March 2026 arXiv papers — page 25

S. -A. Guo, J. Ye, J. -Y. Tan, Z. -W. Zhang

Previous experimental realizations of Dicke model in atomic or ionic systems are based on global observables assuming uniform spin-boson coupling, while inevitable experimental nonuniformity on the one hand requires site-resolved measurement of spin states, and on the other hand provides potential quantum advantage on the simulation of multi-spin distributio

Rodney Jehu-Appiah

I propose Umwelt engineering -- the deliberate design of the linguistic cognitive environment -- as a third layer in the agent design stack, upstream of both prompt and context engineering. Two experiments test the thesis that altering the medium of reasoning alters cognition itself. In Experiment 1, three language models reason under two vocabulary constrai

Tiantong Wang, Minfan Zhao, Jun Shi, Hannan Wang

Recent advancements in deep learning-based interactive segmentation methods have significantly improved pathology image segmentation. Most existing approaches utilize user-provided positive and negative clicks to guide the segmentation process. However, these methods primarily rely on iterative global updates for refinement, which lead to redundant re-predic

Songchen Ma, Hongyi Li, Weihao Zhang, Yonghao Tan

Mixture-of-Experts is a promising approach for edge AI with low-batch inference. Yet, on-device deployments often face limited on-chip memory and severe workload imbalance; the prevalent use of offloading further incurs off-chip memory access bottlenecks. Moreover, MoE sparsity and dynamic gating shift distributed strategies toward much finer granularity and

L. Pagliaro, T. Zingales, G. Piotto, I. Giovannini

Computationally expensive and time-consuming Bayesian atmospheric retrievals pose a significant bottleneck for the rapid analysis of high-quality exoplanetary spectra from present and next generation space telescopes, such as JWST and Ariel. As these missions demand more complex atmospheric models to fully characterize the spectral features they uncover, the

S. Jalalzadeh, R. Jalalzadeh, H. Moradpour

In this work, we investigate the thermodynamics of Schwarzschild black and white holes within a $q$-deformed Wheeler--DeWitt framework. By introducing a $q$-deformed Heisenberg--Weyl algebra at a root of unity, we derive a finite-dimensional Hilbert space, a bounded mass spectrum, and an adiabatic invariant leading to a bounded entropy-mass relation. The def

Ji-Chong Yang, Zhan Zhao, Xiang-Ning Li, Wen-Wen Li

External electromagnetic fields provide a useful probe of QCD matter, but real electric fields are hindered by the sign problem, motivating studies with imaginary electric fields. We investigate mesonic screening correlators in lattice QCD at finite temperature in the presence of such a background using staggered fermions. At low temperature, scalar screenin

Jishad Kumar, Achilleas Lazarides, Tapio Ala-Nissila

We develop an exact framework for quantum Zeno and anti-Zeno dynamics in a broad class of open systems, whose microscopic Hamiltonians are quadratic in bosonic or fermionic operators. We treat the environment through an exact stroboscopic resetting scheme acting at the level of the single-particle density matrix (SPDM). Within this framework, we consider two

Damian Sercombe

We show that any connected algebraic group $G$ over a field admits a nilpotent normal subgroup $Z_\infty(G)$ such that the quotient $G/Z_\infty(G)$ has trivial center. We construct $Z_\infty(G)$ as the final term of the transfinitely extended upper central series of $G$; accordingly, we call it the hypercenter of $G$. We establish several related results abo

Xing Wu

We test the cosmic distance duality relation (CDDR) using two model-independent methods. Method I is based on the PAge parametrization, which characterizes the expansion history in terms of the cosmic age. Parametrizations of possible CDDR violations are constrained using observational data from Type Ia supernovae (SN), baryon acoustic oscillations (BAO), co

Alexey Pavlov, Michael Ruderman

This paper presents an adaptive causal discrete-time filter for derivative estimation, exemplified by its use in estimating relative velocity in a mechatronic application. The filter is based on a constrained least squares estimator with window adaptation. It demonstrates low sensitivity to low-amplitude measurement noise, while preserving a wide bandwidth f

Robin Cockett, Durgesh Kumar, Priyaa Varshinee Srinivasan

This article provides an alternate characterization of dagger categories, which are central to the study of categorical quantum mechanics, in terms of inner product categories. An inner product category is an "achiral involutive" category with an inner product combinator. Inner product categories are, in turn, precisely the same as unitary categories, which

Jian Zhou

We present a large-scale computational study combining arbitrary-precision arithmetic, sequence acceleration, and the PSLQ integer relation algorithm to discover exact closed-form expressions for fundamental constants arising in asymptotic analysis. We compute the Stokes multipliers C_M of the one-dimensional anharmonic oscillators H = p^2/2 + x^2/2 + g x^{2

Sylvy Anscombe

A field is existentially t-henselian if it is has the same existential theory in the first-order language of rings as a field that admits a nontrivial henselian valuation. This property turns out to be equivalent to $\mathbb{Z}$-largeness, which is a property identified in previous work with Fehm, and which holds for $F$ if and only if $tF[\![t]\!]$ is not D

Florentin Koch

When a cognitive system modifies its own functioning, what exactly does it modify: a low-level rule, a control rule, or the norm that evaluates its own revisions? Cognitive science describes executive control, metacognition, and hierarchical learning with precision, but lacks a formal framework distinguishing these targets of transformation. Contemporary art

O. A. Ilinskaya, S. N. Shevchenko

Resonant driving enables coherent control of quantum systems, including single and coupled qubits. From a complementary perspective, transitions of a quantum system can be exploited for the detection of microwave photons. In this work, we theoretically investigate resonant multiphoton excitations in a system of qubits. When the energy of K photons matches th

Wen-Zhao Zhang, Keye Zhang, Jie Li

We present a theoretical framework for the coherent coupling between Stokes and anti-Stokes scattering processes, revealing interference phenomena inaccessible to either process alone. Within a dispersive-interaction model beyond the resolved-sideband limit, we show that classical driving and system linewidth coherently links the two channels, enabling phase

Ke-Jian He, Guo-Ping Li, Li-Fang Li, Xiao-Xiong Zeng

In this work, we investigate the gravitational lensing effects and the dynamic evolution of the shadow of Vaidya black holes by employing backward ray-tracing techniques. Within the celestial sphere framework, the black hole shadow exhibits a complete evolutionary sequence, transitioning from an initial stable configuration through continuous expansion to a

Jingrui Niu, Shengquan Xiang

In this paper, we investigate the quantitative exponential stability of the Korteweg-de Vries equation on a finite interval with its length close to the critical set. Sharp decay estimates are obtained via a constructive PDE control framework. We first introduce a novel transition-stabilization approach, combining the Lebeau--Robbiano strategy with the momen

Shakib Mustavee, Arvind Singh, Shaurya Agarwal

Measuring sediment transport in riverbeds has long been a challenging research problem in geomorphology and river engineering. Traditional approaches rely on direct measurements using sediment samplers. Although such measurements are often considered ground truth, they are intrusive, labor-intensive, and prone to large variability. As an alternative, sedimen

Dongjie Tan, Jianghui Ji, Chunhui Bao, Xiumin Huang

The Closeby Habitable Exoplanet Survey (CHES) aims to achieve microarcsecond-level astrometry of about one hundred nearby FGK-type stars within 10 parsecs to detect Earth-like planets. Such precision exceeds the capability of absolute astrometry relying on Gaia catalogs, whose positional accuracy degrades over time due to error propagation from stellar motio

Laure Dumaz, Hugo Magaldi

Ram\'irez and Rider (2009) established that the hard edge of the spectrum of the $\beta$-Laguerre ensemble converges, in the high-dimensional limit, to the bottom of the spectrum of the stochastic Bessel operator. Using stochastic analysis tools, we prove that, in the high-temperature limit ($\beta \to 0$), the rescaled eigenvalue point process of this opera

Shiri Chechik, Gur Lifshitz, Doron Mukhtar

This paper advances the state of the art in girth approximation within the CONGEST model. Manoharan and Ramachandran [PODC '24] provided the first significant improvement in girth approximation in over a decade. We build on this momentum and make progress on all fronts: we provide a unified family of algorithms yielding girth approximation-round tradeoffs fo

Sonika Piridi, Ranjan Kumar, Divya Pandey, Ananta C. Pradhan

We used observations obtained with the Ultraviolet Imaging Telescope on board the AstroSat satellite to measure the integrated far-ultraviolet (FUV) and optical (V) magnitudes of 30 Galactic globular clusters (GCs). We classified the UV-bright evolved stellar populations of the GCs using FUV$-$V versus FUV color-magnitude diagrams (CMDs) and BaSTI-IAC isochr

Yixing Zhu, Qing Zhang, Wenju Xu, Wei-Shi Zheng

We present YOEO, an approach for object erasure. Unlike recent diffusion-based methods which struggle to erase target objects without generating unexpected content within the masked regions due to lack of sufficient paired training data and explicit constraint on content generation, our method allows to produce high-quality object erasure results free of unw

Xiaoou Luo, Chao He, Zhengbo Yang, Yingjian Wang

Building blocks of life such as amino acids, nucleobases, and fatty acids are central to prebiotic chemistry and represent key targets in the search for planetary biosignatures. In planetary materials, biomolecules typically occur at trace levels within complex matrices, posing substantial analytical challenges, particularly for quantitative characterization

Florentin Koch

Recent work on artificial consciousness shifts evaluation from behaviour to internal architecture, deriving indicators from theories of consciousness and updating credences accordingly. This is progress beyond naive Turing-style tests. But the indicator-based programme remains epistemically under-calibrated: consciousness science is theoretically fragmented,

Rita Pardini, Sofia Tirabassi

Let $V$ be a smooth quasi-projective complex surface with compactification $(X,D)$ and set $\overline P_1(V):=h^0(X,K_X+D)$, $\overline q(V):=h^0(X,\Omega^1_X(\log D))$. We prove that $\overline P_1(V)\ge \overline q(V)-1$ if $V$ has maximal Albanese dimension and $\overline P_1(V)\ge\frac 16( \overline q(V)-5)$ otherwise. Both bounds are sharp.

Shuai Wang, Guochun Wu, Xin Zhong

We establish the global existence of weak solutions to the isentropic compressible Navier-Stokes equations in three-dimensional annular cylinders with Navier-slip boundary conditions, allowing large axisymmetric initial data and vacuum states, provided that the bulk viscosity is sufficiently large. We identify a regime in which compressible and incompressibl

Shuaijun Liu, Xiaoping Xie

In this paper, we propose two monolithic fully discrete finite element methods for fluid-structure interaction (FSI) based on a novel Piola-type Arbitrary Lagrangian-Eulerian (ALE) mapping. For the temporal discretization, we apply the backward Euler method to both the non-conservative and conservative formulations. For the spatial discretization, we adopt a

Haoxian Liu, Hengle Jiang, Lanxuan Hong, Xiaomin Ouyang

Brain-computer interfaces (BCIs) have opened new platforms for human-computer interaction, medical diagnostics, and neurorehabilitation. Wearable BCI systems, which typically employ non-invasive electrodes for portable monitoring, hold great promise for real-world applications, but also face significant challenges of signal quality degradation caused by moti

Junho Kim, Hosu Lee, James M. Rehg, Minsu Kim

Recent progress in video large language models (Video-LLMs) has enabled strong offline reasoning over long and complex videos. However, real-world deployments increasingly require streaming perception and proactive interaction, where video frames arrive online and the system must decide not only what to respond, but also when to respond. In this work, we rev

Rishi Rani, Massimo Franceschetti

We consider the problem of learning-based man-in-the-middle (MITM) attacks in cyber-physical systems (CPS), and extend our previously proposed Bellman Deviation Detection (BDD) framework for model-free reinforcement learning (RL). We refine the standard MDP attack model by allowing the reward function to depend on both the current and subsequent states, ther

Mohammad AL-Smadi

Islamic inheritance law (ilm al-mawar{\i}th) presents a challenging domain for evaluating large language models' structured reasoning capabilities, requiring multi-step legal analysis, rule-based blocking decisions, and precise fractional calculations. We present QU-NLP's submission to the QIAS 2026 shared task on Arabic Islamic inheritance reasoning. Our ap

Yejia Liu, Hengle Jiang, Haoxian Liu, Runxi Huang

3D human pose estimation is a key enabling technology for applications such as healthcare monitoring, human-robot collaboration, and immersive gaming, but real-world deployment remains challenged by viewpoint variations. Existing methods struggle to generalize to unseen camera viewpoints, require large amounts of training data, and suffer from high inference

Yuchen Lian, Pengshuo Duan, Dali Kong

Gaseous giants are characterized by their deep atmospheres, which lack clear boundaries with their interiors; therefore, their internal states could directly influence atmospheric dynamics. So far, most modeling studies have considered deep convection as the primary mechanism by which the interior influences atmospheric dynamics. In this work, we propose ano

Louis Bastogne, Lukas Korosec, Evgenios Stylianidis, Daniel G. Porter

Antiferroelectrics generate significant interest since their polarization versus electric field (PE) curves show typical double-hysteresis loops appealing for various applications. Unfortunately, antiferroelectrics are rare. In magnetic compounds, magnetization versus magnetic field (M-H) curves can show analogous double hysteresis loops not only in antiferr

Sahil Shrivastava

Diabetes mellitus affects over 537 million adults worldwide. Insulin-dependent patients require continuous glucose monitoring and precise dose calculation while operating under strict power budgets on wearable devices. This paper presents PDDS - an in-silico, software-complete research prototype of an event-driven computational pipeline for predictive insuli

Mengyao Wu, Danhua Song, Jie Yang

In this paper, we develop the higher descent equations for higher gauge theories within the framework of 2-term $L_{\infty}$ algebras. Starting from a multilinear symmetric invariant polynomial, we construct a family of higher Chern-Simons type characteristic classes and verify that they satisfy the higher descent equations. These polynomials encode both the

Leah Wrenn Berman, Jürgen Richter-Gebert

Chasles' Quadrilateral Theorem is a classical statement about four tangents to a conic that simultaneously circumscribe a circle. In its various formulations, it relates the concurrence of certain lines to the existence of confocal conics or inscribed circles. We show that several classical and modern versions of this theorem are affected by subtle ambiguiti

Jihun Kim, Javad Lavaei

Dynamical systems can confront one of two extreme types of disturbances: persistent zero-mean independent noise, and sparse nonzero-mean adversarial attacks, depending on the specific scenario being modeled. While mean-based estimators like least-squares are well-suited for the former, a median-based approach such as the $\ell_1$-norm estimator is required f

Xian Wang, Xuanru Cheng, Rongkai Shi, Lei Chen

Virtual Reality (VR) co-manipulation enables multiple users to collaboratively interact with shared virtual objects. However, existing research treats objects as monolithic entities, overlooking scenarios where users need to manipulate different sub-components simultaneously. This work addresses conflict resolution when users select overlapping vertices (non

He Yang, Dongyi Lv, Song Ma, Wei Xi

Dataset condensation aims to synthesize compact yet informative datasets that retain the training efficacy of full-scale data, offering substantial gains in efficiency. Recent studies reveal that the condensation process can be vulnerable to backdoor attacks, where malicious triggers are injected into the condensation dataset, manipulating model behavior dur

Junhao Jia, Huangwei Chen, Ruiying Sun, Yanhui Song

Real-world mathematical modeling is inherently an experiential and collaborative endeavor. Domain experts rarely solve complex problems from scratch; instead, they draw upon analogies from historical cases and subject their hypotheses to rigorous peer scrutiny. However, autonomous agents powered by Large Language Models predominantly rely on isolated reasoni

Yuqi Ping, Huahao Ding, Tianhao Liang, Longyu Zhou

Natural language (NL) navigation for low-altitude unmanned aerial vehicles (UAVs) offers an intelligent and convenient solution for low-altitude aerial services by enabling an intuitive interface for non-expert operators. However, deploying this capability in urban environments necessitates the precise grounding of underspecified instructions into safety-cri

Anh Tung Nguyen, Andreas Hertzberg, André MH Teixeira

This paper addresses the security allocation problem within networked control systems, which consist of multiple interconnected control systems under the influence of two opposing agents: a defender and a malicious adversary. The adversary aims to maximize the worst-case attack impact on system performance while remaining undetected by launching stealthy dat

Thomas Beckers, Anthony Bloch, Leonardo Colombo

Data-driven modeling is playing an increasing role in robotics and control, yet standard learning methods typically ignore the geometric structure of nonholonomic systems. As a consequence, the learned dynamics may violate the nonholonomic constraints and produce physically inconsistent motions. In this paper, we introduce a structure-preserving Gaussian pro

Weihao Tang, Hongjin He

Cartoon-texture image decomposition is a fundamental yet challenging problem in image processing. A significant hurdle in achieving accurate decomposition is the pervasive presence of noise in the observed images, which severely impedes robust results. To address the challenging problem of cartoon-texture decomposition in the presence of heavy-tailed noise,

Dae Ik Kim, Chang-Hwan Lee, Youngman Kim, Sangyong Jeon

Heavy-ion collision is an important tool to understand the dense nuclear matter properties. In order to understand the results of the heavy-ion collision experiments, both theoretical approaches to dense nuclear matter using effective models and the computer simulations with given theoretical models have been performed. Due to the complexity of the system an

Qian Yang, Miaomiao Wang, Abdelhamid Tayebi

This paper proposes a model predictive trajectory tracking approach for quadrotors subject to input constraints. Our proposed approach relies on a hierarchical control strategy with an outer-loop feedback generating the required thrust and desired attitude and an inner-loop feedback regulating the actual attitude to the desired one. For the outer-loop transl

Siying Huang, Yifen Mu, Ge Chen

This paper studies the problem of decentralized learning of Coarse Correlated Equilibrium (CCE) in aggregative Markov games (AMGs), where each agent's instantaneous reward depends only on its own action and an aggregate quantity. Existing CCE learning algorithms for general Markov games are not designed to leverage the aggregative structure, and research on

Oren Goldberg, Noa Mazurski, Uriel Levy

The height of dielectric metasurfaces is largely considered a constant in the fabrication process due to the top-down fabrication approach, resulting in a binary structure. Yet, for the recently introduced Mie voids metasurfaces, controlling the thickness of the voids locally is crucial for achieving significant spectral tuning. In this work we demonstrate M

Minzhang Li, Kuixiang Shao, Xuebing Li, Yuyang Jiao

Automated 3D scene generation is pivotal for applications spanning virtual reality, digital content creation, and Embodied AI. While computer graphics prioritizes aesthetic layouts, vision and robotics demand scenes that mirror real-world complexity which current data-driven methods struggle to achieve due to limited unstructured training data and insufficie

Chiara Amorino, Arnaud Gloter

We investigate the structural foundations of statistical efficiency under $\alpha$-local differential privacy, with a focus on maximizing Fisher information. Building on the role of continuous staircase mechanisms, we identify a fundamental symmetry regarding the extremal values $1$ and $e^{\alpha}$. We demonstrate that when the optimal measure satisfies thi

Mingda Han, Huanqi Yang, Zehua Sun, Wenhao Li

Millimeter-wave (mmWave) radar enables privacy-preserving human activity recognition (HAR), yet real-world deployment remains hindered by costly annotation and poor transferability under domain shift. Although prior efforts partially alleviate these challenges, most still require retraining or adaptation for each new deployment setting. This keeps mmWave HAR

Chenliang Tian, Zebo Yang, Raj Jain, Ramana Kompella

Scalable quantum networks must support concurrent entanglement requests, yet existing routing protocols fail when users compete for shared repeater resources, wasting fragile quantum states. This paper presents RADAR-Q, a resource-aware decentralized routing protocol embedding real-time resource contention into path selection. Unlike prior designs requiring

Mohamed Amine Bergach

We present an optimized Fast Fourier Transform (FFT) implementation for Apple Silicon GPUs, achieving 138.45~GFLOPS for $N\!=\!4096$ complex single-precision transforms -- a 29\% improvement over Apple's highly optimized vDSP/Accelerate baseline (107~GFLOPS). Our approach is grounded in a \emph{two-tier local memory model} that formally characterizes the App

Dae Ik Kim, Chang-Hwan Lee, Kyungil Kim, Youngman Kim

We implement the quark-meson coupling model in Daejeon Boltzmann-Uehling-Uhlenbeck (DJBUU) transport model and perform Au+Au collision simulations at intermediate energies. Results are compared with simulations using a conventional quantum hadrodynamics model. Differences in the maximum density reached during the collisions are interpreted in terms of nuclea

Martina Malberti, Xiaohu Sun

Precision timing has played a critical role in high-energy physics experiments, particularly for particle identification and the suppression of pileup under the challenging conditions expected at future colliders like the High-Luminosity Large Hadron Collider (HL-LHC). Over the past decades, significant advancements in timing measurement technologies have be

Sergey Lesnik, Gregor Olenik, Mark Wassermann

The first OpenFOAM HPC Challenge (OHC-1) was organised by the OpenFOAM HPC Technical Committee (HPCTC) to collect a snapshot of OpenFOAM's computational performance on contemporary production hardware and to compare hardware-constrained submissions with software-track optimisations. Participants ran a common incompressible steady-state RANS case, the open-cl

Fereidoun Sabetghadam

We introduce a homothetic extension of classical Weyl integrable geometry by generalizing the usual linear gauge transformations to affine homothetic transformations centered at a distinguished harmonic, scale-invariant form $\alpha_d$. After relinearizing these affine gauge transformations via a suitable shift of variables, we obtain a twisted exterior calc

Hayeon Jeon, Dakyeom Ahn, Sunyu Pang, Yunseo Choi

Introspection is central to identity construction and future planning, yet most digital tools approach the self as a unified entity. In contrast, Dialogical Self Theory (DST) views the self as composed of multiple internal perspectives, such as values, concerns, and aspirations, that can come into tension or dialogue with one another. Building on this view,

Mingda Han, Huanqi Yang, Chaoqun Li, Wenhao Li

Rapid advances in speech synthesis and audio editing have made realistic forgeries increasingly accessible, yet existing detection methods remain vulnerable to tampering or depend on visual/wearable sensors. In this paper, we present VoxAnchor, a system that physically grounds audio authentication in vocal dynamics by leveraging the inherent coherence betwee

Dae Ik Kim, Chang-Hwan Lee, Kyungil Kim, Youngman Kim

The quark-meson coupling (QMC) model incorporates quark degrees of freedom into the relativistic mean-field (RMF) framework, distinguishing it from traditional quantum hadrodynamics (QHD), which treats nucleons as point-like particles. In this work, we implement the QMC model within the DaeJeon Boltzmann-Uehling-Uhlenbeck (DJBUU) transport code to investigat

Ahmed G. Ghallab, Ian R. Petersen

This paper presents a velocity-free position control strategy for quadrotor unmanned aerial vehicles based on nonlinear negative imaginary (NNI) systems theory. Unlike conventional position control schemes that require velocity measurements or estimation, the proposed approach achieves asymptotic stability using only position feedback. We establish that the

Russell Mizzi

A graph $G$ is \emph{unstable} if its canonical double cover CDC$(G)$ has more automorphisms than Aut$(G)\times \mathbb{Z}_2$. A related problem asks when two non-isomorphic graphs share the same CDC. We unify both via \emph{lifting} and \emph{guided folding}, showing that they are governed by conjugacy classes of strongly switching involutions in Aut(\CDC$(

Baoheng Zhang, Jiahui Liu, Gui Zhao, Weizhou Zhang

Multimodal Large Language Models (MLLMs) perform strong vision-language reasoning under standard conditions but fail in extreme illumination, where RGB inputs lose irrevocable structure and semantics. We propose Event-MLLM, an event-enhanced model that performs all-light visual reasoning by dynamically fusing event streams with RGB frames. Two key components

Lam Pham, Khoi Vu, Dat Tran, David Fischinger

In this paper, we analyze two main factors of Bonafide Resource (BR) or AI-based Generator (AG) which affect the performance and the generality of a Deepfake Speech Detection (DSD) model. To this end, we first propose a deep-learning based model, referred to as the baseline. Then, we conducted experiments on the baseline by which we indicate how Bonafide Res

Dinh-Khoi Vo, Van-Loc Nguyen, Tam V. Nguyen, Minh-Triet Tran

Removing objects from natural images is challenging due to difficulty of synthesizing semantically coherent content while preserving background integrity. Existing methods often rely on fine-tuning, prompt engineering, or inference-time optimization, yet still suffer from texture inconsistency, rigid artifacts, weak foreground-background disentanglement, and

Yi Liu

Scaling laws relate model quality to compute budget (FLOPs), but practitioners face wall-clock time constraints, not compute budgets. We study optimal model sizing under fixed time budgets from 5 minutes to 24 hours on consumer GPUs (RTX 4090). Across 70+ runs spanning 50M--1031M parameters, we find: (1)~at each time budget a U-shaped curve emerges where too

Fulong Ma, Daojie Peng, Jun Ma

Drivable areas and curbs are critical traffic elements for autonomous driving, forming essential components of the vehicle visual perception system and ensuring driving safety. Deep neural networks (DNNs) have significantly improved perception performance for drivable area and curb detection, but most DNN-based methods rely on large manually labeled datasets

Pranav M R, Jayant Chandwani, Ahmed M. Abdelmoniem, Arnab K. Paul

Multimodal federated learning (FL) is essential for real-world applications such as autonomous systems and healthcare, where data is distributed across heterogeneous clients with varying and often missing modalities. However, most existing FL approaches assume uniform modality availability, limiting their applicability in practice. We introduce BLOSSOM, a ta

Chenliang Tian, Zebo Yang, Raj Jain, Ramana Kompella

In quantum networks, one way to communicate is to distribute entanglements through swapping at intermediate nodes. Most existing work primarily aims to create efficient two-party end-to-end entanglement over long distances. However, some scenarios also require remote multipartite entanglement for applications such as quantum secret sharing and multi-party co

A. Baki Kocaballi, Joseph Kizana, Sharon Stein, Simon Buckingham Shum

Seamless AI presents output as a finished, polished product that users consume rather than shape. This risks design fixation: users anchor on AI suggestions rather than generating their own ideas. We propose Generative Friction, which introduces intentional disruptions to AI output (fragmentation, delay, ambiguity) designed to transform it from finished prod

Masih Haseli, Jorge Cortés, Joel W. Burdick

This paper extends the forward-backward consistency index, originally introduced in Koopman modeling of systems without input, to the setting of control systems, providing a closed-form computable measure of accuracy for data-driven models associated with the Koopman Control Family (KCF). Building on a forward-backward regression perspective, we introduce th

Daniel Zantedeschi

We ask what happens when the index set carries modal structure, with possibilities organized into a Kripke frame. We define modal exchangeability as invariance under accessibility-preserving automorphisms that fix a designated base world, and derive a representation theorem for countable frames. The orbit decomposition of the centered symmetry group governs

Soham Bonnerjee, Sayar Karmakar, George Michailidis

We propose a scalable, provably accurate method for localizing an unknown number of multiple axis-aligned anomalous patches in spatial data under a general class of spatial dependence. Motivated by the practical need to detect localized changes rather than completely segment large spatial grids, we first introduce both a naive and a significantly faster inte

Ryotaro Sakamoto, Miyu Suzuki, Hiroyoshi Tamori

A root lattice is a finite rank $\mathbb{Z}$-lattice generated by elements $x$ satisfying $x\cdot x=2$. It is well-known that the root lattices have an $ADE$ classification and they play a prominent role in the study of even unimodular lattices. The notion of root lattices can be naturally generalized to lattices over the ring of integers $\mathcal{O}$ of a

Ahmed M. Benaya, Ali A. Nasir, Khaled M. Rabie, Daniel B. da Costa

Reconfigurable intelligent surfaces have emerged as a cutting-edge technology for next-generation wireless communications that are capable of reconfiguring the wireless environment using a large number of cost-effective reflecting elements. However, a significant body of prior studies has focused on single-layer surfaces that lack the capability of significa

Kai Jiang, Jiaqi Tang, Qilong Zhai, Qi Zhou

Quasiperiodic elliptic operators (QEOs) serve as fundamental models in both mathematics and physics, as exemplified by their role in the numerical modeling of one-dimensional photonic quasicrystals. However, distinct from periodic elliptic operators, approximating eigenpairs for QEOs poses significant challenges, particularly in capturing the full spectral s

Jongmin Lee, Seungyeop Kang, Sungjoo Yoo

Establishing consistent correspondences across images is essential for 3D vision tasks such as structure-from-motion (SfM), yet most existing matchers operate in a pairwise manner, often producing fragmented and geometrically inconsistent tracks when their predictions are chained across views. We propose MV-RoMa, a multi-view dense matching model that jointl

Kesheng Chen, Wenjian Luo, Qi Zhou, Yujiang liu

Traditional multiobjective optimization problems (MOPs) are insufficiently equipped for scenarios involving multiple decision makers (DMs), which are prevalent in many practical applications. These scenarios are categorized as multiparty multiobjective optimization problems (MPMOPs). For MPMOPs, the goal is to find a solution set that is as close to the Pare

Jiannan Wang, Yuyi Mao, Xianghao Yu, Ying-Jun Angela Zhang

Movable antennas (MAs) enable the reconfiguration of array geometry within a bounded region to exploit sub-wavelength spatial degrees of freedom in wireless communication and sensing systems. However, most prior research has predominantly focused on the communication and sensing performance, overlooking the mechanical power consumption inherent in antenna mo

Phat Nguyen, Thang Pham

Multi-agent systems based on large language models (LLMs) for financial trading have grown rapidly since 2023, yet the field lacks a shared framework for understanding what drives performance or for evaluating claims credibly. This survey makes three contributions. First, we introduce a four-dimensional taxonomy, covering architecture pattern, coordination m

Meituan LongCat Team, Bin Xiao, Chao Wang, Chengjiang Li

The prevailing Next-Token Prediction (NTP) paradigm has driven the success of large language models through discrete autoregressive modeling. However, contemporary multimodal systems remain language-centric, often treating non-linguistic modalities as external attachments, leading to fragmented architectures and suboptimal integration. To transcend this limi

Siyi Lang, Hongyi Gao, Yingxin Zhang, Zihao Liu

On-orbit servicing represents a critical frontier in future aerospace engineering, with the manipulation of dynamic non-cooperative targets serving as a key technology. In microgravity environments, objects are typically free-floating, lacking the support and frictional constraints found on Earth, which significantly escalates the complexity of tasks involvi

Jean Douglas Carvalho, Hugo Taciro Kenji, Ahmad Mohammad Saber, Glaucia Melo

Advanced Driver Assistance Systems (ADAS) increasingly rely on learning-based perception, yet safety-relevant failures often arise without component malfunction, driven instead by partial observability and semantic ambiguity in how risk is interpreted and communicated. This paper presents a scenario-centric framework for reproducible auditing of LLM-based ri

Jakwang Kim, Young-Heon Kim, Chan Park

In many statistical settings, two types of data are available: coupled data, which preserve the joint structure among variables but are limited in size due to cost or privacy constraints, and marginal data, which are available at larger scales but lack joint structure. Since standard methods require coupled data, marginal information is often discarded. We p

Zhongyuan Liu, Shaonan Yu, Jianping Li, Pengfei Wan

Panoramic multi-object tracking is important for industrial safety monitoring, wide-area robotic perception, and infrastructure-light deployment in large workspaces. In these settings, the sensing system must provide full-surround coverage, metric geometric cues, and stable target association under wide field-of-view distortion and occlusion. Existing image-

Rachit Agarwal, Abhishek Joshi, Sathish Chalasani, Woo Jin Kim

Object pose estimation is a fundamental task in 3D vision with applications in robotics, AR/VR, and scene understanding. We address the challenge of category-level 9-DoF pose estimation (6D pose + 3Dsize) from RGB-D input, without relying on CAD models during inference. Existing depth-only methods achieve strong results but ignore semantic cues from RGB, whi

Xiaoyang Wang, Han Xu, Lukas Broers, Tomonori Shirakawa

Phase transitions are among the most intriguing phenomena in physical systems, yet their behavior near criticality remain challenging to study using classical algorithms. Parameterized quantum circuits (PQCs) offer a promising approach to investigating such regimes on practical quantum computers. However, in order to use it to probe critical behavior, a PQC

Xulu Zhang, Haoqian Du, Xiaoyong Wei, Qing Li

Lineart colorization is a critical stage in professional content creation, yet achieving precise and flexible results under diverse user constraints remains a significant challenge. To address this, we propose OmniColor, a unified framework for multi-modal lineart colorization that supports arbitrary combinations of control signals. Specifically, we systemat

Yuxi Wang, Jungyeul Park

Bock's 1971 algorithm is an exact primal--dual method for the minimum-cost arborescence problem, but its Algol presentation obscures the interaction of its maintained arrays and label-directed control flow. We provide a self-contained tutorial comprising the original listing, a line-mapped explanation, a circuit-forming three-node example, and a complete

Tanvir Hossain, Muhammad Ifte Khairul Islam, Lilia Chebbah, Charles Fanning

Graph neural networks (GNNs) have achieved strong performance across various real-world domains. Nevertheless, they suffer from oversquashing, where long-range information is distorted as it is compressed through limited message-passing pathways. This bottleneck limits their ability to capture essential global context and decreases their performance, particu

Ojas Chaturvedi, Kayshav Bhardwaj, Tanay Gondil, Benjamin Shiue-Hal Chou

This paper presents the results of the 2025 Automatic Music Transcription (AMT) Challenge, an online competition to benchmark progress in multi-instrument transcription. Eight teams submitted valid solutions; two outperformed the baseline MT3 model. The results highlight both advances in transcription accuracy and the remaining difficulties in handling polyp

Siyu Wu, Lei Shi, Lei Xia, Cenyang Wu

Model visualization (ModelVis) has emerged as a major research direction, yet existing taxonomies are largely organized by data or tasks, making it difficult to treat models as first-class analysis objects. We present a model-centric two-stage framework that employs abstract listeners to capture spatial and temporal model behaviors, and then connects the tra

Truong-Son Hy

Protein fitness optimization is inherently a discrete combinatorial problem, yet most learning-based approaches rely on continuous representations and are primarily evaluated through predictive accuracy. We introduce Q-BIOLAT, a framework for modeling and optimizing protein fitness landscapes in compact binary latent spaces. Starting from pretrained protein

Dong-Hui Yang, Jie Zhong

We establish a mixed observability inequality for a class of degenerate hyperbolic equations on the cylindrical domain $\Omega = \mathbb{T} \times (0,1)$ with mixed Neumann Dirichlet boundary conditions. The degeneracy acts only in the radial variable, whereas the periodic angular variable allows propagation with a strong tangential component, making a direc

K M Ferdous, Dipayan Banik, Kowshik Chowdhury, Shazibul Islam Shamim

AI coding agents are increasingly integrated into modern software engineering workflows, actively collaborating with human developers to create pull requests (PRs) in open-source repositories. Although coding agents improve developer productivity, they often generate code with more bugs and security issues than human-authored code. While human-authored PRs o

Po-Chun Chou, Yen-Chi Lee, Chun-An Yang, Chia-Han Lee

Analytical modeling of field-assisted molecular communication under dynamic electric fields is fundamentally challenging due to the coupling between stochastic transport and complex boundary geometries, which renders conventional partial differential equation (PDE) approaches intractable. In this work, we introduce an effective stochastic modeling approach t