December 2025 arXiv papers — page 86

Jonas Pai, Liam Achenbach, Victoriano Montesinos, Benedek Forrai

Prevailing Vision-Language-Action Models (VLAs) for robotic manipulation are built upon vision-language backbones pretrained on large-scale, but disconnected static web data. As a result, despite improved semantic generalization, the policy must implicitly infer complex physical dynamics and temporal dependencies solely from robot trajectories. This reliance

Matin Mortaheb, Erciyes Karakaya, Sennur Ulukus

Semantic communication aims to transmit information most relevant to a task rather than raw data, offering significant gains in communication efficiency for applications such as telepresence, augmented reality, and remote sensing. Recent transformer-based approaches have used self-attention maps to identify informative regions within images, but they often s

Michael Walter, Freek Witteveen

The random purification channel maps n copies of any mixed quantum state to n copies of a random purification of the state. We generalize this construction to arbitrary symmetries: for any group G of unitaries, we construct a quantum channel that maps states contained in the algebra generated by G to random purifications obtained by twirling over G. In addit

Maria Dincă, Tim Chan, Simon C. Benjamin

Fault-tolerant quantum computers use decoders to monitor for errors and find a plausible correction. A decoder may provide a decoder confidence score (DCS) to gauge its success. We adopt a swim distance DCS, computed from the shortest path between syndrome clusters. By contracting tensor networks, we compare its performance under phenomenological noise to th

Adam Kaufman, James Lucassen, Tyler Tracy, Cody Rushing

Future AI agents might run autonomously with elevated privileges. If these agents are misaligned, they might abuse these privileges to cause serious damage. The field of AI control develops techniques that make it harder for misaligned AIs to cause such damage, while preserving their usefulness. We introduce BashArena, a setting for studying AI control techn

Vira Filatova, Andrii Zelenchuk, Dmytro Filatov

Early-stage candidate validation is a major bottleneck in hiring, because recruiters must reconcile heterogeneous inputs (resumes, screening answers, code assignments, and limited public evidence). This paper presents an AI-driven, modular multi-agent hiring assistant that integrates (i) document and video preprocessing, (ii) structured candidate profile con

Zhenwen Liang, Sidi Lu, Wenhao Yu, Kishan Panaganti

Reinforcement learning has become essential for strengthening the reasoning abilities of large language models, yet current exploration mechanisms remain fundamentally misaligned with how these models actually learn. Entropy bonuses and external semantic comparators encourage surface level variation but offer no guarantee that sampled trajectories differ in

Rohit kumar, Satyabrata Adhikari

Graph-theoretic structures play a central role in the description and analysis of quantum systems. In this work, we introduce a new class of quantum states, called $A_\alpha$-graph states, which are constructed from either unweighted or weighted graphs by taking the normalised convex combination of the degree matrix $D$ and the adjacency matrix $A_G$ of a gr

Oleg Melnikov, Yurii Dorofieiev, Yurii Shakhnovskiy, Huy Truong

This paper presents a unified framework, for the detection, classification, and preliminary localization of anomalies in water distribution networks using multivariate statistical analysis. The approach, termed SICAMS (Statistical Identification and Classification of Anomalies in Mahalanobis Space), processes heterogeneous pressure and flow sensor data throu

Sandro Tacchella, Vasily Belokurov, Harry T. J. Bevins, Roberto Maiolino

Each galaxy is observed only once along its life, making galaxy evolution fundamentally an inverse statistical problem: time-dependent physics must be inferred from ensembles of single-epoch snapshots. To move beyond descriptive scaling relations toward physical regulation mechanisms of star formation, quenching, chemical enrichment and black hole growth, ga

Victor Léger, Florent Chatelain

Partial Least Squares (PLS) is a widely used method for data integration, designed to extract latent components shared across paired high-dimensional datasets. Despite decades of practical success, a precise theoretical understanding of its behavior in high-dimensional regimes remains limited. In this paper, we study a data integration model in which two hig

Harry Lane, Guratinder Kaur, Masahiro Kawamata, Yusuke Nambu

Single crystal spinel CoFe$_2$O$_4$ exhibits the largest room-temperature saturation magnetostriction among non-rare-earth compounds and a high Curie temperature ($T_c \sim 780$ K), properties that are critical to a wide range of industrial and medical applications. Neutron spectroscopy reveals a large band splitting ($\sim$ 60 meV) between two ferrimagnetic

Boyuan Chen, Tianyuan Zhang, Haoran Geng, Caiyi Zhang

General-purpose robots require decision-making models that generalize across diverse tasks and environments. Recent works build robot foundation models by extending multimodal large language models (MLLMs) with action outputs, creating vision-language-action (VLA) systems. These efforts are motivated by the intuition that MLLMs' large-scale language and imag

Chandrasekhar Gokavarapu

Building on the Waldhausen and Quillen models of higher algebraic $K$-theory for exact categories and Waldhausen categories attached to a non-commutative $n$-ary $\Ga$-semiring $(T,\Ga)$, we establish the fundamental formal properties of $K$-theory in this $\Ga$-parametrised, slot-sensitive setting. For the exact/Waldhausen categories of finitely generated b

Binglei Lou, Gautham Duddi Krishnaswaroop, Filip Wojcicki, Ruilin Wu

Accurate and low-latency qubit state measurement is critical for trapped-ion quantum computing. While deep neural networks (DNNs) have been integrated to enhance detection fidelity, their latency performance on specific hardware platforms remains underexplored. This work benchmarks the latency of DNN-based qubit detection on field-programmable gate arrays (F

Wolfgang Arendt, Daniel Daners, Manfred Sauter

A recent result from [AtES24] allows one to define variational solutions of the Dirichlet problem for general continuous boundary data. We establish basic properties of this notion of solution and show that it coincides with the Perron solution. Variational solutions can elegantly be characterised in terms of the given boundary function when the variational

Ines Faria, Matheus Silva, Crystian Saraiva, Jose Soares

Brain metastases affect approximately between 20% and 40% of cancer patients and are commonly treated with radiotherapy or radiosurgery. Early prediction of recurrence following treatment could enable timely clinical intervention and improve patient outcomes. This study proposes an artificial intelligence based approach for predicting brain metastasis recurr

Matilda Häggblom, Minna Hirvonen, Jouko Väänänen

We develop dimension theoretic methods for propositional team based logics. Such quantitative methods were defined for team based first-order logic in a recent paper by Hella, Luosto and the third author and were used to obtain strong hierarchy results in the first-order logic context. We show that in propositional logic and in several important cases, a tea

Nilesh P. Rijhwani, Titus J. Brinker, Peter Neher, Marco Nolden

Accurate disease diagnosis depends on effective collaboration between medical specialties, yet departments often use distinct data systems and proprietary formats. This heterogeneity hinders joint analysis and integration of complementary diagnostic information. The use of separate viewers for each modality further restricts cross-specialty collaboration. Al

Jorge I. Poveda, Andrew R. Teel

This article aims to provide an accessible, tutorial-style introduction to hybrid extremum-seeking systems, which are model-free, feedback-optimization controllers that incorporate hybrid dynamics, meaning both continuous-time and discrete-time behaviors. Such systems arise when advanced control and optimization tools are needed to overcome the limitations o

Ariful Islam Khandaker, Abdullah Al Shafi, Mohiuddin Ahmad

Automatic skin lesion classification from dermoscopy images is important for the early diagnosis of skin diseases such as melanoma. Class imbalance in skin lesion datasets, notably the defects in the representation of malignant(cancerous) cases, is one of the difficulties for deep learning models' performances and generalizations. This paper offers an exhaus

Xiaojie Tao, Rajit Gadh

The increasing penetration of inverter-based renewable generation has significantly reduced system inertia, making modern power grids more vulnerable to rapid frequency deviations following disturbances. While a wide range of flexible resources-including electric vehicles (EVs), data centers, and battery energy storage systems (BESS)-have demonstrated the ph

Manuel M. Müller, Björn Bornkamp, Frank Bretz, Timothy I. Cannings

Subgroup selection in clinical trials is essential for identifying patient groups that react differently to a treatment, thereby enabling personalised medicine. In particular, subgroup selection can identify patient groups that respond particularly well to a treatment or that encounter adverse events more often. However, this is a post-selection inference pr

Mengyuan Xiao, Longji Bing, Guilaine Lagache, Miroslava Dessauges-Zavadsky

The first few billion years of cosmic history witnessed the rapid emergence of the most massive galaxies, yet their true space density, baryon assembly pathways, and early quenching mechanisms remain poorly constrained. Current surveys lack the wide-field, rest-frame FIR sensitivity needed to obtain a complete census of massive systems and to trace their col

Georg Siedel, Rojan Regmi, Abhirami Anand, Weijia Shao

This paper proposes a training data augmentation pipeline that combines synthetic image data with neural style transfer in order to address the vulnerability of deep vision models to common corruptions. We show that although applying style transfer on synthetic images degrades their quality with respect to the common Frechet Inception Distance (FID) metric,

Luigi Caputi, Francesco Vaccarino

In this paper, we study the Hochschild cohomology of diagrams of algebras introduced by Gerstenhaber and Schack and provide computations for filtrations of incidence algebras. Our aims are threefold: firstly, we revisit and explore the connection between the Gerstenhaber-Schack complexes and the Baues-Wirsching cohomology of categories. Secondly, we analyse

Adam Karvonen, James Chua, Clément Dumas, Kit Fraser-Taliente

Large language model (LLM) activations are notoriously difficult to understand, with most existing techniques using complex, specialized methods for interpreting them. Recent work has proposed a simpler approach known as LatentQA: training LLMs to directly accept LLM activations as inputs and answer arbitrary questions about them in natural language. However

Remco van der Hofstad

Percolation is a model for random damage to a network. It is one of the simplest models that displays a phase transition: when the network is severely damaged, it falls apart in many small connected components, while if the damage is light, connectivity is hardly affected. We study the location and nature of the phase transition on random graphs. In particul

Ishan Kalburge, Máté Lengyel

The neural basis of probabilistic computations remains elusive, even amidst growing evidence that humans and other animals track their uncertainty. Recent work has proposed that probabilistic representations arise naturally in task-optimized neural networks trained without explicitly probabilistic inductive biases. However, prior work has lacked clear criter

M. L. L. Dantas, R. Smiljanic, R. S. de Souza, P. B. Tissera

As stars traverse the Milky Way, their orbits evolve through perturbations that alter their orbital radii. These changes arise from two mechanisms: churning, which modifies angular momentum, and blurring, which induces eccentric orbits without major angular momentum change. To assess whether churning or blurring dominates the dynamical evolution of Gaia-ESO

Daniel Nichols, Prajwal Singhania, Charles Jekel, Abhinav Bhatele

Language models (LMs) are becoming increasingly dependent on external tools. LM-based agentic frameworks frequently interact with their environment via such tools to search files, run code, call APIs, etc. Further, modern reasoning-based LMs use tools such as web search and Python code execution to enhance their reasoning capabilities. While tools greatly im

Pablo Otálora, Sigurd Skogestad, José Luis Guzmán, Manuel Berenguel

The industrial production of microalgae is an important and sustainable process, but its actual competitiveness is closely related to its optimization. The biological nature of the process hinders this task, mainly due to the high nonlinearity of the process along with its changing nature, features that make its modeling, control and optimization remarkably

S. Huyan, T. Qian, L. Wang, W. Bi

We report a comprehensive study of pressure-induced evolution of the magnetism and development of superconductivity (SC) in MnBi8Te13, a promising ambient pressure, ferromagnetic (FM) topological insulator candidate. By employing high-pressure electrical transport, magnetoresistance, DC magnetic susceptibility, and X-ray diffraction measurements, we construc

Gongjun Choi, Tony Gherghetta

We study the QCD axion arising from the 5th component of a bulk $U(1)$ gauge field in a five-dimensional warped grand unified theory, and determine the viable range of the axion decay constant $f_a$. Unlike flat extra dimensions, where gauge couplings run quickly above the Kaluza--Klein (KK) scale, the logarithmic running in warped geometries permits substan

Omar-Farouk Adesida, David Quigley, Livia B. Partay

We explore the use of the nested sampling technique to sample the configuration space of non-spherical hard particles. We employ the technique on the hard dumbbell system consisting of two hard spheres connected by a rigid bond, and investigate the phase stability across a wide pressure range and for bond lengths from completely overlapping to tangential har

Peter Humphries

The Wasserstein distance quantifies the distance between two probability measures on a metric space. We prove an analogue of the Berry-Esseen inequality for the Wasserstein distance on a finite area hyperbolic surface. This inequality controls the Wasserstein distance via an average of Weyl sums, which are integrals of Maass cusp forms and Eisenstein series

Chase Walker, Rickard Ewetz

Large language models (LLMs) exhibit remarkable capabilities, yet their reasoning remains opaque, raising safety and trust concerns. Attribution methods, which assign credit to input features, have proven effective for explaining the decision making of computer vision models. From these, context attributions have emerged as a promising approach for explainin

Jiaqi Xu, Cuiling Lan, Xuejin Chen, Yan Lu

Human beings solve complex problems through critical thinking, where reasoning and evaluation are intertwined to converge toward correct solutions. However, most existing large language models (LLMs) treat the reasoning and verification as separate processes: they either generate reasoning without explicit self-checking or rely on external verifiers to detec

Sergi Masot-Llima, Elies Gil-Fuster, Carlos Bravo-Prieto, Jens Eisert

Demonstrating quantum advantage in machine learning tasks requires navigating a complex landscape of proposed models and algorithms. To bring clarity to this search, we introduce a framework that connects the structure of parametrized quantum circuits to the mathematical nature of the functions they can actually learn. Within this framework, we show how fund

H. W. Edler, M. Hoeft, S. Bhagat, A. Basu

The environment plays an important role in shaping the evolution of cluster galaxies through mechanisms such as ram pressure stripping (RPS), whose effect may be enhanced in merging clusters. We investigate a complex of three galaxies UGC 6697, CGCG 097-073, and CGCG 097-079, that are currently undergoing extreme RPS, as evident from their multi-wavelength-d

Xiaodi Li, Dingcheng Li, Rujun Gao, Mahmoud Zamani

Continual learning remains a fundamental challenge in machine learning, requiring models to learn from a stream of tasks without forgetting previously acquired knowledge. A major obstacle in this setting is catastrophic forgetting, where performance on earlier tasks degrades as new tasks are learned. In this paper, we introduce PPSEBM, a novel framework that

Chayan Jain, Rishant Sharma, Archit Garg, Ishan Bhanuka

Generating long, cohesive video stories with consistent characters is a significant challenge for current text-to-video AI. We introduce a method that approaches video generation in a filmmaker-like manner. Instead of creating a video in one step, our proposed pipeline first uses a large language model to generate a detailed production script. This script gu

Tianze Luo, Haotian Yuan, Zhuang Liu

The multi-step denoising process in diffusion and Flow Matching models causes major efficiency issues, which motivates research on few-step generation. We present Solution Flow Models (SoFlow), a framework for one-step generation from scratch. By analyzing the relationship between the velocity function and the solution function of the velocity ordinary diffe

Pavel Sekatski, Jef Pauwels

Entanglement and Bell nonlocality are known to be inequivalent: there exist entangled states that admit a local hidden-variable model for all local measurements. Here we show that this gap disappears in a minimal broadcast extension of the Bell scenario. Assuming only the validity of quantum theory, we prove that for every entangled state $\rho_{AB}$ there e

The Simons Observatory Collaboration, I. Abril-Cabezas, S. Adachi, P. Ade

We present updated forecasts for the scientific performance of the degree-scale (0.5 deg FWHM at 93 GHz), deep-field survey to be conducted by the Simons Observatory (SO). By 2027, the SO Small Aperture Telescope (SAT) complement will be doubled from three to six telescopes, including a doubling of the detector count in the 93 GHz and 145 GHz channels to 48,

Gayatri Ghosh

We investigate flavoured hybrid leptogenesis in a minimal renormalizable $\mathrm{SO}(10)$ framework in which both Type-I and Type-II seesaw interactions contribute to the generation of the cosmological baryon asymmetry. We consider a regime where heavy Majorana neutrinos and electroweak scalar triplets are independently quasi-degenerate, leading to resonant

Tamanna Hossain, Robert L. Logan, Ganesh Jagadeesan, Sameer Singh

State space models (SSMs) are a promising alternative to transformers for language modeling because they use fixed memory during inference. However, this fixed memory usage requires some information loss in the hidden state when processing long sequences. While prior work has studied the sequence length at which this information loss occurs, it does not char

Stefano Facchini, Jacqueline Hodge, Jes Jørgensen, Eva Schinnerer

Over the last 15 years, the Atacama Large Millimeter/submillimeter Array (ALMA) has revolutionized astrophysics by providing unprecedented resolution and sensitivity in observing the cold universe, including the formation of stars, planets, and galaxies. With groundbreaking discoveries ranging from the first detailed images of protoplanetary disks to the kin

John B. Etnyre

In this note, we show that transverse knots have unique standard neighborhoods and prove a structure theorem about non-loose Legendrian knots. We also prove a finiteness result for transverse knots in a tight contact manifold. The common theme of these two results is a general destabilization result for Legendrian knots. As a byproduct of this work, we find

Stephen Tivenan, Indranil Sahoo, Yanjun Qian

Spatial boundaries, such as ecological transitions or climatic regime interfaces, capture steep environmental gradients, and shifts in their structure can signal emerging environmental changes. Quantifying uncertainty in spatial boundary locations and formally testing for temporal shifts remains challenging, especially when boundaries are derived from noisy,

Hongbo Zhao, Meng Wang, Fei Zhu, Wenzhuo Liu

The computational and memory overheads associated with expanding the context window of LLMs severely limit their scalability. A noteworthy solution is vision-text compression (VTC), exemplified by frameworks like DeepSeek-OCR and Glyph, which convert long texts into dense 2D visual representations, thereby achieving token compression ratios of 3x-20x. Howeve

Ratang Sedimo, Ivoline C. Ngong, Jami Lashua, Joseph P. Near

We present the Distributed High-Dimensional Matrix Mechanism (Distributed HDMM), a protocol for answering workloads of linear queries on distributed data that provides the accuracy of central-model HDMM without a trusted curator. Distributed HDMM leverages a secure aggregation protocol to evaluate HDMM on distributed data, and is secure in the context of a m

F. M. Vincentelli, P. Casella, A. Veledina, A. Ambrifi

Accretion onto compact objects is one of the most fundamental phenomena in the astrophysics, powering some of the most luminous objects in the sky. Along with this, accretion has also a key impact on the evolution of the Universe, through the launch of powerful outflows that affect the surrounding medium. In the last years sub-second optical-infrared observa

Sahibpreet Singh, Lalita Devi

This paper examines the admissibility of AI-generated forensic evidence in criminal trials. The growing adoption of AI presents promising results for investigative efficiency. Despite advancements, significant research gaps persist in practically understanding the legal limits of AI evidence in judicial processes. Existing literature lacks focused assessment

Wissam Karroucha, Carlos Oliver, Veronique Stoven, Vincent Mallet

Targeting RNA with small molecules offers significant therapeutic potential. Machine learning could substantially accelerate preclinical drug discovery, from hit identification to lead optimization. Yet a fundamental limitation emerges: drug design machine learning models, tailored for proteins, are not readily applicable to RNAs because of fundamental diffe

Jairo Fúquene-Patiño

In this work, inspired by machine learning techniques, we propose a new Bayesian model for Small Area Estimation (SAE), the Fay-Herriot model with Spectral Clustering (FH-SC). Unlike traditional approaches, clustering in FH-SC is based on spectral clustering algorithms that utilize external covariates, rather than geographical or administrative criteria. A m

Benjamin Desef

This thesis focuses on the intersection of mathematical and computational optimization and quantum information. Main contributions are open-source software code: A hybrid approach mixing "traditional" nonconvex and convex methods can make difficult problems more accessible. A demonstration of how to efficiently implement such an algorithm, avoiding interfaci

Kimberly Matsuda, Yanlai Chen, Yingda Cheng, Fengyan Li

The radiative transfer equation (RTE) is a fundamental mathematical model to describe physical phenomena involving the propagation of radiation and its interactions with the host medium. Deterministic methods can produce accurate solutions without any statistical noise, yet often at a price of expensive computational costs originating from the intrinsic high

Luke Majury, Marie Dominique, Ryan Milligan, Dana-Camelia Talpeanu

Large solar flares (GOES M-class or higher) are usually associated with eruptions of material. However, when considering flare irradiance enhancements and dynamics such as chromospheric evaporation, potential contributions from erupted material have historically been neglected. We analyse nine eruptive M- and X-class flares from 2024 to early 2025, quantifyi

Marc Steinberger, Maxime Siber, Hans-Joachim Egelhaaf, Mingjian Wu

Making solution-cast organic solar cells industrially available generally comes at the cost of significant performance losses compared to device prototypes manufactured under laboratory conditions. Adjusting solvent evaporation kinetics is postulated to recover efficiency. Yet, a comprehensive characterization of their effect, independently of other property

Laura Sánchez-Menguiano, Dimitri A. Gadotti, Almudena Zurita, Estrella Florido

In this study we perform a comparative analysis of the properties of the HII regions located in different areas of barred galaxies, with the aim of investigating the impact of bars on the physical properties of the ionised gas. Based on integral field spectroscopy data for 17 barred galaxies covering approximately the central 6x6 kpc, we detect a total of 22

Gergely Hajdu, Johanna Jurcsik, Márcio Catelan, Grzegorz Pietrzyński

Context. A number of RR Lyrae stars show variable mean magnitudes in the OGLE survey light curves of the Galactic bulge. Hitherto this phenomenon was not studied, as it was generally assumed to be related to problems with the photometry. Aims. We investigate whether the mean magnitude variability of RR Lyrae variables is due to genuine astrophysical phenomen

Yuanhang Li, Yiren Song, Junzhe Bai, Xinran Liang

We propose \textbf{IC-Effect}, an instruction-guided, DiT-based framework for few-shot video VFX editing that synthesizes complex effects (\eg flames, particles and cartoon characters) while strictly preserving spatial and temporal consistency. Video VFX editing is highly challenging because injected effects must blend seamlessly with the background, the bac

Darshita Rathore, Vineet Kumar, Chetna Bansal, Anindya Moitra

Large language models are increasingly adapted to downstream tasks through fine-tuning. Full supervised fine-tuning (SFT) and parameter-efficient fine-tuning (PEFT) methods, such as Low-Rank Adaptation (LoRA), are two dominant approaches. While PEFT methods are widely used for their computational efficiency, the implications of their configurations (e.g., ra

P. P. Avelino, A. R. Gomes, D. A. Tamayo

Bulk viscosity, which characterizes the irreversible dissipative resistance of a fluid to volume changes, has been proposed as a potential mechanism for explaining both early- and late-time accelerated expansion of the Universe. In this work, we investigate two distinct physical scenarios for the origin of bulk viscosity: (1) nonminimal interactions between

Dibyendu Adak, Rujeko Chinomona, Duc P. Truong, Oleg Korobkin

In this work, we develop a space--time Chebyshev spectral collocation method for three-dimensional Maxwell's equations and combine it with tensor-network techniques in Tensor-Train (TT) format. Under constant material parameters, the Maxwell system is reduced to a vector wave equation for the electric field, which we discretize globally in space and time usi

Linnea Evanson, Mingfang Zhang, Hubert Banville, Saarang Panchavati

Decoding speech from brain activity has typically relied on limited neural recordings collected during short and highly controlled experiments. Here, we introduce a framework to leverage week-long intracranial and audio recordings from patients undergoing clinical monitoring, effectively increasing the training dataset size by over two orders of magnitude. W

Sarah Kaißer, Markus Kleffmann, Kristina Schaaff

Gamification is widely used in digital learning. However, most systems neglect age-related differences. This paper investigates how gamification can be designed in an age-aware way to address learners' diverse motivational and cognitive needs. Based on a targeted literature review, we present a mapping of age groups, mechanics, and effects. Furthermore, we d

Maryna Kachanovska, Adrian Savchuk

In the frequency domain wave scattering problems, obstacles can be effectively replaced by point scatterers as soon as the wavelength of the incident wave exceeds significantly their diameter. The situation is less clear in the time domain, where recent works suggest the presence of an additional temporal scale that quantifies the smallness of the obstacle.

Benjamin C. Koenig, Sili Deng

Thermal runaway in lithium-ion batteries is strongly influenced by the state of charge (SOC). Existing predictive models typically infer scalar kinetic parameters at a full SOC or a few discrete SOC levels, preventing them from capturing the continuous SOC dependence that governs exothermic behavior during abuse conditions. To address this, we apply the Kolm

Giovanni Amendola, Pietro Cofone, Marco Manna, Aldo Ricioppo

Recognizing similarities among entities is central to both human cognition and computational intelligence. Within this broader landscape, Entity Set Expansion is one prominent task aimed at taking an initial set of (tuples of) entities and identifying additional ones that share relevant semantic properties with the former -- potentially repeating the process

Jorge Baeza-Ballesteros, Daniel G. Figueroa, Adrien Florio, Joanes Lizarraga

We present a discussion on lattice techniques for the simulation of non-canonical field theory circumstances, complementing our previous monograph (arXiv:2006.15122) on canonical cases. We begin by reviewing basic aspects of lattice field theory, including symplectic and non-symplectic evolution algorithms. We then introduce lattice implementations of non-ca

Chang-Fan Mo, Matthieu J. Mercier, Jacques Magnaudet, Jie Zhang

The wake of a body moving across the isopycnals of a strongly stratified fluid is characterized by the presence of an intense jet which, under certain circumstances, may become unstable. To get insight into the phenomenology of this instability and the underlying mechanisms, we conduct fully-resolved three-dimensional time-dependent simulations of the flow p

Akash Yadav, Ruda Zhang

Reliable forward uncertainty quantification in engineering requires methods that account for aleatory and epistemic uncertainties. In many applications, epistemic effects arising from uncertain parameters and model form dominate prediction error and strongly influence engineering decisions. Because distinguishing and representing each source separately is of

Giovanna Bruno, Rosario Roberto Riso, Henrik Koch, Enrico Ronca

Cavity quantum electrodynamics provides a powerful tool to manipulate material properties, yet it remains a matter of debate whether and how quantized fields affect the periodicity of crystals. Here, we extend Bloch's theorem to crystals under strong light-matter coupling, revealing that polariton quasiparticles preserve lattice periodicity. We introduce a g

Rahmat K. Adesunkanmi, Adel Alaeddini, Mahesh Krishnamurthy

Accurate estimation of a battery's state of charge and state of health is essential for safe and reliable battery management. Existing approaches often decouple these two states, lack stability guarantees, and exhibit limited generalization across operating conditions. This study introduces a unified operator-theoretic framework for aging-aware state of char

Yu Zheng, Jie Hu, Kailun Yang, Jiaming Zhang

Autonomous driving requires a persistent understanding of 3D scenes that is robust to temporal disturbances and accounts for potential future actions. We introduce a new concept of 4D Occupancy Spatio-Temporal Persistence (OccSTeP), which aims to address two tasks: (1) reactive forecasting: ''what will happen next'' and (2) proactive forecasting: "what would

Morgan Coe, Gruffudd Jones, Leah-Nani Alconcel, Marina Gashinova

With the rapidly growing population of resident space objects (RSOs) in the near-Earth space environment, detailed information about their condition and capabilities is needed to provide Space Domain Awareness (SDA). Space-based sensing will enable inspection of RSOs at shorter ranges, independent of atmospheric effects, and from all aspects. The use of a su

Kester Clegg, Richard Hawkins, Ibrahim Habli, Tom Lawton

LLMs (Large Language Models) are increasingly used in text processing pipelines to intelligently respond to a variety of inputs and generation tasks. This raises the possibility of replacing human roles that bottleneck existing information flows, either due to insufficient staff or process complexity. However, LLMs make mistakes and some processing roles are

Fabio Guglietta, Diego Taglienti, Mauro Sbragaglia

We propose an extension of the phenomenological Maffettone-Minale (MM) model (P.L. Maffettone and M. Minale, J. Non-Newton. Fluid Mech. 78, 227-241 (1998)) to describe the time-dependent deformation of a droplet with interfacial viscosity in a shear flow. The droplet, characterised by surface tension $\sigma$, is spherical at rest with radius $R$ and deforms

Haley N. Scolati, Ryan A. Loomis, Anthony J. Remijan, Kin Long Kelvin Lee

High-dimensional astronomical data cubes provide a wealth of spectral and structural information that can be used to study astrophysical and chemical processes. The complexity and sheer size of these datasets pose significant challenges in their efficient analysis, visualization, and interpretation. In specific astronomical use cases, a number of dimensional

Xinshun Feng, Mingzhe Liu, Yi Qiao, Tongyu Zhu

Recent advances in explainable recommendations have explored the integration of language models to analyze natural language rationales for user-item interactions. Despite their potential, existing methods often rely on ID-based representations that obscure semantic meaning and impose structural constraints on language models, thereby limiting their applicabi

Ivan Di Liberti, Nicholas Meadows

We construct classifying $\infty$-topoi by showing that the $(\infty,2)$-category of topoi has weighted limits. We show that several prestacks of interest have a classifying topos, including the prestack of spectra.

Leandro Fiorini Aurichi, Guilherme Eduardo Pinto

We study the existence and cardinality of universal families for classes of rayless graphs. It is known, by a result of Diestel, Halin, and Vogler, that the class of countable rayless graphs does not admit a countable universal family, leaving open the precise complexity of this class. We prove that for every infinite cardinal $\kappa$, the class of rayless

K. E. L. de Farias, M. A. Anacleto, Rafael A. Batista, Iver Brevik

We investigate the thermal Casimir effect for a massless scalar field in the curved spacetime of a neutron star within the Thermo Field Dynamics (TFD) formalism. Starting from the renormalized energy-momentum tensor, we generalize the Stefan-Boltzmann law to include gravitational redshift and curvature corrections governed by the Tolman-Oppenheimer-Volkoff (

Wilson Fung, Lu Guo, Drake Hilliard, Alessandro Casadei

Accurate forecasting of package volumes at delivery stations is critical for last-mile logistics, where errors lead to inefficient resource allocation, higher costs, and delivery delays. We propose OpComm, a forecasting and decision-support framework that combines supervised learning with reinforcement learning-based buffer control and a generative AI-driven

Antonio Galván, Nissim Fraija, Edilberto Aguilar-Ruiz, Hermes León Vargas

This work explores whether hadronic processes could be responsible for the high-energy emission seen in quasars identified by the Large Area Telescope (LAT) instrument aboard the Fermi satellite. In contrast to purely leptonic models, this work investigates whether hadronic mechanisms can explain the observed gamma-ray spectra by analyzing the spectral energ

Mariana Lins Costa

The prevailing technical literature in AI Safety interprets scheming and sandbagging behaviors in large language models (LLMs) as indicators of deceptive agency or hidden objectives. This transdisciplinary philosophical essay proposes an alternative reading: such phenomena express not agentic intention, but structural fidelity to incoherent linguistic fields

Johannes Hägerlind, Bao-Long Tran, Urs Waldmann, Per-Erik Forssén

Estimating camera intrinsics and extrinsics is a fundamental problem in computer vision, and while advances in structure-from-motion (SfM) have improved accuracy and robustness, open challenges remain. In this paper, we introduce a robust method for pose estimation and calibration. We consider a set of rigid cameras, each observing the scene from a different

Harsh Sharma, Himadri Shekhar Dhar

We demonstrate a quadratic enhancement of power in a battery consisting of $N$ two-level systems or spins interacting with two photonic cavity modes, where one of the modes is in the dispersive regime. In contrast to Dicke batteries, the power enhancement arises from a $N^2$ scaling of both quantum correlations and speed of evolution, thus highlighting genui

Carlos Couto, José Mourão, Mário A. T. Figueiredo, Pedro Ribeiro

Near an optimal learning point of a neural network, the learning performance of gradient descent dynamics is dictated by the Hessian matrix of the loss function with respect to the network parameters. We characterize the Hessian eigenspectrum for some classes of teacher-student problems, when the teacher and student networks have matching weights, showing th

FNU Vikas, Paul Gratz, Daniel Jiménez

Conditional branch prediction predicts the likely direction of a conditional branch instruction to support ILP extraction. Branch prediction is a pattern recognition problem that learns mappings between a context to the branch outcome. An accurate predictor reduces the number of instructions executed on the wrong path resulting in an improvement of performan

Shengming Yin, Zekai Zhang, Zecheng Tang, Kaiyuan Gao

Recent visual generative models often struggle with consistency during image editing due to the entangled nature of raster images, where all visual content is fused into a single canvas. In contrast, professional design tools employ layered representations, allowing isolated edits while preserving consistency. Motivated by this, we propose \textbf{Qwen-Image

James Clarke, Hyunjae Lee, Kyla Wong, Julia Glenn

Actin and myosin drive many instances of force generation, deformation, and shape change in cells, tissues, and organisms. In particular, cytoskeletal actomyosin is remarkable in its adaptive architecture, responding to a host of actin-binding proteins. Equally important, however, is actomyosin's interaction with its mechanical environment. Actomyosin contra

Hongbin Na, Zimu Wang, Zhaoming Chen, Peilin Zhou

Psychological defenses are strategies, often automatic, that people use to manage distress. Rigid or overuse of defenses is negatively linked to mental health and shapes what speakers disclose and how they accept or resist help. However, defenses are complex and difficult to reliably measure, particularly in clinical dialogues. We introduce PsyDefConv, a dia

Alexandre Dussolle, Pietro Liò

Going from pure Multilayer Perceptron (MLP) to a learnable graph message-passing mechanism at each layer has been foundational to state-of-the-art results, despite the computational trade-off (e.g. GATs or Transformers). To go a step further, in this work, we introduce N-simplicial attention, going from pairwise token similarity to higher-order interactions,

Tobias Kirschstein, Simon Giebenhain, Matthias Nießner

We introduce FlexAvatar, a method for creating high-quality and complete 3D head avatars from a single image. A core challenge lies in the limited availability of multi-view data and the tendency of monocular training to yield incomplete 3D head reconstructions. We identify the root cause of this issue as the entanglement between driving signal and target vi

R. J. Charity, G. H. Sargsyan, K. D. Launey, T. B. Webb

A triplet of kindred prompt-2p emitters in A=8 nuclei has been demonstrated. Two of these are the ground state of 8C and its isobaric analog state in 8B, both of which are analogs of the halo or thick-skinned nucleus 8He. The third member is the recently found fourth 1+ state in 8B. This new 8B state at E*=8.4 MeV was observed to decay to the ground state of

Giacomo Picardi, Saverio Iacoponi, Matias Carandell, Jorge Aguirregomezcorta

Underwater robotics is becoming increasingly important for marine science, environmental monitoring, and subsea industrial operations, yet the development of underwater manipulation and actuation systems remains restricted by high costs, proprietary designs, and limited access to modular, research-oriented hardware. While open-source initiatives have democra

Shuibai Zhang, Fred Zhangzhi Peng, Yiheng Zhang, Jin Pan

While Diffusion Language Models (DLMs) are theoretically well-suited for iterative refinement due to their non-causal structure, they often fail to reliably revise incorrect tokens in practice. The key challenge lies in the model's inability to distinguish between correct and erroneous tokens in a visible sequence. Standard masked diffusion language model (M