arXiv papers from July 2026

H. De Raedt, K. Michielsen

This review gives a survey of numerical algorithms and software to simulate quantum computers. It covers the basic concepts of quantum computation and quantum algorithms and includes a few examples that illustrate the use of simulation software for ideal and physical models of quantum computers.

Han Gil Choi, Juan Urrutia, Miguel Zumalacárregui

Cold Dark Matter predicts a population of low-mass halos which are sensitive to its fundamental nature and the primordial power spectrum, yet remain undetected. Although elusive, their discovery may be possible thanks to wave-optics lensing of gravitational waves (GWs) by the superposition of many halos along the line of sight. We study the statistical prope

Runhui Huang, Qihui Zhang, Zhe Liu, Yu Gao

In this paper, we propose SpectraReward, a training-free reward function that turns pretrained MLLMs into off-the-shelf reward models for image-generation reinforcement learning. Instead of asking the MLLM to judge a generated image or answer decomposed verification questions, SpectraReward measures how well the original prompt can be recovered from the gene

Daniel Garibi, Ronen Kamenetsky, Hadar Averbuch-Elor, Daniel Cohen-Or

Generating and editing a person's face demands high precision, as even minor modifications can significantly alter a subject's perceived identity. Current personalization and editing methods built on general-purpose text-to-image models, however, often lack the precision required for fine-grained facial edits. We present a method for fine-grained ide

Dian Wang, Jisang Park, Xiaomeng Xu, Han Zhang

Robotic manipulation is inherently multi-frame: local actions may be simple in an end-effector frame, while transport, upright-object handling, and whole-body coordination are better represented in a base-aligned frame. However, modern diffusion-based visuomotor policies typically commit to a single predefined action frame, forcing one denoiser to model acti

Shikai Qiu, Marc Finzi, Yujia Zheng, Kun Zhang

Compression is fundamental to intelligence. A model that can represent its training data as a short code has discovered regularities that enable generalization. Large neural networks may learn functions far simpler than their parameter counts suggest, but it is challenging to construct codes that realize this simplicity. Parameter-based methods such as quant

Abhijit Chakraborty, Bharath Sambasivam, Karunya Shirali, Hunter Nelson

We propose an efficient algorithm based on shadow Hamiltonian simulation to approximately simulate the real-time dynamics of observables under time-independent Hamiltonians. Shadow Hamiltonian simulation works at the level of the operator algebra generated by the observables through commutators with the Hamiltonian. Exactly encoding the quantum state in this

Gabrielle Kaili-May Liu, Areeb Gani, Jacqueline Lu, Jordan Thomas

Metacognition is a foundational component of intelligence critical to effective learning, problem solving, decision-making, communication, and more. In recent years, it has become increasingly recognized as a cornerstone of capable, transparent AI systems. Yet while LLMs have made significant progress across diverse real-world tasks, it is not yet clear when

Tamar Ervin, Trevor A. Bowen, Alfred Mallet, Philip A. Isenberg

Open questions in collisionless plasma dissipation can be addressed using space-based observations in different astrophysical environments, with implications for both astrophysical and laboratory plasma systems. We study a low-$β$, highly imbalanced, sub-Alfvénic stream observed by Parker Solar Probe (PSP) to identify and distinguish between signatures of st

Farid Thaalba, Fernando Abalos, Miguel Bezares

We analyse the initial-value problem of metric higher-derivative effective theories of gravity. We show that any such theory whose characteristic velocities are independent of derivatives of the metric is intrinsically weakly hyperbolic, independently of the gauge fixing. To show this, we identify the spin-$2$ physical sector directly from the characteristic

Liwei Jiang, Ke Tang, Zhe Zhang

We study deterministic first-order minimization of a convex function without prior knowledge of the objective's growth, smoothness regime, or associated parameters. We develop anytime, parameter-free bundle-level methods that adapt simultaneously to these unknown properties and attain best-known oracle complexities. For nonsmooth Lipschitz objectives sat

Yubo Wang, Zhiyue Lu

For a reversible system relaxing to equilibrium, the obvious fastest strategy is to lower all kinetic barriers (open all gates). We find that such intuition holds at three levels: the all-open-gate strategy achieves the highest local conductance, it maximizes the instantaneous speed of approach in every $f$-divergence, and it simultaneously maximizes all rel

Francesco Verdiani, Emanuele Castorina, Ennio Salvioni, Emiliano Sefusatti

We present updated bounds on ultra-light axions (ULAs) as a subcomponent of dark matter, derived from the full-shape analysis of the DESI Data Release 1 galaxy power spectra combined with Cosmic Microwave Background (CMB) data from ACT and Planck. We focus on the mass window $10^{-32}\,\mathrm{eV}\leq m_a \leq 10^{-24}\,\mathrm{eV}$, employing state-of-the-a

Tiberiu Musat, Tiago Pimentel, Nicholas Zucchet, Thomas Hofmann

We present a theoretical framework to explain the emergence of inductive reasoning abilities in Transformer language models. While previous works on Transformer learning dynamics have so far been mostly tied to specific tasks, we study a generalized class of inductive tasks that unifies several synthetic tasks known in the literature, including in-context n-

Yunhai Feng, Natalie Leung, Jiaxuan Wang, Lujie Yang

Recent work in humanoid whole-body control has found success with a simple recipe: retarget human motion to robot kinematic references, then train policies via reinforcement learning (RL) to track them. But how does this recipe transfer to dexterous manipulation? The answer is not obvious, as manipulation involves complex, contact-rich dynamics and requires

Esteban U. Vega Barajas

Institutions collect far more open-ended teaching-evaluation feedback than they read. A prior study introduced a validated protocol for classifying such comments by thematic category and sentiment, built from a documented annotation guide, an intra-annotator reliability measurement, stratified cross-validation, and a held-out evaluation on a Spanish institut

Luyining Gan, Jie Han, Bin Wang

A $4$-uniform $2$-cycle in a $4$-uniform hypergraph of length $t$ is a cyclic ordering of $2t$ vertices $v_1v_2\cdots v_{2t}v_1$ such that $v_{2i+1}v_{2i+2}v_{2i+3}v_{2i+4}$ are edges for $0\le i\le t-1$ while the addition is modulo $2t$. For every $γ>0$ and large $n$, we characterize the $n$-vertex $4$-uniform hypergraphs such that every triple of vertices

Zixiang Xu, Sixian Li, Huaxing Liu, Xiang Wang

Existing studies of LLM-as-judge scoring bias work predominantly at the input-output level: they perturb inputs, measure score deltas, and propose prompt-level mitigations. We argue that the same biases admit a representation-level account in the judge's hidden state, complementary to the input-output view and operationally useful in ways it does not aff

Andrew Weldon, Casey Papovich, Justin Spilker, Robert C. Kennicutt

We investigate the properties of ionized gas outflows in nearby star-forming galaxies from the final Data Release of the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) Survey. Using spatially resolved spectroscopy, we search for signatures of ionized outflows within physically motivated outflow apertures. We find significant evidence for additio

Quentin Griette, Hiroshi Matano

We consider a two-species reaction-diffusion system in one space dimension that is derived from an epidemiological model in a spatially periodic environment with two types of pathogens: the wild type and the mutant. The system is of a hybrid nature, partly cooperative and partly competitive, but neither of these entirely. As a result, the comparison principl

Chamindu Liyanage, Chirantha Kurukulasuriya, Chathuni Wijegunawardana, Wikum Kumara

Sensing and early detection of small unmanned aerial systems (sUAS) are critically important in modern-day defense. In dense urban and indoor environments, detection becomes extremely challenging due to dense multipath, fading, low-altitude flight, and non-line-of-sight (NLOS) radio-frequency propagation. This paper presents a continuous-wave multiple-input

Kejun Liu

The memory kernel of an open quantum system obeys Kramers--Kronig (KK) relations if and only if its Laplace transform is analytic in the upper half-plane -- a property known as Hardy-space analyticity. Here we show that non-unitary exchange statistics, the defining property of paraparticles, intrinsically breaks Hardy-space analyticity. The metric $η$ that g

Carlos J. Vargas, Caroline Kilbourne, Haeun Chung, Erika Hamden

The circumgalactic medium (CGM) -- the multiphase gas reservoirs surrounding galaxies -- remains the least understood component of the baryon cycle governing galaxy growth, despite its central role in the Astro2020 Decadal Survey's priorities. Existing constraints come almost exclusively from pencil-beam absorption spectroscopy, leaving the spatial struc

Weikun Zhu, Natalie Ngoh, Shelly Ben-David, Maxwell Conte

Quantum light sources capable of generating single photons are fundamental building blocks for photonic quantum technologies. In the ongoing search for an ideal quantum emitter, inorganic halide perovskite nanocrystals have emerged as a promising source of single photons. Their unique optical response, with an unmatched ease of synthetic tunability, stands o

Junnosuke Koizumi

We completely determine the magnitude homology of tope graphs of real hyperplane arrangements. Their ranks can be described as the Hilbert functions of the Stanley--Reisner rings of certain simplicial complexes naturally associated with the arrangements. For Coxeter arrangements, this gives a computation of the magnitude homology of the Cayley graph of the c

Shijie Wang, Honglu Zhou, Ziyang Wang, Ran Xu

Current Video Large Language Models (Video LLMs) excel in question answering (QA) but largely operate as black boxes, providing textual answers without verifiable visual grounding. Existing explainability efforts rely on textual rationales or sparse bounding boxes, which struggle to capture complex video dynamics such as occlusions and non-rigid deformations

Mohammad Aqib

We investigate almost Ricci--Bourguignon solitons on three-dimensional contact metric manifolds. Under natural curvature assumptions, we show that the additional freedom introduced by allowing the soliton function to vary is rigidly constrained by the contact geometry. Using a local orthonormal \(φ\)-basis on the non-Sasakian region, we derive the full compo

Francesco Sorrenti, Erick Pastén, Leonardo Giani

We develop a kinematic framework that relates the monopole, dipole, and quadrupole of the luminosity distance to an ellipsoidal peculiar velocity field describing the dynamics of the Laniakea supercluster. By properly accounting for the transformations between the CMB and Laniakea reference frames and selecting Type Ia supernovae within the volume associated

Nishant Aggarwal, Ayushi Dubal, Sreeraj Kannakarankodi, Ian McDougall

Can large language models perform deep technical comprehension of computer architecture papers -- not summarization, but structured critique that names the core mechanism, surfaces buried assumptions, and connects a contribution beyond its own scope? We study Gauntlet, an open-source pipeline that analyzes a paper through five independent expert-persona revi

Ari Krishna

Let $V$ be a six-dimensional complex vector space and let $G=\operatorname{Gr}(2,Λ^2V^\vee)$ parametrize pencils of skew-symmetric $6\times6$ matrices. The group $\operatorname{PGL}(V)$ has $11$ orbits on $G$, classified by the Jordan-Kronecker canonical form. We compute the Chow class of every orbit closure in the Schubert basis of $\operatorname{CH}^*(G)$.

Adam Bílek, Ryszard Kukulski, Paulina Lewandowska, Łukasz Pawela

We investigate the resilience to noise of single-qubit quantum key distribution (QKD) protocols in the scenario of security against independent eavesdropping attacks and key distillation based on one-way classical communication. To this end, we introduce a noise-based metric that quantifies the efficiency of QKD protocols. Within this framework, we analyze t

Xinzhao Wang, Shuo Zhou, Ziruo Wang, Pei Zeng

Product formulas are among the most practical approaches to Hamiltonian simulation, requiring no ancillary qubits and exhibiting error bounds governed by nested commutators rather than only by Hamiltonian norms. Their circuit size, however, scales polynomially with the inverse precision. We develop a high-order nested-commutator compensation (HNCC) algorithm

Deniz Kerimoglu, Junnosuke Kamohara, Jiyeon Maeng, Ziwon Yoon

Bipedal robots are challenging to control because they operate close to instability, where small variations in foot-terrain contact can rapidly destabilize locomotion. On rigid terrain, bipedal robots mitigate this fragility by using well-established contact mechanics and control strategies. On flowable surfaces such as granular slopes, foot contact can indu

Luca Bianchi, Carlo Marconi, Riccardo Cioli, Jan Sperling

The Kirkwood-Dirac quasiprobability provides an operational representation of a quantum state, whose negativity serves as a measure of nonclassicality. Despite its fundamental importance, the extremal values of the Kirkwood-Dirac negativity are still unknown in the general case. We investigate the Kirkwood-Dirac quasiprobability of an arbitrary quantum state

Robert Szafarczyk

We develop and study an obstruction for lifting schemes over the integers to spectral schemes over the sphere spectrum. This extends a result of Nikolaus for rings, which states that a necessary condition for liftability is existence of a $\hatδ$-structure. We prove descent properties for $\hatδ$-rings, define $\hatδ$-schemes, and prove an analogous statemen

Valery V. Ryzhikov

Some results in ergodic theory on joinings and multiple mixing and the proof of Steve Kalikow's theorem on 3-fold mixing of 2-fold mixing rank one automorphisms are discussed.

Shokhzod Jumaniyozov, Javlon Rayimbaev, Yassine Sekhmani, Satimbay Palvanov

We study the thermodynamic properties and radiation characteristics of a regular compact object obtained by applying the Simpson-Visser regularisation to the Schwarzschild black hole in modified gravity. The resulting SV-MOG spacetime, whose lapse function involves both the MOG coupling parameter and the black bounce parameter, smoothly interpolates between

Fran Sučić, Leo Vitasović, Nikola Petrušić

We present Need for Speed Sort (NFS Sort), a recursive distribution-based sorting algorithm designed for numeric arrays. The algorithm partitions elements into equal-width value intervals, recursively refines dense buckets, and propagates analytical interval bounds between recursive calls, avoiding repeated scans for local minima and maxima. NFS Sort combine

Lingkai Kong, Zijian Wu, Yuzhe Gu, Haiteng Zhao

Large language models (LLMs) have achieved remarkable performance on high-school and olympiad-style mathematics, yet their capabilities on advanced mathematics remain poorly understood. Existing benchmarks, however, fall short in both scope and evaluation granularity: they provide limited disciplinary coverage and often rely on final-answer correctness or co

Sabyasachi Maulik, Soumen Pari

In this paper, we study codimension-two holography in a de Sitter (dS) wedge setup, based on the idea of wedge holography. We consider a $d+1$-dimensional Anti-de Sitter (AdS) bulk spacetime bounded by two end-of-the-world branes with $d$-dimensional de Sitter geometry. We propose that this configuration is holographically dual to a conformal field theory (C

Senrui Chen, Marco Fanizza, Filippo Girardi, Ludovico Lami

The sample complexity is the minimum number of copies required to learn an accurate classical description of a quantum state. Bosonic and fermionic Gaussian quantum states are families of quantum states that play a key role in quantum science and technology, from quantum optics and many-body physics to quantum chemistry, quantum computing, and quantum inform

Paul Steinacker, Amanda E. Seedhouse, Nard Dumoulin Stuyck, Tuomo Tanttu

Silicon spin qubits are a leading candidate for large-scale quantum computing owing to their compatibility with semiconductor manufacturing. However, scaling to useful fault-tolerant processors will likely generate thermal loads that exceed the cooling power available at millikelvin temperatures. Raising the operating temperature eases cooling requirements b

Robert Fleischer, Maria Laura Piscopo, K. Keri Vos, B. Yağmur Zubaroğlu

We investigate the Standard Model (SM) description of the singly Cabibbo-suppressed charm decays $D^0\to π^-π^+$, $D^0\to K^-K^+$ and $D^0\to K_{\rm S}^0K_{\rm S}^0$. Using factorisation together with isospin symmetry, we constrain non-factorisable effects and the associated $U$-spin breaking required by the measured branching fractions. We find that correct

Kerui Chen, Jinglu Wang, Xiaoyi Zhang, Yan Lu

Recent Multimodal Large Language Models (MLLMs) achieve strong performance on single-view video understanding benchmarks. However, sports videos involve dense occlusion, rapid motion, and complex interactions that are difficult to resolve from a single viewpoint. In practice, sports events are recorded from multiple camera angles, providing complementary evi

Junrui Zhang, Zemin Chen, Lusi Li, Mohammad Ghasemigol

Quantum Neural Networks (QNNs) are a promising framework for quantum machine learning on near-term quantum devices, but their security risks remain insufficiently understood. Studies have shown that QNNs are vulnerable to backdoor attacks, yet existing quantum backdoors mostly rely on a fixed trigger shared by all poisoned inputs. This fixed-trigger design i

Narges Rashidi, Maryam Roushan

We investigate linear and nonlinear primordial perturbations in an anisotropic Dirac-Born-Infeld (DBI) inflationary model with a non-vacuum initial state. Using the Arnowitt-Deser- Misner (ADM) formalism, we expand the action up to second and third order in the curvature perturbation and derive the corresponding scalar and tensor power spectra, as well as th

Stephen Li, Alice C. Quillen, Adam E. Rubinstein, Dominique Segura-Cox

Using a 1D mass and heat transport model, we numerically integrate heat flow and gas transport in a porous body exposed to a transient heating event while embedded in a protoplanetary disk. When small icy grains are heated, volatiles sublimate, enriching the disk with volatile gases. When a porous body enters this heated, volatile-rich environment, volatile

Rahul Kumar Walia, Boris Georgiev, Chi-kwan Chan

We establish a universal connection between the classical energy conditions and directly observable black hole properties. By imposing the energy conditions locally at the photon sphere, we derive analytic and testable bounds on the shadow size, Lyapunov exponent, Lyapunov time, and photon-ring time delay that apply to all static, spherically symmetric black

Divya Mereddy, Jeevan Beedareddy

This paper presents a cascaded Low-Rank Adaptation (LoRA)-based multimodal fusion framework for action and activity recognition in healthcare-oriented training environments. The proposed architecture combines parameter-efficient modality-specific adaptation with sequential fusion, enabling modalities to be integrated in stages without retraining previously l

Caleb Robinson, Anthony Ortiz, Simone Fobi Nsutezo, Cameron Birge

When a large disaster strikes, responders need a map of which buildings are damaged within hours. The models that do well on public benchmarks assume matched before-and-after imagery and a training set drawn from similar past events, and neither is usually available for a new disaster in its first day. We present HASTE (High-speed Assessment and Satellite Tr

Chu Zhang, XiaoKe Zeng, Jin Zhang, Ruoyu Wen

Creativity often flourishes in collaboration, such as when designers brainstorm a new app together, or storytellers collectively build a world with elements of each person's narrative. However, collaborative storytelling can have challenges for its participants, such as when they disagree about the plot proposed, or when different ideas become fragmented

Zihan Su, Teng Hu, Jiangning Zhang, Ruiyan Wang

Autoregressive diffusion models have enabled high-quality video generation, yet their sequential nature inherently suffers from error accumulation. In long-horizon video synthesis, minor prediction deviations compound over time, inevitably leading to unconstrained generative drift, structural collapse, and severe visual degradation. To address this, we propo

Till Preuster, Manuel Schaller, Anton Schiela, Martin Stoll

Many numerical algorithms for optimal control leverage an elimination of the state via the control-to-state map such as condensed approaches or preconditioned conjugate gradient methods for the optimality system. As such, the norm of the control-to-state map directly enters the convergence estimates for these methods, e.g., via the condition number of the as

Alexandru A. Popa, Jack Walsh

We study twisted Weyl group multiple Dirichlet series attached to symmetrizable Kac-Moody root systems, using the Chinta-Gunnells method to construct their $p$-parts. Our main result is a decomposition theorem for functions invariant under the twisted Chinta-Gunnells action: under natural analytic hypotheses, such a function has a unique expansion in terms o

Nastassia Pouradier Duteil, David Poyato, David N. Reynolds

In this article, we study a new class of collective motion for identical coupled Stuart-Landau oscillators on graphs. This model was previously known to converge to the synchronized state for a certain class of initial data. Here, we show that when the interaction matrix is circulant, there exists another class of attractors, in which the particles are unifo

Thea Sukianto, Donata Giglio, Mikael Kuusela

Estimating ocean heat content (OHC) with reliable uncertainties is critical for understanding and monitoring the evolution of Earth's climate, as the ocean has stored most of the energy accumulated in the climate system due to Earth Energy Imbalance. Here, we use Argo profiling float data from 2004-2022 to map OHC. As fewer Argo observations are availabl

Omar Bachain, Mohamed Amazioug, Rachid Ahl Laamara

We investigate multiparameter quantum estimation in a molecular dimer composed of two dipole--dipole interacting two-level systems, focusing on the simultaneous estimation of temperature $T$ and detuning $Λ$. By employing a vectorization approach to derive the quantum Fisher information matrix, we analyze the precision limits of both simultaneous and individ

Huy Che, Dinh-Duy Phan, Duc-Lung Vu

License plate character detection is a crucial component of intelligent transportation systems, where high accuracy and computational efficiency are required for real-time deployment. Although recent deep learning-based methods have substantially improved detection performance, many high-accuracy models rely on large-scale architectures that incur substantia

Wenzheng Dong, Andrew G. Green, Vlatko Vedral, Jinzhao Sun

Which properties of a quantum many-body system are operationally accessible is a central question underlying spectroscopy, thermodynamics, and quantum information science. Conventional response theory answers this question within a system-only paradigm: one perturbs and measures the matter itself, obtaining susceptibility built from causally ordered nested c

Matteo Beccaria, Eleonora Alfinito

We study the low-temperature expansion of the disk partition function $Z(β)$ of the double-scaled SYK model (DSSYK) at fixed coupling $λ=2p^{2}/N$, where $N$ is the number of Majorana fermions and $p$ is the number of fermions in each interaction term, both taken to infinity. We show that the exact Bessel-function representation of $Z(β)$, expanded at large

Siddhartha Ganguly, Ashwin Aravind, Souvik Das, Masaaki Nagahara

This article presents a novel, numerically viable algorithm for solving sparse robust optimal control problems in continuous time. We consider a constrained linear noisy system governed by an ordinary differential equation (ODE), with an $L^1$-type objective function in line with the sparse optimal control literature. The resulting optimal control problem is

Romain Amigon

Neural Architecture Search (NAS) has automated the design of deep learning models but traditionally requires massive computational resources, often measured in thousands of GPU-days. In this paper, we propose a frugal and memetic NAS framework designed to democratize architecture design on consumer-grade hardware. Our approach combines the global macro-searc

Indrajit Paul, Kishan Deka, Tuhin Ghosh, Siddharth Maharana

The two WALOP instruments, built for the PASIPHAE survey, will measure the linear polarization of large numbers of stars in the Galactic polar regions in the SDSS-$r$ band. They are designed with a wide field-of-view, enabling measurement of the Stokes parameters $I$, $q$, and $u$ for multiple stars simultaneously within a $35^\prime \times 35^\prime$ region

Daijiro Hitotsuyanagi, Hiroto Yamada, Daisuke Yamashiki, Kazuma Ishihara

Superflares are energetic explosions on stellar surface with energies of 10^33-10^36 erg, significantly exceeding those of typical solar flares. While previous studies have suggested that these events are driven by magnetic energy stored in large starspots, the detailed time-resolved relationship between starspot area and flare activity on individual stars h

Omar Bachain, Mohamed Amazioug, Rachid Ahl Laamara

We investigate multiparameter quantum estimation in a Raman-coupled two-qubit system at thermal equilibrium. Analytical expressions for the quantum Fisher information matrix are derived to characterize the simultaneous estimation of the temperature and Raman coupling strength. The corresponding quantum Cramér--Rao bounds are obtained and compared with those

Hyung-Jin Yoon

Finite Reliability Representations (FRR) certify when a cell-constant policy is sufficient for reliable decision-making in a partially observed system with a known physical noise floor. In practice, however, sensing and execution noise can be latent and context-dependent. This paper develops a certificate-aware active disambiguation framework for an unknown

David Könen, Lara Löhken, Michael Stiglmayr

In multi-objective combinatorial optimization, unsupported non-dominated points typically outnumber supported points and are often significantly more challenging to compute. Recent studies show that extreme supported non-dominated points provide high-quality representations of the non-dominated set for certain binary problems. We demonstrate that this observ

Keith L. Chambers, Richard M. White, Colin R. Goding, Helen M. Byrne

Cancer progression is driven by the ability of cells with identical driver mutations to adopt biologically distinct adaptive phenotypes. Yet the population dynamics implied by intratumour phenotypic heterogeneity is poorly understood. Melanoma, a highly aggressive skin cancer, represents an excellent model to explore phenotype-switching, in part because phen

Peter Dunson, Ciprian M. Crainiceanu

Bayesian Factor Models (BFM) are well-established models that decompose the observed variability in a set of mean-zero, independent, and uncorrelated factors (random effects). While Factor Analysis (FA) was introduced in 1904 by Spearman, there has been renewed interest in inferential and computational methods that can adapt to large and complex modern data

Kaixin Ma, Di Feng, Alexander Metz, Jiarui Lu

We introduce MM-ToolSandBox, a benchmark and evaluation framework for visually grounded tool-calling agents. The framework provides a stateful execution environment spanning 500+ tools across 16 application domains, supporting multi-image, multi-turn tasks where agents must ground progressively arriving visual inputs into executable tool calls while handling

Sharath S. Girimaji

Since the foundational studies in the late nineteenth century, fluid turbulence has stood as a profound, unsolved challenge in classical physics. Much of this enduring difficulty stems from non-equilibrium turbulence, where the lack of a unifying physical framework for macroscopic coherent structures has hampered predictive flow modeling. Here, we establish

Bijan Mazaheri, Jiaqi Zhang, Caroline Uhler

Causal discovery algorithms learn a network that describes the causal dependencies among random variables. A common workflow involves first utilizing conditional independence properties on observational data to determine partially directed causal relationships, then applying interventions to orient the unknown causal directions. A critical assumption for the

Xuhui Yang, Guofu Cao

Characterizing emission and decay time spectra in multi-component fluorescent media is essential for identifying intrinsic material properties and optimizing detectors. However, wavelength evolution from the secondary inner filter effect (IFE) distorts these observable spectra. While Monte Carlo (MC) ray-tracing can simulate this distortion, accumulating ade

Daiki Saito, Atsushi Naruko

We investigate the long-wavelength evolution of linear perturbations in a homogeneous and anisotropic background with a scalar field coupled to a vector field. Using the spatial gradient expansion in the uniform-$\mathcal{N}$ gauge in which the number of $e$-folds is unperturbed, we derive the complete set of superhorizon solutions and establish their corres

Changyu You, Rong-Gen Cai, Tao Yang

The dark energy equation of state (EoS) and a possible dark-sector interaction are degenerate at the level of background expansion: the same expansion history may be interpreted as time-varying dark energy, energy exchange with dark matter, or a mixture of both. We introduce a data-driven, nonparametric expansion--growth framework that breaks this degeneracy

Qinfeng Li, Weihong Xie, Hang Yang

In earlier work, we posed a stability question for Serrin's overdetermined problem under Dirichlet perturbations and proved that the answer is negative in dimensions $n\ge3$. Here we resolve the question in the planar convex class and obtain a sharp quantitative theory without any a priori geometric nondegeneracy. Let $u_Ω$ solve \[ -Δu_Ω=1\ \text{in }Ω,

Pingshi Yu, Chengsong Tan, Nicolas Wu, Alastair Donaldson

Randomised testing is a widely-used approach to software validation, yet its theoretical foundations remain thin. In particular, the fundamental question of what it means for a set of inputs to be \emph{generable} has gone unanswered in both the literature and folklore. We present the first complexity-theoretic foundations for random generators in software t

Giulia Di Fede, Salvatore Andolina

Large Language Models (LLMs) can make exploratory search more efficient but may undermine the reflection and iterative sensemaking needed in unfamiliar domains. Existing LLM tools often prioritize rapid answers over supporting users in tracking how their understanding evolves and how well their strategies align with their goals. We present TrailLM, a system

Stefano Ronchi

In this thesis we define $n$-duals of VB $n$-groupoids over Lie $n$-groupoids and study their properties. For $n = 0$ this returns the dual vector bundle construction, while for $n = 1$ this returns Pradines's construction of the dual of a VB groupoid over a Lie groupoid, which includes the cotangent symplectic groupoid of Coste, Dazord and Weinstein. Fo

Antonio San Martin, Catherine Trekker

This paper proposes a human-centered artificial intelligence (HCAI) framework for AI-assisted lexicography. While generative AI offers significant opportunities to enhance lexicographic work, it also raises concerns regarding the future role of lexicographers and the preservation of linguistic and cultural diversity. Drawing on HCAI principles and previous a

Wen Ying, Adil Rahman, Erzhen Hu, Seongkook Heo

Virtual Reality (VR) offers potential for productivity work by creating expansive displays anywhere, yet current systems often rely on external input devices that limit the on-the-go use of mobile VR. We introduce HandPad, a suite of bare-hand interaction techniques that leverage the benefits of asymmetric bimanual coordination and self-haptic support. HandP

Berta Martínez-Pàmias, Miquel Rudé, Cristina Masoller

Quantum random number generators, at the core of digital trust infrastructures, rely on quantum entropy sources (QESs) to produce randomness from physical processes. The quantum origin certification of a QES requires a physical model compatible with the measured signal of the device. Here, we study Quside Technologies' phase-diffusion QES consisting of a

Buffoli Fabio

We present HAMCOR (Hamiltonian-based AGN Multi-constraint CORonal inference framework), a geometry-agnostic method for inferring the X-ray coronal structure of accreting black holes using reverberation-lag measurements. Unlike conventional template-fitting approaches, HAMCOR reframes coronal geometry inference as the ground-state selection of a physical Hami

Bertuel Tangue Ndawa, Ferdinand Ngakeu, Nasser Saipele Nansidi

Let $M$ be a manifold endowed with a bi-Lagrangian structure $(ω,\mathcal{F}_{1},\mathcal{F}_{2})$. Thus, $ω$ is a symplectic form, and $(\mathcal{F}_{1},\mathcal{F}_{2})$ is a pair of transverse Lagrangian foliations on the symplectic manifold $(M,ω)$. We prove that, if $M$ is parallelizable, then every bi-Lagrangian structure on $M$ naturally induces bi-La

Chu Zhang, Simai Huang, Shaohua Wu, Yihuan Chen

Climate change is a source of anxiety about the future. Understanding how people express themselves about climate change enables us to address such concerns. To study climate change expression on social media, we analyzed 200 TikTok videos tagged with #climatechange, identifying four categories of content: expression-feelings, views-appeals, news-information

Alfonso M. Ganan-Calvo

Gas-focused liquid microjets -- the flow-focusing sample delivery on which serial femtosecond crystallography depends -- reach speeds several times the pressure-driven (Bernoulli) bound, unexplained by continuum, local-equilibrium models that do not resolve the rarefied, hypersonic expansion of the focusing gas. We resolve that expansion with a deterministic

Yu-Han Huang, Chih-Kai Yang, Ke-Han Lu, An-Yu Cheng

Large audio-language models (LALMs) often underperform on fine-grained, non-semantic attributes of speech, such as a speaker's emotion, despite strong performance on speech content. Improving this without the cost of retraining calls for an effective inference-time intervention, yet most existing methods intervene only after the audio encoder and operate

Zhipeng Xu

We give three explicit quantum Latin squares of order $6$ with cardinalities $19$, $21$, and $23$, where vectors differing only by a global phase are counted as identical. The first two examples arise from normalized Schur products of columns of complex Hadamard matrices. For cardinality $19$, a Butson-type matrix over eighth roots of unity has the unique no

Rajam Elancheliyan, Jean Marc Fromental, Edouard Chauveau, Domenico Truzzolillo

Yield-stress fluids undergo a singular solid-to-liquid transition at a critical stress threshold. While conventionally investigated under steady shear, large-amplitude oscillatory tests force these materials to cyclically navigate between arrested and fluidized states. Here, we uncover a hidden universality in their non-linear oscillatory response: at suffic

Seung Hyun Hahm, Minh T. Dinh, SouYoung Jin

Long-form audio description (AD) requires more than describing visible actions: it must preserve characters, events, relationships, and story context across scenes so that blind and low-vision (BLV) audiences can follow a film. Modern video-language models (VLMs) are effective on short clips, but they often treat each moment independently, producing descript

Shahaf Aharony Shapira, Gene Chen, Marija Vucelja, Oren Raz

The Mpemba effect is the counterintuitive phenomenon in which an initially hotter system cools faster than a colder, otherwise identical system. It has been experimentally demonstrated in various classical overdamped systems. Here, we explore the existence of the same effect in a regime where inertia cannot be neglected, namely, the underdamped regime. We co

Raktim Bhattacharya

Selective state-space models such as Mamba route information through a bank of first-order modes whose input coupling is set by a learned selection mechanism. We give an exact instrument for measuring how a trained model uses these modes. Because the state matrix is diagonal, each channel's output decomposes exactly into per-mode contributions, and a per

Himadri Halder, Rohit Kumar

The aim of this paper is twofold. First, we establish a Riesz conjugate theorem for weighted harmonic Bergman spaces. More precisely, we prove that if $f=u+iv$ is a harmonic $K$-quasiregular mapping in $\mathbb{D}$ and the real part $u$ belongs to the weighted harmonic Bergman space $a_α^p$, $0<p<\infty$, then the imaginary part $v$ also belongs to the same

Sebastian Weyrer, Johannes Gerstmayr, Aki Mikkola, Grzegorz Orzechowski

Model order reduction decreases the dimension of a mechanical system by introducing modal coordinates that retain important dynamic characteristics. Sliding beams, as found in telescopic structures, pose a fundamental challenge. Fixed modal coordinates fail to capture evolving system properties, and updating the modal basis during simulation causes modal coo

Chunhe Xiong, Sunho Kim, Long Long, Junde Wu

We investigate the private capacity of quantum channels using the recently proposed quantum convolution theory for discrete-variable quantum systems. We focus on the role of the magic resource played in this framework. Firstly, for a large class of convolutional channels, we find that the private capacity is zero if the fixed environmental state is a stabili

Sheng Li, Jing Li, Felix Schijve, Jun Hu

Automatic speech recognition (ASR) has become a critical component of modern robotic systems because it is one of the most natural and intuitive ways for humans to interact with robots. A commonly used method is to directly use API services online. But is that all we can do? This article provides an overview of how ASR technologies are integrated into variou

A. F. Zakharov

A. A. Friedmann (04.06.1888 -- 16.09.1925) proposed the first physical cosmological models in 1920s. Despite the fact that Friedmann&#39;s works were very famous soon after their publication, the study of dynamic models of the Universe in the USSR was actually banned in the 30s - 50s of the last century, and Soviet philosophers and propagandists wrote that m

Chenhaoyue Wang, Amartya S. Banerjee

Electronic flat bands in an elemental two-dimensional material provide an attractive setting for electron interactions competing with suppressed kinetic energy. Here we propose a buckled silicon kagome lattice (SiKL), an unfunctionalized six-atom monolayer of bond-linked Si$_3$ triangles and dodecagonal pores. Its planar parent hosts a dispersionless Kohn--S

Y. Zhang, T. Adkins, M. Barnes, A. V. Dudkovskaia

Local flux-tube gyrokinetic simulations of ion-scale turbulence in tokamak plasmas at finite plasma beta are conducted to investigate the generation of zonal flows via turbulent stresses. A parameter scan in the safety factor $q$ and electron beta $β_e$ reveals a transition from low- to high-transport states when $β_{\mathrm{eff}} \equiv q^2β_e$ exceeds a ce

Tian-Yi Zhang, Peng-Yi Liu, Yu-Fei Sun, Ai-Min Guo

Chiral molecules have attracted broad interdisciplinary interest for their ability to produce highly spin-polarized current. This phenomenon, known as the chiral-induced spin selectivity effect, holds great potential in the field of spintronics. Here, we propose to combine chiral molecules with altermagnets to construct highly efficient and tunable spin valv