April 2026 arXiv papers — page 58

Guancheng Wang, Qinghua Xu, Lionel C. Briand, Zhaoqiang Guo

Large language models (LLMs) have recently shown strong potential for generating project-level unit tests. However, existing state-of-the-art approaches primarily rely on execution-path information to guide prompt construction, which is often insufficient for complex software systems with rich inter-class dependencies, deep call chains, and intricate object

Ayushi Mehrotra, Dipkamal Bhusal, Michael Clifford, Nidhi Rastogi

Feature attribution methods explain the predictions of deep neural networks by assigning importance scores to individual input features. However, most existing methods focus solely on marginal effects, overlooking feature interactions, where groups of features jointly influence model output. Such interactions are especially important in image classification

Yang Dan, Dallas R. Trinkle

The energy density method is generalized to include spin polarization with the full formalism derived based on spin-density functional theory, which aims at decomposing the total energy into well-defined atomic energies. The method involves two steps: (1) decomposing the total energy into spin-polarized energy density functions in real space, and (2) integra

Vivek Upadhyay, Amaresh Chakrabarti

Background: The classroom discourse analysis has been transformed by the growing use of audio-video multimodal data, which demands analytical methods that balance interpretive depth with computational scalability. Methods: This study introduces the Audio Video Verbal Analysis (AVVA) framework, adapted from the Verbal Analysis method to integrate qualitative

Shay Allen Logan

I introduce PEDAL -- a probabilistic epistemic logic meant to capture, in propositional dynamic terms, the epistemic state of an agent engaged in checking whether a program meets its specification. Semantically, PEDAL is built `on top of' PDL and uses probability measures defined on the set of possible program valuations of an otherwise-specified PDL-model.

Anna Zhang, Qinglan Luo, London Bielicke, Eunice Jun

The design of scientific experiments deserves its own variation of formal verification to catch cases where scientists made important mistakes, such as forgetting to take confounding variables into account. One of the most fundamental underpinnings of science is causality, or what it means for interventions in the world to cause other outcomes, as formalized

Daqian Cheng, Xuchu Ding, Yujia Wu, Xiang Zhang

Localization for autonomous vehicles on highways remains under-explored compared to urban roads, and state-of-the-art methods for urban scenes degrade when directly applied to highways. We identify key challenges including environment changes under information homogeneity, heavy occlusion, degraded GNSS signals, and stringent downstream requirements on accur

Yan Jin, Jizhao Zang, Sarang Yeola, Alexa R. Carollo

Spatially periodic systems of coupled bosons are governed by on-site interactions and tunneling between sites, opening a rich phase space of many-body behavior. Here, we explore nanophotonic control of collective many-body light states in a driven-dissipative Kerr microresonator. We demonstrate a non-equilibrium Mott insulator to superfluid transition that a

Jackson Brough

Real numbers in constructive mathematics have always seemed to require compromises of one form or another. Classical proofs of Cauchy completeness require countable choice, Bishop's setoid construction introduces persistent bookkeeping overhead on every definition and theorem, and Dedekind cuts force cumbersome universe-level tracking in predicative type the

Etha Tianze Hua, Tian Yun, Ellie Pavlick

We define and investigate source-modality monitoring -- the ability of multimodal models to track and communicate the input source from which pieces of information originate. We consider source-modality monitoring as an instance of the more general binding problem, and evaluate the extent to which models exploit syntactic vs. semantic signals in order to bin

Ignasi Sole

Portamento in string performance has been studied primarily as a binary presence-or-absence phenomenon, with existing research measuring frequency of occurrence and, less commonly, duration in milliseconds. This paper introduces a third quantitative descriptor; the spectrographic gradient of the portamento slide, measured in Hz/second, and demonstrates its m

Brian VanVoorst, Nicholas Walczak, Christopher Gilleo, Charles Meissner

This paper introduces EgoMAGIC (Medical Assistance, Guidance, Instruction, and Correction), an egocentric medical activity dataset collected as part of DARPA's Perceptually-enabled Task Guidance (PTG) program. This dataset comprises 3,355 videos of 50 medical tasks, with at least 50 labeled videos per task. The primary objective of the PTG program was to dev

Roman Parpalak, Denis Utkin

Given $n$ lines in general position in the plane, how many bounded triangular faces can the arrangement have? We construct a straight-line affine arrangement of $19$ lines satisfying the conditions of the iterative construction by Bartholdi, Blanc, and Loisel, thereby obtaining an infinite series of straight-line arrangements attaining the maximum number of

Eduardo Said Merin-Martinez, Andres Mendez-Vazquez, Eduardo Rodriguez-Tello

Kolmogorov-Arnold Networks (KANs) are a recent neural network architecture offering an alternative to Multilayer Perceptrons (MLPs) with improved explainability and expressibility. However, KANs are significantly slower than MLPs due to the recursive nature of B-spline function computations, limiting their application. This work addresses these issues by pro

Rajeev Dutt, Bhawna Sahni, Yao Zhao, Yuji Go

The electronic and thermoelectric (TE) transport properties of 13 n-type and p-type half-Heusler alloys are computationally examined using Boltzmann transport. The electronic scattering times resulting from all relevant phonon interactions and ionized impurity scattering (IIS) are fully accounted for using ab initio extracted parameters. We find that at room

Cooper Veit

Every ML kernel ships with an implicit contract about what it computes. People rarely write the contract down. When two kernels disagree -- when a matmul on AMD produces a different gradient than the same matmul on NVIDIA, when a fused attention kernel silently downcasts an accumulator, when an out-of-bounds access returns zero on one stack and garbage on an

João Mattos, Arlei Silva

We propose Mochi, a Graph Foundation Model that addresses task unification and training efficiency by adopting a meta-learning based training framework. Prior models pre-train with reconstruction-based objectives such as link prediction, and assume that the resulting representations can be aligned with downstream tasks through a separate unification step suc

Michael Neumann, Lasse Bischof, Maria Rauschenberger, Eva-Maria Schön

The rapid development of GenAI technologies is transforming learning, assessment, and academic production in higher education. Despite increasing student adoption, many institutions lack operational mechanisms to systematically align regulations and curricula with evolving generative artificial intelligence practices, creating regulatory ambiguity and academ

Beatriz Souza, Chang Lou, Suman Nath, Michael Pradel

Complex software systems often suffer from silent failures, i.e., violations of the intended semantics that do not cause explicit errors. A promising approach to detect such errors is to use system-specific runtime checkers that monitor the execution of a system and check for violations of the intended semantics. However, writing such checkers for a given so

Yang Lu, Rabimba Karanjai, Lei Xu, Weidong Shi

AI research pipelines can now generate academic work that may satisfy existing peer review standards for quality, novelty, and methodological rigor. However, the publication system was built around the assumption that research is produced by human authors. It therefore lacks a clear way to evaluate work when the knowledge claim may be valid but the producer

Juniper Lovato, Laurent Hébert-Dufresne, Mohsen Ghasemizade, Jonathan St-Onge

Privacy preferences are not fixed individual traits, they depend on context and lived experiences. In this study, we analyze 2,912 survey responses from 782 college students collected over seven survey periods during 2023 and 2024. We ask about their usage of social media, the security settings of their accounts, and measure their comfort in sharing personal

Tanmay Gautam, Alireza Bahramali, Sandeep Atluri

Automated red-teaming methods for large language models typically optimize attack prompts within a fixed, human-designed strategy, leaving the attack strategy itself unchanged. We instead optimize the strategy. We propose AutoRISE, a method that searches over executable attack programs rather than individual prompts. At each iteration, a coding agent edits a

Patrick S. Noerr, Ahmed A. Abushawish, Gulcin Pekkurnaz, Padmini Rangamani

Neuronal function requires precise spatial organization of mitochondria to meet localized energetic demand. However, the physical constraints governing mitochondrial transport in axons remain poorly defined. Bidirectional motor-driven trafficking inherently introduces the potential for collisions, but the implications of these interactions for transport fail

Beomchang Kim, Subhrajyoty Roy, Priyam Das

Optimization over the Stiefel manifold $\mathrm{St}(p,d)$, the set of $p \times d$ column-orthonormal matrices, is fundamental in statistics, machine learning, and scientific computing, yet remains challenging in the presence of non-convex, non-smooth, or black-box objectives. Existing methods largely rely on either convex relaxations or gradient-based Riema

Abigail McClain Gomez, Fiona Abney-McPeek, Hong-Ye Hu, Susanne F. Yelin

Measurement-only circuits provide a minimal setting in which repeated local projections can either generate or suppress many-body entanglement, giving rise to measurement-induced phase transitions and dynamical regimes, that might have no unitary counterpart. Here we investigate entanglement and information transitions in one-dimensional measurement-only Cli

Vittoria Tommasini, Nils L. Vu, Mark A. Scheel, Saul A. Teukolsky

Reducing orbital eccentricity in numerical relativity simulations of binary black holes is essential for producing astrophysically relevant gravitational wave models, as many of these systems are expected to be near-circular in nature. Standard eccentricity reduction procedures rely on iterative schemes, often requiring four or more trial simulations to achi

Maurice Funk, Daumantas Kojelis

We study the expressive power of message-passing aggregate-combine-readout graph neural networks (ACR-GNNs). Particularly, we focus on the first-order (FO) properties expressible by this formalism. While a tight logical characterisation remains a difficult open question, we make two contributions towards answering it. First, we show that sum aggregation and

Simon Santschi, Niels C. Vooijs

We complete Maksimova's classification of the normal extensions of S4 with interpolation. In particular, we prove Craig interpolation for the six extensions of S4 for which Craig interpolation was still open. The proof strategy builds upon the ideas of Smory\'nski, but employs a novel approach using Fine's frame formulas for splitting clusters.

Goran Erceg, James Kelly, Judy Kennedy, Christopher Mouron

Recent work of Piotr Oprocha and his collaborators has provided a number of delicate examples of dynamical systems separating specification, shadowing, and periodic-point density, primarily in symbolic or totally disconnected spaces. The goal of the present paper is to demonstrate that similar - and in some cases sharper - separations occur on the Lelek fan,

Deepank Girish, Yi Hao Chan, Sukrit Gupta, Jing Xia

Several brain foundation models (FM) have recently been proposed to predict brain disorders by modelling dynamic functional connectivity (FC). While they demonstrate remarkable model performance and zero- or few-shot generalization, the salient features identified as potential biomarkers are yet to be thoroughly evaluated. We propose RE-CONFIRM, a framework

M. L. McLanahan, A. P. Ramirez

We measure the colossal permittivity in single crystal Fe$_2$TiO$_5$ using broadband spectroscopy in the frequency range 20 Hz - 1 MHz. The relaxation response is analyzed using a Debye-like model with Arrhenius activation in two different ways and yields an energy barrier of 286.1 $\pm$ 2.8 meV. DC transport yields an activation energy of 288.8 $\pm$ 2.8 me

Emily Zhou, Marcus Ma, Kleanthis Avramidis, Gabor Mihaly Toth

Eye movement and memory retrieval are deeply and bidirectionally intertwined, however existing literature is generally confined to controlled lab settings. We investigate the relationship between eye gaze and memory recall in free-form autobiographical recall, which comprises both autonoetic consciousness -- the ability to mentally place oneself in the past

Felix Leonhard Janzen, Lukas Moddemann, Alexander Diedrich, Oliver Niggemann

In many cyber-physical systems, especially in critical applications such as aeroplanes, data to train anomaly detection and diagnosis algorithms is lacking due to data protection issues and partial observability. To combat this inherent lack of data, we introduce a high-fidelity, physics-informed co-simulation of a common aircraft main-fuel-pump system model

Xinyu Zhang, Ameer Qaqish, D. Y. Lin, Didong Li

In many longitudinal studies, a large number of variables are measured repeatedly over time, with substantial missing data. Existing methods, such as probabilistic principal component analysis (PPCA), are ill-equipped to handle such incomplete, high-dimensional longitudinal data, as they fail to account for the nested sources of variation and temporal depend

Siddhesh Raut, Ethan Nadler, Andrew Benson

We improve upon the parametric model for the evolution of the density profiles of self-interacting dark matter (SIDM) halos introduced in Yang et al. (2024b), by considering the effects of mass accretion on a SIDM halo's gravothermal evolution. The original parametric model accurately predicts parameters $V_{\max}$ and $R_{\max}$, but with a tendency to over

Jonathan Gagné, Jacqueline K. Faherty, Azul Ruiz Diaz, Louis-Philippe Coulombe

We present a Python spectrophotometry extraction tool tailored for fast-moving point sources detected in the SPHEREx mission, and use it to construct a set of 0.75-5.0 $\mu$m low-resolution ($\lambda/\Delta\lambda \sim 50$) spectrophotometry data products based on the SPHEREx Quick Release 2 (QR2) for a set of 6003 L0-Y1 ultracool dwarfs: 2050 known ultracoo

Ron Shnapp, Stefano Brizzolara

We introduce a modal representation for Lagrangian trajectories in turbulence, termed Lagrangian Proper Orthogonal Decomposition (LPOD). An ensemble of particle trajectories is used to construct velocity time series, which are normalized independently for each trajectory to isolate fluctuations. Principal Component Analysis is then applied to the resulting d

Zhimu Zhou, Yanpeng Zhao, Qiuyu Liao, Bo Zhao

Visual planning represents a crucial facet of human intelligence, especially in tasks that require complex spatial reasoning and navigation. Yet, in machine learning, this inherently visual problem is often tackled through a verbal-centric lens. While recent research demonstrates the promise of fully visual approaches, they suffer from significant computatio

Tim Alhäuser, Heinz-Peter Breuer

We explore the performance of the time-convolutionless (TCL) projection operator technique using the Fano-Anderson model as a test case. Comparing the exact TCL master equation with an expansion in powers of the strength of the system-environment coupling, we analyze the transient dynamics as well as the steady-state behavior. For a Lorentzian spectral densi

Stylianos A. Tsilioukas

We report the design, construction, and classroom use of a low-cost inertia dynamometer, built as a year-long project-based learning (PBL) activity with adult students at a Greek Evening Vocational High School (EPAL). The apparatus consists of a machined steel drum of calculated moment of inertia $I = 0.6507~\mathrm{kg\,m^2}$, mounted on a student-welded fra

Shaun Fallat, Samir Mondal, Hristo Sendov

In this framework, the extremal case corresponds to the tightest nontrivial relaxation in this hierarchy, in which every proper principal submatrix is constrained to be positive semidefinite, while the global positive semidefiniteness condition is governed by the determinant. In this paper, we study the determinants of locally positive semidefinite matrices

Joshua O'Connor

We outline the analysis of three-particle interactions and decay processes for leading-order modifications in a Lorentz-violating quantum field theory.

Luka Andrenšek

We investigate equations in Kiselman's semigroup $K_n$, generated by $a_1, \dots, a_n$. Let $f$ denote the zero element of $K_n$. We prove that if $y \in K_n$ lies in the subsemigroup generated by $a_2, \dots, a_n$, then $x y = f$ implies $x = f$. In contrast, the equation $x a_1 = f$ admits non-trivial solutions. We describe the solution set of this equatio

Ran Raz

We prove a lower bound of $\Omega\left(n^{1.5}\right)$ for the number of product gates in non-commutative arithmetic circuits for an explicit $n$-variate degree-$n$ polynomial $f_{n}$ (over every field). We observe that this implies that over certain non-commutative rings $R$, any arithmetic circuit that computes the induced polynomial function $f_{n}: R^n \

Junjie Zhao, Guangming Liang, Xiaonan Liu, Dongzhu Liu

Accurate yet low-latency channel state information (CSI) acquisition is essential for multiple-input multiple-output (MIMO) communication systems. While advanced deep generative models, such as score-based and diffusion models, enable high-fidelity CSI reconstruction from limited pilot observations, they often suffer from high inference latency. To achieve a

Casandra D. Monroe

We define branched bending deformations as deformations supported on a piecewise totally geodesic complex of $(n-1)$-dimensional faces meeting along $(n-2)$-dimensional branching loci. These are a generalization of bending deformations, as introduced by Johnson and Millson. We give a lower bound on the dimension of the (infinitesimal) deformation space suppo

Eduardo Miranda

This paper describes a group interview technique designed to support documentless process assessments while promoting at the same time collaboration among assessment participants. The method was successfully used in one consulting assignment where it got previously discording participants, talking to each other and agreeing on the issues. The technique borro

Anamta Khan, Ratna Kandala, Deepti, Sheza Munir

Social media platforms have become primary channels for health information in the Global South. Using gomutra (cow urine) discourse on YouTube in India as a case study, we present a post-facto Large Language Model (LLM)-assisted discourse analysis of 30 multilingual transcripts showing that promotional content blends sacred traditional language with pseudo-s

Jessica Moosmann, Irdin Pekaric, Giovanni Apruzzese

Security Operations Centers (SOCs) are pivotal in modern enterprises. Tasked to monitor complex network environments constantly under attack, SOCs can be active 24/7 and can include hundreds of operators supported by state-of-the-art technologies. Abundant research has studied the internal processes of SOCs, highlighting their pros and cons, as well as the c

Alexander Stuy, Nodin Weddington

An artificial world of barriers and plains scattered with food is used to test the feasibility of using genetic algorithms to optimize hebbian neural networks to perform on problems without apriori knowledge of the problem domain. A formal L-System based genetic alphabet for neural networks, titled Lsys, and a neural network genetic modeling tool titled Wp1h

Grigory Sapunov

We study learned memory tokens as a computational scratchpad for a single-block Universal Transformer with Adaptive Computation Time (ACT) on Sudoku-Extreme, a combinatorial reasoning benchmark. Memory tokens are empirically necessary: no configuration without them reaches non-trivial performance. The optimal count has a sharp lower threshold (T=0 always fai

Tancredi Schettini Gherardini

This thesis discusses exotic 7-spheres, i.e. manifolds that are homeomorphic but not diffeomorphic to the ordinary 7-sphere, using a set of analytical and computational tools from theoretical physics. The theory of fibre bundles and instantons, together with their relation to Yang-Mills theory, are reviewed, before presenting a generalisation of self-duality

Haipeng An, Haoming Nie

Solar-reflected dark matter (SRDM) consists of dark-matter particles up-scattered and accelerated by energetic electrons in the solar interior, producing a high-velocity tail that can enhance signals in direct-detection experiments, especially for MeV-scale masses. We consider an inelastic dark matter (iDM) model, in which solar scattering populates the exci

Santiago Ojeda-Ramirez, Eva Durall Gazulla, Kylie Peppler

Who gets to decide how generative AI tools enter students' classrooms? We report on a five-week participatory design program in which three 11th-grade Latinx students and three high school teachers in California negotiated how generative AI tools would be used and taught about in learning environments. Drawing on video recordings and designed artifacts, we a

Chong Wang, Yanqi Zhang, Zhezhen Jin, Annette O'Connor

Network meta-analysis (NMA) combines direct and indirect comparisons across a treatment network, but exact contribution decompositions that reproduce NMA estimates are lacking, especially for multi-arm trials with correlated contrasts. We develop a contrast-space projection formulation of NMA that expresses the estimator as a linear mapping of observed pairw

Haohan Xu, Jason Bohne, Pawel Polak, Yurij Baransky

We study the detection of transient liquidity erosion ("crumbling quotes") in electronic limit order books, where observable quote deterioration may reflect either mechanical liquidity withdrawal or informational repricing. Using the ABIDES agent-based simulator, we construct a multi-agent environment in which crumbling emerges from stochastic regime switche

Jayashis Das, Vatsana Tiwari, Manish Kumar, Auditya Sharma

We investigate the effect of a periodic electric field drive on the generalized Aubry-Andr\'e model, also known as the Ganeshan-Pixley-Das Sarma (GPD) model, which is well known as a host of mobility edges. Our study of the Floquet spectrum of the driven GPD model uncovers the emergence of two distinct Floquet mobility edges, a delocalized--localized (DL) ed

Ricardo G. Sanfelice, Berk Altin

The problem of controlling hybrid dynamical systems using model predictive control (MPC) is formulated and sufficient conditions for asymptotic stability of a set are provided. Hybrid dynamical systems are modeled in terms of hybrid equations, involving a differential equation and a difference equation with inputs and constraints. The proposed hybrid MPC alg

Huan Liu, Kaushik Bhattacharya

Molluscan shells come in various shapes and sizes. Despite this diversity, each species produces a shell with a characteristic shape that is independent of environmental conditions. We seek to understand this robust complexity. We are guided by two principles in the spirit of D'Arcy Thompson. First, the growth is governed by the repeated and continuous a

LHCb collaboration, R. Aaij, M. Abdelfatah, A. S. W. Abdelmotteleb

This paper presents the first measurement of the forward-backward asymmetry, $A_{\rm FB}$, and the flat term, $F_{H}$, that parameterise the angular distribution of the $B^+\to\pi^+\mu^+\mu^-$ decay. The proton-proton collision dataset used in the analysis corresponds to an integrated luminosity of 9 fb$^{-1}$, collected with the LHCb experiment between 2011

Santiago Ojeda-Ramirez, Symone Gyles, Kylie Peppler

As generative AI systems increasingly mediate learning, they are often treated as authoritative sources of knowledge. This perspective paper introduces community-based AI learning as a framework that repositions authority, grounding AI engagement in learners' lived and community-based epistemologies. Drawing from community-driven learning and constructionist

I. N. Khoroshiy, A. Podlesnyak, D. Menzel, M. C. Rahn

Chiral magnetic textures, such as skyrmions, are of great interest to the condensed matter community due to their novel transport properties. The stabilization of topologically non-trivial magnetic phases, like the skyrmion lattice in MnSi, is governed by underlying magnetic interactions which can be probed via measurements of spin-wave excitations. Here, we

Laki Iinbor, Zhiyang Dou, Wojciech Matusik

We introduce Soft Anisotropic Diagrams (SAD), an explicit and differentiable image representation parameterized by a set of adaptive sites in the image plane. In SAD, each site specifies an anisotropic metric and an additively weighted distance score, and we compute pixel colors as a softmax blend over a small per-pixel top-K subset of sites. We induce a sof

Nicholas Owens, James Wadsley, Robert Wissing, Ben Keller

In this paper, we introduce the first implementation of magnetic field-aligned hyperbolic diffusion for standard smoothed particle (magneto-)hydrodynamics (SPH), and its linear-exact gradient extension (LESPH). Hyperbolic diffusion differs from traditional parabolic methods by incorporating the physical characteristic speed of diffusing particles and is comp

Jeremy Klotz, Shree K. Nayar

Solar panels are increasingly deployed in cities on rooftops, walls, and urban infrastructure. Although the panel costs have fallen in recent years, the soft costs of installing them have not. These soft costs include assessing the illumination (irradiance) of a panel, which is typically performed using a 3D model that fails to capture small nearby structure

Benjamin Savino, Wen Wu

Direct numerical simulations of an accelerating turbulent boundary layer (TBL) over a smooth wall and a wall fully covered with streamwise-aligned riblets are performed to investigate drag modulation and its underlying mechanisms. The riblet-scale flow is resolved using an immersed boundary method. Starting from a zero-pressure-gradient (ZPG) TBL at Re=6800,

M. Carducci, D. Meloni, M. Parriciatu, J. T. Penedo

We investigate theories of flavour based on genus $g=2$ modular invariance and analyze how fermion mass hierarchies can be generated in this context, in the vicinity of invariant points or regions in moduli space where a residual symmetry is preserved. We apply this mechanism of modular proximity-induced hierarchies to the quark sector, with the vacuum expec

Luciano Loris Viteritti, Riccardo Rende, Christopher Roth, Anirvan Sengupta

The two-dimensional Hubbard model at finite doping hosts competing or intertwined orders, resulting in conflicting conclusions from different computational approaches regarding its ground state. We show that a key source of such discrepancies is the bias encoded in the variational ansatz. We consider three different Transformer backflow fermionic wave functi

Euclid Collaboration, S. H. Vincken, K. Rojas, M. Melchior

We present AstroVink, a vision transformer classifier designed for automated identification of strong lens candidates in Euclid imaging. We build upon the DINOv2 encoder, fine tuned to distinguish between lens and non-lens galaxies. Our base model, trained on simulated strong lens systems and labelled non lenses, recovers 88 of the 110 lens candidates within

Francesco D'Eramo, Silvia Manconi, Tommaso Sassi

We present a framework in which three classes of dark matter number-changing processes can affect both the relic abundance via thermal freeze-out in the early universe and the generation of indirect cosmic-ray signals today. These processes are: (i) direct annihilations into Standard Model final states; (ii) annihilations into metastable on-shell mediators t

John Orlowski-Scherer, Anna Kofman, Tanay Bhandarkar, Mark Devlin

The Simons Observatory Large Aperture Telescope Receiver (SO LATR) is a next generation Cosmic Microwave Background camera equipped with > 60, 000 detectors operating at 100 mK. Maintaining these detectors at the correct temperatures and locations requires stiff, cryogenically insulating struts. In this paper we report the design and performance of a novel g

Tom Drechsler, Matthias Vojta

The Ising chain in a transverse field stands out as a paradigmatic example for a quantum phase transition. CoNb$_2$O$_6$ has been discussed as a material realization of this physics, but it was later realized that its magnetic exchange couplings are more complicated, taking the form of twisted Kitaev chains. Here we study a three-dimensional model for CoNb$_

Javier Fuentes-Martín, Javier López Miras, Adrián Moreno-Sánchez

At finite temperature, the decoupling of heavy Matsubara modes allows a four-dimensional quantum field theory to be matched onto a purely spatial, three-dimensional effective field theory (EFT). This dimensional reduction is a crucial prerequisite for the precise computation of thermal observables, most prominently those related to cosmological phase transit

João Barata, Matvey V. Kuzmin, Ian Moult, Andrey V. Sadofyev

We study energy-energy correlators (EECs) in many-body quantum states, focusing on the matter produced in the aftermath of heavy-ion collisions. We analyze the angular structure of EECs in the collinear limit and identify a sequence of dynamical regimes. At the largest angular separations within the small-angle regime, the observable is dominated by disconne

Motoo Suzuki

We argue that kinetic mixing between topological flux sectors generates an effective shift of the QCD $\bar\theta$ angle, thereby inducing CP-violating effects. To demonstrate this mechanism, we analyze a $(1+1)$-dimensional $U(1)\times U(1)$ gauge theory as a controlled setting, where kinetic mixing leads to observable shifts in electric fluxes. We then ext

Rinchen Sherpa, Anuvab Sarkar, Tarak Nath Maity, Paramita Dutta

Novel target materials with anisotropic response will play a key role in detecting low-mass dark matter in upcoming experiments. Bilayer graphene is one such material that has been proposed for the detection of sub-MeV mass dark matter particles via electronic excitations. In this work, we calculate scattering rate via a massive mediator in bilayer graphene.

Erwin H. Tanin, Yikun Wang

Sufficiently strong electric fields can produce charged-particle pairs via the Schwinger effect. We argue that steep matter-density gradients, as can arise in neutron star interiors, would analogously produce neutrino-antineutrino pairs. We then discuss observational signatures of these gradient-produced (anti)neutrinos and how they could provide new probes

Daniel Stapleton, Svea Hernandez

The STIS team monitors the time dependent sensitivity (TDS) of each grating with one from a set of three secondary CALSPEC standard stars: GRW+70D5824, AGK+81D266, and BD+28D4211. Here, we use the three primary CALSPEC White Dwarf standard stars, dubbed the standard star "triad" (GD71, GD153, G191B2B), as an independent set of standards to verify the accurac

Yen-Siang Wu, Rundong Luo, Jingsen Zhu, Tao Tu

How can we tell whether a video has been sped up or slowed down? How can we generate videos at different speeds? Although videos have been central to modern computer vision research, little attention has been paid to perceiving and controlling the passage of time. In this paper, we study time as a learnable visual concept and develop models for reasoning abo

Nicolae Filat, Ahmed Hussain, Konstantinos Kalogiannis, Elena Burceanu

Streaming Continual Learning (CL) typically converts a continuous stream into a sequence of discrete tasks through temporal partitioning. We argue that this temporal taskification step is not a neutral preprocessing choice, but a structural component of evaluation: different valid splits of the same stream can induce different CL regimes and therefore differ

MD Nahidul Hasan Sabit

We study spectral properties of quantum many-body Hamiltonians through a subsystem-based framework. Given a Hamiltonian of the form $H = \sum_{X \subseteq \Lambda} \Phi(X)$ acting on a tensor product Hilbert space, we associate to each subset $S \subseteq \Lambda$ a subsystem Hamiltonian $H_S$ and its spectrum $\mathcal{S}(S) = \sigma(H_S)$. This produces a

Benjamin Kohler, David Zollikofer, Johanna Einsiedler, Alexander Hoyle

Recent work has used LLM agents to reproduce empirical social science results with access to both the data and code. We broaden this scope by asking: Can they reproduce results given only a paper's methods description and original data? We develop an agentic reproduction system that extracts structured methods descriptions from papers, runs reimplementations

Hao-Yu Hsu, Tianhang Cheng, Jing Wen, Alexander G. Schwing

Understanding human activities and their surrounding environments typically relies on visual perception, yet cameras pose persistent challenges in privacy, safety, energy efficiency, and scalability. We explore an alternative: 4D perception without vision. Its goal is to reconstruct human motion and 3D scene layouts purely from everyday wearable sensors. For

Matt Larson, Ethan Partida

We prove the Hard Lefschetz theorem and Hodge-Riemann relations for certain rings which resemble the cohomology rings of projectivizations of globally generated vector bundles over toric varieties. This proves new cases of the standard conjecture of Hodge type and gives Bloch-Gieseker-type results for tautological classes of matroids.

Isabella Liu, An-Chieh Cheng, Rui Yan, Geng Chen

Long-horizon manipulation remains challenging for vision-language-action (VLA) policies: real tasks are multi-step, progress-dependent, and brittle to compounding execution errors. We present LoHo-Manip, a modular framework that scales short-horizon VLA execution to long-horizon instruction following via a dedicated task-management VLM. The manager is decoup

Natalie Collina, Jiuyao Lu, Georgy Noarov, Aaron Roth

We study the minimax sample complexity of multicalibration in the batch setting. A learner observes $n$ i.i.d. samples from an unknown distribution and must output a (possibly randomized) predictor whose population multicalibration error, measured by Expected Calibration Error (ECE), is at most $\varepsilon$ with respect to a given family of groups. For ever

Ceyuan Yang, Zhijie Lin, Yang Zhao, Fei Xiao

We present Omni, a unified multimodal model natively trained on diverse modalities, including text, images, videos, 3D geometry, and hidden representations. We find that such training enables Context Unrolling, where the model explicitly reasons across multiple modal representations before producing predictions. This process enables the model to aggregate co

D. Pauli, T. N. Parsons, R. K. Prinja

Massive stars with their strong ionizing radiation and strong stellar winds are the key feedback agents of the universe. Stellar winds of massive stars are often measured by fitting resonance lines in the UV using non-LTE stellar atmosphere models. So far, the line formation regions of these lines have not been measured empirically, preventing a comparison t

Siddhant Midha, Yifan F. Zhang, Daniel Malz, Dmitry A. Abanin

Belief propagation has recently emerged as a powerful framework for evaluating tensor networks in higher dimensions, combining computational efficiency with provable analytical guarantees. In this work, we develop the first end-to-end theory of tensor network belief propagation for a class of projected entangled pair states satisfying \emph{strong injectivit

Arunabh Majumdar

We present CrossCommitVuln-Bench, a curated benchmark of 15 real-world Python vulnerabilities (CVEs) in which the exploitable condition was introduced across multiple commits - each individually benign to per-commit static analysis - but collectively critical. We manually annotate each CVE with its contributing commit chain, a structured rationale for why ea

Zhiqiu Xu, Shibo Jin, Shreya Arya, Mayur Naik

As frontier language models attain near-ceiling performance on static mathematical benchmarks, existing evaluations are increasingly unable to differentiate model capabilities, largely because they cast models solely as solvers of fixed problem sets. We introduce MathDuels, a self-play benchmark in which models occupy dual roles: each authors math problems u

Kuan Heng Lin, Zhizheng Liu, Pablo Salamanca, Yash Kant

We present Vista4D, a robust and flexible video reshooting framework that grounds the input video and target cameras in a 4D point cloud. Specifically, given an input video, our method re-synthesizes the scene with the same dynamics from a different camera trajectory and viewpoint. Existing video reshooting methods often struggle with depth estimation artifa

Songen Gu, Yuhang Zheng, Weize Li, Yupeng Zheng

Recently, end-to-end robotic manipulation models have gained significant attention for their generalizability and scalability. However, they often suffer from limited robustness to camera viewpoint changes when training with a fixed camera. In this paper, we propose VistaBot, a novel framework that integrates feed-forward geometric models with video diffusio

Pegah Khayatan, Jayneel Parekh, Arnaud Dapogny, Mustafa Shukor

Despite impressive progress in capabilities of large vision-language models (LVLMs), these systems remain vulnerable to hallucinations, i.e., outputs that are not grounded in the visual input. Prior work has attributed hallucinations in LVLMs to factors such as limitations of the vision backbone or the dominance of the language component, yet the relative im

Bartosz Balis, Michal Orzechowski, Piotr Kica, Michal Dygas

Scientific workflow systems automate execution -- scheduling, fault tolerance, resource management -- but not the semantic translation that precedes it. Scientists still manually convert research questions into workflow specifications, a task requiring both domain knowledge and infrastructure expertise. We propose an agentic architecture that closes this gap

David Kremer, Nicolas Dupuis

Peaked quantum circuits, whose output distribution is sharply concentrated on a single bitstring, have emerged as a promising candidate for verifiable quantum advantage, as the correctness of the quantum output can be checked by simply comparing against the known peak. Recent work by Gharibyan et al. arXiv:2510.25838 claimed heuristic quantum advantage using

Elena Bernardelli, Elena Gaburro, Michael Dumbser

We present a novel structure-preserving semi-implicit finite volume method on vertex-based staggered meshes for the compatible discretization of first order systems of time-dependent partial differential equations (PDEs). The method preserves divergence-free and curl-free vector fields exactly thanks to the compatible vertex-staggered discretization of the s

Bingcong Li, Yilang Zhang, Georgios B. Giannakis

Low-rank adaptation (LoRA) has emerged as the de facto standard for parameter-efficient fine-tuning (PEFT) of foundation models, enabling the adaptation of billion-parameter networks with minimal computational and memory overhead. Despite its empirical success and rapid proliferation of variants, it remains elusive which architectural choices, optimization t

Yanran Zhang, Wenzhao Zheng, Yifei Li, Bingyao Yu

In recent years, significant progress has been made in both image generation and generated image detection. Despite their rapid, yet largely independent, development, these two fields have evolved distinct architectural paradigms: the former predominantly relies on generative networks, while the latter favors discriminative frameworks. A recent trend in both

Max Defez, Filippo Quarenghi, Mathieu Vrac, Stephan Mandt

Deep-learning video super-resolution has progressed rapidly, but climate applications typically super-resolve (increase resolution) either space or time, and joint spatiotemporal models are often designed for a single pair of super-resolution (SR) factors (upscaling spatial and temporal ratio between the low-resolution sequence and the high-resolution sequen