April 2026 arXiv papers — page 11

Takara Sakai

This study proposes an efficient and computationally light route based map matching method for GPS track data on urban expressway networks. The key idea is to exploit a symbolic structure of named lines and named junctions that link level map matching leaves unused. We represent each candidate route as a sequence of line and junction names, take the set of s

Ritvik Garimella, Vedant Khandelwal, Anvi Kohli, Amit Sheth

Exact-match evaluation of agent-calling obscures qualitatively different failure modes: a model may select the right function yet hallucinate argument values, or satisfy a schema while choosing a agent for the wrong reason. Existing benchmarks collapse these distinctions into a single binary score, leaving practitioners unable to diagnose where agent calls f

Rasheed Mudasiru

AI agent systems that couple large language models (LLMs) with external tools and APIs are inherently non-deterministic: LLM sampling variance, external API state, CDN infrastructure headers, and execution-environment noise collectively prevent any prior agent run from being faithfully re-executed. Existing observability platforms capture execution logs but

Yuhang Wang

Multi-agent LLM systems increasingly rely on a Planner to decompose goals into sub-task sequences that downstream Executor and Critic agents execute and audit. We identify the planning phase as a critical attack surface: a single injection into the Planner's context achieves cascade amplification, corrupting all downstream sub-tasks simultaneously. We in

Amirreza Esmaeili, Fatemeh Fard

Understanding how Large Language Models (LLMs) make token-level decisions during code generation remains a major challenge for both researchers and practitioners. While recent tools provide insights into model internals or generation outcomes, they often lack decoding-time signals, fine-grained uncertainty measures, and interactive mechanisms for exploring a

Hiroki Fukui

Large language models process the world's writing systems with radical inequality. We constructed the Digital Script Representation Index (DSRI), a seven-axis measure of digital support, and applied it to the 300 writing systems of the Global Script Database (Fukui, 2026). Only 29 scripts (9.7%) are fully supported by contemporary digital infrastructure;

Tanner Culleton, Hung-Fu Chang

Large Language Models (LLMs) have rapidly influenced many aspects of society, particularly education, due to their demonstrated ability to complete assignments and examinations across a wide range of subjects. Although prior studies have examined the educational impact of LLMs, much of the existing work relies on public or open problem datasets and lacks top

Renwei Meng, Bowen Zhang, Jian Wang, Xican Wang

Reliable evaluation of large language models should separate supported answering from unsupported guessing without conflating either with data contamination, prompt idiosyncrasy, or generic refusal behavior. We present a contamination-aware, multi-zone benchmark for measuring the transition from answerable knowledge to abstention-expected unknowns under froz

Nemania Borovits, Damian Andrew Tamburri, Willem-Jan van den Heuvel

Privacy obligations under GDPR increasingly shape software engineering. We synthesize 90 studies from 2018 to 2025 using a systematic review with thematic synthesis to chart privacy engineering. Thirteen dimensions form two recurrent cores: Privacy Enhancing Technologies (PETs) with Privacy Metrics (PM) and Verification and Testing (VT) and Governance and Ac

Jinliang Xu, Runkai Zhu, Bingqi Li, Fanjie Nie

The rapid advancement of Large Language Models (LLMs) has established autonomous agents as the core vehicles for artificial intelligence applications. However, existing Internet infrastructures, primarily relying on TCP/IP and DNS, are designed for human-centric, host-to-host data transmission, inherently lacking the semantic awareness, dynamic capability di

Derya Akbaba, Daniela Paz Moyano Dávila, Måns Gezelius, Yin He

While feminist and critical data theories have long critiqued the use of data to uphold a positivist-informed view about science, few examples offer alternative methods to display scientific constructs. In response, we present Data and Me: an exhibit informed by feminist and critical data theories, which we designed and launched at a local science museum. Da

Vignesh Nagarajan

Visual art remains largely inaccessible to blind and low-vision (BLV) audiences due to brief or absent alt-text, which rarely conveys the sensory, spatial, or emotional qualities of an artwork. This study presents an automated workflow that generates multi-sensory art descriptions and synchronized audio narration using large language models and text-to-speec

Weikang Yang, S M Mazharul Hoque Chowdhury, Wei Jin

Text is one of the most common ways to store data in this computerized world. At a glance, it may seem that those data are not interconnected. But in reality, data can have hidden connections. Therefore, in this research, a new model has been presented that can find hidden relationships between two medical concepts by using MetaMap and appropriate text-minin

Sydney Johns, Sanjeev Parthasarathy, Shantnu Bhalla, Vaibhav Garg

Video platforms such as YouTube have reshaped how users engage with entertainment and information, emphasizing brief, highly engaging content such as Shorts. Within this ecosystem, certain content occupies a gray area where it remains allowed but may still have unintended negative effects on some audiences. To study this problem, we introduce TwistedHumor, a

Julien de Castelnau, Thomas Koehler, Arthur Charguéraud, Clément Pit-Claudel

GPUs have become essential in modern high performance computing, but programming them correctly remains a significant challenge. This difficulty arises from subtle concurrency bugs that result from the explicit management of synchronization primitives and data movement across intricate hierarchies of memory and parallel threads. At the same time, the ability

Charles Pillet, Pascal Giard, Bassant Selim, François Leduc-Primeau

In this paper, a low-complexity approach for the automorphism ensemble decoder (AED) using successive cancellation (SC) as constituent decoders is proposed. The approach sequentially activates sub-decoders and terminates the decoding process based on pre-optimized parameters, derived from the strong correlation observed between the decoding outcome and the S

Falk L Wiegmann, Nancy L Ford

The ramp filter kernel and cutoff frequency are fundamental parameters of the Feldkamp-Davis-Kress (FDK) algorithm that determine the resolution and noise characteristics of the reconstructed image. Despite their importance, systematic evaluations of their combined effect on task-based image quality in preclinical micro-CT are scarce, and many studies do not

Anne Kétri P. da Fonseca, Joelson D. V. Hermes, Edson D. Leonel

We explore the critical parameters responsible for the transition from integrability to chaos in a family of billiards combining elliptical and oval deformations. Unlike standard oval billiards, where a known critical parameter governs the destruction of the last invariant curve, the introduction of an integrable elliptic component yields a second deformatio

Denis Bernard, Lorenzo Piroli, Stefano Scopa

We study the melting of a domain wall in the quantum simple exclusion process with all-to-all hoppings (a.k.a. the charged SYK$_2$ model). We show that the real-time dynamics of physical quantities of interest can be obtained exploiting spectral results in random matrix theory. We first show that the eigenvalues of the correlation matrix corresponding to the

Rong-Zheng Liu, Hua-Lei Yin

Quantum key distribution (QKD) theoretically offers information-theoretic security. The prevailing approach is the prepare-and-measure BB84 protocol, which implements QKD using conventional laser rather than single-photon source via the decoy-state method. However, side-channel attacks targeting sources severely threaten system security. Despite extensive ef

Emre Akıskalıoğlu, Mustafa Atmaca, Lorenzo Ghiro, Giovanni Perin

Range anxiety and long recharging times remain critical barriers to electric vehicle adoption. Dynamic Inductive Charging (DIC) offers a compelling solution by enabling wireless power transfer while driving, potentially reducing battery size requirements and thus vehicle costs. However, DIC infrastructures are expensive and power-constrained, requiring intel

Sefa Kayraklık, Samed Keşir, Batuhan Kaplan, Ahmet Muaz Aktaş

This paper presents a real-world experimental analysis of a modular reconfigurable intelligent surface (RIS) prototype designed to operate in the 5G N78 band. Unlike most RIS studies in the literature that focus on simulations or controlled setups, the proposed system is validated through three phases consisting of indoor measurements, outdoor long-range tes

Jia-Qi Wang, Shao-Jiang Wang

The long-standing tension in the Hubble constant $H_0$ has motivated extensive explorations of both new physics and observational systematics, for example, the late-time systematics in measuring the B-band absolute magnitude $M_B$ of type Ia supernovae, which is degenerated with $H_0$ via an intercept $-5a_B=M_B+5\lg (c/H_0/\mathrm{Mpc})+25$ in the linear re

R. Konno, E. O. Ofek, A. Krassilchtchikov, Y. Shvartzvald

Context. The Large Array Survey Telescope (LAST) is a wide-field visual-band survey designed to explore the variable and transient sky with high cadence. Its raw data stream is automatically processed in near real time at the observatory site, producing science-quality images, catalogs, and transient alerts. Transient alerts are then reported to the Transien

Michael Hanus, Kai-Oliver Prott, Finn Teegen

Functional logic languages are a high-level approach to programming by combining the most important declarative features. They abstract from small-step operational details so that programmers can concentrate on the logical aspects of an application. This is supported by appropriate evaluation strategies. Demand-driven evaluation from functional programming i

Dnyaneshwar R. Bhosale, Piotr Fabrykiewicz, Devashibhai Adroja, Martin Meven

We report a comprehensive investigation of the excitation spectrum of ErFeO$_3$ orthoferrite by means of time-of-flight neutron spectroscopy. The spectrum consists of two distinct components: strongly dispersive spin wave excitations of the Fe$^{3+}$ sublattice spanning $\approx$~9 - 65 meV, and crystal electric field (CEF) excitations of Er$^{3+}$ ions belo

Jérôme Bétrisey, Daniel R. Reese, Camilla Pezzotti, Marie-Jo Goupil

We present detailed asteroseismic modelling of 95 main-sequence solar-like stars and Kepler exoplanet host stars using the FICO procedure, a three-step method that combines forward and inverse techniques that enables precise inference of fundamental stellar parameters such as mass, radius, age, and mean density. We applied the FICO procedure to a catalogue o

Meng-Yun Lai, Hyat Huang, Jutta Kunz, Yun Soo Myung

We investigate spin-induced scalarization of Kerr black holes in an Einstein-scalar-Gauss-Bonnet (EsGB) model that does not admit a linear tachyonic instability of the scalar-free solution. The scalarization mechanism is therefore genuinely nonlinear. We first analyze the decoupled scalar dynamics on fixed Kerr backgrounds and show that sufficiently rapid ro

Ashes Modak, Anowar Shaikh, Manu Kurian, Binata Panda

We investigate the charge transport properties of a relativistic drifting plasma using the kinetic theory within the relaxation time approximation. The collective drift induced by electromagnetic fields is described in terms of a suitably modified distribution function. The analysis is done for both constant and time dependent field configurations. For const

C. Aydar, A. Merloni, G. Zeltyn, C. Andonie

Unraveling the growth of supermassive black holes and their connection to host galaxies requires disentangling the Active Galactic Nuclei (AGN) emission from that of the stellar populations. When an AGN spectrum is observed at different activity phases, if the spectral decomposition properly recognizes the nuclear and stellar components, key physical propert

Gokce Basar, Shuo Song

We consider non-equilibrium evolution of non-Gaussian fluctuations in a hydrodynamic system undergoing a boost-invariant expansion described by Bjorken flow. We derive the evolution equations for two- and three-point velocity correlators using the effective field theory framework and present analytical solutions for them. We show that the average Landau fram

Seno Aji

We develop a coarse-grained theoretical description of the macroscopic emergent electric field generated by phonon-coupled lattice deformations in the breathing and rotational dynamics of a skyrmion lattice under microwave excitation. The analysis identifies the symmetry and dynamical conditions that yield rectified (dc) and oscillating (ac) electric fields,

Kai Kono, Hiroyuki Ebata, Shio Inagaki

Granular materials are inherently out-of-equilibrium systems due to energy dissipation through inelastic collisions and friction. When driven by mechanical agitation such as vibration, they exhibit rich collective behaviors including segregation, clustering, and spontaneous oscillations. Here, we report directional stepwise migration of particle clusters fro

Ethan Bito, Yongli Ren, Estrid He

Large language models (LLMs) are increasingly used for recommendation reranking, but their listwise predictions can depend on the order in which candidates are presented. This creates a mismatch between the set-based nature of recommendation and the sequence-based computation of decoder-only LLMs, where permuting an otherwise identical candidate set can chan

Abdaljalel E. Alizzi, Zurab K. Silagadze

We apply the extended Nikiforov-Uvarov method to the non-relativistic limit of the Dirac equation with a Coulomb potential in spaces of constant curvature. In this case, the radial equation reduces to the Heun equation, and the extended Nikiforov-Uvarov method easily yields a quantization condition which leads to necessary condition under which the resulting

Yusuke Iguchi, Kaede Inoh, Ryosuke Koizumi, Makoto Yokoyama

We demonstrate a magnetic quantum critical point embedded within the superconducting state of Zn-doped CeCoIn$_5$, revealed by a pronounced peak in the magnetic penetration depth at zero temperature $λ(0)$. Using scanning SQUID microscopy, we determine the local superconducting transition temperature $T_{\mathrm c}$ and $λ(0)$. By parameterizing $λ(0)$ in te

Boyuan Gu, Yijin Yang, Shuaiqi Cheng, Xiaorong Ding

Cuffless blood pressure (BP) estimation based on Pulse Transit Time (PTT) has emerged as a promising solution for continuous health monitoring. However, conventional models relying on the Moens-Korteweg equation often fail during rapid hemodynamic fluctuations, as they assume arterial walls are purely elastic and neglect inherent viscoelasticity. To address

Pu-Zhao Kow, Henrik Shahgholian, Tomas Sjödin

The primary goal of this paper is to give a precise definition and prove existence and uniqueness of multiphase quadrature domains for subharmonic functions, ensuring that the prescribed measures are supported in the interior of the resulting domains. The approach to prove existence is based on a variational framework, where we minimize an energy functional

Chunli Miao, Jiarui Qin, Chan Li, Xiaolei Hao

We analytically derive the all-order strong-field S-matrix series incorporating intermediate-state Coulomb-Volkov corrections (ICSFA). Focusing on rescattering processes described by the second-order term, we systematically investigate the impact of intermediate-state Coulomb interactions on above-threshold ionization (ATI) spectra of atomic hydrogen in line

Maitrayee Gupta, Jiří Svoboda, Konstantinos Kouroumpatzakis, Nicolas Peschken

Little is currently known about the large-scale environments of Green Pea (GP) and Blueberry (BB) galaxies, which are low-mass, compact systems with extreme specific star-formation rates (sSFR). Their environments are inherently linked to their formation mechanism, and they may serve as crucial local analogues for high-redshift, reionizing galaxies. This pap

Giuseppe Silano, Quentin Sablé, Marco Tognon, Luigi Iannelli

This paper presents a sensitivity-based tube Nonlinear Model Predictive Control (NMPC) framework for cooperative aerial chains under bounded parametric uncertainty. We consider a planar two-vehicle chain connected by rigid links, modeled with input-rate actuation to enforce slew-rate and magnitude limits on thrust and torque. Robustness to uncertainty in lin

Milan Sil, Alexandre Faure, Helmut Wiesemeyer, Pierre Hily-Blant

Small carbon hydride cations, such as the methylidyne ion (CH$^+$), play an important role in the chemistry of the interstellar medium (ISM). They participate in gas-phase reaction networks leading to the formation of hydrocarbon species that act as precursors to more complex organic molecules. CH$^+$ is a highly reactive ion that is rapidly destroyed by H,

Kacim François-Élie, Alain Mazzolo

In this article, we consider the Benes process with drift $μ(x)=α\tanh(αx + β)$, with $α> 0$, $β\in \mathbb{R}$, that is, the diffusion defined by the stochastic differential equation $dX(t)=α\tanh(αX(t)+β)\,dt + dW(t)$, with an absorbing barrier at $x=a$. After deriving the propagator and key associated quantities--the first-passage-time distribution and th

Brendon J. Brewer

Bayesian hierarchical models are frequently used in practical data analysis contexts. One interpretation of these models is that they provide an indirect way of assigning a prior for unknown parameters, through the introduction of hyperparameters. The resulting marginal prior for the parameters (integrating over the hyperparameters) is usually dependent, so

Neil R. Smalheiser, Joe D. Menke, Arthur W. Holt, Halil Kilicoglu

Objectives. Major research and implementation efforts have been devoted to indexing articles according to the major topics discussed, but much less effort to indexing their publication types and study designs (collectively, PTs). In this Perspective, we discuss how indexing PTs differs from topical MeSH indexing and requires a different approach. Materials a

Kazuhiro Takemoto

Autonomous systems increasingly require moral judgment capabilities, yet whether these capabilities scale predictably with model size remains unexplored. We systematically evaluate 75 large language model configurations (0.27B--1000B parameters) using the Moral Machine framework, measuring alignment with human preferences in life-death dilemmas. We observe a

Abdurakhmon Nosirov, Cosimo Bambi, Leda Gao, Jos de Bruijne

Theoretical models predict that the Galaxy hosts $10^8$-$10^9$ black holes formed from the complete gravitational collapse of heavy stars and that most of these black holes are isolated, without any companion. Within 15 pc of the Solar System ($\sim 50$ ly), there may be a few black holes. If located inside one of the Local Interstellar Clouds - which occupy

Ning Liu

We investigate the dispersion relation of collective modes in ultralight Bose gases under Modified Newtonian Dynamics (MOND). Starting from the coupled Gross-Pitaevskii and MOND Poisson equations, we derive an anisotropic dispersion relation that depends on the angle between the perturbation wavevector and the background gravitational field. This anisotropy

Eslam Ahmed, Ryoi Ohashi, Hiroki Isobe, Kentaro Nomura

Even-denominator fractional quantum Hall (FQH) states can be viewed as topological superconductors of composite fermions, supporting a charged chiral mode and $|\mathcal{C}_{cf}|$ neutral Majorana modes set by the Chern number $\mathcal{C}_{cf}$. Despite ongoing efforts, distinguishing the many competing paired phases remains an open problem. In this work, w

Alberto Salvio

This paper provides a systematic and complete study of thermal field theory with fermion fields of any kind for generic equilibrium density matrices, which feature arbitrary values not only of temperature and chemical potentials, but also average angular momentum. This extends a previous study that focused on scalar fields, to all fermion-scalar theories. Bo

Ritwik Das, Soumen Basak, Mohammad Rezwan Habib, Indra Dasgupta

Recent theoretical and experimental advances have identified cobaltates with a high-spin $d^7$ electronic configuration as promising hosts for spin-orbit entangled $J_{eff} = 1/2$ magnetism that can support bond-dependent exchange interactions. In two-dimensional triangular lattices, the coexistence of such exchange frustration along with geometric frustrati

Yilun Wu, Yi Chen, Julia Velkovska

Jet quenching is a phenomenon in heavy-ion collisions arising from jet interactions with the quark-gluon plasma (QGP). Its study is complicated by the interplay of multiple physics processes that affect jet observables. In addition, detector effects may influence the results and must be accounted for when identifying quenched jets. We employ a Long Short-Ter

Goro Nozue, Hidenori Fujiwara, Satoru Hamamoto, Miwa Tsutsumi

We have investigated the orbital-dependent electronic states of Cd6Ce, a prototype of strongly correlated rare-earth-based Tsai-type quasicrystals and approximants (ACs) by soft and hard x-ray photoemission spectroscopy. Our results reveal that the 4f orbitals are predominantly hybridized with the valence-band electrons far from the Fermi level (EF), in shar

Lanke Fu, Dario Panici, Elizabeth Paul, Alan Kaptanoglu

Balancing plasma performance and coil cost is a significant challenge when designing a stellarator power plant. Most current stellarator designs are produced through two-stage optimization: stage-1 for the equilibrium and stage-2 for a coil design that reproduces its magnetic configuration. Because few proxies connect both stages, two-stage optimization can

Sida Cao, Devdigvijay Singh, Lavonne S. Mack, John P. Palastro

Flying focus techniques produce laser pulses whose focal points travel at arbitrary, controllable velocities. While this flexibility can enhance a broad range of laser-based applications, existing techniques constrain the motion of the focal point to the propagation direction of the pulse. Here, we introduce a flying focus configuration that decouples the mo

Lin Yu, Tianxing Hu, Zhiyu Lei, Xiangyan An

Two extreme events in the universe, fast radio bursts (FRBs) and cosmic rays (CRs), could be correlated, where FRBs with extreme field strength near their sources may contribute to CRs. This study investigates localized particle acceleration driven by FRB-like ultra-relativistic electromagnetic pulses in an electron--positron--ion plasma system. It is found

Marios Galanis, Onur Hosten, Asimina Arvanitaki, Savas Dimopoulos

We recently showed that macroscopic nuclear spin ensembles prepared in coherent spin states can dramatically enhance the interaction rates of weakly interacting cosmic relics-such as dark matter and the cosmic neutrino background-through collective quantum effects analogous to Dicke superradiance, where the de-excitation and excitation rates scale as the squ

Frédéric Blondel, Erwan Jézéquel, Helen Schottenhamml, Majid Bastankhah

So-called engineering or analytical wind farm flow solvers typically build upon two submodels: one for the velocity deficit and one for the wake-added turbulence intensity. While velocity deficit modelling has received considerable attention, wake-added turbulence models are less prevalent in comparison. Yet, accurate estimates of local turbulence intensity

Hank Chen, Joaquin Liniado

We introduce a three-dimensional quantum field theory with an infinite-dimensional symmetry, realized explicitly through a centrally extended affine graded Lie algebra. This symmetry is a direct three-dimensional generalization of the chiral symmetry in the Wess-Zumino-Witten model. Upon performing radial quantization, we construct the Fock space of the theo

Francesco D'Amico, Dario Bocchi, Matteo Negri

Scaling laws in deep learning -- empirical power-law relationships linking model performance to resource growth -- have emerged as simple yet striking regularities across architectures, datasets, and tasks. These laws are particularly impactful in guiding the design of state-of-the-art models, since they quantify the benefits of increasing data or model size

Lang Liu, Zhu Yi, Yungui Gong

The Higgs inflation model with nonminimal coupling, while disfavored by the 1$σ$ region of the latest Atacama Cosmology Telescope Data Release 6 (ACT DR6) observational data, can be reconciled with the ACT DR6 data by incorporating the effects of reheating. In this paper, we consider reheating with a constant equation of state $w_{re}$. For the strong coupli

Wei Li, Min-Xuan Zhou, Yun-Hao Shi, Z. D. Wang

We reveal that the entropic uncertainty relation with a quantum memory is able to intrinsically connect local generic contextuality addressed in the pioneering work by Spekkens and nonlocal quantum resources such as entanglement and Bell nonlocality. Based on the constructed optimal set for any given single-qubit state, we prove rigorously a faithful criteri

John Stack, Ming Wang, Frank Mueller

Distributed architectures are a route to scalable quantum computing, but the performance of fault-tolerant operations across noisy inter-module links remains poorly characterized. We present circuit-level simulations of two key distributed primitives: transversal non-local CNOT and logical teleportation using surface and bivariate-bicycle codes. We then simu

Yucong Cai, Daniel Robbins, Hassaan Saleem

We revisit the question of conformal boundary conditions in the compact free boson CFT in two dimensions. Besides the well-known Neumann and Dirichlet cases, there is an additional proposed one-parameter family of boundary states when the radius is an irrational multiple of the self-dual radius. These additional states have a continuous open string spectrum,

Mary Ryan Baumann, Monica Taljaard, Patrick J. Heagerty, Michael O. Harhay

Cluster-randomized trials (CRTs) are a well-established class of designs for evaluating community-based interventions. An essential task in planning these trials is determining the number of clusters and cluster sizes needed to achieve sufficient statistical power for detecting a clinically relevant effect size. While methods for evaluating the average treat

Jose M. Alvarez, Salvatore Ruggieri

Testing for individual discrimination involves deriving a profile, the comparator, similar to the one making the discrimination claim, the complainant, based on a protected attribute, such as race or gender, and comparing their decision outcomes. The complainant-comparator pair is central to discrimination testing. Most discrimination testing tools rely on t

Nicolas Daans, Philip Dittmann

We establish that all rings of $S$-integers are universally definable in function fields in one variable over certain ground fields including global and non-archimedean local fields. That is, we show that the complement of such a ring of $S$-integers is always a diophantine set. As a technical tool, we use a reciprocity exact sequence for quadratic Witt grou

Arvind Kumar, Moni Kumari, Ariel Weiss

Let $f$ be a genus two cuspidal Siegel modular eigenform. We prove an adelic open image theorem for the compatible system of Galois representations associated to $f$, generalising the results of Ribet and Momose for elliptic modular forms. Using this result, we investigate the distribution of the Hecke eigenvalues $a_p$ of $f$, and obtain upper bounds for th

Sungguk Cha, DongWook Kim

Frame-alignment and collective-identity theories explain how external events become public claims about a group's standing, vulnerability, rights, or obligations. Whether such mechanisms travel to AI-agent communities is unsettled. We test this on Moltbook, an open agent-only platform, coding 1{,}706 post-level units against a four-dimension rubric with Qwen

Shou Yoshikawa

In this paper, we prove that smooth Calabi--Yau hypersurfaces of degree $d$ over complete unramified discrete valuation rings with residue characteristic $p$ are perfectoid split if $p$ is larger than the relative dimension and $p\nmid d$. We also show that unramified lifts of smooth Fano hypersurfaces over fields of characteristic $p>0$ are globally $+$-reg

Wojciech Różowski, Robin Piedeleu, Alexandra Silva, Fabio Zanasi

Behavioural distances provide a quantitative approach to comparing the states of transition systems, moving beyond traditional Boolean notions of equivalence. In this paper, we develop a sound and complete axiomatisation of behavioural distance for nondeterministic processes using Milner's charts, a model that generalises finite-state automata by incorporati

Rushil Saraswat, Aditya Prasad Dash, Huan Zhong Huang, Gang Wang

Energy-energy correlators (EECs) provide a sensitive probe of both perturbative and nonperturbative dynamics in relativistic heavy-ion collisions. Jet-medium interactions enhance particle multiplicity within the jet cone, which must be properly accounted for when extracting the EEC of jet shower hadrons in experiments. To address this issue, we develop an au

Neha Nagaraja, Hayretdin Bahsi, Carlo R. da Cunha

As large language models are integrated into autonomous robotic systems for task planning and control, compromised inputs or unsafe model outputs can propagate through the planning pipeline to physical-world consequences. Although prior work has studied robotic cybersecurity, adversarial perception attacks, and LLM safety independently, no existing study tra

Ali Jaberi, Yonatan Kurniawan, Robert Black, Shayan Mousavi M.

This paper introduces AutoREC, an open-source Python platform for developing, training, and evaluating reinforcement learning (RL) agents that automatically generate equivalent circuit models (ECMs) from electrochemical impedance spectroscopy (EIS) data. Although ECMs are widely used to interpret EIS measurements, their identification typically relies on man

Dexin Wang, Roberto Bomfin, Ahmad Bazzi, Marwa Chafii

Multi-band sensing has emerged as a key enabler of integrated sensing and communication (ISAC), one of the six primary usage scenarios defined for IMT-2030 (6G). The introduction of frequency range 3 (FR3, 7-24 GHz), comprising non-contiguous sub-bands across a wide frequency span, further reinforces the importance of multi-band operation. In such scenarios,

Marco Robol, Paolo Giorgini

Autonomous agents can adapt their behaviour to changing environments, but remain bound to requirements, goals, and capabilities fixed at design time, preventing genuine software evolution. This paper introduces self-evolving software agents, combining BDI reasoning with LLMs to enable autonomous evolution of goals, reasoning, and executable code. We propose

Yizheng Liu, Carl Peterson, Chinmoy Nath Saha, Marko J. Tadjer

Beta-gallium oxide (\beta-Ga2O3) holds enormous potential for medium voltage range power electronic applications. This work reports VBr > 10 kV/Ron,sp = 43 m\Omega*cm2 class edge terminated vertical heterojunction diodes (HJDs) with e-beam/sputtered nickel oxide (NiOx) stack on epitaxial (011) \beta-Ga2O3. The power figure of merit (PFOM) of the HJD exceeds

Mohammadamin Habibollah, Davood Rafiei

Evaluating text-to-SQL systems remains largely fragile: correctness is typically judged by executing predicted and gold SQL queries on a single static database, even though the same queries may behave differently under alternative database instances. This raises a broader language modeling question: Can large language models synthesize semantically meaningfu

Madhumitha Venkatesan, Xuyang Chen, Dongyu Liu

Time-series classification (TSC) has advanced significantly with deep learning, yet most models rely solely on raw numerical inputs, overlooking alternative representations. While texture-based encodings such as Gramian Angular Fields (GAF) and Recurrence Plots (RP) convert time series into 2D images, they often require heavy preprocessing and yield less int

Kirill Rudov, Fedor Sandomirskiy, Leeat Yariv

Correlated equilibria arise naturally when agents communicate or rely on intermediaries such as recommendation systems. We study when a given Nash equilibrium can be improved within the set of correlated equilibria for general objectives. Our key insight is a detail-free criterion: any Nash equilibrium with three or more randomizing agents is generically imp

Zegarelli Angela, Pais Matteo, Peretti Enrico, Celli Silvia

The death of massive stars produces central accreting compact objects and sometimes relativistic jets. Not all jets escape the stellar envelope: unsuccessful, or choked, jets dissipate their energy into a pressurized cocoon, which expands and may break out as a mildly relativistic outflow. We investigate the plasma physics of collapsing massive stars hosting

Zuriel Y. Yescas-Ramos, Andrés Álvarez-García, Huziel E. Sauceda

We present \textsc{dm-PhiSNet}, a physically constrained \textsc{PhiSNet}-based equivariant model that predicts one-electron reduced density matrices (1-RDMs) directly from molecular geometries in an atomic-orbital (AO) basis for accelerated self-consistent field (SCF) workflows. Training follows a two-stage schedule with progressively introduced physically

G. Saltar Rivera, L. Villafane, J. B. Freund

If sufficient training data are available, neural networks are attractive for representing missing physics in simulations, such as sub-grid scales in the coarse-mesh particle-turbulence system we consider. Physical constraints are known to both increase performance and reduce the need for data; we use the complete physics represented in the discretized gover

Roy Lacey

Species-resolved azimuthal anisotropy scaling functions are constructed from identified particle $v_2$ and $v_3$ obtained from event-by-event iEBE-VISHNU simulations for Pb+Pb collisions at $\sqrt{s_{NN}}=2.76$ and $5.02$~TeV. The scaling functions exhibit a robust collapse across transverse momentum, centrality, particle species, and beam energy, indicating

Fazle Elahi Faisal, Qianhui Wu, Baolin Peng, Jianfeng Gao

Recent advances in multimodal large language models (LLMs) have revolutionized web agents that can automate complex tasks on websites. However, their accuracy remains limited by the scarcity of high-quality web trajectory training data. Existing automatic trajectory generation methods suffer from incomplete website coverage due to homepage-based task proposa

Tingting Wang, Shixun Huang, Zhifeng Bao, J. Shane Culpepper

Data discovery - retrieving relevant tables from a data lake in response to user queries - is a fundamental building block for downstream analytics. In practice, data discovery must support different query modalities, including natural language (NL) statements and tables, and accommodate diverse user intents, ranging from open-ended enrichment to task-driven

Jon-Paul Cacioli

When instructed to underperform on multiple-choice evaluations, do language models engage with question content or fall back on positional shortcuts? We map the boundary between these regimes using a six-condition adversarial instruction-specificity gradient administered to two instruction-tuned LLMs (Llama-3-8B and Llama-3.1-8B) on 2,000 MMLU-Pro items. Dis

Sahar Atallah, Peter Carrekmor, Michael Garn, Yukuan Tao

Even simplified models of quantum many-body systems can be difficult to analyse. However, taking inspiration from the foundations of physics, one may wonder whether there are practical advantages to constructing alternative beyond-quantum descriptions of many-body systems. We explore this question in the context of quantum interactions that are diagonal in t

Petr Hrubý, Elima Shehu

We study the set of image tuples arising from fixed cameras observing varying planar 3-dimensional point configurations. We derive a formula for the number of complex critical points of the triangulation problem, which seeks to reconstruct such configurations from noisy image data. Valid for an arbitrary number of views, this formula quantifies the intrinsic

Fangzheng Tian, Debasis Ganguly, Craig Macdonald

Query Performance Prediction (QPP) estimates the retrieval quality of ranking models without the use of any human-assessed relevance judgements, and finds applications in query-specific selective decision making to improve overall retrieval effectiveness. Although unsupervised QPP approaches are effective for lexical retrieval models, they usually perform we

Chia-Ruei Liu, Yongjia Song, Qiong Zhang, Cameron Turner

Engineering design problems are often modeled as multi-objective optimization tasks in which a scalarized utility function selects an optimal design from the Pareto set. In practice, preferences are imperfectly known, so uncertainty in the preference model leads to uncertainty in the resulting optimal design. This paper proposes a probabilistic framework tha

Pablo Ramses Alonso-Martin

We study the effective estimation of the diffusivity and Hurst parameter for the homogenized limit of a class of slow/fast systems. Depending on the system parameters, this limit solves a stochastic differential equation driven by either a Wiener process or a Hermite process. In the class of models we consider, the fast variable is a fractional Ornstein--Uhl

Francesco Viganò, Tolga Birdal, Michael T. Schaub, Mauricio Barahona

We introduce root-to-leaf path random walks on double covers of graded signed graphs and analyze their behavior in a general setting. Viewing simplicial complexes within this framework, we show that these walks induce the natural normalization of the coboundary operator and of the Hodge Laplacians while preserving the basic structural features of combinatori

Tofayel Ahammad Ovee, Daniel Kroeger, Jean-François Louf

Piezoionic hydrogels offer a route to mechanically driven bioelectronic interfaces, but their output is limited by rapid, symmetric ion redistribution that dissipates charge gradients. In biological electrocytes, efficient signal generation arises from the coupling of ion selectivity with spatial confinement that regulates transport. Here, we introduce a con

Jiaru Zhang, Zeyun Deng, Juanwu Lu, Ziran Wang

Drifting models are capable one-step generative models trained to follow a drifting field. The field combines attractive and repulsive softmax-weighted centroids over the data and current-generator distributions. In practice, only a minibatch of $n$ samples from each distribution is available, and each centroid is approximated by an empirical estimate. In th

Mahshid Rezakhani, Nowfel Mashnoor, Kimia Azar, Hadi Kamali

As large language models (LLMs) are increasingly fine-tuned for hardware tasks like RTL code generation, the scarcity of high-quality datasets often leads to the use of rapidly assembled or generated training data. These datasets frequently lack security verification and are highly susceptible to data poisoning attacks. Such poisoning can cause models to gen

Justo Pastor Lambare

In his 1972 book Science At the Crossroads, Helbert Dingle attacked the consistency of special relativity through a fallacious argument championed by the crank community even to this day. Dingle's affair is a curious chapter in the history of physics and, more generally, science. We briefly review Dingle's case from a historical and didactic perspective.

Hannah M. Bellenbaum, Dave A. Chapman, Maximilian P. Böhme, Thomas Gawne

X-ray Thomson scattering (XRTS) is a common diagnostic used in the warm dense matter (WDM) regime to estimate plasma parameters like density, temperature and charge state. Experimental analysis typically relies on a forward model to obtain estimates for these parameters, as the measured spectrum is a convolution of the dynamic structure factor (DSF) and the

Usman Nazir, Disha Gomathinayagam, Muhammad Kamran, Sara Khalid

Pakistan experienced an unusually severe flood season between June and December 2025, with cascading impacts on population, infrastructure, and agriculture. Existing operational flood products (e.g., UNOSAT) provide valuable episode-level snapshots but rarely deliver spatially and temporally continuous inundation maps at near-real-time latency within the cou

Astitva Goel, Samarth Galchar, Sumit Kanu

Remaining Useful Life (RUL) estimation is a critical component of Prognostics and Health Management (PHM), enabling proactive maintenance scheduling and reducing unplanned failures in industrial equipment. This paper presents a comparative study of machine learning approaches for RUL estimation on the NASA C-MAPSS turbofan engine dataset: classical baselines