semantic exploration, and linguistic innovation emerge within a collaborative storytelling paradigm that paired human participants with a large language model (LL

Haya Diwan, Lisa Hellerstein, Nicole Megow, Jens Schlöter

Research in explorable uncertainty addresses combinatorial optimization problems where there is partial information about the values of numeric input parameters, and exact values of these parameters can be determined by performing costly queries. The goal is to design an adaptive query strategy that minimizes the query cost incurred in computing an optimal s

Amir Targholizadeh, Grigoriy Y. Nikulin, Pankaj K. Jha

Over the past decade, multi-element superconducting nanowire single-photon detectors (SNSPDs) have emerged as the leading single-photon detection technology due to their exceptional system detection efficiency (SDE), ultrahigh timing precision, negligible dark counts, etc. However, achieving these performances with a \textit{single-element} SNSPD has been an

Jofre Costa, Eric Luu, David R. Wood, Jung Hon Yip

Hadwiger's Conjecture states that every graph with chromatic number $k$ contains a complete graph on $k$ vertices as a minor. This conjecture is a tremendous strengthening of the Four-Colour Theorem and is regarded as one of the most important open problems in graph theory. The case of Hadwiger's Conjecture for graphs with $\alpha(G) = 2$ has garnered much a

Alan R. Vazquez, Kilian M. Rother, Marco V. Charles-Gonzalez

Two-level fractional factorial designs permit the study multiple factors using a limited number of runs. Traditionally, these designs are obtained from catalogs available in standard textbooks or statistical software. However, modern Large Language Models (LLMs) can now produce two-level fractional factorial designs, but the quality of these designs has not

Ju Zhuoxuan, Doroslovacki Milos

In communication networks, channel estimation and user localization are challenging problems in harsh environments or signal-blocked areas. This paper introduces a novel approach to minimize the Mean Squared Error (MSE) in channel estimation between mobile users and rectangular Reconfigurable Intelligent Surfaces (RIS) within wireless communication systems.

Anjali Sarawgi, Esteban Garces Arias, Christof Zotter

This paper presents the first end-to-end pipeline for Handwritten Text Recognition (HTR) for Old Nepali, a historically significant but low-resource language. We adopt a line-level transcription approach and systematically explore encoder-decoder architectures and data-centric techniques to improve recognition accuracy. Our best model achieves a Character Er

Allen W. Herman, Bobby Miraftab

We study when the adjacency matrix of a Cayley graph factors as the product of two adjacency matrices of Cayley graphs. Let $G$ be a finite group and let $U\subseteq G\setminus \{e\}$ be symmetric. Writing $A(G;U)$ for the adjacency matrix of the Cayley graph of $G$ with respect to $U$, we prove that for symmetric subsets $S,T,U$ of $G\setminus \{e\}$, $A(G;

Alireza Moayedikia, Alicia Troncoso

Training large neural networks and merging task-specific models both exploit low-rank structure and require parameter importance estimation, yet these challenges have been pursued in isolation. Current workflows compute curvature information during training, discard it, then recompute similar information for merging--wasting computation and discarding valuab

Alif Munim, Jun Ma, Omar Ibrahim, Alhusain Abdalla

Large language models (LLMs) have rapidly advanced in clinical decision-making, yet the deployment of proprietary systems is hindered by privacy concerns and reliance on cloud-based infrastructure. Open-source alternatives allow local inference but often have large model sizes that limit their use in resource-constrained clinical settings. Here, we benchmark

Kunjal Panchal, Saayan Mitra, Somdeb Sarkhel, Haoliang Wang

Recent advances in video-language models have enabled powerful applications like video retrieval, captioning, and assembly. However, executing such multi-stage pipelines efficiently on mobile devices remains challenging due to redundant model loads and fragmented execution. We introduce Atom, an on-device system that restructures video-language pipelines for

Bri Holt

Structured generation for LLM tool use highlights the value of compact DSL intermediate representations (IRs) that can be emitted directly and parsed deterministically. This paper introduces axial grammar: linear token sequences that recover multi-dimensional structure from the placement of rank-specific separator tokens. A single left-to-right pass assigns

Zenan Yang, Yuanliang Li, Jingwei Zhang, Yongjie Liu

Accurate fault diagnosis and quantification are essential for the reliable operation and intelligent maintenance of photovoltaic (PV) arrays. However, existing fault quantification methods often suffer from limited efficiency and interpretability. To address these challenges, this paper proposes a novel fault quantification approach for PV strings based on a

Eugenio Simao

Motivation: Biological Petri Nets (Bio-PNs) model biochemical pathways where multiple reactions simultaneously affect shared metabolites through convergent production or regulatory coupling. However, classical Petri net independence theory requires transitions to share no places -- a constraint that fails to capture biological reality. This mismatch prevents

Brian N. Bailey

This work presents a new framework for procedural generation of dynamic 3D plant model geometries, which has been implemented in the Helios modeling system. Key goals of this work were to develop a model that 1) has a generalized set of parameters that are conserved across species, which are botanically-consistent and readily measurable; 2) significantly red

Kleuton A. L. Lima, José A. dos S. Laranjeira, Bill D. A. Huacarpuma, Nicolas F. Martins

We introduce 3D-DPhyne, a novel three-dimensional (3D) carbon allotrope derived from the dodecaphenylyne framework, and investigate its structural, electronic, optical, and mechanical properties using first-principles calculations. The proposed structure forms a tetragonal, topologically complex network of four-, six-, and twelve-membered carbon rings with m

Julie Clutterbuck, Frieder Jäckel, Xuan Hien Nguyen

We show that for every $n \geq 2$ and $D > 0$ there exist a convex domain $\Omega \subseteq \mathbb H^n$ with diameter $D$ and a convex potential $V$ on $\Omega$ such that the fundamental gap of the operator $-\Delta+V$ is strictly smaller than the fundamental gap of $-\Delta$. In comparison to previous work, this result requires more refined control of the

Jiongxiao Wang, Qiaojing Yan, Yawei Wang, Yijun Tian

Large Language Model (LLM)-based agents have demonstrated remarkable capabilities in complex reasoning and multi-turn interactions but struggle to continuously improve and adapt when deployed in new environments. One promising approach is implementing skill libraries that allow agents to learn, validate, and apply new skills. However, current skill library a

Matthias Diener, Matthew J. Smith, Michael T. Campbell, Kaushik Kulkarni

MIRGE is a computational approach for scientific computing based on NumPy-like array computation, but using lazy evaluation to recast computation as data-flow graphs, where nodes represent immutable, multi-dimensional arrays. Evaluation of an array expression is deferred until its value is needed, at which point a pipeline is invoked that transforms high-lev

Justin Li, Efe Sencan, Jasper Zheng Duan, Vitus J. Leung

Machine learning models, particularly deep neural networks, have demonstrated strong performance in classifying complex time series data. However, their black-box nature limits trust and adoption, especially in high-stakes domains such as healthcare. To address this challenge, we introduce UniCoMTE, a model-agnostic framework for generating counterfactual ex

Michiel P. Dubbelman, Heng Wu, Joost Aretz, Yaojia Wang

Josephson junctions (JJs), devices consisting of two superconductors separated by a barrier, are of great technological importance, being a cornerstone of quantum information processing. Classical understanding of superconductor-insulator-superconductor JJs is that conventional insulator's properties, other than magnetism, do not significantly influence the

Bjorna Qesaraku, Jan Steckel

Maritime autonomous systems require robust predictive capabilities to anticipate vessel motion and environmental dynamics. While transformer architectures have revolutionized AIS-based trajectory prediction and demonstrated feasibility for sonar frame forecasting, their application to maritime radar frame prediction remains unexplored, creating a critical ga

Tamara Happé, Jasper Wijnands, Paolo Scussolini, Peter Pfleiderer

Due to climate change, heatwaves are becoming more frequent and intense, with western Europe experiencing the strongest trends in the Northern Hemisphere mid-latitudes. Part of the temperature trends are caused by circulation changes, which are not accurately captured in climate models. Here we deploy Deep Learning techniques to classify European heatwaves b

Bruno Duchesne, Christopher-Lloyd Simon

For $n\in \mathbb{N}$, consider a hyperbolic $n$-dimensional simplex $\Delta$, defined by $1+n$ points in the compactified hyperbolic space $\mathbf{H}^n \sqcup \partial \mathbf{H}^n$. For each integer $m\le n$, denote $\delta^n_m(\Delta)\in [0,+\infty]$ the Hausdorff distance between its skeleta of dimensions $n$ and $m$. In particular, $\delta^n_{n-1}(\Del

Liam McAllister, Andreas Schachner

These lectures provide a self-contained introduction to flux compactifications of type IIB string theory on Calabi-Yau orientifolds. The first lecture begins with geometric foundations, then presents vacuum solutions in Calabi-Yau compactifications, as well as the geometry and physics of the moduli problem. The second lecture develops the classical theory of

Junying Wang, Yuanlu Xu, Edith Tretschk, Ziyan Wang

The creation of photorealistic dynamic hair remains a major challenge in digital human modeling because of the complex motions, occlusions, and light scattering. Existing methods often resort to static capture and physics-based models that do not scale as they require manual parameter fine-tuning to handle the diversity of hairstyles and motions, and heavy c

Timo Pierre Schrader, Lukas Lange, Tobias Kaminski, Simon Razniewski

The rise of large language models (LLMs) has sparked interest in coding assistants. While general-purpose programming languages are well supported, generating code for domain-specific languages remains a challenging problem for LLMs. In this paper, we focus on the LLM-based generation of code for Answer Set Programming (ASP), a particularly effective approac

Salar Hashemitaheri, Ian Harris

Online support groups for smoking cessation are economical and accessible, yet they often face challenges with low user engagement and stigma. The use of an automatic conversational agent would improve engagement by ensuring that all user comments receive a timely response.). We address the challenge of insufficient high-quality data by employing a two-level

Toshiaki Hori, Jonathan DeCastro, Deepak Gopinath, Avinash Balachandran

We propose a new approach for solving planning problems with a hierarchical structure, fusing reinforcement learning and MPC planning. Our formulation tightly and elegantly couples the two planning paradigms. It leverages reinforcement learning actions to inform the MPPI sampler, and adaptively aggregates MPPI samples to inform the value estimation. The resu

Gabriel Gomez, Jose F. Rodriguez

Gravitational wave oscillations arise from the exchange of energy between the metric perturbations and additional tensor modes. This phenomenon can occur even when the extra degrees of freedom consist of a triplet of massive Abelian vector fields, as in Multi-Proca dark energy models. In this work, we study gravitational wave oscillations in this class of mo

Cole Wyeth, Marcus Hutter

We generalize the AIXI reinforcement learning agent to admit a wider class of utility functions. Assigning a utility to each possible interaction history forces us to confront the ambiguity that some hypotheses in the agent's belief distribution only predict a finite prefix of the history, which is sometimes interpreted as implying a chance of death equal to

Ernő Buzás, Attila Szilva

Independent voters play an increasingly decisive role in contemporary elections, yet their collective behavior remains poorly understood. This paper investigates how a minority of voters with greater flexibility in their political preferences influences opinion formation in polarized electorates. Using a modified Deffuant model, we show that even simple hete

Hamed Ajorlou, Gonzalo Mateos, Luana Ruiz

Assessing homophily in large-scale networks is central to understanding structural regularities in graphs, and thus inform the choice of models (such as graph neural networks) adopted to learn from network data. Evaluation of smoothness metrics requires access to the entire network topology and node features, which may be impractical in several large-scale,

Michael H. Coen

Dialogue topic segmentation supports summarization, retrieval, memory management, and conversational continuity. Despite decades of work, evaluation practice remains dominated by strict boundary matching and F1-based metrics. Modern large language model (LLM) based conversational systems increasingly rely on segmentation to manage conversation history beyond

Jonathan Junné, Raphael Winter, Havva Yoldaş

We consider the spatially homogeneous Landau equation for multiple species with different masses. As in the single-species case, the singularity of the collision operator is determined by a parameter $\gamma \in [-3,1]$, where $\gamma = -3$ corresponds to Coulomb interactions. We prove that if $\gamma\geq -\sqrt{8}$ in the cross-interaction operators, then t

Ashley Colley, Kuisma Hurtig, Juri Etto, Emma Kirjavainen

Plastics recycling depends on everyday sorting practices and on how recycling services are communicated and experienced. Virtual reality (VR) can present these practices and services in situated, interactive form, yet its role in service design for plastics recycling is still emerging. This paper examines how VR tools can contribute to designing plastics rec

Zachary J. Wegert, Ben Wilks, Ngamta Thamwattana, Vivien J. Challis

Gaining insight into the interaction between flexible piezoelectric structures and ocean waves can inform the development of compact, high-efficiency wave-energy converters that harvest renewable energy from the marine environment. In this paper, the problem of wave energy absorption by floating and submerged piezoelectric plates is investigated. The equatio

Panagiota Gatoula, George Dimas, Dimitris K. Iakovidis

Synthetic medical image data can unlock the potential of deep learning (DL)-based clinical decision support (CDS) systems through the creation of large scale, privacy-preserving, training sets. Despite the significant progress in this field, there is still a largely unanswered research question: "How can we quantitatively assess the similarity of a synthetic

Vatsal Baherwani, Yixuan Ren, Abhinav Shrivastava

Text-to-video diffusion models synthesize temporal motion and spatial appearance through iterative denoising, yet how motion is encoded across timesteps remains poorly understood. Practitioners often exploit the empirical heuristic that early timesteps mainly shape motion and layout while later ones refine appearance, but this behavior has not been systemati

Saraswathy Amjith, Mihika Dusad, Neha Muramalla, Shweta Shah

Chain-of-thought (CoT) prompting has become central to mathematical reasoning in large language models, yet models remain brittle to early errors: a single arithmetic slip or unjustified inference typically propagates uncorrected to an incorrect final answer. We investigate whether training on intentionally flawed reasoning traces can teach models to detect

Rohini Uma-Vaideswaran, P. K. Yeung

A fundamental relation in Lagrangian Kolmogorov theory is concerned with inertial range scaling of the second-order velocity structure function over intermediate time lags at sufficiently high Reynolds numbers. Significant theoretical support for asymptotic constancy of the scaling constant ($C_0$) is known, but limitations in the range of time scales access

Jiakun Fan, Yanglin Zhang, Xiangchen Li, Dimitrios S. Nikolopoulos

Diffusion Large Language Models (dLLMs) have emerged as a promising alternative to Autoregressive Models (ARMs), utilizing parallel decoding to overcome sequential bottlenecks. However, existing research focuses primarily on kernel-level optimizations, lacking a holistic serving framework that addresses the unique memory dynamics of diffusion processes in pr

Louis Gass, Domenico Marinucci, Giovanni Peccati, Francesca Pistolato

A vast literature over the past fifteen years has been devoted to the study of the geometric properties of Gaussian random waves. In this work, we investigate the geometric behavior of \emph{uniform random waves}, a much less studied non-Gaussian model in which the $L^2$ norm is constrained to be exactly equal to one in every realization (a normalization tha

Pranav Shetty, Mirazul Haque, Petr Babkin, Zhiqiang Ma

Training data detection is critical for enforcing copyright and data licensing, as Large Language Models (LLM) are trained on massive text corpora scraped from the internet. We present SPECTRA, a watermarking approach that makes training data reliably detectable even when it comprises less than 0.001% of the training corpus. SPECTRA works by paraphrasing tex

Tao Ji, Javier Serna, Gregory J. Herczeg, Shinsuke Takasao

The proliferation of high time-resolution and decades-long monitoring of classical T Tauri stars provides a vast opportunity to test the variability of the star-disk connections. However, most monitoring surveys use single broad-band filters, which makes the conversion of photometric variability into accretion rate difficult. In this study, we analyze accret

Zhenyu Liu, Yunzhen Liu, Zehao Fan, Garrett Gagnon

Mixture-of-Experts (MoE) models scale capacity via sparse activation but stress memory and bandwidth. Offloading alleviates GPU memory by fetching experts on demand, yet token-level routing causes irregular transfers that make inference I/O-bound. Static uniform quantization reduces traffic but degrades accuracy under aggressive compression by ignoring exper

Thiago Ferreira, Jhon Yana Galarza, Henrique Reggiani, Kiersten M. Boley

Planet formation in the Galactic thick disc is expected to be inefficient---low solid reservoirs, short disc lifetimes, and harsh irradiation environments should conspire to inhibit the assembly of planetary bodies---yet, planets are there, and they are stranger than we expected. Here, we present a homogeneous characterisation of 32 exoplanetary systems orbi

Gábor Kun, László Márton Tóth

In 2021, motivated by graph limit theory Lov\'asz extended most of the theory of flows to a measure theoretic setting. Using this framework, the first author constructed $d$-regular treeings that are measurably bipartite, and have no nonzero measurable circulations, that is, flows without sources or sinks. In particular, these treeings do not admit a measura

Ziao Yang, Chidiebere I. Nwaogbo

Kagome lattice bilayers offer unique opportunities for engineering electronic properties through interlayer stacking and strain. We report a comprehensive first-principles study of Pd$_3$O$_2$Cl$_2$ kagome bilayers, examining four stacking configurations (AA, AA$'$, AB, AB$'$). Our calculations reveal dramatic stacking-dependent band gap modulation from 0.08

Primoz Moravec

We study discrete torsion for the $n$--torus with finite symmetry group $G$ from the Dijkgraaf--Witten viewpoint. A class in $H^n(G,U(1))$ assigns a phase to each flat $G$--bundle, equivalently to each commuting $n$--tuple in $G$ up to conjugation. We introduce the subgroup $\Br^n(G)\subseteq H^n(G,U(1))$ of \emph{untwisted} classes, those whose Dijkgraaf--W

Ohoud Alzahrani, Russell Beale, Bob Hendley

Social bots are now deeply embedded in online platforms for promotion, persuasion, and manipulation. Most bot-detection systems still treat behavioural features as static, implicitly assuming bots behave stationarily over time. We test that assumption for promotional Twitter bots, analysing change in both individual behavioural signals and the relationships

Suhaib Abdurahman, Farzan Karimi-Malekabadi, Chenxiao Yu, Nour S. Kteily

Human conflict is often attributed to threats against material conditions and symbolic values, yet it remains unclear how they interact and which dominates. Progress is limited by weak causal control, ethical constraints, and scarce temporal data. We address these barriers using simulations of large language model (LLM)-driven agents in virtual societies, in

Dhruvesh Patel, Durga Prasad Maram, Sai Sreenivas Chintha, Benjamin Rozonoyer

In recent years, there has been a resurgence of interest in non-autoregressive text generation in the context of general language modeling. Unlike the well-established autoregressive language modeling paradigm, which has a plethora of standard training and inference libraries, implementations of non-autoregressive language modeling have largely been bespoke

Aqsa Shaikh, Omar Madany, Benjamin Kincaid, Lubos Mitas

We employ correlation-consistent effective core potentials (ccECPs) to perform exact or nearly exact correlation and total energy calculations for the fifth-row elements (Rb-Xe). Total energies are calculated using various correlated methods: configuration interaction (CI), coupled-cluster (CC) up to perturbative quadruple excitations whenever feasible, and

Muhayy Ud Din, Jan Rosell, Waseem Akram, Irfan Hussain

Simulation is essential for developing robotic manipulation systems, particularly for task and motion planning (TAMP), where symbolic reasoning interfaces with geometric, kinematic, and physics-based execution. Recent advances in Large Language Models (LLMs) enable robots to generate symbolic plans from natural language, yet executing these plans in simulati

Madjid Eshaghi Gordji, Mohamadali Berahman

Trust serves as a fundamental pillar of human interactions, playing a crucial role in economic, social, and political relationships. While traditional models of trust primarily focus on the decision making of the first player, this paper introduces an innovative approach that shifts the emphasis to the decision making processes of the second player. The prop

Monika Zamojska, Jarosław A. Chudziak

LLM-powered agents are now used in many areas, from customer support to education, and there is increasing interest in their ability to act more like humans. This includes fields such as social, political, and psychological research, where the goal is to model group dynamics and social behavior. However, current LLM agents often lack the psychological depth

Lyes Smaili, Soulaimane Berkane

This paper develops a smooth safety-filtering framework for nonlinear control-affine systems under limited perception. Classical Control Barrier Function (CBF) filters assume global availability of the safety function - its value and gradient must be known everywhere - an assumption incompatible with sensing-limited settings, and the resulting filters often

Pierre-Henri Chaudouard

Let $p\geq 1$. The symmetric space $S=GL(2p+1)/GL(p+1)\times GL(p)$ (over a number field) is not cuspidal in the sense that its automorphic spectrum does not contain any cuspidal representation of $GL(2p+1)$. In this article, we compute the spectral decomposition of its relatively cuspidal part: this is, by definition, the part of the spectrum that is induce

Brian Nelson, Behrouz Farhang-Boroujeny

Recently, filter bank multi-carrier spread spectrum (FBMC-SS) technology has been proposed for use in ultra-wideband (UWB) communication systems. It has been noted that, due to the spectral partitioning properties of the filter banks, a UWB signal can be synthesized and processed using a parallel set of signal processors operating at a moderate rate. This tr

Rajiv Thummala, Gregory Falco

Spacecraft increasingly rely on heterogeneous computing resources spanning onboard flight computers, orbital data centers, ground station edge nodes, and terrestrial cloud infrastructure. Selecting where a workload should execute is a nontrivial multi objective problem driven by latency, reliability, power, communication constraints, cost, and regulatory fea

Khushboo Thaker, Yony Bresler

Deploying accurate Text-to-SQL systems at the enterprise level faces a difficult trilemma involving cost, security and performance. Current solutions force enterprises to choose between expensive, proprietary Large Language Models (LLMs) and low-performing Small Language Models (SLMs). Efforts to improve SLMs often rely on distilling reasoning from large LLM

Bhrij Patel, Davide Belli, Amir Jalalirad, Maximilian Arnold

Function calling agents powered by Large Language Models (LLMs) select external tools to automate complex tasks. On-device agents typically use a retrieval module to select relevant tools, improving performance and reducing context length. However, existing retrieval methods rely on static and limited inputs, failing to capture multi-step tool dependencies a

John K. Nino

We report the discovery of a positive correlation between supermassive black hole mass and dark matter halo concentration at fixed halo mass in cosmological hydrodynamical simulations. Analyzing central galaxies in TNG100 (N = 18,954), EAGLE (N = 1,522), and CAMELS-TNG (N = 6,664), we find partial correlation coefficients of r = +0.24, +0.34, and +0.66 respe

Haoye Lu, Yaoliang Yu, Darren Lo

In many real-world scenarios, obtaining fully observed samples is prohibitively expensive or even infeasible, while partial and noisy observations are comparatively easy to collect. In this work, we study distribution restoration with abundant noisy samples, assuming the corruption process is available as a black-box generator. We show that this task can be

O. G. Turutanov

The Nobel Prize in Physics 2025 has been awarded to John Clarke, John Martinis, and Michel Devoret for "the discovery of macroscopic quantum mechanical tunnelling and energy quantisation in an electric circuit". The paper explains the essence of their studies and shows in a historical context the importance of earlier research in superconductivity and quantu

Jannis Blauth, Christian Nöbel, Rico Zenklusen

One of the most elementary spreading models on graphs can be described by a fire spreading from a burning vertex in discrete time steps. At each step, all neighbors of burning vertices catch fire. A well-studied extension to model fire containment is to allow for fireproofing a number $B$ of non-burning vertices at each step. Interestingly, basic computation

Muhammad Zeeshan Karamat, Sadman Saif, Christiana Chamon Garcia

Large Language Models (LLMs) deployed in practical and safety-critical settings are increasingly susceptible to bit-flip faults caused by hardware degradation, cosmic radiation, or deliberate fault-injection attacks such as Rowhammer. These faults silently corrupt internal parameters and can lead to unpredictable or dangerous model behavior. Localizing these

Jack Y. Araz, Michael Spannowsky

Machine-learning techniques are essential in modern collider research, yet their probabilistic outputs often lack calibrated uncertainty estimates and finite-sample guarantees, limiting their direct use in statistical inference and decision-making. Conformal prediction (CP) provides a simple, distribution-free framework for calibrating arbitrary predictive m

Russell Zimmerman, Shashank Kumar, Shiva Kant Tiwari, Md Ehsanuzzaman

Squeezed light is a key resource in quantum metrology and quantum information science. It is primarily generated through nonlinear optical interactions, where the degree of squeezing is set by the nonlinearity of the medium. Here, we modulate the third-order nonlinear response of a dielectric with strong ultrafast laser fields to control squeezed light gener

Alejandro Ranchal-Pedrosa, Benjamin Marsh, Lefteris Kokoris-Kogias, Alberto Sonnino

Modern blockchains increasingly adopt multi-proposer (MCP) consensus to remove single-leader bottlenecks and improve censorship resistance. However, MCP alone does not resolve how users should disseminate transactions to proposers. Today, users either naively replicate full transactions to many proposers, sacrificing goodput and exposing payloads to MEV, or

Eric Chesebro

This paper develops a computational framework for studying the hyperbolic geometry of 2-bridge link complements and Kleinian groups generated by two parabolic elements. The framework is built on Sakuma-Weeks triangulations and introduces a family of Farey recursive polynomials. For a rational number which determines a hyperbolic 2-bridge link, this paper pro

Yinxu Tang, Chengsong Huang, Jiaxin Huang, William Yeoh

Knowledge Graph Question Answering (KGQA) has largely focused on entity-centric queries that return a single answer entity. However, many real-world questions are inherently relational, aiming to understand how entities are associated rather than which entity satisfies a query. In this work, we introduce relation-centric KGQA, a complementary setting in whic

P. Saracco, P. Conconi, C. Arcidiacono, H. Mahmoodzadeh

A deep understanding of the life-cycle of galaxies, particularly those of high mass, requires clarifying the mechanisms that regulate star formation (SF) and its abrupt shutdown (quenching), often capable of stopping SF rates of hundreds of solar masses per year. What initially triggers quenching, and what sustains the quiescent state thereafter, especially

Ali Eslami, Jiangbo Yu

Agentic AI is increasingly being explored and introduced in both manually driven and autonomous vehicles, leading to the notion of Agentic Vehicles (AgVs), with capabilities such as memory-based personalization, goal interpretation, strategic reasoning, and tool-mediated assistance. While frameworks such as the OWASP Agentic AI Security Risks highlight vulne

Nicholas A. Pape

Yahtzee is a classic dice game with a stochastic, combinatorial structure and delayed rewards, making it an interesting mid-scale RL benchmark. While an optimal policy for solitaire Yahtzee can be computed using dynamic programming methods, multiplayer is intractable, motivating approximation methods. We formulate Yahtzee as a Markov Decision Process (MDP),

Min-Jung Kim, Jeongho Kim, Hoiyeong Jin, Junha Hyung

Recent progress in video diffusion models has spurred growing interest in camera-controlled novel-view video generation for dynamic scenes, aiming to provide creators with cinematic camera control capabilities in post-production. A key challenge in camera-controlled video generation is ensuring fidelity to the specified camera pose, while maintaining view co