March 2026 arXiv papers — page 48

Ziyan Wang, Peng Chen, Ding Li, Chiwei Li

Learning diverse and high-fidelity traffic simulations from human driving demonstrations is crucial for autonomous driving evaluation. The recent next-token prediction (NTP) paradigm, widely adopted in large language models (LLMs), has been applied to traffic simulation and achieves iterative improvements via supervised fine-tuning (SFT). However, such metho

Hongrui He, Hao Lyu, Xueting Si

Grain boundary migration governs microstructural evolution in crystalline materials, directly influencing mechanical properties such as strength and thermal stability. Disconnections, which are line defects formed at grain boundaries in response to local curvature, have been identified as critical carriers of boundary migration. Here, we investigate the glid

Ray-Yuan Chung, Xuhai Xu, Ari Pollack

Large language model based health agents are increasingly used by health consumers and clinicians to interpret health information and guide health decisions. However, most AI systems in healthcare operate in siloed configurations, supporting individual users rather than the multi-stakeholder relationships central to healthcare. Such use can fragment understa

Dohwan Ko, Jinyoung Park, Seoung Choi, Sanghyeok Lee

Mixture-of-Experts (MoE) has emerged as an effective approach to reduce the computational overhead of Transformer architectures by sparsely activating a subset of parameters for each token while preserving high model capacity. This paradigm has recently been extended to Vision-Language Models (VLMs), enabling scalable multi-modal understanding with reduced c

Hiroto Masaoka, Tetsuya Hashimoto, Shotaro Yamasaski, Yuhei Iwata

We present a search for neutrino counterparts to fast radio bursts (FRBs) using temporal and spatial cross-matching between the Second CHIME/FRB catalogue and the IceCube high-energy alert-track catalogue ICECAT1. Because current FRB--neutrino models do not provide a unique consensus on emission ordering, our primary significance test adopts a two-sided, ord

Guang Yang, Ziye Geng, Yihang Chen, Changqing Luo

Task-agnostic model fingerprinting has recently gained increasing attention due to its ability to provide a universal framework applicable across diverse model architectures and tasks. The current state-of-the-art method, MetaV, ensures generalization by jointly training a set of fingerprints and a neural-network-based global verifier using two large and div

Xiaowei Zhu, Yubing Ren, Fang Fang, Shi Wang

The rapid advancement of large language models has increasingly blurred the boundary between human-written and AI-generated text, raising societal risks such as misinformation dissemination, authorship ambiguity, and threats to intellectual property rights. These concerns highlight the urgent need for effective and reliable detection methods. While existing

Duo-Lun Ge, Zhi-Wei Liu, Li-Sheng Geng

Understanding near-threshold strong interactions is essential for disentangling hadronic molecules and compact multiquark states in heavy-flavor spectroscopy. In this context, the doubly charmed tetraquark candidate $T_{cc}(3875)^+$ serves as a critical benchmark because it lies very close to the $D^*$-$D$ thresholds. Motivated by the interaction ambiguity r

Yuhang Zhou, Zhuokai Zhao, Ke Li, Spilios Evmorfos

Feature selection is a crucial step in large-scale industrial machine learning systems, directly affecting model accuracy, efficiency, and maintainability. Traditional feature selection methods rely on labeled data and statistical heuristics, making them difficult to apply in production environments where labeled data are limited and multiple operational con

Guoyi Fu, Shanshan Fu, Xiaoguang Li, Jian Zhang

This paper is concerned with the stability of standing waves for the mass-critical Hartree equation with a focusing perturbation by the variational method. The profile decomposition theory is employed to prove the attainability of the cross constrained variational problem, and then the comparison of two cross constrained variational problems is derived. The

Xianliang Zhou, Fei Yang, Miao Liu, Yin Shi

One of the primary reasons that superconductivity in underdoped cuprates is enigmatic is that it emerges from an incoherent Fermi-arc state, so the applicability of the Bardeen-Cooper-Schrieffer (BCS) theory is questionable. Here we approach this problem by investigating unconventional $d$-wave superconductivity in a recently proposed solvable model for stro

Amarnath Chakraborty, Aleksandr Rodin, Shaffique Adam, Giovanni Vignale

We investigate the interplay of in-plane magnetic and transverse electric fields in AB-stacked bilayer graphene. In prior work neglecting trigonal warping, we demonstrated that this configuration induces an insulator-metal transition purely via orbital effects, albeit requiring impractically large magnetic fields ($>100$ T). Here, we extend the analysis to t

Wenyi Xu, Feiran Zhu, Songyang Li, Renzhe Zhou

Kuaishou serving hundreds of millions of searches daily, the quality of short-video search is paramount. However, it suffers from a severe Matthew effect on long-tail queries: sparse user behavior data causes models to amplify low-quality content such as clickbait and shallow content. The recent advancements in Large Language Models (LLMs) offer a new paradi

Ruicheng Ao, Jiashuo Jiang, David Simchi-Levi

Firms that price perishable resources -- airline seats, hotel rooms, seasonal inventory -- now routinely use demand predictions, but these predictions vary widely in quality. Under hard capacity constraints, acting on an inaccurate prediction can irreversibly deplete inventory needed for future periods. We study how prediction uncertainty propagates into dyn

Weiwei Fang, Lin Li, Kaize Shi, Yu Yang

High-fidelity social simulation is pivotal for addressing complex Web societal challenges, yet it demands agents capable of authentically replicating the dynamic spectrum of human interaction. Current LLM-based multi-agent frameworks, however, predominantly adhere to static interaction topologies, failing to capture the fluid oscillation between cooperative

Parth Gaba, Emiliano De Cristofaro

In this paper, we study how different Reddit communities discuss generative AI in high school education, focusing on learning, academic integrity, AI detection, and emotional framing. Using 3,789 posts from five education-related subreddits, we compare student, teacher, and mixed communities using a pipeline that combines keyword retrieval, human-validated r

Rui Zhang, Xian-Zhe Tao, Yibo Chen, Jiawei Zhang

Haplotype phasing, the process of resolving parental allele inheritance patterns in diploid genomes, is critical for precision medicine and population genetics, yet the underlying optimization is NP-hard, posing a scalability challenge. To address this, we introduce QHap, a haplotype phasing algorithm that leverages quantum-annealing-inspired optimization. B

Kamran Ahmad Awan, Sonia Khan, Eman Abdullah Aldakheel, Saif Al-Kuwari

Connected and automated vehicles require city-scale coordination under strict latency and reliability constraints. However, many existing approaches optimize communication and mobility separately, which can degrade performance during network outages and under compute contention. This paper presents QIVNOM, a quantum-inspired framework that jointly optimizes

Haoge Chang, Zeyang Yu

This article studies randomization inference for treatment effects in randomized controlled trials with attrition, where outcomes are observed for only a subset of units. We assume monotonicity in reporting behavior as in \cite{lee2009training} and focus on the average treatment effect for always-reporters (AR-ATE), defined as units whose outcomes are observ

Huanshuo Dong, Hao Wu, Hong Wang, Qin-Yi Zhang

Long-term fluid dynamics forecasting is a critically important problem in science and engineering. While neural operators have emerged as a promising paradigm for modeling systems governed by partial differential equations (PDEs), they often struggle with long-term stability and precision. We identify two fundamental failure modes in existing architectures:

Ziqi Gao, Nicha Dvornek, Xiaoran Zhang, Gigi Galiana

Low-field (LF) magnetic resonance imaging (MRI) improves accessibility and reduces costs but generally has lower signal-to-noise ratios and degraded contrast compared to high field (HF) MRI, limiting its clinical utility. Simulating LF MRI from HF MRI enables virtual evaluation of novel imaging devices and development of LF algorithms. Existing low field sim

Yinyi Luo, Hrishikesh Gokhale, Marios Savvides, Jindong Wang

Despite significant progress in text-to-image generation, aligning outputs with complex prompts remains challenging, particularly for fine-grained semantics and spatial relations. This difficulty stems from the feed-forward nature of generation, which requires anticipating alignment without fully understanding the output. In contrast, evaluating generated im

Alejandro Ayala, Santiago Bernal-Langarica, José Jorge Medina-Serna, Ana Julia Mizher

In heavy-ion collisions, an excess in photon production, together with a larger than expected positive elliptic flow, has been observed, a phenomenon commonly referred to as the direct photon puzzle. In this work we study the mechanism of photon production arising from gluon splitting and fusion during the pre-equilibrium stage in the presence of magnetic fi

Lijie Ji, Sabrina Rashid, Yanlai Chen, Zhu Wang

Traditional projection-based reduced-order modeling approximates the full-order model by projecting it onto a linear subspace. With a fast-decaying Kolmogorov $n$-width of the solution manifold, the resulting reduced-order model (ROM) can be an efficient and accurate emulator. However, for parametric partial differential equations with slowly decaying Kolmog

Dingjie Song, Tianlong Xu, Yi-Fan Zhang, Hang Li

Assessing student handwritten scratchwork is crucial for personalized educational feedback but presents unique challenges due to diverse handwriting, complex layouts, and varied problem-solving approaches. Existing educational NLP primarily focuses on textual responses and neglects the complexity and multimodality inherent in authentic handwritten scratchwor

Jiapeng Li, Changsheng You, Guoliang Cheng, Haobin Sun

For extremely large-scale arrays (XL-arrays), the discrete Fourier transform (DFT) codebook, conventionally used in the far-field, has recently been employed for near-field beam training. However, most existing methods rely on the line-of-sight (LoS) dominant channel assumption, which may suffer degraded communication performance when applied to the general

Hiroyuki Tetsuka, Minoru Hirano

Wireless bioelectronic interfaces are increasingly used to control tissue-engineered biohybrid robotic systems. However, a unifying engineering framework linking device design to system-level control remains underdeveloped. Here, we propose that wireless control in biohybrid robotics can be formulated as a coupled co-design problem of integrating signal deli

Jiaqing Zhang, Hao Wang, Mingjia Yin, Bo Chen

Modern deep recommender models are trained under a continual learning paradigm, relying on massive and continuously growing streaming behavioral logs. In large-scale platforms, retraining models on full historical data for architecture comparison or iteration is prohibitively expensive, severely slowing down model development. This challenge calls for data-e

Oliver Heinzel, Smajil Halilovic, Thomas Hamacher, Michael Ulbrich

Closed-loop shallow geothermal systems are one of the key technologies for decarbonizing the residential heating and cooling sector. The primary type of these systems involves vertical borehole heat exchangers (BHEs). During the planning phase, it is essential to find the optimal design for these systems, including the depth and spatial arrangement of the BH

Di Yang

Okounkov [36] proved a remarkable formula relating $n$-point GUE (Gaussian unitary ensemble) correlators of a fixed genus to Witten's intersection numbers of the same genus. The partition function of GUE correlators is a tau-function for the Toda lattice hierarchy. In this note, based on the knowledge of these two statements we give a new proof of the Witten

Javad Pourmostafa Roshan Sharami

Machine Translation (MT) and Quality Estimation (QE) perform well in general domains but degrade under domain mismatch. This dissertation studies how to adapt MT and QE systems to specialized domains through a set of data-focused contributions. Chapter 2 presents a similarity-based data selection method for MT. Small, targeted in-domain subsets outperform mu

Ruopeng Xu, Songling Zhang, Zhaohui Yang, Yixuan Chen

In this paper, we investigate the performance of a fluid antenna relay (FAR)-assisted downlink communication system utilizing non-orthogonal multiple access (NOMA). The FAR, which integrates a fluid antenna system (FAS), is equipped on an autonomous aerial vehicle (AAV), and introduces extra degrees of freedom to improve the performance of the system. The tr

ZeBin Ji, Yang Hu, Xiuli Bi, Bo Liu

It is essential for understanding neural network decisions to interpret the functionality (also known as concepts) of neurons. Existing approaches describe neuron concepts by generating natural language descriptions, thereby advancing the understanding of the neural network's decision-making mechanism. However, these approaches assume that each neuron has we

Elizabeth J. Iles, Finn A. Pal, Joss Bland-Hawthorn, Ken Freeman

A bar-like central feature is commonly observed in both nearby and distant spiral-type galaxies, including the Milky Way. While many methods exist to categorise this morphology, no one method has emerged as the field-wide standard. To develop a rigorous and consistent method for identifying these bars, we investigate a classification scheme based on dynamica

Vo Thanh Phat

This paper studies the convexity properties of nonsmooth extended-real-valued weakly convex functions, a class of functions that is central to modern optimization and its applications. We establish new characterizations of convexity using second-order generalized derivative tools, including subgradient graphical derivatives, second subderivatives, and second

Chenfeng Cao, Yifan Tang, Jens Eisert

Fermionic linear optics (FLO) with Gaussian resources is efficiently classically simulable. We show that this is no longer the case for such quantum circuits for fermions with internal degrees of freedom, equipped with mid-circuit number monitoring and classical feedforward. In our architecture, the measurement record routes the selected blocks into a fixed-

Thomas Sinclair

We introduce a canonical operator-theoretic construction associated to a finite geometric lattice, in which a simple nonassociative ``diamond product'' on the lattice basis gives rise to a family of creation operators indexed by atoms and a corresponding self-adjoint Hamiltonian on $\mathbb R[L]$. A key structural feature is that the Hamiltonian changes rank

Wenwen Zhou, Xiaodong Feng, Ling Guo, Hao Wu

Model correction is essential for reliable PDE learning when the governing physics is misspecified due to simplified assumptions or limited observations. In the machine learning literature, existing correction methods typically operate in parameter space, where uncertainty is often quantified via sampling or ensemble-based methods, which can be prohibitive a

Se Yan, Han Zhong, Zemin, Zhong

This paper provides some of the first large-scale descriptive evidence on how consumers adopt and use platform-embedded shopping AI in e-commerce. Using data on 31 million users of Ctrip, China's largest online travel platform, we study "Wendao," an LLM-based AI assistant integrated into the platform. We document three empirical regularities. First, adoption

Moshood A. Fakorede, Krishna Upadhyay, A. B. Siddique, Umar Farooq

Large language models (LLMs) have shown strong performance on automated software engineering tasks, yet existing benchmarks focus primarily on library-style repositories, leaving mobile application development largely unexplored despite its framework-specific build systems, heterogeneous artifact types, and coordinated multi-file fix requirements. We introdu

Minhua Zhou, Xinling Wu, Lei Xu, Nannan Chen

This study investigates the X-ray characteristics of jet-associated radio-quiet AGNs across distinct optical/UV to X-ray correlation phases. Quasi-simultaneous optical/UV/X-ray observations of NGC 4051 from May-June 2009, obtained through Swift and XMM-Newton, reveal a temporal dichotomy: a strong optical/UV to X-ray correlation dominates the initial observa

Dong-Yang Gao, Hui-Gen Liu, Ming Yang, Ji-Lin Zhou

The traditional definition of the circumstellar habitable zone (HZ) focuses on liquid water, but neglects the crucial role of ultraviolet (UV) radiation in prebiotic chemistry. Low-mass stars typically emit insufficient UV radiation for photochemistry throughout the liquid water HZs during quiescent states. However, frequent flares can provide substantial UV

Jie Zhu, Yimin Tian, Boyang Li, Kehao Wu

This paper introduces \textbf{FinMCP-Bench}, a novel benchmark for evaluating large language models (LLMs) in solving real-world financial problems through tool invocation of financial model context protocols. FinMCP-Bench contains 613 samples spanning 10 main scenarios and 33 sub-scenarios, featuring both real and synthetic user queries to ensure diversity

Peiju Liu, Jinming Liu, Xipeng Qiu, Xuanjing Huang

Vision-Language-Action (VLA) models excel in robotic manipulation but suffer from significant inference latency due to processing dense visual tokens. Existing token reduction methods predominantly rely on attention magnitude as a static selection. In this work, we challenge this assumption, revealing that high-attention tokens are task-dependent and can eve

Alejandro Alvarado, Andres Anabalon, Mariano Chernicoff, Julio Oliva

We compute the one-loop contribution to the semiclassical partition function of near-extremal, asymptotically AdS black holes in five-dimensional Einstein-Gauss-Bonnet gravity. In the absence of an exact analytic rotating solution at finite Gauss-Bonnet coupling $\alpha$, we restrict to static, charged configurations and evaluate the contribution to $Z_{\tex

Jenna Kang, Colin Groth, Tong Wu, Finley Torrens

Predicting human gaze in video is fundamental to advancing scene understanding and multimodal interaction. While traditional saliency maps provide spatial probability distributions and scanpaths offer ordered fixations, both abstractions often collapse the fine-grained temporal dynamics of raw gaze. Furthermore, existing models are typically constrained to s

Aleksey Mohov, Maxim Lyutikov

We perform MHD simulations of AGN-blown bubbles in the Intercluster Medium (ICM) containing large-scale coherent magnetic fields. We assume that bubbles, created by the intermittent jets from Active Galactic Nuclei, quickly relax to the Woltjer-Taylor spheromak-like state, with internal plasma beta-parameter $\sim 1$. We demonstrate that such bubbles rising

Xuepeng Jing, Wenhuan Lu, Hao Meng, Zhizhi Yu

Human trajectory forecasting is important for intelligent multimedia systems operating in visually complex environments, such as autonomous driving and crowd surveillance. Although Conditional Flow Matching (CFM) has shown strong ability in modeling trajectory distributions from spatio-temporal observations, existing approaches still focus primarily on super

Xiyang Wu, Guangyao Shi, Qingzi Wang, Zongxia Li

Vision-language-action (VLA) models enable robots to follow natural-language instructions grounded in visual observations, but the instruction channel also introduces a critical vulnerability: small textual perturbations can alter downstream robot behavior. Systematic robustness evaluation therefore requires a black-box attacker that can generate minimal yet

Sungho Moon, Seunghun Lee, Jiwan Seo, Sunghoon Im

We propose Context-aware Video-text Alignment (CVA), a novel framework to address a significant challenge in video temporal grounding: achieving temporally sensitive video-text alignment that remains robust to irrelevant background context. Our framework is built on three key components. First, we propose Query-aware Context Diversification (QCD), a new data

Moein Shahiki Tash, Zahra Ahani, Mohim Tash, Mostafa Keikhay Farzaneh

The growing prominence of cryptocurrencies has triggered widespread public engagement and increased speculative activity, particularly on social media platforms. This study introduces a novel classification framework for identifying predictive statements in cryptocurrency-related tweets, focusing on five popular cryptocurrencies: Cardano, Matic, Binance, Rip

Gerui Chen, Zien Gao, Xin Lan, Jie-qiang Wu

By using Wald formalism, we show that the identification between the bulk and boundary conserved quantities induced by the perturbation of generic non-electromagnetic matter field holds not only on top of the asymptotically flat stationary spacetimes but also on top of the asymptotically AdS stationary ones. We further show that such an identification reduce

Yifeng Zhang, Jieming Chen, Tingguang Zhou, Tanishq Duhan

Multi-Agent Self-Driving (MASD) systems provide an effective solution for coordinating autonomous vehicles to reduce congestion and enhance both safety and operational efficiency in future intelligent transportation systems. Multi-Agent Reinforcement Learning (MARL) has emerged as a promising approach for developing advanced end-to-end MASD systems. However,

Xibei Chen, Yifeng Zhang, Yuxiang Xiao, Mingfeng Fan

Recent advances in robotics, automation, and artificial intelligence have enabled urban traffic systems to operate with increasing autonomy towards future smart cities, powered in part by the development of adaptive traffic signal control (ATSC), which dynamically optimizes signal phases to mitigate congestion and optimize traffic. However, achieving effecti

Yingying Chen, Zihao Jiao, Haizhen Wang, Dehui Li

Interlayer excitons (IXs) in two-dimensional (2D) van der Waals heterostructures have attracted considerable attention due to their unique optical and electronic properties. Owing to the spatially indirect nature, the radiative emission efficiency highly sensitive to interlayer twist angles. Further considering that their uniformly oriented out-of-plane dipo

J. V. Zamperlini, C. C. Barros

This work investigates curvature-induced modifications to the Yukawa potential in static, spherically symmetric spacetimes described by Tolman metrics, focusing on their implications for compact stellar objects, with particular application to solutions IV and VI. Motivated by the interplay of quantum interactions and strong gravitational fields in systems li

Teppei Takamatsu, Shou Yoshikawa

We study the perfectoid pure threshold with respect to $p$, an invariant of singularities in mixed characteristic $(0,p)$ arising from perfectoid purity. In this paper, we compute perfectoid pure thresholds for lifts of rational double points. We show that the set of such thresholds is contained in $\mathbb{Q}$ and satisfies the ascending chain condition. In

Ho Truong Nam Hai, Augusto Ducati Luchessi, Kaveh Edalati

Microalgae are an abundant bioorganic material source and play a significant role in life on Earth by conducting photosynthesis for carbon dioxide (CO2) capture and its conversion to oxygen (O2). In this study, a combination of microalgae as a negative-CO2-emitting sacrificial agent with the traditional photocatalytic water-splitting process using brookite T

Somayeh Sadat Mehrnia, Fatemeh Razavi, Helia Abedini, Niloofar Rahimi

Accurate identification of EGFR and KRAS mutations is essential for precision therapy in non-small cell lung cancer (NSCLC), but tissue genotyping is invasive and may not capture tumor heterogeneity. CT-based radiogenomics offers a noninvasive alternative, although generalization across centers remains challenging. We benchmarked handcrafted radiomics featur

Patrick Daniels, Pol van Hoften, Dongryul Kim, Mingjia Zhang

We construct Igusa stacks for all Shimura varieties of abelian type and derive consequences for the cohomology of these Shimura varieties. As an application, we prove that the Fargues--Scholze local Langlands correspondence agrees with the semi-simplification of the local Langlands correspondences constructed by Arthur, Mok and others, for all classical grou

Jiuqi Wei, Xiaodong Lee, Botao Peng, Quanqing Xu

Locality-sensitive hashing (LSH) is a well-known solution for approximate nearest neighbor (ANN) search with theoretical guarantees. Traditional LSH-based methods mainly focus on improving the efficiency and accuracy of query phase by designing different query strategies, but pay little attention to improving the efficiency of the indexing phase. They typica

Jiuqi Wei, Zhenyu Liao, Ruoyu Han, Quanqing Xu

Approximate Nearest Neighbor Search (ANNS) in high-dimensional Euclidean spaces is a fundamental problem with broad applications. Subspace Collision is a newly proposed ANNS framework that provides a novel paradigm for similarity search and achieves superior indexing and query performance. However, the subspace collision framework remains data-agnostic and q

Yassien Shaalan

Deploying neural networks on microcontrollers is constrained by kilobytes of flash and SRAM, where 1x1 pointwise (PW) mixers often dominate memory even after INT8 quantization across vision, audio, and wearable sensing. We present HYPER-TINYPW, a compression-as-generation approach that replaces most stored PW weights with generated weights: a shared micro-ML

Asimina S. Hamakiotes, Sung Min Lee, Jacob Mayle, Tian Wang

Given non-CM elliptic curves $E_1$ and $E_2$ over $\mathbb{Q}$, we study the natural density of primes $p$ of good reduction for which the orders of the groups $E_1(\mathbb{F}_p)$ and $E_2(\mathbb{F}_p)$ are coprime. This problem may be viewed as an elliptic curve analogue of the classical question concerning the density of coprime integer pairs. Motivated b

Papri Dey

Bayesian inference over positive semidefinite (PSD) matrix-valued parameters arises in structured covariance estimation, graph-Laplacian precision models, and multi-output graph learning, but Euclidean proposals often mix poorly near the cone boundary. We propose \ConeMALA, a geometry-aware Metropolis-adjusted Langevin algorithm whose proposal geometry is in

Jing Yang, Krithika Dharanikota, Emily Jia, Haiwei Chen

Accurately modeling how real-world materials reflect light remains a core challenge in inverse rendering, largely due to the scarcity of real measured reflectance data. Existing approaches rely heavily on synthetic datasets with simplified illumination and limited material realism, preventing models from generalizing to real-world images. We introduce a larg

Hiroshi Inazawa

This paper introduces a neural network model that learns multiple attributes as images and performs associated, sequential recall of the learned memories. Briefly, the model presented here is an associative memory model that extends previous models [1] by increasing the number of attributes. In the real world, memory recall generates a chain of associations

Yongchao Huang

Self-supervised representation learning often relies on deterministic predictive architectures to align context and target views in latent space. While effective in many settings, such methods are limited in genuinely multi-modal inverse problems, where squared-loss prediction collapses towards conditional averages, and they frequently depend on architectura

Mónica Agüero, Juliana Bourdieu, Alejandro Hnilo, Marcelo Kovalsky

Collapse-locality is an untested loophole in the violation of Bell's inequalities. The core of the argument is that the time value of photon detection is delayed by the time Tc required by the collapse of its quantum state. The value of Tc is given by the underlying theory of quantum collapse, and is mostly unknown. Depending on the value of Tc, detections i

Yunes Alqudsi, Murat Makaraci

Coordinating teams of aerial robots in cluttered three-dimensional (3D) environments requires a principled integration of discrete mission planning-deciding which robot serves which goals and in what order -- with continuous-time trajectory synthesis that enforces collision avoidance and dynamic feasibility. This paper introduces IMD-TAPP (Integrated Multi-D

Aleksandar Radak, Mingyu Fan, Bassam Nima, Yuiki Takahashi

Precision measurements of time-reversal (T) symmetry violating moments probe physics beyond the Standard Model. We show that precision spectroscopy of paramagnetic lanthanide and actinide ions doped into noncentrosymmetric crystals offers a promising platform for extending the sensitivity of searches for T-violation in nuclear physics. The unpaired valence e

Jiajun Wang

In this paper, we prove polynomial growth bounds for the Sobolev norms of solutions to the fractional nonlinear Schr\"odinger equation on the torus \T^d (d \ge 2), following and extending a result of Joseph Thirouin on \T [Thi17]. The key ingredient is the establishment of Strichartz estimates for the fractional Schr\"odinger equation on \T^d. To this end, w

Yalu Zhu, Feng Liu

Two data-driven modal analysis approaches, proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD), are applied to analyze the unsteady flow obtained by solving the Reynolds-averaged Navier-Stokes (RANS) equations in a 1.5-stage axial turbine. The reduced-order reconstructed pressure, dominant mode shapes, and dynamic features of these dom

Gesina Schwalbe, Mert Keser, Moritz Bayerkuhnlein, Edgar Heinert

Vision-language model (VLM) encoders such as CLIP enable strong retrieval and zero-shot classification in a shared image-text embedding space, yet the semantic organization of this space is rarely inspected. We present a post-hoc framework to explain, verify, and align the semantic hierarchies induced by a VLM over a given set of child classes. First, we ext

TJ Dunham

We prove that platform-deterministic inference is necessary and sufficient for trustworthy AI. We formalize this as the Determinism Thesis and introduce trust entropy to quantify the cost of non-determinism, proving that verification failure probability equals 1 - 2^{-H_T} exactly. We prove a Determinism-Verification Collapse: verification under determinism

Anisha Roy, Dip Roy

Large language models (LLMs) are increasingly used to generate financial alpha signals, yet growing evidence shows that LLMs memorize historical financial data from their training corpora, producing spurious predictive accuracy that collapses out-of-sample. This memorization-induced look-ahead bias threatens the validity of LLM-based quantitative strategies.

Haresh Rengaraj Rajamohan, Yuxuan Chen, Kyunghyun Cho, Cem M. Deniz

This study assesses whether self-supervised learning (SSL) improves knee osteoarthritis (OA) modeling for diagnosis and prognosis relative to ImageNet-pretrained initialization. We compared (i) image-only SSL pretrained on knee radiographs from the OAI, MOST, and NYU cohorts, and (ii) multimodal image-text SSL pretrained on hospital knee radiographs paired w

Alexander Roman, Marco Knipfer, Jogi Suda Neto, Konstantin T. Matchev

Magic and entanglement are two measures that are widely used to characterize quantum resources. We study the interplay between magic and entanglement in two-qubit systems, focusing on the two extremes: maximal magic and minimal magic for a given level of entanglement. We quantify magic by the R\'{e}nyi entropy of order 2, $M_2$, and entanglement by the concu

Shengxian Li, Jiaren Yuan, Tao Ouyang, Anlian Pan

The two-dimensional ferroelectric (FE) material CuInP$_2$S$_6$ (CIPS) has garnered considerable interest due to its out-of-plane ferroelectricity at room temperature. However, a notable discrepancy exists between experiments and density functional theory (DFT) calculations regarding the ground state of CIPS thin films: experiments suggest a state with net po

Jason Zhao

Given an initial data configuration $(A^{\mathrm{in}}, \phi^{\mathrm{in}})$ on $\mathbb R^2$ such that the self-dual abelian Higgs energy is near the minimum energy within its topological class, we prove that its evolution under the self-dual abelian Higgs gradient flow in temporal gauge converges exponentially as $t \to \infty$ with respect to the $(H^1 \ti

Irene S. Gabashvili

Community associations rely heavily on resident surveys to guide decisions about amenities, infrastructure, and services. However, survey responses reflect perceptions that may not directly correspond to underlying operational conditions. This study bridges that gap by calibrating survey-based satisfaction measures against objective utilization data. Using p

Vasu Srinivasan, Dhriti Vasu

We present a Sovereign AI architecture for clinical triage in which all inference is performed on-device and inbound data is delivered via a physically unidirectional channel, implemented using receive-only broadcast infrastructure or certified hardware data diodes, with no return path to any external network. This design removes the network-mediated attack

Yan Meng, Jack Cook, X. Y. Han, Kaan Duman

Accurate surgical phase recognition is essential for analyzing procedural workflows, supporting intraoperative decision-making, and enabling data-driven improvements in surgical education and performance evaluation. In this work, we present a comprehensive framework for phase recognition in pituitary tumor surgery (PTS) videos, combining self-supervised repr

Baraa Hikal, Jonas Becker, Bela Gipp

This paper describes LogSigma, our system for SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis (DimABSA). Unlike traditional Aspect-Based Sentiment Analysis (ABSA), which predicts discrete sentiment labels, DimABSA requires predicting continuous Valence and Arousal (VA) scores on a 1-9 scale. A central challenge is that Valence and Arousal di

Max Holschneider, Saetbyeol LeeYouk

AI chatbots have quietly become the world's most popular therapists, coaches, and confidants. Users of cloud-based LLM services are increasingly shifting from simple queries like idea generation and poem writing, to deeply personal interactions. As Large Language Models increasingly assume the role of our confessors, we are witnessing a massive, unregulated

Sampada Deglurkar, Ebonye Smith, Jingqi Li, Claire J. Tomlin

Reachability computations that rely on learned or estimated models require calibration in order to uphold confidence about their guarantees. Calibration generally involves sampling scenarios inside the reachable set. However, producing reasonable probabilistic guarantees may require many samples, which can be costly. To remedy this, we propose that calibrati

Yalu Zhu, Feng Liu, William A. Sirignano

An in-house large-eddy simulation (LES) code is applied to compute the chemically reacting flow in a turbine stage to analyze the influence of fuel injection and combustion on its aerodynamic and thermodynamic performance. Two reacting cases--with four and sixteen fuel injectors at the inlet for each stator passage--are computed and compared against two nonr

Fan Zou, Elena Gallo, Zihao Zuo, Edmund Hodges-Kluck

We reassess the direct collapse black hole (DCBH) interpretation of UHZ1 (UNCOVER-26185), a gravitationally lensed galaxy at $z_\mathrm{spec}=10.054$. That interpretation rests on a hard ($2-7$ keV) X-ray excess detected with Chandra, attributed to a Compton-thick AGN with an inferred $2-10$ keV luminosity of $L_\mathrm{X,int}\sim10^{46}~\mathrm{erg~s^{-1}}$

Youqian Qi, Mingzhe Guo, Zhenghua Huang, Tom Van Doorsselaere

Coronal plumes and chromospheric jet-like structures are believed to be highly dynamic. We report the first direct observations of a propagating kink wave in a chromospheric jet-like structure and its associated plumelet structure in the upper corona of the solar polar region, using data from the High Resolution Imager (HRI) of the Extreme Ultraviolet Imager

Ilkin Aliyev, Jesus Lopez, Tosiron Adegbija

Spiking Neural Networks (SNNs) offer inherent advantages for low-power inference through sparse, event-driven computation. However, the theoretical energy benefits of SNNs are often decoupled from real hardware performance due to the opaque relationship between training-time choices and inference-time sparsity. While prior work has focused on weight pruning

P. Horak, I. Semaev

Let n denote the number of variables and m the number of equations in a sparse polynomial system over the binary field. We study the inconsistency probability of randomly generated sparse polynomial systems over the binary field, where each equation depends on at most k variables and the number of variables grows. Associating the system with a hypergraph, we

Kamer Ali Yuksel, Hassan Sawaf

Modern machine learning is still largely organized around a single recipe: choose a parameterized model family and optimize its weights. Although highly successful, this paradigm is too narrow for many structured prediction problems, where the main bottleneck is not parameter fitting but discovering what should be computed from the data. Success often depend

Lucas Miranda, Carlos Banjar, Daniel Menasche, Anton Kocheturov

Assessing the security posture of Industrial Control Systems (ICS) is critical for protecting essential infrastructure. However, the complexity and scale of these environments make it challenging to identify and prioritize potential attack paths. This paper introduces a semi-automated approach for generating attack graphs in ICS environments to visualize and

Oleg D. Lavrentovich

Design of soft matter capable of controllable microscale dynamics is a frontier of modern science. Jiahao Chen et al. demonstrate that an electric field can drive particle-like solitons-skyrmions in a chiral nematic along a preprogrammed trajectory with a variable speed within a two-dimensional plane. The effect is rooted in flexoelectric polarization of a d

Olivier Bernardi, Neha Goregaokar

The Pak-Stanley labeling is a bijection between the regions of the $m$-Shi arrangement and the $m$-parking functions. Mazin generalized this labeling to every deformation of the braid arrangement and proved that this labeling is always surjective onto a set of directed multigraph parking functions. We provide a right inverse to the generalized Pak-Stanley la

Sanjay Mishra

Retrieval-augmented generation (RAG) faces a fundamental three-way tension: deeper retrieval improves factual grounding but inflates token costs and end-to-end latency. Static retrieval configurations cannot resolve this tension across heterogeneous query workloads -- simple definitional queries waste budget on unnecessary context, while complex analytical p

Michael Todasco, Joselyn Cesare

Public concern about an "AI penalty" suggests that labeling content as AI-generated may negatively influence how it is evaluated. We tested this claim in a preregistered experiment (N = 254, per protocol) using a pure attribution design: participants read one of two ~200-word vignettes and were randomly assigned to see it labeled as Human-written, AI

VD Doske

We present the Consilium Protocol, a Byzantine Fault Tolerance-derived architecture for structured multi-model AI deliberation that treats inter-model disagreement as epistemic signal rather than error. The protocol assigns engineered cognitive personas to language models -- separating what a model is from how it reasons -- and introduces an In-Sample/Out-of

Beyza Kalkanli, Tom Bu, Mahsa Shakeri, Alexander Fix

Head-mounted devices integrated with eye tracking promise a solution for natural human-computer interaction. However, they typically require per-user calibration for optimal performance due to inter-person variability. A differential personalization approach using Siamese architectures learns relative gaze displacements and reconstructs absolute gaze from a

Jiasen Guo, Ryan P. Siebenaller, Michael A. Susner, Jiaqiang Yan

We report a magnetic and neutron diffraction study on the ground state magnetism and field evolution of single crystal van der Waals multiferroic CuCrP2S6. The ordered moments align along the b axis in the A-type antiferromagnetic configuration with a spin-flop transition along the same direction. Field application along a introduces a smooth transition to a