March 2026 arXiv papers — page 4

Dario Antolini, Edoardo Ballico, Alessandro Oneto

The Hadamard rank of a point with respect to a projective variety is, if it exists, the minimum number of points of the variety whose coordinate-wise product is the given point. We classify the projective varieties for which the Hadamard rank is finite for any point. As a by-product we obtain the finiteness of the Hadamard rank with respect to varieties of t

Peng Gang

How reliably can structured intent representations preserve user goals across different AI models, languages, and prompting frameworks? Prior work showed that PPS (Prompt Protocol Specification), a 5W3H-based structured intent framework, improves goal alignment in Chinese and generalizes to English and Japanese. This paper extends that line of inquiry in thr

Xiaoshan Huang, Conrad Borchers, Jiayi Zhang, Susanne P. Lajoie

Effective collaboration requires teams to manage complex cognitive and emotional states through Socially Shared Regulation of Learning (SSRL). Physiological synchrony (i.e., longitudinal alignment in physiological signals) can indicate these states, but is hard to interpret on its own. We investigate the physiological and conversational dynamics of four medi

Shuto Shibasaki, Fumiya Mohri, Takashi Tsuchimochi

Near-degenerate electronic structures remain a major challenge for conventional single-reference density functional theory (DFT). To address this problem, we propose time-dependent $\Delta$SCF (TD$\Delta$SCF), a novel linear-response scheme in which a non-Aufbau $\Delta$SCF determinant serves as the reference for a subsequent TDDFT calculation. In contrast t

Xi Du, Bin Yue, Furen Deng, Yidong Xu

During Cosmic Dawn, Pop III stars could be formed in minihalos through molecular hydrogen (H$_2$) cooling. The minimum halo mass required for H$_2$ cooling is highly sensitive to Lyman-Werner (LW) radiation, which dissociates H$_2$ and regulates star formation. However, the efficiency of LW feedback remains poorly constrained due to the lack of direct observ

Luan Borges Teodoro Reis Sena, Francisco Galuppo Azevedo

Interpretability is central for scientific machine learning, as understanding \emph{why} models make predictions enables hypothesis generation and validation. While tabular foundation models show strong performance, existing explanation methods like SHAP are computationally expensive, limiting interactive exploration. We introduce ShapPFN, a foundation model

Xiang Zhang, Giuseppe Caire, Mingyue Ji

The packet type (PT)-based framework~\cite{zhang2026taming} provides a systematic and principled approach to designing device-to-device (D2D) coded caching schemes that achieve reduced \sbp while preserving the optimal communication rate. However, existing PT designs rely exclusively on homogeneous \sbp, where all packets have an identical size regardless of

Noor Khalal, Chakib Fettal, Lazhar Labiod, Mohamed Nadif

Machine-learning-based code vulnerability detection (CVD) has progressed rapidly, from deep program representations to pretrained code models and LLM-centered pipelines. Yet dependable vulnerability labeling remains expensive, noisy, and uneven across projects, languages, and CWE types, motivating approaches that reduce reliance on human labeling. This surve

Xin Jin, Priyam Srivastava, Ronghe Wang, Yuqing Li

Quantum sensing technologies offer transformative potential for ultra-sensitive biomedical sensing, yet their clinical translation remains constrained by classical noise limits and a reliance on macroscopic ensembles. We propose a unifying generational framework to organize the evolving landscape of quantum biosensors based on their utilization of quantum re

Fernando Hernando, Gregorio Quintana-Ortí

We present a new general method for performing basic arithmetic in the finite field~$\mathbb{F}_p$ for any prime $p>2$ by using traditional binary operations over~$\mathbb{F}_2$. Our new approach is efficient and competitive with current state-of-art methods. We apply our new arithmetic method to the computation of the minimum Hamming distance of random line

Vanessa Emanuela Guarino, Claudia Winklmayr, Jannik Franzen, Josef Lorenz Rumberger

Uncertainty Quantification (UQ) is crucial for ensuring the reliability of automated image segmentations in safety-critical domains like biomedical image analysis or autonomous driving. In segmentation, UQ generates pixel-wise uncertainty scores that must be aggregated into image-level scores for downstream tasks like Out-of-Distribution (OoD) or failure det

Ilyes Jaouedi, Gilles Chardon, José Picheral

We address the problem of localizing multiple sources in 3D by combining sensor array measurements with camera observations. We propose a fusion framework extending the covariance matrix fitting method with an unbalanced optimal transport regularization term that softly aligns sensor array responses with visual priors while allowing flexibility in mass alloc

Takeshi Kurata

The term XR is currently widely used as an expression encompassing Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR). However, there is no clear consensus regarding its origin or meaning. XR is sometimes explained as an abbreviation for Extended Reality, but multiple interpretations exist regarding its etymology and formation process. This

G. Alencar, V. H. U. Borralho

In this work, we obtain regular solutions with a dynamical cosmological function in unimodular gravity. This alternative theory to General Relativity imposes an additional condition on the spacetime volume element by fixing the metric volume density to a prescribed non-dynamical quantity, which can be represented by a constant in unimodular coordinates. In t

Soveatin Kuntur, Nina Smirnova, Anna Wroblewska, Philipp Mayr

This paper investigates sentence-level text reuse in multilingual journalism, analyzing where reused content occurs within articles. We present a weakly supervised method for detecting sentence-level cross-lingual reuse without requiring full translations, designed to support automated pre-selection to reduce information overload for journalists (Holyst et a

Daniel Stern, Brian Grefenstette

Yes.................

Shasha Yu, Fiona Carroll, Barry L. Bentley

As AI becomes increasingly embedded across societal domains, understanding how future AI practitioners, particularly technology students, perceive its risks is essential for responsible development and adoption. This study analyzed responses from 139 students in Computer Science, Data Science/Data Analytics, and other disciplines using both explicit AI risk

Pavel S. Avdienko, Lukas Hanschke, Quirin Buchinger, Nikolai Bart

We report a scalable molecular beam epitaxy strategy to achieve a low density of O-band electrically tunable InAs/InGaAs quantum dots (QDs) on GaAs(001) substrates. Our approach is based on a gradient deposition of InAs in the sub-ML regime and subsequent capping with an InGaA strain-reducing layer to redshift the emission wavelength. For different growth co

Balázs Pozsgay, István Vona

We explore the capability of a Large Language Model (LLM) to perform specific computations in mathematical physics: the task is to compute the coordinate Bethe Ansatz solution of selected integrable spin chain models. We select three integrable Hamiltonians for which the solutions were unpublished; two of the Hamiltonians are actually new. We observed that t

Yuhang Yang, Fan Zhang, Huaijin Pi, Shuai Guo

Digital characters are central to modern media, yet generating character videos with long-duration, consistent multi-view appearance and expressive identity remains challenging. Existing approaches either provide insufficient context to preserve identity or leverage non-character-centric information as the memory, leading to suboptimal consistency. Recognizi

Tomoki Mihara

We formulate and verify a non-Archimedean analogue of Chase's lemma. Following the framework by K.\ Eda removing restriction of cardinality from analogy on direct product between countability and non-$\omega_1$-measurability, we extend the non-Archimedean analogue of Chase's lemma to a non-Archimedean counterpart of the extension by K.\ Eda of the extension

Serkan Kirbas, Federica Sarro, David Williams

As software in industry grows in size and complexity, so does the volume of engineering data that companies generate and use. Ideally, this data could be used for many purposes, including informing decisions on engineering priorities. However, without a structured representation of the links between different aspects of software development, companies can st

Jonas Landsgesell, Pascal Knoll, Tizian Wenzel

Tabular foundation models such as TabPFN and TabICL already produce full predictive distributions, yet prevailing regression benchmarks evaluate them almost exclusively via point-estimate metrics (RMSE, $R^2$). This discards precisely the distributional information these models are designed to provide - a critical gap for high-stakes domains where not all ki

Raül Pérez-Gonzalo, Andreas Espersen, Søren Forchhammer, Antonio Agudo

Transferring large volumes of high-resolution images during wind turbine inspections introduces a bottleneck in assessing and detecting severe defects. Efficient coding must preserve high fidelity in blade regions while aggressively compressing the background. In this work, we propose an end-to-end deep learning framework that jointly performs segmentation a

Oğuzhan Ersoy, Nikolay Blagoev, Jona te Lintelo, Stefanos Koffas

Decentralised post-training of large language models utilises data and pipeline parallelism techniques to split the data and the model. Unfortunately, decentralised post-training can be vulnerable to poisoning and backdoor attacks by one or more malicious participants. There have been several works on attacks and defenses against decentralised data paralleli

Andrey Egorov

We investigate lower bounds for the number of ideal and finite vertices of right-angled hyperbolic polyhedra of finite volume. We use a geometric method of orthogonal gluings to establish new bounds in low dimensions, specifically $v_\infty(P^5) \ge 3$ and $v_{fin}(P^7) \ge 4$. By combining these initial bounds with double counting arguments and recurrence r

Anirudh Raman, Olivier Jaubert, Mark Wrobel, Tina Yao

Purpose: To investigate whether synthetically generated fractal data can be used to train deep learning (DL) models for dynamic MRI reconstruction, thereby avoiding the privacy, licensing, and availability limitations associated with cardiac MR training datasets. Methods: A training dataset was generated using quaternion Julia fractals to produce 2D+time ima

Hans Riess, Yujun Huang, Matthew Klawonn, Gioele Zardini

Monotone co-design enables compositional engineering design by modeling components through feasibility relations between required resources and provided functionalities. However, its standard boolean formulation cannot natively represent quantitative criteria such as cost, confidence, or implementation choice. In practice, these quantities are often introduc

Giuseppe Scarlato, Antonio Cicone, Marco Donatelli

In the analysis of real-world data, extracting meaningful features from signals is a crucial task. This is particularly challenging when signals contain non-stationary frequency components. The Iterative Filtering (IF) method has proven to be an effective tool for decomposing such signals. However, such a technique cannot handle directly data that have been

Recai Erdem

The (old) cosmological constant problem consists of two different problems. The first is the huge discrepancy between the value of the cosmological constant deduced from observations and its value expected from cosmological constant-like theoretical contributions (such as vacuum expectation value of Higgs potential). The second problem is why the value of th

Edgardo Brigatti

We propose new analytical tools for describing growth-rate distributions generated by stationary time-series. Our analysis shows how deviations from normality are not pathological behaviour, as suggested by some traditional views, but instead can be accounted for by clean and general statistical considerations. In contrast, strict normality is the effect of

Georgii Mikriukov, Grégoire Montavon, Marina M. -C. Höhne

Post-hoc explanation methods are widely used to interpret black-box predictions, but their generation is often computationally expensive and their reliability is not guaranteed. We propose epistemic uncertainty as a low-cost proxy for explanation reliability: high epistemic uncertainty identifies regions where the decision boundary is poorly defined and wher

Francisco Galuppo Azevedo, Clarissa Lima Loures, Denis Oliveira Correa

Training relational foundation models requires learning representations that transfer across tasks, yet available supervision is typically limited to a small number of prediction targets per database. This task scarcity causes learned representations to encode task-specific shortcuts that degrade transfer even within the same schema, a problem we call label

Claire Lamman

We present galactic constellations: charming shapes in large cosmological surveys. By exploring a dense subset of DESI's first data release, we discover distinctive constellations including "Pisces Grandis", "The DESI Stick Woman", and "W". We additionally develop a public website for anyone to explore DESI data, find their own constellations, and share thei

Amirreza Alasti, Efe Erdal, Yücel Celik, Theresa Eimer

Reinforcement Learning (RL) agents often struggle with efficiency and performance in complex environments. We propose a novel framework that uses a Large Language Model (LLM) to dynamically generate a curriculum over available actions, enabling the agent to incorporate each action individually. We apply this framework to the game of Blackjack, where the LLM

Vestislav Apostolov, Abdellah Lahdili, Chung-Ming Pan

We prove that for any smooth polarized complex $n$-dimensional manifold $(X, L_X)$ which admits an extremal K\"ahler metric in $c_1(L_X)$, and for any integer $k$ large enough (in terms of a bound depending on $(X, L_X)$), the $(n+k+1)$-dimensional complex cone $\mathcal{Y}:= \overline{(L_X \otimes \mathcal{O}_{\mathbb{P}^k}(1))^{\times}}$ with section $X \t

Dan Petersen, Victor Roca i Lucio, Sinan Yalin

The classical bar-cobar adjunction between dg algebras and dg coalgebras goes back to the origins of differential homological algebra as developed by Cartan, Eilenberg, Moore, and many others, and is part of the broader framework of Koszul duality. In recent years, several $\infty$-categorical analogues of this adjunction have been developed, notably by Luri

Bruno Le Floch

In his quest to disprove a claim by Peirce that all lattices are distributive, Ernst Schr\"oder considered 135 years ago a list of 990 equational laws on quasigroups, analogous to associativity, such as $(x // y) * z = (y // x) \backslash\backslash z$. A quasigroup is a non-associative analogue of groups, specifically a set equipped with multiplication and r

Zhihong Cui, Haoran Tang, Tianyi Li, Yushuai Li

Trajectory planning for autonomous driving increasingly leverages large language models (LLMs) for commonsense reasoning, yet LLM outputs are inherently unreliable, posing risks in safety-critical applications. We propose C-TRAIL, a framework built on a Commonsense World that couples LLM-derived commonsense with a trust mechanism to guide trajectory planning

Nelly Elsayed

Assistive technologies increasingly support independence, accessibility, and safety for older adults, people with disabilities, and individuals requiring continuous care. Two major categories are virtual assistive systems and robotic assistive systems operating in physical environments. Although both offer significant benefits, they introduce important secur

Jordan Berthoumieu

This article provides a naturel sequel of previous works [6, 4] regarding the stability of travelling waves for a general one-dimensional Schr\"odinger equation (N LS) with non-zero condition at infinity. The aim of this article is twofold. First, we prove the asymptotic stability of well-prepared chains of dark solitons and secondly, we construct an asympto

Tomoki Mihara

We propose a new frame work of $p$-adic neural network. Unlike the original $p$-adic neural network by S.\ Albeverio, A.\ Khrennikov, and B.\ Tirrozi using a family of characteristic functions indexed by hyperparameters of precision as activation functions, we use a single injective $p$-adic character on the topological Abelian group $\mathbb{Z}_p$ of $p$-ad

Ionel Lazanu, Konstantin Zioutas

Macroscopic dark matter with dominating strong interactions, supposed to be composites, represents an alternative to the most popular WIMP particles. Predicted in various models as strangelets, nuclearites, nuggets, having different internal structures and properties, but not yet observed experimentally, these forms of dark matter are associated with the exi

Breno C. Bispo, Stefania Sardellitti, Juliano B. Lima, Fernando A. N. Santos

Brain connectomics is still largely dominated by pairwise-based models, such as graphs, which cannot represent circulatory or higher-order functional interactions. In this paper, we propose a multimodal framework based on Topological Signal Processing (TSP) that models the brain as a higher-order topological domain and treats functional interactions as discr

Yinuo Liu, Zi Qian, Heng Zhou, Jiahao Zhang

Interleaved text-and-image generation represents a significant frontier for Multimodal Large Language Models (MLLMs), offering a more intuitive way to convey complex information. Current paradigms rely on either image generation or retrieval augmentation, yet they typically treat the two as mutually exclusive paths, failing to unify factuality with creativit

Mst. Fahmida Sultana Naznin, Adnan Ibney Faruq, Mushfiqur Rahman, Niloy Kumar Mondal

Automated radiology report summarization aims to distill verbose findings into concise clinical impressions, but existing multimodal models often struggle with visual noise and fail to meaningfully improve over strong text-only baselines in the FINDINGS $\to$ IMPRESSION transformation. We challenge two prevailing assumptions: (1) that more visual input is al

Rin I. Yamada, Kazuki Tokuda, Yasuo Fukui, Daiki Adachi

The Large Magellanic Cloud (LMC) exhibits vigorous high-mass star formation, including the HII regions 30~Dor that is the most active site of star formation in the local group. The present paper focuses on the Giant Molecular Cloud (GMC) in the HII region N113 in the central part of the LMC. Based on the $^{12}$CO($J$ =1-0) and $^{13}$CO($J$ = 1-0) data at a

Natalia Saburova

We consider discrete Schr\"odinger operators $H_{\mu Q}=\Delta+\mu Q$ with real periodic potentials $Q$ on periodic graphs, where $\Delta$ is the adjacency operator and $\mu\in\mathbb R$ is a coupling constant. The spectra of the operators consist of a finite number of closed intervals (bands). In the large coupling regime, we obtain an asymptotic upper boun

Ruslan Maksimau

We study the stabiliser formalism for qudits of arbitrary dimension $d$. In the free case, we show that the basic theorem of the stabiliser formalism remains valid: if the stabiliser subgroup $H$ is free as a $Z/dZ$-module and contains no non-trivial scalars, then the protected space $V^H$ is naturally identified with the state space of a smaller number of q

Daniil Fisher, Valentin Khrulkov, Mikhail Saygin, Ivan Oseledets

T-count minimization is an NP-hard problem that arises in fault-tolerant quantum compilation. In the parity-matrix representation, which captures the non-Clifford part of a quantum circuit, algebraic optimizers such as TODD can achieve state-of-the-art results. However, heuristics fixed in advance determine which transformation is applied, limiting the explo

Daban Q. Jaff, Mohammad Mohammadamini

FLEURS offers n-way parallel speech for 100+ languages, but Northern Kurdish is not one of them, which limits benchmarking for automatic speech recognition and speech translation tasks in this language. We present FLEURS-Kobani, a Northern Kurdish (ISO 639-3 KMR) spoken extension of the FLEURS benchmark. The FLEURS-Kobani dataset consists of 5,162 validated

Prasanjit Dey, Soumyabrata Dev, Bianca Schoen-Phelan

Accurate assessment of atmospheric nitrogen dioxide (NO$_2$) and sulfur dioxide (SO$_2$) is essential for understanding climate-air quality interactions, supporting environmental policy, and protecting public health. Traditional monitoring approaches face limitations: satellite observations provide broad spatial coverage but suffer from data gaps, while grou

Daniil Domaretskiy, Ned Hayward, Van Huy Nguyen, Simone Benaglia

Disorder masks much of the rich physics in two-dimensional electronic systems, with charged impurities often the limiting factor. In graphene, progress in reducing disorder has largely stagnated since boron nitride encapsulation was introduced a decade ago. Here we show that a brief deep-UV exposure enhances the electronic quality of encapsulated graphene -

Edvard Bakhitov

This paper develops a penalized GMM (PGMM) framework for automatic debiased inference on functionals of nonparametric instrumental variable estimators. We derive convergence rates for the PGMM estimator and provide conditions for root-n consistency and asymptotic normality of debiased functional estimates, covering both linear and nonlinear functionals. Mont

Rakhimov Kamoladdin

In this work, initial-boundary value problems for the time-fractional Airy equation are considered on different intervals. We study the properties of potentials for this equation and, using these properties, construct solutions to the considered problems. The uniqueness of the solution is proved using an analogue of the Gronwall-Bellman inequality and an a p

Cristina Butucea, Jean-François Delmas, Anne Dutfoy, Antoine Schoonaert

We study various properties of $f$-divergences and Csisz\'ar indices between two probability distributions in very general setups for the convex function $f$ and for the probability distributions. We establish general structural properties of $f$-divergences and show how they are inherited by the associated Csisz\'ar indices, including monotonicity and invar

Wolf Cukier, Dominic Samra, Vighnesh Nagpal, Diana Powell

Speculative fiction has long served an inspiration for genuine scientific inquiry. One notable work that has almost acted in this manner is the the seminal comedic speculative fiction work Cloudy with a Chance of Meatballs. While exoplaneteers reference this work frequently, we have never engaged with the central prediction of this work... until now! We perf

Yi Zhang, Zixing Wang, Fulvio Forni

We present a passive, data-driven velocity control method for nonlinear robotic manipulators that achieves better tracking performance than optimized PID with comparable design complexity. Using only three minutes of probing data, a VRFT-based design identifies passive iFIR controllers that (i) preserve closed-loop stability via passivity constraints and (ii

Thomas Eiter, Jan Giesselmann, Robert Lasarzik, Philipp Öffner

We consider a structure-preserving finite-volume scheme for the Euler-Korteweg (EK) and Navier-Stokes-Korteweg (NSK) equations. We prove that its numerical solutions converge to energy-variational solutions of EK or NSK under mesh refinement. Energy-variational solutions constitute a novel solution concept that has recently been introduced for hyperbolic con

Luke Weisenbach, Sophie L. Newman, Kieran Graham, Sai S. Dhavala

Recent cosmological surveys and datasets have highlighted a variety of tensions to the concordance model of our universe, $\Lambda$CDM. Of particular interest is the Hubble tension, the $5.5\sigma$ discrepancy between measurements of the Hubble constant $H_0$ using high redshift CMB data from Planck ($67.27\pm0.60$km$\text{s}^{-1}\text{Mpc}^{-1}$) and low re

Alessio Giarnetti, Simone Marciano, Davide Meloni

The unprecedented precision now being achieved in the measurement of the Pontecorvo--Maki--Nakagawa--Sakata (PMNS) lepton mixing matrix opens a new window onto the underlying structure of the neutrino mass matrix and the possibly associated flavor symmetries. In this work, we investigate the constraints imposed on the unitary matrix $U_\nu$ that diagonalises

Marie-Therese Sekwenz, Kyle Beadle, Simon Parkin

The European Union's Digital Services Act (DSA) introduced regulatory mechanisms which serve as a way to manage harmful content online. The recognition of Trusted Flaggers (TFs) is one such mechanism which accredits entities with experience, platform independence, and skill in identifying and reporting illegal content. With the DSA's TF role being roughly on

Dounia Nanadoumgar-Lacroze, Monica Seglar-Arroyo, Mario Martinez, Lluïsa-Maria Mir

The suspended end mirror of the input mode cleaner cavity in the Advanced Virgo Plus interferometer was equipped with an instrumented baffle in spring 2021, serving as a demonstrator of the technology in preparation for the installation of large instrumented baffles in the main arms of the interferometer. This baffle includes tens of sensors positioned near

Artem Averin

Applications of the Ryu-Takayanagi formula to evaporating black holes are known to reproduce the Page curve, although leaving open the microscopic origin of the necessary black hole degrees of freedom responsible for the entanglement and microstates. Here, we fill this gap by utilizing a recently proven generalized Ryu-Takayanagi formula which keeps the cont

Rui Ai, Yu Pan, David Simchi-Levi, Chonghuan Wang

In user-agent interaction scenarios such as recommendation, brainstorming, and code suggestion, Large Language Models (LLMs) often generate sets of candidate recommendations where the objective is to maximize the collective utility of the entire set rather than individual candidates independently. However, existing reinforcement learning post-training paradi

Khanh-Hung Giang-Tran, Soroosh Shafiee, Nam Ho-Nguyen

This paper addresses constrained smooth saddle-point problems in settings where projection onto the feasible sets is computationally expensive. We bridge the gap between projection-based and projection-free optimization by introducing a unified dual dynamic smoothing framework that enables the design of efficient single-loop algorithms. Within this framework

David L. Cole, Michael Lau, Xinliang Dai, Sambuddha Chakrabarti

Generation and Transmission Expansion Planning (GTEP) problems co-optimize generation and transmission expansion, enabling them to provide better planning decisions than traditional Generation Expansion Planning or Transmission Expansion Planning problems, but GTEPs can be computationally complex or intractable. Benders Decomposition (BD) has been applied to

Julian Berger, Pantelis P. Analytis, Ville Satopää, Ralf H. J. M. Kurvers

Artificial intelligence (AI) is broadly deployed as an advisor to human decision-makers: AI recommends a decision and a human accepts or rejects the advice. This approach, however, has several limitations: People frequently ignore accurate advice and rely too much on inaccurate advice, and their decision-making skills may deteriorate over time. Here, we comp

Chenlong He, Leilei Huang, Wei Li, Hanyang Cui

Existing mezzanine image codecs lack specialized screen content coding tools and therefore struggle to maintain high image quality under bandwidth constraints, especially in areas with dense text. Although distribution codecs offer advanced screen content compression techniques, their high computational complexity makes them impractical for mezzanine coding.

Carlos García Vera, Norbert Heuer, Dirk Praetorius

We propose a least-squares penalization as a means to extend the discontinuous Petrov-Galerkin (DPG) method with optimal test functions to a class of semilinear elliptic problems. The nonlinear contributions are replaced with independent unknowns so that standard DPG techniques apply to the then linear problem with non-trivial kernel. The nonlinear relations

Bukunmi G. Odunlami, Marcos Netto, Hai Lin

Discrete events alter how parameter influence propagates in hybrid systems. Prevailing Fisher information formulations assume that sensitivities evolve smoothly according to continuous-time variational equations and therefore neglect the sensitivity updates induced by discrete events. This paper derives a Fisher information matrix formulation compatible with

Benjamin Josef Schüßler, Jakob Prange

With the ever-growing urgency of sustainability in the economy and society, and the massive stream of information that comes with it, consumers need reliable access to that information. To address this need, companies began publishing so called Environmental, Social, and Governance (ESG) reports, both voluntarily and forced by law. To serve the public, these

Paola Stolfi, Elia Onofri, Gabriella Bretti

The structure of the nonlinear inverse problem arising from capillarity-driven imbibition in porous media is investigated, considering a degenerate parabolic PDE with compactly supported diffusivity and boundary-driven fluxes as the governing forward model. The inverse problem -- inferring hydraulic model parameters from sparse integral absorption measuremen

Victor G. Lopez, Malte Heinrich, Matthias A. Müller

In the reinforcement learning literature, strong theoretical guarantees have been obtained for algorithms applicable to LTI systems. However, in the nonlinear case only weaker results have been obtained for algorithms that mostly rely on the use of function approximation strategies like, for example, neural networks. In this paper, we study the applicability

Bozhen Zhou, Qi Zhao, Pan Zhang

Recent studies have shown that Trotter errors are highly initial-state dependent and that standard upper bounds often substantially overestimate them. However, the mechanism underlying anomalously small Trotter errors and a systematic route to identifying error-resilient states remain unclear. Using interaction-picture perturbation theory, we derive an analy

Yaoming Shi

We study two $(1+1)$-dimensional systems, denoted $(R0)$ and $(Z0)$, that are rigorously derived from the three-dimensional axisymmetric Euler equations in a signed polar formulation on the meridian plane. The main point of view in this revision is that these $(1+1)$D systems are not ad hoc model equations and not merely ``symmetry-axis reductions.'' Rather,

Juan Pablo Bertucci, Theo Hofman, Mauro Salazar

Electrification of marine transport is a promising solution to reduce sector greenhouse gas emissions and operational costs. However, the large upfront cost of electric vessels and the required charging infrastructure can be a barrier to the development of this technology. Optimization algorithms that jointly design the charging infrastructure and the operat

Lionel Alexandre Truflandier

In this work, we propose a simple method to resolve the eigenstates located at the band gap edges of an electronic eigenspectrum using only the quasi-purified one-particle density matrix as input. The theoretical framework relies on the decomposition of the occupation number variance into a particle and hole moment. These moments, when purified using power n

Ali Saadat, Jacques Fellay

Functional evidence is essential for clinical interpretation of genomic variants, but identifying relevant studies and translating experimental results into structured evidence remains labor intensive. We developed a benchmark based on ClinGen curated annotations to evaluate two large language models (LLMs), a non reasoning model (gpt-4o-mini) and a reasonin

Adar Avsian, Larry Heck

Large language models (LLMs) are increasingly deployed in multi-agent settings where communication must balance informativeness and secrecy. In such settings, an agent may need to signal information to collaborators while preventing an adversary from inferring sensitive details. However, existing LLM benchmarks primarily evaluate capabilities such as reasoni

Zhen Zhang, Jujia Zhao, Xinyu Ma, Xin Xin

Cold-start recommendation remains a central challenge in dynamic, open-world platforms, requiring models to recommend for newly registered users (user cold-start) and to recommend newly introduced items to existing users (item cold-start) under sparse or missing interaction signals. Recent generative recommenders built on pre-trained language models (PLMs) a

Yi Chen, Yuying Ge, Hui Zhou, Mingyu Ding

The development of Vision-Language-Action (VLA) models has been significantly accelerated by pre-trained Vision-Language Models (VLMs). However, most existing end-to-end VLAs treat the VLM primarily as a multimodal encoder, directly mapping vision-language features to low-level actions. This paradigm underutilizes the VLM's potential in high-level decision m

Moo K. Chung, Luigi Maccotta, Aaron Struck

Causal inference in brain networks has traditionally relied on regression-based models such as Granger causality, structural equation modeling, and dynamic causal modeling. While effective for identifying directed associations, these methods remain descriptive and acyclic, leaving open the fundamental question of intervention: what would the causal organizat

Minyoung E. Kim, Dae Hee Yun, Aditi V. Patel, Madeline Hon

Unprecedented visual details of biological structures are being revealed by subcellular-resolution whole-brain 3D microscopy data, enabled by recent advances in intact tissue processing and light-sheet fluorescence microscopy (LSFM). These volumetric data offer rich morphological and spatial cellular information, however, the lack of scalable data processing

Isabella Leone Zimmel, Alberto Ramos

The decoupling strategy allows one to obtain the value of the strong coupling in QCD from the running in pure gauge. Here we present our strategy to determine the running in the $SU(3)$ Yang-Mills theory. We use a finite-volume scheme with twisted boundary conditions and a step-scaling approach based on a gradient-flow coupling. We show preliminary results f

A. P. Jones

This work studies the likely dust seeding processes arising from alkali metal and alkaline earth ionisation, epoxidation (epoxide bond formation via oxygen atom insertion into C=C bonds), and grain charge disproportionation (the existence around the uncharged state of oxidised cationic and reduced anionic states) at (sub-)nanometre size scales. The chemical,

Haida Li, Hongguang Liu

In Loop Quantum Gravity, the quantum action of the volume operator is crucial in understanding quantum dynamics. In this work, we implement a generalized numerical algorithm that can compute the quantum action of the volume operator on a broad class of gauge-variant and gauge-invariant spin-network states. This algorithm is later used to calculate the cohere

Philipp Reiser

We classify which of the 672 oriented diffeomorphism types of closed, simply-connected spin 7-manifolds with the cohomology ring of $S^2\times S^5$ admit a free circle action. In addition, we show that whenever such an action exists, there exist infinitely many pairwise non-equivalent free circle actions. Finally, in almost all cases where such an action exi

Yan Lin, Jilin Hu, Shengnan Guo, Christian S. Jensen

Microscopic road-network weights represent fine-grained, time-varying traffic conditions obtained from individual vehicles. An example is travel speeds associated with road segments as vehicles traverse them. These weights support tasks including traffic microsimulation and vehicle routing with reliability guarantees. We study the problem of time-varying mic

Ilse Fischer, Moritz Gangl

Fully inhomogeneous spin Hall-Littlewood symmetric rational functions $F_\lambda$ arise as partition functions of certain path configurations in the $\mathfrak{sl}_2$ higher spin six vertex models. They are multiparameter generalizations of the classical Hall-Littlewood symmetric polynomials. We establish two new generalizations of the classical Littlewood i

Phil M. Korngut, James J. Bock, Samuel Condon, C. Darren Dowell

The SPHEREx near-infrared space telescope is an all-sky spectroscopic survey mission launched on March 12th, 2025 UTC. In addition to providing the community with a spectral database applicable to a wide range of investigations, it is optimized to address three core science goals: to survey the large scale structure of the Universe for signatures of non-Gaus

Amit Bengal, Teddy Lazebnik

Scientific research increasingly depends on multi-author collaboration, yet the systems used to allocate authorship credit remain vulnerable to conflict, strategic behavior, and project breakdown. Although prior work has shown that authors may rationally issue ultimatums over authorship order within a single manuscript, much less is known about how such beha

Yan Hao, Daniel J. Graham, Marc-Thorsten Hütt

In many systems, communication proceeds by broadcasting rather than single source-target routing, but network structures that maximize signal lifetime are not well understood. Degree correlations are known to influence robustness and spreading, yet their effect on signal persistence has remained unclear. Here we introduce Copy-Spread-Annihilate dynamics, a m

Gaurab Baral, Junxiu Zhou

Automated processing of structured documents such as government forms, healthcare records, and enterprise invoices remains a persistent challenge due to the high degree of layout variability encountered in real-world settings. This paper introduces AutoFormBench, a benchmark dataset of 407 annotated real-world forms spanning government, healthcare, and enter

Hongliang Jiang

The SCFT/VOA correspondence provides a powerful framework for studying 4d $\mathcal N=2$ superconformal field theories (SCFTs) through the mathematical machinery of 2d vertex operator algebras (VOAs). It captures the Schur operators of the underlying SCFT, whose spectrum is encoded by the Schur index and its refinement, the Macdonald index. While the Schur i

Han Deng, Anqi Zou, Hanling Zhang, Ben Fei

Scientific discovery increasingly depends on high-throughput characterization, yet automation is hindered by proprietary GUIs and the limited generalizability of existing API-based systems. We present Owl-AuraID, a software-hardware collaborative embodied agent system that adopts a GUI-native paradigm to operate instruments through the same interfaces as hum

Anne-Sophie Kaloghiros, Yuchen Liu, Andrea Petracci, Junyan Zhao

We study the K-moduli stack of prime Fano threefolds of genus twelve, known as $V_{22}$. We prove that its boundary, which parametrizes singular members, is purely divisorial and consists of four irreducible components corresponding to the four families of Prokhorov's one-nodal $V_{22}$. A key ingredient is a modular relation between Fano threefolds $X$ and

Haida Li, Hongguang Liu

We derive an asymptotic expansion for off-diagonal coherent-state matrix elements of non-polynomial operators in gauge theories admitting holomorphic coherent-state representations. The derivation combines stationary-phase analysis with an operator-level treatment of the Taylor remainder, and yields explicit semiclassical error control under stated assumptio

Dániel Marx, Marcin Pilipczuk, Michał Pilipczuk

Given an $H$-minor-free graph $G$ and an integer $k$, our main technical contribution is sampling in randomized polynomial time an induced subgraph $G'$ of $G$ and a tree decomposition of $G'$ of width $\widetilde{O}(k)$ such that for every $Z\subseteq V(G)$ of size $k$, with probability at least $\left(2^{\widetilde{O}(\sqrt{k})}|V(G)|^{O(1)}\right)^{-1}$,

Michele Caselli, Luca Gennaioli

We introduce a simple new method, based on the Caffarelli-Silvestre extension and a Duhamel-type formula, to derive exact pointwise identities for fractional commutators and nonlinear compositions associated with the fractional Laplacian on general Riemannian manifolds. As applications, we obtain a pointwise fractional Leibniz rule, a fractional Bochner's fo