December 2025 arXiv papers — page 66

Tin Stribor Sohn, Maximilian Dillitzer, Jason J. Corso, Eric Sax

Vision-language navigation requires agents to reason and act under constraints of embodiment. While vision-language models (VLMs) demonstrate strong generalization, current benchmarks provide limited understanding of how embodiment -- i.e., the choice of physical platform, sensor configuration, and modality alignment -- influences perception, reasoning, and

Samidh Chakrabarti, David Willner, Kevin Klyman, Tiffany Saade

This paper details the methodology behind CoPE, a policy-steerable small language model capable of fast and accurate content labeling. We present a novel training curricula called Contradictory Example Training that enables the model to learn policy interpretation rather than mere policy memorization. We also present a novel method for generating content pol

Yonatan Betancur-Ocampo, Guillermo Monsivais, Vít Jakubský

We review recent advances in Klein and anti-Klein tunneling in one- and two-dimensional materials. Using a general tight-binding framework applied to multiple periodic systems, we establish the criteria for the emergence of Klein tunneling based on the conservation of an effective reduced pseudospin. The inclusion of higher-order terms in the wave vector lea

Benjamin D. Kim, Daniel Alabi, Lav R. Varshney

We explore connections between secret sharing and secret key agreement, which yield a simple and scalable multiterminal key agreement protocol. In our construction, we use error-correcting codes, specifically Reed-Solomon codes with threshold reconstruction, to ensure no information is leaked to an eavesdropper. We then derive novel bounds for both full-rank

Alberto Purpura, Emily Chen, Swapnil Shinde

Reasoning Large Language Models (LLMs) have shown promising results when tasked with solving complex problems. In this paper, we propose and evaluate a multi-stage workflow that leverages the capabilities of fine-tuned reasoning LLMs to assist in the review process of marketing content, making sure they comply with a given list of requirements. The contribut

Mithila Mangedarage, Keith Pedersen, Zack Sullivan

Recent progress in the study of QCD phenomena with energy correlators motivates novel approaches to explore the information contained in the QCD radiation spectrum. Fox-Wolfram moments are a set of observables that characterize the angular distribution of energy flow in high-energy collisions. Contrary to their conventional application, they are a class of c

Félix Cabello Sánchez, Jesús M. F. Castillo, Alberto Salguero-Alarcón, Nazaret Trejo-Arroyo

We compute the derived functors of (the functors associated to) the ideal of compact operators in Banach spaces and obtain new results about the extension and lifting of compact operators.

Shad Durussel, Gergely Molnár, Jean-François Molinari

We investigate dynamic crack propagation and fragmentation with the phase-field fracture approach. The method was chosen for its ability to yield crack paths that are independent of the underlying mesh, thanks to the damage regularization zone. In dynamics, we observe a progressive widening of this regularization zone and attribute it to an unphysical trappi

Christian Carrick, Bertrand Guillou, Sarah Petersen

We compute the $RO(C_2)$-graded real Brown--Peterson homology of the representation-loop space $\Omega^\rho S^{\rho + 1}$, where $\rho$ is the regular representation of the cyclic group of order two. This calculation gives a $C_2$-equivariant analogue of the classical computation of Brown--Peterson homology of the double loop space $\Omega^2 S^3$ due to Rave

Moussa Labbadi, Denis Efimov, Leonid Fridman

In this paper, the feasibility of recently developed higher order delayed sliding mode controllers is addressed. With this aim the robustness against the measurement noise and mismatched perturbations for the systems governed by such controllers is established using ISS implicit Lyapunov-Razumikhin function approach. To illustrate proposed results, a simulat

Gregorio Paci, Sergey N. Solodukhin

We study the conformal field theory defined by the fourth-order operator on four-dimensional manifolds with boundaries, reformulating it through an auxiliary field so that the dynamics become second order. Within this framework, we compute the heat kernel of $\Box^2$ in flat space exactly, together with the associated Seeley-DeWitt coefficients for a broad c

Sascha Kurz

In the bounded confidence model the opinions of a set of agents evolve over discrete time steps. In each round an agent averages the opinion of all agents whose opinions are at most a certain threshold apart. Here we assume that the opinions of the agents are elements of the real line. The details of the dynamics are determined by the initial opinions of the

Joyful E. Mdhluli

This paper serves as a practical guide for individuals and organisations seeking to design, implement, and evaluate astronomy-for-development initiatives, as well as those preparing proposals for the International Astronomical Union's Office of Astronomy for Development (IAU OAD) annual Call for Proposals. The paper aims to outline how systematic evidence co

Shubham Kumar Nigam, Tanuj Tyagi, Siddharth Shukla, Aditya Kumar Guru

This paper presents an early exploration of reinforcement learning methodologies for legal AI in the Indian context. We introduce Reinforcement Learning-based Legal Reasoning (ReGal), a framework that integrates Multi-Task Instruction Tuning with Reinforcement Learning from AI Feedback (RLAIF) using Proximal Policy Optimization (PPO). Our approach is evaluat

Shirsa Maitra, Tathagata Banerjee, Anushka De, Diganta Mukherjee

This study aims to provide a data-driven approach for empirically tuning and validating rating systems, focusing on the Elo system. Well-known rating frameworks, such as Elo, Glicko, TrueSkill systems, rely on parameters that are usually chosen based on probabilistic assumptions or conventions, and do not utilize game-specific data. To address this issue, we

Alison L. Coil, David S. N. Rupke, Serena Perrotta, Saloni Agrawal

Odd Radio Circles (ORCs) are a new class of extragalactic object, with large rings of faint radio continuum emission typically spanning 100s of kpc; their origins are unknown. Previous optical spectroscopy of the central galaxy in ORC4, a classic isolated ORC, revealed spatially-extended ionized gas with strong [OII] emission and line ratios consistent with

A. Damonte, I. Pillitteri, A. Maggio, A. García Muñoz

Stellar soft X-ray ([1, 100] Angstrom) and Extreme Ultraviolet (also EUV, [100, 920] Angstrom; jointly, XUV) radiation affects the evolution and chemistry of exoplanet atmospheres. It is however uncertain to what extent the radiation's short-term variability contributes to these effects. We are interested in what this variability might imply for planets arou

Carlos Blanco, Benjamin Lillard, Jack D. Shergold

Scintillating molecular crystals have emerged as prime candidates for directional dark matter detector targets. This anisotropy makes them exquisitely sensitive due to the daily modulation induced by the directional dark matter wind. However, predicting the interaction rate for arbitrary molecules requires accurate modeling of the many-body ground as well as

Nikos Spyropoulos, Marinos Manolesos, George Papadakis

This paper presents a comprehensive numerical investigation of a NACA0012 undergoing Stall Flutter Limit Cycle Oscillations (LCO) across distinct fluid dynamics regimes. It accurately models Small Amplitude Oscillations (SAO) in the transitional Reynolds regime and Large Amplitude Oscillations (LAO) in the moderate regime, observed in different experimental

José Rodal

We present the first fully explicit, continuous, analytically derived warp-drive spacetime within General Relativity whose shift-vector flow is kinematically irrotational. Building on Santiago \emph{et al.} that scalar-potential, zero-vorticity warp fields are Hawking-Ellis Type I for unit lapse and flat spatial slices, we supply a closed-form scalar potenti

Yu Fang, Kanchana Ranasinghe, Le Xue, Honglu Zhou

Vision-Language-Action (VLA) models have achieved remarkable progress in robotic manipulation by mapping multimodal observations and instructions directly to actions. However, they typically mimic expert trajectories without predictive motion reasoning, which limits their ability to reason about what actions to take. To address this limitation, we propose jo

Alexey S. Koshelev, Oleg Melichev, Leslaw Rachwal

We consider the most general covariant gravity action up to terms that are quadratic in curvature. These can be endowed with generic form factors, which are functions of the d'Alembert operator. If they are chosen in a specific way as an exponent of an entire function, the theory becomes ghost-free and renormalizable at the price of non-locality. Furthermore

Alexander J. Grutter, Nurul Fitriyah, Brian B. Maranville, Saurav Prakash

High-temperature and high-magnetic-field-induced re-entrant superconductivity has been discovered in the infinite-layer nickelate $\mathrm{Sm_{1-x-y} Eu_x Ca_y Ni O_2}$ (SECNO). Infinite-layer nickelates are the closest known analogues of high-$\mathrm{T}_c$ cuprate superconductors, yet they host distinct magnetic ground states. Using low-energy muon spin re

Shubham Kumar Nigam, Parjanya Aditya Shukla, Noel Shallum, Arnab Bhattacharya

Handwritten text recognition (HTR) and machine translation continue to pose significant challenges, particularly for low-resource languages like Marathi, which lack large digitized corpora and exhibit high variability in handwriting styles. The conventional approach to address this involves a two-stage pipeline: an OCR system extracts text from handwritten i

Soumava Paul, Prakhar Kaushik, Ankit Vaidya, Anand Bhattad

We address semantic 3D part segmentation: decomposing objects into parts with meaningful names. While datasets exist with part annotations, their definitions are inconsistent across datasets, limiting robust training. Previous methods produce unlabeled decompositions or retrieve single parts without complete shape annotations. We propose ALIGN-Parts, which f

Philip Goyal

For a century, quantum theory has posed a fundamental challenge to philosophical thinking. On its face, it repudiates many of the key features of the mechanical conception of physical reality. However, the challenge of developing a precise, coherent alternative to that conception has yet to be met. Here, I argue that a major hindrance to the project of quant

Raffaele Tito D'Agnolo, Manuel Ettengruber, Lian-Tao Wang

Theories with a large number of long-lived metastable vacua are our only concrete explanation for the puzzling value of the Cosmological Constant (CC). The energy scales where these vacua are realized are unknown. In this work, we consider the possibility that a sector of this landscape of vacua is within experimental reach and discuss its signatures at coll

M. Grant Roberts, Aarna Garg, Tesla Jeltema, Stefano Profumo

We predict the effective clustering bias parameter, $b_{\rm{eff}}$, at $z\sim5$ for Little Red Dots (LRDs) seeded by Ultra-Strongly Self-Interacting Dark Matter (uSIDM). From our model, we find that $b_{\rm{eff}}\sim4.5$, thus we infer that LRDs seeded by uSIDM would populate halos of typical masses $\sim 8\times10^{10}~M_{\odot}$; this bias factor is consis

Alexandra E. Moylett, Bhargavi Jonnadula

Floquet codes have recently emerged as a new family of error-correcting codes, and have drawn significant interest across both theoretical and practical quantum computing. A central open question has been how to implement logical operations on these codes. In this work, we show how two techniques from static quantum error-correcting codes can also be impleme

Maria Ramos, Timothy Cohen, Mariangela Lisanti

It is plausible that the dark matter particles have non-gravitational interactions among themselves. If such self interactions are large enough, they could leave an imprint on the morphology of galaxies. These effects can be studied with numerical simulations, which serve as the primary tool to predict the non-linear evolution of galactic structure. A standa

Matteo Cantiello, Jake B. Hassan, Rosalba Perna, Philip J. Armitage

The JWST discovery of "Little Red Dots" (LRDs) has revealed a population of compact, red sources at $z \sim 5-10$ that likely host supermassive black holes (SMBHs). Recent observations of the gravitationally lensed LRD R2211-RX1 reveal century-scale photometric variability and a hysteresis loop in the luminosity-temperature plane, strongly suggesting that th

Arthur Hebecker, Severin Lüst, Andreas Schachner, Simon Schreyer

We analyse warping corrections to the scalar potential in flux compactifications of Type IIB string theory, focusing on their effect on $F$-term de Sitter uplifting in Calabi-Yau orientifold models. A systematic inverse-volume expansion allows us to derive the four-dimensional off-shell potential in the presence of warping and non-ISD 3-form fluxes. This cor

Yutaka Fujita, Rohta Takahashi, Norita Kawanaka

In the standard Galactic cosmic-ray (CR) paradigm, protons are accelerated up to ~1 PeV by Galactic sources. While supernova remnants (SNRs) have been traditionally considered as the primary accelerators, recent observations by LHAASO and HAWC have detected very-high-energy (VHE) gamma rays exceeding 100 TeV from several microquasars, suggesting that these X

Kenneth Higginbotham

Recent work by Engelhardt, Gesteau, and Harlow applies proposals for incorporating observers into holographic maps to study the Antonini-Rath puzzle for closed universes. In a new form of ``observer complementarity,'' they find that an AdS bulk observer measures a SWAP test to determine that there is no closed universe in the bulk, contrary to the (limited)

Shilong Zhang, He Zhang, Zhifei Zhang, Chongjian Ge

Modern Latent Diffusion Models (LDMs) typically operate in low-level Variational Autoencoder (VAE) latent spaces that are primarily optimized for pixel-level reconstruction. To unify vision generation and understanding, a burgeoning trend is to adopt high-dimensional features from representation encoders as generative latents. However, we empirically identif

Qianwei Wang, Bowen Li, Zhanpeng Luo, Yifan Xu

Long-horizon robotic tasks are hard due to continuous state-action spaces and sparse feedback. Symbolic world models help by decomposing tasks into discrete predicates that capture object properties and relations. Existing methods learn predicates either top-down, by prompting foundation models without data grounding, or bottom-up, from demonstrations withou

Ananta R. Bhattarai, Helge Rhodin

Monocular depth estimation remains challenging, as foundation models such as Depth Anything V2 (DA-V2) struggle with real-world images that are far from the training distribution. We introduce Re-Depth Anything, a test-time self-supervision framework that bridges this domain gap by fusing foundation models with the powerful priors of large-scale 2D diffusion

Byungjun Kim, Taeksoo Kim, Junyoung Lee, Hanbyul Joo

Recent progress in 3D reconstruction has made it easy to create realistic digital twins from everyday environments. However, current digital twins remain largely static and are limited to navigation and view synthesis without embodied interactivity. To bridge this gap, we introduce Dexterous World Model (DWM), a scene-action-conditioned video diffusion frame

Mirjam Cvetič, Ron Donagi, Jonathan J. Heckman, Max Hübner

Anomalies of a quantum field theory (QFT) constitute fundamental non-perturbatively robust data. In this paper we extract anomalies of 5D superconformal field theories (SCFTs) directly from the underlying extra-dimensional geometry. We show that all of this information can be efficiently extracted from extra-dimensional $\eta$-invariants, bypassing previousl

Yuzhe Zhu

We consider the Fisher information for spatially homogeneous multi-species Landau system. We show that the mass-weighted Fisher information is monotone decreasing in time along the solutions of the Landau system with a general class of interaction potentials.

Stéphane Bessy, Daniel Gonçalves, Amadeus Reinald, Dimitrios M. Thilikos

We investigate the problem of strong connectivity augmentation within plane oriented graphs. We show that deciding whether a plane oriented graph $D$ can be augmented with (any number of) arcs $X$ such that $D+X$ is strongly connected, but still plane and oriented, is NP-hard. This question becomes trivial within plane digraphs, like most connectivity augmen

Jonathon Fox, William J Buchanan, Pavlos Papadopoulos

With the increase in deep learning, it becomes increasingly difficult to understand the model in which AI systems can identify objects. Thus, an adversary could aim to modify an image by adding unseen elements, which will confuse the AI in its recognition of an entity. This paper thus investigates the adversarial robustness of LLaVA-1.5-13B and Meta's Llama

Junyu Zhang, Yifan Sun, Tianang Leng, Jingyan Shen

Despite the superior performance of Large Reasoning Models (LRMs), their reasoning behaviors are often counterintuitive, leading to suboptimal reasoning capabilities. To theoretically formalize the desired reasoning behaviors, this paper presents the Laws of Reasoning (LoRe), a unified framework that characterizes intrinsic reasoning patterns in LRMs. We fir

Vongani H. Maluleke, Kie Horiuchi, Lea Wilken, Evonne Ng

Understanding and generating multi-person interactions is a fundamental challenge with broad implications for robotics and social computing. While humans naturally coordinate in groups, modeling such interactions remains difficult due to long temporal horizons, strong inter-agent dependencies, and variable group sizes. Existing motion generation methods are

Aditya Gahlawat, Ahmed Aboudonia, Sandeep Banik, Naira Hovakimyan

Imitation learning (IL) enables autonomous behavior by learning from expert demonstrations. While more sample-efficient than comparative alternatives like reinforcement learning, IL is sensitive to compounding errors induced by distribution shifts. There are two significant sources of distribution shifts when using IL-based feedback laws on systems: distribu

Robin Schimmelpfennig, Mark Díaz, Vinodkumar Prabhakaran, Aida Davani

Over a billion users globally interact with AI systems engineered to mimic human traits. This development raises concerns that anthropomorphism, the attribution of human characteristics to AI, may foster over-reliance and misplaced trust. Yet, causal effects of humanlike AI design on users remain untested in ecologically valid, cross-cultural settings, leavi

Tomer Borreda, Fangqiang Ding, Sanja Fidler, Shengyu Huang

We present RadarGen, a diffusion model for synthesizing realistic automotive radar point clouds from multi-view camera imagery. RadarGen adapts efficient image-latent diffusion to the radar domain by representing radar measurements in bird's-eye-view form that encodes spatial structure together with radar cross section (RCS) and Doppler attributes. A lig

Xinru Wang, Ming Yin, Eunyee Koh, Mustafa Doga Dogan

As multi-agent systems powered by Large Language Models (LLMs) are increasingly adopted in real-world workflows, users with diverse technical backgrounds are now building and refining their own agentic processes. However, these systems can fail in opaque ways, making it difficult for users to observe, understand, and correct errors. We conducted formative in

Wisnu Uriawan, Ahmad Badru Al Husaeni, Dzakwanfaiq Nauval, Farid Muhtar Fathir

This study presents the development of a marker-based augmented reality (AR) application designed to visualize the content of Surah al-Fil as an interactive and context-rich medium for Islamic education. Using a research and development approach, the system was developed through structured stages including data collection, user requirement analysis, interfac

Youssef Tawfik, Shan Hao, Thomas P. Purdy

Many optical measurement techniques, such as light scattering from wavelength-scale particles or detecting motion from a surface with an optical lever, encode information in a complex radiation pattern. Extracting all available information is essential for many quantum-enhanced sensing protocols but is often impractical, as it requires many channels to spati

Zhaonan Li, Shijie Lu, Fei Wang, Jacob Dineen

End-to-end Vision-language Models (VLMs) often answer visual questions by exploiting spurious correlations instead of causal visual evidence, and can become more shortcut-prone when fine-tuned. We introduce VISTA (Visual-Information Separation for Text-based Analysis), a modular framework that decouples perception from reasoning via an explicit information b

Lucas L. Brugger, Cristhiano Duarte, Bruno F. Rizzuti

With 2025 being declared the Year of Quantum Science and Technology, our contribution seeks to provide a fresh perspective on Schr\"odinger's cat thought experiment. We reinterpret this experiment by viewing it through the lens of quantum theory as a generalisation of classical probability, rooted in a Bayesian subjectivist framework. In this revised approac

Landon Taylor, Joshua Jeppson, Ahmed Irfan, Lukas Buecherl

Highly-concurrent system models with vast state spaces like Chemical Reaction Networks (CRNs) that model biological and chemical systems pose a formidable challenge to cutting-edge formal analysis tools. Although many symbolic approaches have been presented, transient probability analysis of CRNs, modeled as Continuous-Time Markov Chains (CTMCs), requires ex

Kristoffer Wickstrøm, Teresa Dorszewski, Siyan Chen, Michael Kampffmeyer

Current approaches for designing self-explainable models (SEMs) require complicated training procedures and specific architectures which makes them impractical. With the advance of general purpose foundation models based on Vision Transformers (ViTs), this impracticability becomes even more problematic. Therefore, new methods are necessary to provide transpa

Callum Deakin, Xi Chen

This paper analyzes the performance of spectro-temporal unitary transforms for coherent optical modulation. Unlike conventional IQ modulation, such transforms are based on a cascade of phase modulators and dispersive elements, so are theoretically lossless and not limited by the bandwidth of the constituent modulators. We analyse the performance limits and d

Anjun Chu, Joyce Kwan, Eric Yilun Song, Seth Hew Peng Chew

Realizing and controlling the unconventional pairing featured by topological superconductors remains a central challenge. We introduce a cavity QED quantum simulator that engineers competing chiral $p_x+ip_y$ and $d_{x^2-y^2}+id_{xy}$ orders by tailoring cavity-mediated couplings between atomic pseudospins that emulate momentum-dependent pairing channels. Th

Pedro C. Ormonde, Matthew Stasolla, Alec Menzer, Joseph Zhu

New free-swimming experiments and simulations are conducted on a pair of three-dimensional, bio-robotic swimmers composed of a body and tail section based on Yellowfin tuna, Thunnus albacares. It is discovered that the pair converges spontaneously to a side-by-side schooling formation that is stable to perturbations in the swimming direction at a fixed later

Laksha Pradip Das, Diksha Garg, Maria Vittoria Garzelli, Mary Hall Reno

The all-sky very-high-energy ($10^4-10^6$ GeV) atmospheric muon flux measured by IceCube shows a spectral hardening at the highest energies, indicating the presence of a prompt component. IceCube has also measured the atmospheric muon neutrino flux at high energy. However, since this flux is dominated by astrophysical neutrinos, only an upper bound can be pl

Frederick A. Gent, Mordecai-Mark Mac Low, Maarit J. Korpi-Lagg, Touko Puro

Magnetic fields are critical at many scales to galactic dynamics and structure, including multiphase pressure balance, dust processing, and star formation. Dynamo action determines their dynamical structure and strength. Simulations of combined large- and small-scale dynamos have successfully developed mean fields with strength and topology consistent with o

Hye-Young Jo, Mose Sakashita, Aditi Mishra, Ryo Suzuki

AI video generation has lowered barriers to video creation, but current tools still struggle with inconsistency. Filmmakers often find that clips fail to match characters and backgrounds, making it difficult to build coherent sequences. A formative study with filmmakers highlighted challenges in shot composition, character motion, and camera control. We pres

Dominik Szczepaniak, Monika Harvey, Fani Deligianni

Cognitive training for sustained attention and working memory is vital across domains relying on robust mental capacity such as education or rehabilitation. Adaptive systems are essential, dynamically matching difficulty to user ability to maintain engagement and accelerate learning. Current adaptive systems often rely on simple performance heuristics or pre

Dmitry Chicherin, Johannes Henn, Yongqun Xu, Shun-Qing Zhang

Employing a cutting-edge bootstrap method, we analytically compute the three-loop pentagonal Wilson loop with Lagrangian insertion in planar $\mathcal{N}=4$ super-Yang-Mills theory. This object is conjectured to coincide with the maximally transcendental part of the four-loop five-point all-plus amplitude in pure Yang-Mills theory. Our starting point is an a

C. A. Breu, D. I. Pontin, E. Priest, I. De Moortel

A large part of the hot corona consists of magnetically confined, bright plasma loops. These observed loops are in turn structured into bright strands. We investigate the relationship between magnetic field geometry, plasma properties and bright strands with the help of a 3D resistive MHD simulation of a coronal loop rooted in a self-consistent convection zo

Yu-Che Wu, Kuo-Chih Chang, Shu-Chun Chu

Digital lasers control the laser beam by dynamically updating the phase patterns of the spatial light modulator (SLM) within the laser cavity. Due to the presence of nonlinear effects, such as mode competition and gain saturation in digital laser systems, it is often necessary to rely on specifically manually tailored approach or iteration processes to find

Herlock Rahimi

Score-based diffusion models currently constitute the state of the art in continuous generative modeling. These methods are typically formulated via overdamped or underdamped Ornstein--Uhlenbeck-type stochastic differential equations, in which sampling is driven by a combination of deterministic drift and Brownian diffusion, resulting in continuous particle

Tim Whittaker, Seth Taylor, Elsa Cardoso-Bihlo, Alejandro Di Luca

Terrain-following coordinates in atmospheric models often imprint their grid structure onto the solution, particularly over steep topography, where distorted coordinate layers can generate spurious horizontal and vertical motion. Standard formulations, such as hybrid or SLEVE coordinates, mitigate these errors by using analytic decay functions controlled by

Karina I. Mazzitello, T. Molina Blanco, C. P. Marcel, V. P. Masson

This study investigates the influence of heater material properties on nucleate pool boiling using a comprehensive simulation model. Copper and silicon oxide are selected as reference materials due to their properties as excellent and poor heat conductors, respectively. The model integrates well-known heat transfer mechanisms, allowing for the assessment of

Haiwen Feng, Long Lian, Lisa Dunlap, Jiahao Shu

A key challenge in evaluating VLMs is testing models' ability to analyze visual content independently from their textual priors. Recent benchmarks such as BLINK probe visual perception through visual prompting, where questions about visual content are paired with coordinates to which the question refers, with the coordinates explicitly marked in the image it

Baohua Yan, Jennifer Kava, Qingyuan Liu, Xuan Di

Standard diffusion models (DMs) rely on the total destruction of data into non-informative white noise, forcing the backward process to denoise from a fully unstructured noise state. While ensuring diversity, this results in a cumbersome and computationally intensive image generation task. We address this challenge by proposing new forward and backward proce

Romain Gicquaud

We prove a Poincar\'e-Sobolev type inequality on compact Riemannian manifolds where the deviation of a function from a biased average, defined using a density, is controlled by the unweighted Lebesgue norm of its gradient. Unlike classical weighted Poincar\'e inequalities, the density does not enter the measure or the Sobolev norms, but only the reference av

Christine Berkesch, Lauren Cranton Heller, Gregory G. Smith, Jay Yang

For any toric ideal $I$ in a polynomial ring $S$, we provide a combinatorial description of a free resolution of the integral closure of the $S$-module $S/I$. These new complexes arise from an extension of Bayer--Sturmfels' theory of cellular free resolutions. As applications, we unify several constructions for a resolution of the diagonal embedding of a tor

Alexander Keimer, Lukas Pflug, Jakob Rodestock

This contribution considers optimal control problems subject to nonlocal conservation laws -- those in which the velocity depends nonlocally (i.e., via a convolution) on the solution -- and the so-called singular limit. First, the existence of minimizers is demonstrated for a broad class of optimal control problems, involving optimization over the initial da

Kevin Bitterlich, Daniel Rudolf, Björn Sprungk

Slice sampling is a well-established Markov chain Monte Carlo method for (approximate) sampling of target distributions which are only known up to a normalizing constant. The method is based on choosing a new state on a slice, i.e., a superlevel set of the given unnormalized target density (with respect to a reference measure). However, slice sampling algori

Ghilles Ainouche, Resmi Sudheer, Susree Mohapatra, Boning Yu

We carry out temperature-dependent scanning tunneling microscopy (STM) studies of the charge density wave (CDW) compound ZrTe$_3$ which is intentionally doped with Hf. Previous bulk studies tie Hf doping to an enhancement of the CDW transition temperature (T$_{CDW}$). In our work, by combining STM measurements with density functional theory (DFT) calculation

Sofia Chiarenza, Alex Krolewski, Marco Bonici, Edmond Chaussidon

We present the first measurement of local-type primordial non-Gaussianity from the cross-correlation between $1.2$ million spectroscopically confirmed quasars from the first data release (DR1) of the Dark Energy Spectroscopic Instrument (DESI) and the Planck PR4 CMB lensing reconstructions. The analysis is performed in three tomographic redshift bins coverin

Hassan Khalid, Muhammad Mahad Khaliq, Muhammad Jawad Bashir

The rapid evolution of Artificial intelligence in healthcare has opened avenues for enhancing various processes, including medical billing and transcription. This paper introduces an innovative approach by integrating AI with Locally Linear Embedding (LLE) to revolutionize the handling of high-dimensional medical data. This AI-enhanced LLE model is specifica

Balram Singh, Ram Prakash Sharma, Somnath Dey

Plant diseases pose a significant threat to global food security, necessitating accurate and interpretable disease detection methods. This study introduces an interpretable attention-guided Convolutional Neural Network (CNN), CBAM-VGG16, for plant leaf disease detection. By integrating Convolution Block Attention Module (CBAM) at each convolutional stage, th

Li Hua Yu

We introduce the two-point propagation field (TPPF), a real-valued, phase-sensitive quantity defined as the functional derivative of the single-photon detection probability with respect to an infinitesimal opaque perturbation placed between the source and detection slits. The TPPF is analytically derived and shown to exhibit a stable, high-frequency sinusoid

Rai M. Menezes, Clecio C. de Souza Silva

Synthetic antiferromagnetic (SAF) skyrmions are nanoscale composite textures that exhibit high-speed, Hall-free current-driven motion and recently demonstrated self-propulsion. These remarkable properties rely on the stability of the SAF skyrmion's topological bound state, whose underlying mechanisms remain unclear. Here, using an atomistic spin model, we an

Lokendra Kumar, Neelesh S. Upadhye, Kannan Piedy

Semantic Textual Similarity (STS) research has expanded rapidly since 2021, driven by advances in transformer architectures, contrastive learning, and domain-specific techniques. This survey reviews progress across six key areas: transformer-based models, contrastive learning, domain-focused solutions, multi-modal methods, graph-based approaches, and knowled

Irma Avdic, David A. Mazziotti

We introduce an entanglement witness that identifies off-diagonal long-range order (ODLRO) -- a distinctive form of entanglement -- in systems containing both fermionic and bosonic particles. By analyzing the particle-hole reduced density matrices of each subsystem, the approach detects ODLRO independently in both fermionic and bosonic sectors and identifies

Sterley Labady, Youssef Mesri, Daniel Pino Munoz, Baptiste Flipon

Materials performance is deeply linked to their microstructures, which govern key properties such as strength, durability, and fatigue resistance. EBSD is a major technique for characterizing these microstructures, but acquiring large and statistically representative EBSD maps remains slow, costly, and often limited to small regions. In this work, we introdu

Laura Doval, Alex Smolin

We study mechanism design when a designer repeatedly uses a fixed mechanism to interact with strategic agents who learn from observing their allocations. We introduce a static framework, calibrated mechanism design, requiring mechanisms to remain incentive compatible given the information they reveal about an underlying state through repeated use. In single-

ATLAS Collaboration

A search for heavy spin-1 and spin-2 resonances decaying into a top-antitop-quark pair has been performed with 140 fb$^{-1}$ of proton-proton collision data collected by the ATLAS experiment at the Large Hadron Collider at a centre-of-mass energy of $\sqrt{s}=13$ TeV. Final states with either exactly one electron or muon, or exactly two leptons ($ee$, $\mu\m

Mariana Bergonzi, Joaquín Fernández, Ernesto Kofman

This work proposes a methodology to develop new numerical integration algorithms for ordinary differential equations based on state quantization, generalizing the notions of Linearly Implicit Quantized State Systems (LIQSS) methods. Using this idea, two novel sub-families of algorithms are designed that improve the performance of current LIQSS methods while

Guofang Wang, Mingwei Zhang

For non-trivial solutions to the zero mode equation on a closed spin manifold \[D \varphi=iA\cdot \varphi,\] we first provide a simple proof for the sharp inequality \eq{ \norm{A}_{L^n}^2 \ge \frac {n}{4(n-1)} Y(M,[g]), } where $Y(M,[g])$ is the Yamabe constant of $(M,g)$, which was obtained by Frank-Loss and Reuss. Then we classify completely the equality c

Ran Gong, Xiaohan Zhang, Jinghuan Shang, Maria Vittoria Minniti

Generalist robot learning remains constrained by data: large-scale, diverse, and high-quality interaction data are expensive to collect in the real world. While simulation has become a promising way for scaling up data collection, the related tasks, including simulation task design, task-aware scene generation, expert demonstration synthesis, and sim-to-real

Mengkun Chen, Sanidhya D. Tripathi, James W. Tunnell

Raman spectroscopy enables non-destructive, label-free molecular analysis with high specificity, making it a powerful tool for biomedical diagnostics. However, its application to biological tissues is challenged by inherently weak Raman scattering and strong fluorescence background, which significantly degrade signal quality. In this study, we present a simu

Sarah Rastegar, Violeta Chatalbasheva, Sieger Falkena, Anuj Singh

Text-to-image (T2I) diffusion models generate high-quality images but often fail to capture the spatial relations specified in text prompts. This limitation can be traced to two factors: lack of fine-grained spatial supervision in training data and inability of text embeddings to encode spatial semantics. We introduce InfSplign, a training-free inference-tim

Corey M. Abramson

This chapter demonstrates how computational social science (CSS) tools are extending and expanding research on aging. The depth and context from traditionally qualitative methods such as participant observation, in-depth interviews, and historical documents are increasingly employed alongside scalable data management, computational text analysis, and open-sc

François Golse, Nikolai Leopold, Norbert J. Mauser, Jakob Möller

We prove the mathematically rigorous (semi-)classical limit $\hbar \to 0$ of the Dirac equation with time-dependent external electromagnetic field to relativistic Vlasov equations with Lorentz force for electrons and positrons. In this limit antimatter and spin remain as intrinsically relativistic effects on a classical level. Our global-in-time results use

Pedro Agostini, Tolga Altinoluk, Néstor Armesto, Guillaume Beuf

We develop a Gaussian model to evaluate the decorated dipole and quadrupole operators that arise beyond the eikonal approximation in the Color Glass Condensate framework. While the method is general and applicable to arbitrary beyond-eikonal Wilson line structures, we employ it for dijet production in deep inelastic scattering at next-to-eikonal accuracy. Af

Celia Julliot, Gabriel M. Dallago, Amir Nejati, Abdoulaye B. Diallo

Detecting walking pattern abnormalities in dairy cows early on holds the potential to reduce the occurrence of clinical lameness. This study aimed to predict gait scores in non-clinically lame dairy cows by using gait attributes based on kinematic data. Markers were placed on 20 anatomical landmarks on 12 dairy cows. The cows were walked multiple times throu

Carlos Vélez García, Miguel Cazorla, Jorge Pomares

We present Planning as Descent (PaD), a framework for offline goal-conditioned reinforcement learning that grounds trajectory synthesis in verification. Instead of learning a policy or explicit planner, PaD learns a goal-conditioned energy function over entire latent trajectories, assigning low energy to feasible, goal-consistent futures. Planning is realize

Ariel Pacetti, Lucas Villagra Torcomian

In this article we study solutions to the generalized Fermat equation $x^q+y^p+z^r=0 $ using hypergeometric motives within the framework of the modular method. In doing so, we give an explicit description of the ramification behavior at primes dividing $2qr$ and analyze the contribution of trivial solutions. We identify a general obstruction to the modular m

Rajni Dabas, Samir Khuller, Emilie Rivkin

Our first focus is the Capacitated Partition Vertex Cover (C-PVC) problem in hypergraphs. In C-PVC, we are given a hypergraph with capacities on its vertices and a partition of the hyperedge set into $\omega$ distinct groups. The objective is to select a minimum size subset of vertices that satisfies two main conditions: (1) in each group, the total number o

A. Giri, J. Kim, C. Drischler, Ch. Elster

We extend the active learning emulators for two-body scattering in coordinate space with error estimation, recently developed by Maldonado et al. [Phys. Rev. C 112, 024002], to coupled-channel scattering in momentum space. Our full-order model (FOM) solver is based on the Lippmann-Schwinger integral equation for the scattering $t$-matrix as opposed to the ra

Phani Pavan Kambhampati, Chainesh Gautam, Jagan Palaniswamy, Madhav Rao

Recent advancements in robotic rehabilitation therapy have provided modular exercise systems for post-stroke muscle recovery with basic control schemes. But these systems struggle to adapt to patients' complex and ever-changing behaviour, and to operate within mobile settings, such as heat and power. To aid this, we present NeuRehab: an end-to-end framework

Andrés Ordóñez, David Ayuso, Piero Decleva, Letizia Fede

We show that the photoelectron angular distributions produced by elliptical and cross-polarized two-color laser fields interacting with randomly oriented chiral molecules decompose into four irreducible representations of the $D_{2h}$ point group. One of these ($A_u$) corresponds to a non-dichroic enantiosensitive (NoDES) contribution. This NoDES contributio

Sidhartha Patnaik, Kumarasamy Sakthivel

This paper investigates the time-optimal control problem for the Landau-Lifshitz-Bloch (LLB) equation, a macroscopic model that characterizes magnetization dynamics in ferromagnetic materials across a wide temperature range, including near and above the Curie temperature. We analyze the LLB system on bounded domains in one, two, and three dimensions, establi