April 2026 arXiv papers — page 152

Chris Sainsbury, Feng Dong, Andreas Karwath

Autoregressive models can predict clinical events, but generating patient-conditioned multi-step trajectories that respond to intervention tokens and testing whether those responses preserve known pharmacological associations has received limited attention. We present FlatASCEND, a 14.5M-parameter autoregressive clinical sequence model using flat composite t

Georgios Vasileiou, Lantian Zhang, Silun Zhang

Incentive design problems consider a system planner who steers self-interested agents toward a socially optimal Nash equilibrium by issuing incentives in the presence of information asymmetry, that is, uncertainty about the agents' cost functions. A common approach formulates the problem as a Mathematical Program with Equilibrium Constraints (MPEC) and optim

Yuan Zhang, Yu Wang, Keke Huang, Zhongqi Sun

State estimation constitutes a core task in monitoring, supervision, and control of dynamic systems. This paper proposes a data-driven framework for the design of state observers for descriptor systems. Necessary and sufficient conditions for the existence of a standard state observer are derived purely from data under mild assumptions. When the system is su

Sajad Ashouri, Arash Hajikhani, Ari Hyytinen, Petri Rouvinen

To better align theories of paradigm shifting discoveries and empirics identifying them, we pro-pose a novel measure that incorporates a discovery impact, novelty, and tendency to break with the past into a single, coherent measure. Calibration using the National Inventor Hall of Fame data reveals that impact, novelty, and disruptiveness are strict complemen

Ismo Tähtinen, Timo Asikainen, Kalevi Mursula

Context. Solar dipole captures important information about the large-scale solar magnetic field. The evolution of the solar magnetic field including the solar dipole can be simulated with a surface flux transport (SFT) model, but these simulations are more extensive than is necessary to produce the evolution of the dipole alone. Aims. We present a dipole flu

Jurek Frey, Katharina Senkalla, Philipp J. Vetter, Fedor Jelezko

Color centers associated with group-IV dopants in diamond with long-lived nuclear spins have emerged as major candidates for distributed quantum computing nodes and quantum repeaters. Several proof-of-principle experiments have already been demonstrated. A key operation for long-distance entanglement-distribution protocols are fast and robust gates between t

Veronica Orsanigo, Thomas Louf, Eleonora Andreotti, Elisa Leonardelli

Since their inception in 1984, the European Framework Programmes (FPs) have funded collaborative R&D to promote excellence, cohesion, and competitiveness in a growing European Union. However, their integrative impact and the evolution of the research landscape alongside its collaborative structures remain insufficiently understood. In this longitudinal study

Eric Chiu, Dominik Kempa

Rank and select queries are basic operations on sequences, with applications in compressed text indexes and other space-efficient data structures. One of the standard data structures supporting these queries is the wavelet tree. In this paper, we study wavelet forests, that is, wavelet-tree structures based on the fixed-block compression boosting technique.

Anbang Ruan

The dominant paradigm of local multi-agent systems -- orchestrated, enterprise-bounded pipelines -- is being superseded by internet-wide agent societies in which autonomous agents discover each other through open registries, interact without central orchestrators, and generate emergent social behaviors. We argue that governing such societies requires institu

Nico Holzinger, Matthias Althoff

We propose a simple yet effective divide-and-discard (DD) approach to guaranteed state estimation for nonlinear discrete-time systems. Our method iteratively subdivides interval enclosures of the state and propagates them forward in time using a mean-value enclosure. The central idea is to rely on repeated refinement of simple sets rather than on more comple

John H. J. Einmahl, Chen Zhou

We consider multivariate extreme value statistics for independent but nonidentically distributed random vectors. In particular, the data may have varying tail copulas and also heteroscedastic marginal distributions. Assuming smoothly changing tail copulas, we propose a nonparametric estimator for the integrated tail copula and establish its asymptotic behavi

Shiyu Teng, Jiaqing Liu, Hao Sun, Yu Li

Depression remains widely underdiagnosed and undertreated because stigma and subjective symptom ratings hinder reliable screening. To address this challenge, we propose a coarse-to-fine, multi-stage framework that leverages large language models (LLMs) for accurate and interpretable detection. The pipeline performs binary screening, five-class severity class

Mansi Anil Patil, Devarshi Dhoble, Shivaram Kubakaddi, Mamta Raturi

This work reports back-end-of-line (BEOL) compatible, thin-film transistors (TFTs) with sub-10 nm tungsten-doped tin oxide (TWO) channels deposited by atomic layer deposition (ALD) at 150 $^\circ$C. TFTs with undoped SnO$_{\mathrm{x}}$, undoped WO$_{\mathrm{x}}$, and W-doped SnO$_{\mathrm{x}}$ channels with W concentrations of 5% and 10% were investigated. T

Shkelqim Sherifi

Deep learning has markedly advanced image based plant disease diagnosis as improved hardware and dataset quality have enabled increasingly accurate neural network models. This paper presents PD36 C, a compact convolutional neural network (1,250,694 parameters and 4.77 MB) for plant disease classification. Trained with TensorFlow Keras on the New Plant Diseas

Dongxu Wei, Qi Xu, Zhiqi Li, Hangning Zhou

3D scene generation has long been dominated by 2D multi-view or video diffusion models. This is due not only to the lack of scene-level 3D latent representation, but also to the fact that most scene-level 3D visual data exists in the form of multi-view images or videos, which are naturally compatible with 2D diffusion architectures. Typically, these 2D-based

Xiaoyu Ma, Yiwen Li, Haoyue Liu, Zhichao Wang

Automatic prompt optimization (APO) hinges on the quality of its evaluation signal, yet scoring every prompt candidate on the full training set is prohibitively expensive. Existing methods either fix a single evaluation subset before optimization begins (principled but prompt-agnostic) or adapt it heuristically during optimization (flexible but unstable and

Alexande Brea Rodriguez

The first observation of a charmless purely baryonic decay, $\Lambda_b^{0} \to \Lambda p \bar{p}$, is reported using the full Run 2 LHCb dataset, corresponding to an integrated luminosity of $6.0~fb^{-1}$. The branching fraction is measured relative to that of the topologically similar normalisation mode $\Lambda_b^{0} \to \Lambda K^+K^-$. A simultaneous fit

Ammar Bhilwarawala, Likhamba Rongmei, Harsh Sharma, Arya Jena

IoT botnet detection has advanced, yet most published systems are validated on a single dataset and rarely generalise across environments. Heterogeneous feature spaces make multi-dataset training practically impossible without discarding semantic interpretability or introducing data integrity violations. No prior work has addressed both problems with a forma

David Sanz Ruiz, David Soriano

This work describes topological magnon transport in zigzag CrI$_3$ nanoribbons (ZNR) in presence of edge strain. Exchange coupling terms under strain are obtained from first-principles calculations, and the topological properties are introduced \emph{via} second-neighbor Dzyaloshinskii-Moriya interactions. The magnon Hamiltonian is calculated using linear sp

Yilong Liu, Xixun Lin, Pengfei Cao, Ge Zhang

Large language models (LLMs) have demonstrated impressive capabilities in utilizing external tools. In practice, however, LLMs are often exposed to tools that are irrelevant to the user's query, in which case the desired behavior is to refrain from invocations. In this work, we identify a widespread yet overlooked mechanistic flaw in tool refusal, which we t

Chenlin Zhou, Sihang Guo, Jiaqi Wang, Dongyang Ma

Spiking Transformers, which combine the scalability of Transformers with the sparse, energy-efficient property of Spiking Neural Networks (SNNs), have achieved impressive results in neuromorphic and vision tasks and attracted increasing attention. However, existing directly trained spiking transformers primarily focus on vision tasks. For language modeling w

Yiran Ling, Wenxuan Li, Siying Dong, Yize Zhang

Robot grasping of desktop object is widely used in intelligent manufacturing, logistics, and agriculture.Although vision-language models (VLMs) show strong potential for robotic manipulation, their deployment in low-level grasping faces key challenges: scarce high-quality multimodal demonstrations, spatial hallucination caused by weak geometric grounding, an

Anya Nordskova, Michel Van den Bergh

Non-commutative crepant resolutions (NCCRs) are non-commutative versions of classical crepant resolutions in algebraic geometry. For 3-dimensional terminal Gorenstein singularities Iyama and Wemyss proved that all NCCRs are connected by mutations, which may be viewed as a non-commutative analogue of Kawamata's result that all crepant resolutions are connecte

Édouard Bonnet, Hung Le, Marcin Pilipczuk, Michał Pilipczuk

Fat minors are a coarse analogue of graph minors where the subgraphs modeling vertices and edges of the embedded graph are required to be distant from each other, instead of just being disjoint. In this paper, we give a coarse analogue of the classic theorem that an $n$-vertex graph excluding a fixed minor admits a balanced separator of size $O(\sqrt{n})$. S

Ville J. Härkönen

We consider symmetries and reduction in non-relativistic many-body quantum mechanics, with the aim of identifying physically meaningful observables in systems such as molecules and crystalline solids. To this end, we propose a unified framework based on two additional postulates supplementing the standard quantum-mechanical formalism. For stable systems, the

Lucía Olano-Vegas, Davide Spirito, Evgeny Modin, Pavlo Solokha

Tetragonal InSeI is an interesting low-dimensional metal chalcohalide due to its composition and anisotropic crystal structure composed of helical chains, which give rise to optoelectronic properties with potential application in photodetectors, optical thermometers, and spintronic devices. However, experimental works lack on the study of its anisotropic or

Ayoub Ghriss

We introduce the Structured Sparsity Specification (S$^3$), an algebraic framework for defining, composing, and implementing structured sparse patterns. S$^3$ specifies sparsity through three components: a View that reshapes the tensor via layout composition, a Block specification that defines the atomic pruning unit, and the sparsity decision Scope. Both Bl

Kingshuk Adhikary

Accessing quantum advantage (QA) is a legitimate task in energy harvesting devices, and it is potentially reshaping thermodynamic concepts. In this respect, the resourceful quantum non-Gaussian (QNG) states are promising candidates that precisely enable universal quantum operations to enhance thermodynamic performance with capabilities beyond what Gaussian s

Erica Cau, Andrea Failla, Giulio Rossetti

Large Language Models (LLMs) have demonstrated an unprecedented ability to simulate human-like social behaviors, making them useful tools for simulating complex social systems. However, it remains unclear to what extent these simulations can be trusted to accurately capture key social mechanisms, particularly in highly unbalanced contexts involving minority

Dario Rancati, Jan Maas, Francesco Locatello

Diffusion-based models on continuous spaces have seen substantial recent progress through the mathematical framework of gradient flows, leveraging the Wasserstein-2 (${W}_2$) metric via the Jordan-Kinderlehrer-Otto (JKO) scheme. Despite the increasing popularity of diffusion models on discrete spaces using continuous-time Markov chains, a parallel theoretica

Yihao Zhang, Kai Wang, Jiangrong Wu, Haolin Wu

Large Language Models (LLMs) face prominent security risks from jailbreaking, a practice that manipulates models to bypass built-in security constraints and generate unethical or unsafe content. Among various jailbreak techniques, multi-turn jailbreak attacks are more covert and persistent than single-turn counterparts, exposing critical vulnerabilities of L

Hiroshi Okada, Labh Singh

We propose a radiative Dirac neutrino mass model stabilized by a non-invertible fusion rule originating from a $Z_3 \times Z_3'$ gauging. The imposed symmetry forbids tree-level Yukawa couplings and ensures that neutrino masses are generated only at the one-loop level through the exchange of exotic fermions and inert scalars. This minimal framework simul

Luc Ramsès Talla Waffo

We study the second-order differential operators \(\mathcal D_{\Xi}\) and \(\mathcal D_{\Lambda}\) associated with the rescaled polynomial families \((\widetilde{\Xi}_n)\) and \((\widetilde{\Lambda}_n)\), and more generally the polynomial sequences generated by iterating these operators from an arbitrary linear initial datum \(cx-d\). We establish structural

Lei Xiong, Huaying Yuan, Zheng Liu, Zhao Cao

Leveraging Multi-modal Large Language Models (MLLMs) to accelerate frontier scientific research is promising, yet how to rigorously evaluate such systems remains unclear. Existing benchmarks mainly focus on single-document understanding, whereas real scientific workflows require integrating evidence from multiple papers, including their text, tables, and fig

Leonard Bärmann, Joana Plewnia, Alex Waibel, Tamim Asfour

Robots must verbalize their past experiences when users ask "Where did you put my keys?" or "Why did the task fail?" Yet maintaining life-long episodic memory (EM) from continuous multimodal perception quickly exceeds storage limits and makes real-time query impractical, calling for selective forgetting that adapts to users' notions of relevance. We present

Elaine Lau, Markus Dücker, Ronak Chaudhary, Hui Wen Goh

Existing AI benchmarks lack the fidelity to assess economically meaningful progress on professional workflows. To evaluate frontier AI agents in a high-value, labor-intensive profession, we introduce BankerToolBench (BTB): an open-source benchmark of end-to-end analytical workflows routinely performed by junior investment bankers. To develop an ecologically

Hiroshi Okamoto

We show that quantum computational imaging is advantageous in the setting of low-dose electron microscopy of beam-sensitive specimens. Two qudits placed near the electron beam enable full transfer of quantum information between the electron microscope and a quantum computer in the proposed scheme, providing the specimen is a phase object. We present a quantu

Bronislav Sidik, Dror Mizrahi

We present 3D-Anchored Lookahead Planning (3D-ALP), a System 2 reasoning engine for robotic manipulation that combines Monte Carlo Tree Search (MCTS) with a 3D-consistent world model as the rollout oracle. Unlike reactive policies that evaluate actions from the current camera frame only, 3D-ALP maintains a persistent camera-to-world (c2w) anchor that survive

Kalyan Banerjee, Kalyan Chakraborty, Azizul Hoque

We start with $n$-torsions in the Jacobian of an $m$-gonal curve and produce $n$-torsions in the class group of certain number field $K$.

Yuqi Zhang, Zetai Cen, Haeran Cho

We study the problems arising from modeling high-dimensional tensor-valued time series under a Tucker decomposition-based factor model with multiple structural change points. First, we propose an algorithm for detecting the multiple change points, which utilizes the low-rank structure of the data for statistical and computational efficiency. Also, the multi-

Rui Song, Lida Shi, Ruihua Qi, Yingji Li

In recent years, rapid advances in Multimodal Large Language Models (MLLMs) have increasingly stimulated research on ancient Chinese scripts. As the evolution of written characters constitutes a fundamental pathway for understanding cultural transformation and historical continuity, how MLLMs can be systematically leveraged to support and advance text evolut

Xue Zhang, Bang Huang, Mohamed-Slim Alouini

The rapid growth of the low-altitude economy (LAE) is making aerial systems an important part of future digital infrastructure. Although major advances have been achieved in unmanned aerial vehicle (UAV) platforms, communications, and autonomous control, environmental perception remains a key bottleneck to reliable and scalable LAE operations. Existing sensi

Yang Liu, Enxi Wang, Yufei Gao, Weixin Zhang

Despite the success of reinforcement learning for large language models, a common failure mode is reduced sampling diversity, where the policy repeatedly generates similar erroneous behaviors. Classical entropy regularization encourages randomness under the current policy, but does not explicitly discourage recurrent failure patterns across rollouts. We prop

Fumihiko Asano, Yuxuan Xiang, Yanqiu Zheng, Cong Yan

This paper proposes novel passive-dynamic walkers formed by two cross-shaped frames and eight viscoelastic elements. Since it is a combination of two four-legged rimless wheels via viscoelastic elements, we call it viscoelastically-combined rimless wheel (VCRW). Two types of VCRWs consisting of different cross-shaped frames are introduced; one is formed by c

Dieter Verbruggen, Hazem Sallouha, Sofie Pollin

Spectrum sharing and dynamic spectrum reuse are becoming increasingly critical in modern wireless networks to address spectrum scarcity. However, these techniques inevitably increase Cross-Technology Interference (CTI). In this context, the Open Radio Access Network (O-RAN), as a modern and disaggregated network architecture, necessitates accurate, low-laten

Ting-Ting Ge, Qi-Hang Wu, Pak-Hin Thomas Tam, Jie Feng

We present an updated analysis of the gigaelectronvolt (GeV) gamma-ray emission from the shell-type supernova remnant (SNR) RX J0852.0-4622 (Vela Jr) using 15 yr of Fermi Large Area Telescope (Fermi-LAT) data. We quantitatively model the GeV morphology and find that it is best described by the masked H.E.S.S. shell template, indicating that the embedded puls

Judit Donada, Laia Casamiquela, Friedrich Anders, Lola Balaguer-Núñez

Out of all the discovered open clusters (OCs) that are located in the inner part of the Galaxy, only a small fraction has been observed with high-resolution spectroscopy. An intriguing population of inner-disc OCs at relatively high altitudes ($Z$) from the Galactic plane remains poorly studied. There are few reliable detections of such OCs, and their occurr

Miraç Kerem Maden, Asghar Ullah, Baris Coskunuzer, Özgür E. Müstecaplıoğlu

The reliable operation of finite-time quantum heat engines is fundamentally limited by control imperfections that induce nonadiabatic phase accumulation and quantum friction, degrading the stability of the thermodynamic cycle. Traditional monitoring relies on energetic observables such as instantaneous cycle work; however, under finite-time driving, these qu

Abhinaba Basu

At K=16 tokens (0.4% of a 4K context), every existing KV-cache compression method achieves 0% on credential retrieval. The failure mode is dormant tokens: credentials, API keys, and configuration values that receive near-zero attention but become essential at generation time. Because these tokens lack the statistical signals that eviction policies rely on, n

Kihyuk Lee

Background: Large language models (LLMs) have been explored as tools for generating personalized exercise prescriptions, yet the consistency of outputs under identical conditions remains insufficiently examined. Objective: This study evaluated the intra-model consistency of LLM-generated exercise prescriptions using a repeated generation design. Methods: Six

Junjie Ye, Zhaolin Wang, Yuanwei Liu, Peichang Zhang

A mutual coupling-aware beamforming design for continuous aperture array (CAPA)-aided multi-user systems is investigated. First, a transmit coupling kernel is characterized to explicitly capture the mutual coupling effects inherent in CAPAs, based on which a mutual coupling-aware sum-rate maximization functional optimization problem is formulated. To address

Carlos Oscar S. Sorzano, B. Pueche-Granados

Heavy-tailed impact distributions, intrinsic uncertainty, and the high costs of proposal-based peer review increasingly challenge research funding decisions. Using large-scale bibliometric data, we show that past scientific performance provides statistically meaningful, though imperfect, information about future productivity and impact across multiple dimens

Scott Duke Kominers

We prove a structural upper bound on the $p$-adic valuation of denominators of rationals belonging to a missing-digit set $K_{m,D}$, generalizing a key step in recent work of Lin, Wu, and Yang [arXiv:2603.24614] on reciprocals of factorials. For a rational $\frac{r}{Q}$ with $\gcd(Q,m)=1$ and a fixed prime $p_0\nmid m$, membership in $K_{m,D}$ forces $\nu_{p

Benjamin Lange, Geoff Keeling, Kyle Pedersen, Carmen Heringer

Bottom-up responsible innovation initiatives seek to empower technology development teams to engage in ethical reflection, yet such interventions frequently fail to achieve practitioner engagement. Why do some ethics interventions succeed while others are dismissed as irrelevant, adversarial, or disconnected from work? This paper proposes epistemic trust --

Nicholas Suits, Masudul Imtiaz

Everything that exists has a natural frequency; this material characteristic is something that must be known and fully understood. If we fail to predict, measure, and address potential natural frequency concerns, it could significantly reduce the life span of our equipment or cause it to fail immediately when put into service. There are a few methodologies u

Chentong Wang, Jincheng Gao, Fei Zhu, Jie Chen

Hyperspectral unmixing (HU) is crucial for analyzing hyperspectral imagery, yet achieving accurate unmixing remains challenging. While traditional methods struggle to effectively model complex spectral-spatial features, deep learning approaches often lack physical interpretability. Unrolling-based methods, despite offering network interpretability, inadequat

Wenfei Liang, Wee Peng Tay

In federated learning (FL), accommodating clients with diverse resource constraints remains a significant challenge. A widely adopted approach is to use a shared full-size model, from which each client extracts a submodel aligned with its computational budget. However, regardless of the specific scoring strategy, these methods rely on the same global backbon

Suresh Govindarajan, Akhila Sadanandan

We update the holomorphic modular bootstrap incorporating a recent result that computes the exact S-matrix within the Modular Linear Differential Equation (MLDE) setting. Further, using knowledge of the allowed exponents modulo one, we obtain admissible solutions to all MLDE's with up to six characters and Wronskian index < 6 and one accessory parameter with

Mehmet Parlak, Islam Guven

Conventional microwave engineering education relies heavily on analytical methods, canonical circuit topologies, and intuition-driven design, which have proven effective at microwave frequencies. However, as systems increasingly operate in the millimeter-wave and terahertz regimes, parasitic effects, process-dependent electromagnetic interactions, and ultra-

Cornelius Fritz, Michael Schweinberger

We introduce R package iglm, which implements a comprehensive framework for studying relationships among predictors and outcomes under interference. The implemented regression framework facilitates the study of spillover and other phenomena in connected populations and has important advantages over existing packages, among them scalability and provable theor

Abeer Mostafa, Raneen Younis, Zahra Ahmadi

Spatio-temporal processes often exhibit highly heterogeneous and non-intuitive responses to localized disruptions, limiting the effectiveness of conventional message passing approaches in modeling local heterogeneity. We reformulate spatio-temporal forecasting as the problem of learning information flow over locally structured spaces, rather than propagating

Anton Pakhunov

We present mycelium-index, a streaming approximate nearest neighbor (ANN) index for high-dimensional vector spaces, inspired by the adaptive growth patterns of biological mycelium. The system continuously adapts its topology through myelial edge decay and reinforcement, a traffic-driven living hierarchy, and hybrid deletion combining O(1) bypass for cold nod

Komla Domelevo, Stefanie Petermichl

We define a time faithful dyadic shift operator of complexity one, that is an antisymmetric antiinvolution. We show that the Hilbert transform with values in a Banach space is $L^p$ bounded if and only if the dyadic shift is -- with a linear two sided norm dependence. The results reduce the famous UMD conjecture to a pair of simple dyadic operators.

Fan Xu, Zhi-an Huang, Haohuai He, Yidong Song

Accurate prediction of antibody-antigen binding affinity is fundamental to therapeutic design, yet remains constrained by severe label sparsity and the complexity of antigenic variations. In this paper, we propose AbLWR (Antibody-antigen binding affinity List-Wise Ranking), a novel framework that reformulates the conventional affinity regression task as a li

Xin Zhou, Ji Yang, Yang Su, Xuepeng Chen

We investigate the relationships between LHAASO TeV gamma-ray sources and various kinds of objects, including pulsar wind nebulae (PWNe), supernova remnants (SNRs), HII regions, microquasars, and OB associations. We propose a Randomization-Adjusted Overlap Correlation (RAOC) method to statistically assess association probabilities and evaluate association pr

Hagai Aronowitz, Zvi Kons, Avihu Dekel, George Saon

Speaker-Attributed Automatic Speech Recognition (SAA) enhances traditional ASR systems by incorporating relative speaker identity tags directly into the transcript (e.g., [Speaker 1]:, [Speaker 2]:). In this work, we extend the capabilities of Granite-speech, a state-of-the-art speech-aware Large Language Model (LLM) originally trained for transcription and

Xiaolong Wang, Biaolin Li, Xiaoli Wang

As a special type of bilinear systems, K-power bilinear systems possess a special coupled structure along with nice properties in practice. In this paper, we investigate the data-driven counterpart of balanced truncation for K-power systems. As the standard balanced truncation is performed based on the subsystems of K-power systems, the main idea is to appro

Hande Tuncel Golpek, Mehmet Ali Bilici, Aysun Aytac

Networks are inherently vulnerable to vertex failures, making the analysis of their structural robustness a fundamental problem in graph theory. In this study, we investigate the closeness and vertex residual closeness of graphs, with a particular focus on the middle graph representations of certain special graph classes, which provide a richer structural fr

Mathieu Darnas, Mathilde Ouvrier-Buffet, Antoine Faurie, Jean-Baptiste Casanova

Scalable spin-based quantum computing demands precise and stable control of a large number of gate-defined quantum dots while minimizing wiring complexity and thermal load. Control architectures based on sample-and-hold (SH) multiplexing techniques offer a promising solution by enabling sequential programming of several gate voltages using a limited number o

XuDong Wang, Bin Qi, Shouyu Wang, Chen Liu

Conventionally, octupole deformation in nuclei has been attributed to strong $\Delta l=3$ couplings between opposite-parity single-particle orbitals. In this work, we demonstrate that the often-overlooked $\Delta l=1$ mode also plays an important role. Taking orbitals near the octupole magic number $N = 134$ as a benchmark, we systematically evaluate the $\D

Arpan Sur, Kawshik Nath, Ahmed Zubair

Nature-inspired hyperuniform disorder offers a promising route to broadband light trapping in ultrathin perovskite solar cells by avoiding narrowband, illumination-sensitive responses commonly associated with periodic nanophotonic textures. Here, we introduce a nature-inspired ingenious hyperuniform nanohole architecture integrated into the front glass of a

Víctor Francés-Belda, Carlos Sanmiguel Vila, Rodrigo Castellanos

Accurate and efficient surrogate models for aerodynamic surface pressure fields are essential for accelerating aircraft design and analysis, yet deterministic regressors trained with pointwise losses often smooth sharp nonlinear features. This work presents a conditional denoising diffusion probabilistic model for predicting surface pressure distributions on

Arnaud Pierens, Min-Kai Lin

The regions of protoplanetary discs where planets can form are believed to be weakly ionised, suggesting thereby that non-ideal magneto-hydrodynamics (MHD) effects play an important role in the disc dynamics and in the planet formation process. In particular, the combined effect of ohmic resistivity and ambipolar diffusion can be responsible for launching MH

Ruta Binkyte, Sharif Abuaddba, Chamikara Mahawaga, Ming Ding

This paper introduces AI as a Research Object (AI-RO), a paradigm for governing the use of generative AI in scientific research. Instead of debating whether AI is an author or merely a tool, we propose treating AI interactions as structured, inspectable components of the research process. Under this view, the legitimacy of an AI-assisted scientific paper dep

Zhixin Lin, Jungang Li, Dongliang Xu, Shidong Pan

Mobile GUI agents powered by Multimodal Large Language Models (MLLMs) can execute complex tasks on mobile devices. Despite this progress, most existing systems still optimize task success or efficiency, neglecting users' privacy personalization. In this paper, we study the often-overlooked problem of agent personalization. We observe that personalization can

Zhixiang Lu, Jionglong Su

Multimodal Large Language Models (MLLMs) in healthcare suffer from severe confirmation bias, often hallucinating visual details to support initial, potentially erroneous diagnostic hypotheses. Existing Chain-of-Thought (CoT) approaches lack intrinsic correction mechanisms, rendering them vulnerable to error propagation. To bridge this gap, we propose Dialect

Beibei Wang, Bo Jiang, Ziyan Zhang, Jin Tang

Graph Data Prompt (GDP), which introduces specific prompts in graph data for efficiently adapting pre-trained GNNs, has become a mainstream approach to graph fine-tuning learning problem. However, existing GDPs have been respectively designed for distinct graph component (e.g., node features, edge features, edge weights) and thus operate within limited promp

Rehan Ahmad, Muhammad Umar Farooq, Qihang Feng, Thomas Hain

Speech recognition systems often struggle with data domains that have not been included in the training. To address this, unsupervised domain adaptation has been explored with ensemble and multi-stage teacher-student training methods reducing the word error rate. Despite improvements, the error rate remains much higher than that achieved with supervised in-d

Ruifeng Gao, Sen Li, Jue Wang, Qiuming Zhu

Channel gain maps (CGMs) enable propagation-aware services in edge-intelligent wireless communication networks, while diffusion-based CGM construction is memory intensive for on-device training or adaptation. This letter proposes InvDiff-CGM, an invertible diffusion framework that constructs CGMs from sparse measurements and environmental priors. By adopting

Leanne Vis, Maxim Pisarenco, Bart M. N. Smets, Fons van der Sommen

Geodesic tracking on the projective line bundle $\R^2 \times P^1 $ has many uses, including the segmentation of objects in images. However, global tracking requires expensive distance map computations. We provide a practical solution to this problem by introducing a snake model on $\R^2 \times P^1$, where we only compute the distance map where needed. Our me

Emanuele Borgonovo, Francesco Cappelli, Xuefei Lu, Elmar Plischke

Feature importance methods using unrestricted permutations are flawed due to extrapolation errors; such errors appear in all non-trivial variable importance approaches. We propose three new approaches: conditional model reliance and Knockoffs with Gaussian transformation, and restricted ALE plot designs. Theoretical and numerical results show our strategies

Jianuo Cao, Yuxin Chen, Masayoshi Tomizuka

Training language-conditioned whole-body controllers for humanoid robots demands large-scale motion-language datasets. Existing approaches based on motion capture are costly and limited in diversity, while text-to-motion generative models produce purely kinematic outputs that are not guaranteed to be physically feasible. We present CLAW, a pipeline for scala

Ünsal Öztürk, Vedrana Krivokuća Hahn, Sushil Bhattacharjee, Sébastien Marcel

Face recognition embeddings encode identity, but they also encode other factors such as gender and ethnicity. Depending on how these factors are used by a downstream system, separating them from the information needed for verification is important for both privacy and fairness. We propose Variational Latent Entropy Estimation Disentanglement (VLEED), a post-

Guoxin Yu, Chulun Zhou, Lemao Liu, Qi Wang

Evaluating the quality of model responses remains challenging in generative tasks with long-form answers, as the expected answers usually contain multiple semantically distinct yet complementary factors that should be factorized for fine-grained assessment. Recent evaluation methods resort to relying on either task-level rubrics or question-aware checklists.

Ondrej Majer, Krishna Manoorkar, Wolfgang Poiger, Igor Sedlár

In various computational systems, accessing information incurs time, memory or energy costs. However, standard epistemic logics usually model the acquisition of evidence as a cost-free process, which restricts their applicability in environments with limited resources. In this paper, we bridge the gap between qualitative epistemic reasoning and quantitative

Tencent Hunyuan Team

Advances in Multimodal Large Language Models (MLLMs) are transforming video captioning from a descriptive endpoint into a semantic interface for both video understanding and generation. However, the dominant paradigm still casts videos as monolithic narrative paragraphs that entangle visual, auditory, and identity information. This dense coupling not only co

Shuide Wen, Beier Ku

Building on the Agentic ROI framework proposed by Liu et al. (2026), this paper introduces knowledge compounding as a new measurable concept in the empirical economics of LLM agents and validates it through a controlled four-query experiment on Qing Claw, an industrial-grade C# reimplementation of the OpenClaw multi-agent framework. Our central theoretical c

Chihiro Tabata, Koji Kaneko, Akiko Nakao, Takashi Ohhara

The magnetic order of EuIr$_4$In$_2$Ge$_4$, which crystallizes in a Dresselhaus-type noncentrosymmetric tetragonal structure, was investigated using two complementary single-crystal neutron diffraction approaches. Time-of-flight single-crystal diffraction reveals antiferromagnetic Bragg reflections with propagation vector $q = (1, 0, 0)$ below the N\'{e}el t

Karl Sigfrid, Ellinor Fackle-Fornius, Frank Miller

Long questionnaires increase the response burden for patients and healthcare workers. In the treatment of Parkinson's disease, the MDS-UPDRS questionnaire to track disease progression may be underutilized due to time requirements. While reduced item sets have been studied using Fisher information from Item Response Theory (IRT) models, optimal selection meth

Shroddha Mukhopadhyay, Maria Antonietta Vincenti, Radu Malureanu, Crina Cojocaru

We present a combined experimental and theoretical study of enhanced third-harmonic generation (THG) in silicon nitride metasurfaces. These structures exhibit strong resonant nonlinear responses, enabling up to two orders of magnitude enhancement in THG compared to a flat silicon nitride etalon, driven by strong electromagnetic field localization. We investi

Bhargav Nath, Mehulkumar Lakhadive, Anshu Sharma, Basuraj Bhowmik

Reliable modal identification from output-only vibration data remains a challenging problem under measurement noise, sparse sensing, and structural variability. These challenges intensify when global modal quantities and spatially distributed mode shapes must be estimated jointly from frequency-domain data. This work presents a physics-aware variational grap

Cedric J. Gommes

The pore-surface and surface-surface correlation functions are structural characteristics that play an important role in theoretical materials science and in small-angle scattering theory. Exact analytical expressions for the surface correlation functions are available only for very few models, and we here derive such expressions for the general class of dea

Nicolas Dupré

Let $\mathfrak{F}$ be a non-archimedean local field of residue characteristic $p$ and $G$ be one of the groups $\mathrm{GL}_2(\mathfrak{F})$, $\mathrm{SL}_2(\mathfrak{F})$ or $\mathrm{PGL}_2(\mathfrak{F})$. Let $\mathcal{H}_G$ denote the pro-$p$ Iwahori-Hecke algebra of $G$ over $\overline{\mathbb{F}}_p$. We study the homotopy category $\mathrm{Ho}(\mathcal{

Jiaqi Wu, Zhen Wang, Enhao Huang, Kangqing Shen

Text-guided multispectral object detection uses text semantics to guide semantic-aware cross-modal interaction between RGB and IR for more robust perception. However, notable limitations remain: (1) existing methods often use text only as an auxiliary semantic enhancement signal, without exploiting its guiding role to bridge the inherent granularity asymmetr

Dominic P. Fischer, Zachary Hopton, Jannis Vamvas

Lemmatization -- the task of mapping an inflected word form to its dictionary form -- is a crucial component of many NLP applications. In this paper, we present RUMLEM, a lemmatizer that covers the five main varieties of Romansh as well as the supra-regional standard variety Rumantsch Grischun. It is based on comprehensive, community-driven morphological dat

You Su, Yonghong Song, Jingqi Chen, Zehan Wen

Change detection is a fundamental task in remote sensing, aiming to quantify the impacts of human activities and ecological dynamics on land-cover changes. Existing change detection methods are limited to predefined classes in training datasets, which constrains their scalability in real-world scenarios. In recent years, numerous advanced open-vocabulary sem

Ya-nan Guan, Shaonan Zhang, Hang Guo, Yawen Wang

In this paper, we present a comprehensive overview of the NTIRE 2026 3rd Restore Any Image Model (RAIM) challenge, with a specific focus on Track 3: AI Flash Portrait. Despite significant advancements in deep learning for image restoration, existing models still encounter substantial challenges in real-world low-light portrait scenarios. Specifically, they s

Zhuoyu Wu, Wenhui Ou, Pei-Sze Tan, Wenqi Fang

Retrieving procedure-oriented evidence from materials science papers is difficult because key synthesis details are often scattered across long, context-heavy documents and are not well captured by paragraph-only dense retrieval. We present RECIPER, a dual-view retrieval pipeline that indexes both paragraph-level context and compact large language model-extr

Jie Shi

This paper combines the decomposition technique ($\sigma$-stability) in random functional analysis with the deterministic theory of asymptotically pointwise contractions to provide a complete self-contained derivation of a fixed point theorem for random asymptotically pointwise contractions. We assume the contraction function is linear $\psi(t)=\lambda t$ ($

Jaehong Kim, Jihong Park, Changsheng You, Seung-Woo Ko

Multi-path sensing, which aims to extract the geometric attributes of multiple propagation paths, is expected to be a key functionality of 6G. A movable antenna (MA) can enable this functionality by creating a synthetic aperture through sequential mechanical motion. However, existing MA-based sensing methods typically rely on exhaustive scanning over the ent