April 2026 arXiv papers — page 55

Yiming Wang, Sujit K. Ghosh

A nonparametric model using a sequence of Bernstein polynomials is constructed to approximate arbitrary isotropic covariance functions valid in $\mathbb{R}^\infty$ and related approximation properties are investigated using the popular $L_{\infty}$ norm and $L_2$ norms. A computationally efficient sieve maximum likelihood (sML) estimation is then developed t

Soohwan Lee, Kyungho Lee

AI-mediated Communication (AIMC) systems increasingly aim to protect minority voices by anonymizing or proxying their input, but anonymity and authenticity are not the same construct. This position paper draws on an ongoing empirical study comparing two LLM-powered minority support strategies in hierarchical group decision-making. We found that relaying mino

Boris Velasevic, Nicolas Lanzetti, Eric Mazumdar

We study linear quadratic dynamic games where players are uncertain about each other's control policies or goals and consequently seek to be strategically robust. Building on recent work on strategically robust and risk-averse game theory, we first formalize the problem of strategically robust linear quadratic dynamic games. We show that these can be rewritt

Qi Lü, Bowen Ma, Hanxiao Wang

This paper addresses a Stackelberg stochastic linear-quadratic (LQ) differential game under closed-loop information, a problem inherently time-inconsistent. Existing approaches rely on solving two coupled Hamilton-Jacobi-Bellman (HJB) equations derived via time discretization and a limiting argument, whose convergence remains an open problem. We propose an a

Yasumichi Matsuzawa

We present a direct derivation of Arai's effective Hamiltonian in non-relativistic quantum electrodynamics without relying on the scaling limit. Our result applies to a broader class of potentials, including the Rollnik class and confining potentials such as the harmonic potential.

Tommaso Zaccherini, Siyuan Liu, Dimos V. Dimarogonas

This paper addresses decentralized control of large-scale heterogeneous multi-agent systems subject to bounded external disturbances and limited communication, with the objective of satisfying cooperative Signal Temporal Logic (STL) specifications. The considered specifications involve spatiotemporal tasks that require collaboration among multiple agents, in

Tabinda Sarwar, Farhad Moghimifar, Cong Duy Vu Hoang, Xiaoxiao Ma

NL2SQL systems deployed in industry settings often encounter ambiguous or unanswerable queries, particularly in interactive scenarios with incomplete user clarification. Existing benchmarks typically assume a single source of ambiguity and rely on user interaction for resolution, overlooking realistic failure modes. We introduce Clarity, a framework for auto

Long Cheng, Ritchie Zhao, Timmy Liu, Mindy Li

Sparse-attention decoders rely on exact Top-K selection to choose the most important key-value entries for each query token. In long-context LLM serving, this Top-K stage runs once per decode query and becomes a meaningful latency bottleneck even when the indexer and attention kernels are already highly optimized. We present \textbf{Guess-Verify-Refine (GVR)

Jeonggon Kim, Heejoon Moon, Je Hyeong Hong

Privacy-Preserving Image Queries (PPIQ) are an emerging mechanism for cloud-based visual localization, enabling pose estimation from obfuscated features instead of private images or raw keypoints. However, the main approaches for PPIQ, primarily geometry-based and segmentation-based obfuscation, both suffer from vulnerabilities to recent privacy attacks. In

Anuradha Gupta, Kajal Negi

In this paper, we have studied the hyponormality and invertibility of the operator of type $wT_{\varphi}+T_{\psi}$ where $w$ is any non-zero complex number and $T_{\varphi}, T_{\psi} $ are Toeplitz operators. We have also studied hyponormality when the symbol of Toeplitz operator is a linear functional.

Simon Liebl, Ian Ferguson, Andreas Aßmuth, Natalie Coull

A growing number of Internet of Things (IoT) devices are used across consumer, medical, and industrial domains. They interact with their environment through sensors and actuators and connect to networks such as the Internet. Because sensors may collect sensitive data and actuators can trigger physical actions, security, privacy, and safety are major challeng

Manuel Alejandro Borroto Santana, Erica Coppolillo, Francesco Calimeri, Giuseppe Manco

Large Language Models (LLMs) have demonstrated remarkable performance across a broad spectrum of tasks, including natural language understanding, dialogue systems, and code generation. Despite evident progress, less attention has been paid to their effectiveness in handling declarative paradigms such as Answer Set Programming (ASP), to date. In this paper we

Taro Yaoyama, Sangwon Lee, Minoru Matsubara, Kenzo Kodera

Enhancing seismic fragility and risk assessment of nuclear power plants relies on accurate prediction of reactor building responses to seismic hazards, which can be further improved through dynamic analysis of high-fidelity finite element (FE) models. However, FE models often exhibit non-negligible discrepancies from actual structures due to various sources

Ioan Pădurean, Béla Genge, Roland Bolboacă

This paper proposes a resource-aware allocation model for layered intrusion detection in het erogeneous networks. Monitoring traffic at higher protocol layers improves the ability to detect sophisticated attacks, but it also increases computational and storage costs. The problem is formu lated as an integer linear program that assigns a single monitoring dep

Sheng-Wen Li, Zeyang Liao, Mao-Xin Liu

Understanding quantum system dynamics driven by nonclassical light pulses is challenging, particularly for general light states with large photon numbers. Here we introduce an efficient framework that makes this task tractable. By introducing a pulse-shaped P-representation, the exact quantum evolution is decomposed into a mixture of many independent quasi-c

Ran Zhao, Sheng Jin, Size Wu, Kang Liao

Recent text-to-image (T2I) models have demonstrated impressive capabilities in photorealistic synthesis and instruction following. However, their reliability in knowledge-intensive settings remains largely unexplored. Unlike natural image generation, knowledge visualization requires not only semantic alignment but also strict adherence to domain knowledge, s

Theodore Modis

Societal complexity may be at a historical peak. Distinct from entropy, complexity tends to rise as systems move away from order, crest at an intermediate state, and decline as entropy continues increasing. The use of a thermodynamic analogy and the timing of major technological milestones, from fire to artificial intelligence, shows that the acceleration an

Izabela A. Wrona, Yinwei Li, Radoslaw Szczesniak, Artur P. Durajski

We present a comprehensive first-principles investigation of a hypothetical cubic Pm-3m phase of the ternary hydride NaAlH3, focusing on its lattice dynamics, electronic structure, and electron-phonon-mediated superconducting properties at ambient pressure. Using density functional theory and the Migdal-Eliashberg formalism, we find an exceptionally strong e

Héctor Vaquero del Pino, François Gay-Balmaz, Hiroaki Yoshimura, Lock Yue Chew

Stochastic field theories are often constructed phenomenologically, without a systematic assessment of thermodynamic consistency or local detailed balance. This may hinder a physical description of irreversibility at the field-theoretic level beyond the standard statistical formulation of stochastic thermodynamics. Here, we develop a variational formulation

Yuxuan Wang, María José Belda, Fernando Castro, Katzalin Olcoz

Modern computing workloads commonly involve matrix-matrix multiplication (mmul) as a core computing pattern. Coarse-Grained Reconfigurable Arrays (CGRAs) can flexibly and efficiently support it, since they combine operation-level reconfigurability and high energy efficiency. However, mapping computational kernels that include mmul with state-of-the-art compi

Jai G Singla, Hinal B Patel, Nitant Dube

Synthetic image generation is one of the crucial input for planetary missions. It enables researchers and engineers to visualize planned planetary missions, test imaging systems and plan exploration activities in a virtual environment before actual deployment. Image simulation is essential for assessing landing sites, detecting hazards, and validating naviga

Lukáš Lachman, Carlos E. Lopetegui-González, Massimo Frigerio, Mattia Walschaers

Entanglement is a key resource for many quantum applications. Understanding fundamental properties of entangled states is an important step towards their practical exploitation. We characterize entanglement in the context of Gaussian and non-Gaussian processes and identify entangled states that cannot be produced by any Gaussian evolution acting on separable

Chang Sun, Zhiqiang Que, Bakhtiar Zadeh, Qibin Liu

Lookup-table (LUT) based neural networks can deliver ultra-low latency and excellent hardware efficiency on FPGAs by mapping arithmetic operations directly onto the logic primitives. However, state-of-the-art LUT-aware training (LAT) approaches remain difficult to use in practice: they are often orders of magnitude slower to train than conventional networks,

Ishaan Gakhar, Harsh Nandwani

The classification of legal documents from an unstructured data corpus has several crucial applications in downstream tasks. Documents relevant to court filings are key in use cases such as drafting motions, memos, and outlines, as well as in tasks like docket summarisation, retrieval systems, and training data curation. Current methods classify based on pro

Yuan Xiao, Jiaming Wang, Yuchen Chen, Wei Song

The widespread availability of large-scale code datasets has accelerated the development of code large language models (CodeLLMs), raising concerns about unauthorized dataset usage. Dataset poisoning offers a proactive defense by reducing the utility of such unauthorized training. However, existing poisoning methods often require full dataset poisoning and i

Maximilian Wachter, Sebastian Murgul, Michael Heizmann

Rhythm transcription is a key subtask of notation-level Automatic Music Transcription (AMT). While deep learning models have been extensively used for detecting the metrical grid in audio and MIDI performances, beat-based rhythm quantization remains largely unexplored. In this work, we introduce a novel deep learning approach for quantizing MIDI performances

Towhidul Islam, Mufti Mahmud

Alzheimer's disease (AD) is a progressive neurodegenerative disorder and a major cause of dementia. Structural MRI is widely used to analyze AD-related brain atrophy; however, most deep learning methods rely on computationally expensive 3D convolutional neural networks (CNNs), limiting deployment in resource-constrained settings. This work introduces two mai

Pratishtha Agnihotri, Sarika Jalan

The impact of external forcing is well studied in the Kuramoto model without inertia, but remains unclear for inertial Kuramoto oscillators (KMI) with bimodal intrinsic frequency distributions. This article fills that gap, showing that competition between external forcing and intrinsic bimodality can suppress the intermediate standing wave states of bimodal

Andreas Mueller

The Lie group SE(3) of isometric orientation preserving transformation is used for modeling multibody systems, robots, and Cosserat continua. The use of these models in numerical simulation and optimization schemes necessitates the exponential map, its right-trivialized differential (often referred to as tangent operator), as well as higher derivatives in cl

Peter Vergeer

This paper studies expected performance and practical feasibility of the most commonly used classes of source-level likelihood-ratio (LR) systems when applied to a trace-reference comparison problem. The paper compares performance of these classes of LR systems (used to update prior odds) to each other and to the use of prior odds only, using strictly proper

Xiaofeng Zhou, Linfeng Du, Guangyu Hu, Sharad Sinha

High-level synthesis (HLS) transforms an algorithmic description of hardware from a higher abstraction (e.g., C/C++) into a register-transfer level (RTL) design, offering reduced development time and greater flexibility in design space exploration. However, such machine-generated RTL designs may contain major functional bugs or security vulnerabilities due t

HyoJae Kang, Yeong Jae Park, Hyunmok Jung, Joonho Lee

This study investigates the kinematic role of palm degrees of freedom (DoF) in enhancing thumb opposability in a five-finger robotic hand. A hand model consisting of a five DoF thumb and four fingers with three to four DoF is analyzed, where palm motion is introduced between adjacent fingers. To quantitatively evaluate thumb-finger interaction, the overlap w

Joonmyung Choi, Sanghyeok Lee, Jongha Kim, Sehyung Kim

Recent advances in vision-language models have demonstrated remarkable performance across diverse multi-modal tasks, including document question answering that leverages structured visual cues from text, tables, and figures. However, unlike natural images, document images contain large backgrounds and only sparse supporting evidence, leading to the inefficie

Matilde Fiore, Andrea Bresciani, Miguel Alfonso Mendez, Jeroen van Beeck

Modern container ships face higher wind loads due to increased windage areas, making accurate predictions of wind loads essential for mooring design. Existing empirical models, largely developed for container ships with smaller windage areas and simpler geometrical configurations than those of modern large-scale vessels, often lack accuracy and do not accoun

Nazim I. Mahmudov

We introduce and study the finite-approximate solvability of operator equations \(Lu = h\) in a Hilbert space setting, where a bounded operator \(L \colon U \to H\) is paired with a finite-dimensional constraint operator \(\pi \colon H \to H_0\). The objective is to match exactly the prescribed component \(\pi h\) while approximating the remainder. We prove

Soohwan Lee, Kyungho Lee

As multi-agent AI systems become more common, users increasingly encounter not a single AI voice but a collective one. This shift introduces social dynamics, such as consensus, dissent, and gradual convergence, that can trigger cognitive biases and distort human judgment. We present findings from a controlled experiment (N = 127) comparing three multi-agent

Youichi Okita, Haruhiro Katayose

Audio effects play an essential role in sound design. This research addresses the task of audio effect estimation, which aims to estimate the configuration of applied effects from a wet signal. Existing approaches to this problem can be categorized into predictive approaches, which use models pre-trained in a data-driven manner, and search-based approaches,

Jiajia Li, Zhenzhen Yu, Zhenghao Fu, Guozheng Xu

Early diagnosis and assessment of repetitive subconcussive (rSC) brain injuries are crucial for early clinical intervention. Conventional methods, largely relying on slow fMRI, fail to capture millisecond-level early cortical dynamics, particularly spatiotemporal features associated with pre-configuration dynamics. This study introduces a novel approach inte

Aofan Liu, Jingxiang Meng

Iterative self-correction is increasingly deployed in agentic LLM systems, yet whether repeated refinement improves or degrades performance remains inconsistent across models. We recast self-correction as a closed-loop feedback-control problem in which the same model is both controller and plant, and analyze its error dynamics via a two-state Markov model ov

You Jun Wang

Let $f$ be the Hecke eigenform for the modular group $SL_2(\mathbb{Z})$, and $L(s, \text{sym}^2 f)$ be the symmetric square $L$-function associated with $f$. For $\frac{1}{2}<\sigma<1$, define $m(\sigma)$ as the supremum of all numbers $m$ such that \[ \int_{1}^T|L(\sigma+it, \text{sym}^2 f)|^m \text{d}t\ll_f T^{1+\varepsilon}, \] where $\epsilon>0$ is an ar

Dharshan Kumaran, Viorica Patraucean, Simon Osindero, Petar Veličković

Large language models can detect their own errors and sometimes correct them without external feedback, but the underlying mechanisms remain unknown. We investigate this through the lens of second-order models of confidence from decision neuroscience. In a first-order system, confidence derives from the generation signal itself and is therefore maximal for t

Ganesh M. Balasubramaniam, Xiao-Liu Chu, Radhika V. Nair, Matthew R. Foreman

Lensless optical imaging eliminates the need for refractive optics, enabling compact and low-cost cameras with a large field-of-view, supporting point-of-care diagnostics and industrial monitoring. Practical deployments, however, remain constrained by ill-posed image reconstruction pipelines that require multiple measurements, careful calibration or object-s

Jiafu Hao, Chentao Yue, Wanchun Liu, Branka Vucetic

This paper presents a semantic-enhanced receiver framework for transmitting natural language sentences over noisy wireless channels using multiple short block codes. After ASCII encoding, the sentence is divided into segments, each independently encoded with a short block code and transmitted over an AWGN channel. At the receiver, segments are decoded in par

Cheng Liu, Hoongwah Siew, Hong-Xuan Jiang, Yosuke Mizuno

Broad, skewed iron K$\alpha$ emission lines in the X-ray spectra of accreting black holes encode key information about the spacetime geometry of the innermost disk. While the Kerr metric is standard for spin measurements, horizonless alternatives like traversable "Kerr-like" wormholes can mimic many black hole signatures, challenging current data interpretat

Shruthi C. Bhat, B. R. Srivatsa Kumar

In this research article, we obtain few theta function identities of level ten employing Ramanujan's $_1 \psi_1$ summation formula. Using these identities, we derive a new modular equation of degree five. Further, we establish Eisenstein series identities of level ten using Bailey's very-well poised $_6 \psi_6$ summation formula.

Ayan Datta, Zhixue Zhao, Bhuvanesh Verma, Radhika Mamidi

Large Language Models often achieve strong performance by generating long intermediate chain-of-thought reasoning. However, it remains unclear when a model's final answer is actually determined during generation. If the answer is already fixed at an intermediate stage, subsequent reasoning tokens may constitute post-decision explanation, increasing inference

Heinz H. Bauschke, Tran Thanh Tung

Motivated by Ridgway's proof of the perceptron algorithm, we study a simple subgradient method for convex inequality systems in Hilbert space. Assuming strict feasibility and bounded subgradients, we establish finite termination for several natural step sizes. We also examine what can go wrong without strict feasibility: finite convergence may fail even for

Chen Xu, Xianghao Yu

Reconfigurable antenna systems (RASs), such as fluid antennas and movable antennas, are poised to play a pivotal role in sixth-generation (6G) systems by dynamically adapting the antenna elements for system performance enhancement. However, unlocking their full potential requires channel models that accurately capture the influence of antenna configurations

Anushree Dutta, Pintu Mandal, Nabanita Deb

The performance of a quadrupole mass filter (QMF) is highly sensitive to deviations from ideal electrode geometry. In this work, we investigate the effect of small inward and outward tilting of cylindrical rods on the resolution and transmission characteristics of a QMF. Such geometric perturbations introduce an axial variation in the radial confinement pote

Toshiyuki Kobayashi

We propose a structural framework for branching multiplicities in representation theory, emphasizing their behavior under variation of infinitesimal characters. For the orthogonal reductive pairs $(G,G')$ with complexified Lie algebras $(\mathfrak{o}(n+1,\mathbb{C}), \mathfrak{o}(n,\mathbb{C}))$, we show that branching multiplicities are governed by universa

Fahmida Alam, Mihai Surdeanu, Ellen Riloff

Large language models (LLMs) struggle with relation completion (RC), both with and without retrieval-augmented generation (RAG), particularly when the required information is rare or sparsely represented. To address this, we propose a novel multi-stage paraphrase-guided relation-completion framework, RC-RAG, that systematically incorporates relation paraphra

Wenhui Huang, Songyan Zhang, Collister Chua, Yang Liang

Urban transportation systems face growing safety challenges that require scalable intelligence for emerging smart mobility infrastructures. While recent advances in foundation models and large-scale multimodal datasets have strengthened perception and reasoning in intelligent transportation systems (ITS), existing research remains largely centered on microsc

Yeongseong Jo, Santosh Nadimpalli, Akash Yadav

We prove that for any pair of irreducible principal series representations $(\pi_1,\pi_2)$ of $\operatorname{GL}_n(\mathbb{R})$ in general position, the notions of exceptional pole of type 1 and type 2 coincide. Using this identification, we express the Rankin--Selberg $L$-function $L(s,\pi_1\times\pi_2)$ in terms of the exceptional $L$-factors attached to t

Andrea Angino, Ken Trotti, Diego Ulisse Pizzagalli, Rolf Krause

Gaseous microemboli (GME) represent a common complication of cardiac structural interventions across both surgical and transcatheter approaches. Intraoperative transesophageal echocardiography (TEE) represents a convenient methodology to monitor and visualize the presence of circulating GME. However, their detection and quantification are far from trivial du

Konstantin Borovkov

One says that the local large deviation principle (LLDP) is satisfied for a family of random vectors $\{\zeta_T\}_{T\ge 0}$ in $\mathbb R^d,$ $d\ge 1,$ if there exists a function $D:\mathbb R^d\to [0,\infty],$ $D\not \equiv \infty,$ such that, for any $\alpha\in \mathbb R^d$, \[ \lim_{T\to \infty}T^{-1}\ln \mathbf{P} (|\zeta_T -\alpha|<\varepsilon_T)= - D(\a

Chenyuan Zhang, Katherine Ip, Hamid Rezatofighi, Buser Say

Goal recognition aims to infer an agent's goal from observations of its behaviour. In realistic settings, recognition can benefit from exploiting hierarchical task structure and reasoning under uncertainty. Planning-based goal recognition has made substantial progress over the past decade, but to the best of our knowledge no existing approach jointly integra

Gurpreet Singh, Jeewan C. Pandey, Subhajeet Karmakar

We present a detailed X-ray study of the quiescent and flaring coronae of three active main-sequence K-type stars, V834 Tau, LQ Hya, and BY Dra, using \textit{XMM-Newton} observations. The quiescent coronae are well described by two-temperature thermal plasma models, with cool and hot components at 0.26-0.30 keV and 0.93-1.01 keV, respectively. Despite simil

Bilal Yousuf, Zsofia Lendek, Lucian Busoniu

We address the problem of searching for an unknown number of stationary targets at unknown positions with a mobile agent. A probability hypothesis density filter is used to estimate the expected number of targets under measurement uncertainty. Existing planners, such as Active Search (AS) and its Intermittent variant (ASI), achieve accurate detection but req

Mrityunjoy Mandal, Arnaud G Malan, Prince Nchupang, Jan Nordström

This paper presents a high-order accurate Continuous Galerkin Finite Element Method (CGFEM) for solving the initial boundary value problems governed by the Incompressible Navier-Stokes (INS) equations. We discretize the INS equations using the CGFEM approach in Summation-By-Parts (SBP) form. Lagrange polynomials of up to 4th order are employed. The boundary

Samir K. Vaidya, Kalpesh M. Popat

The energy of a graph $G$ is the sum of the absolute values of the eigenvalues of the adjacency matrix of $G$. Some variants of energy can also be found in the literature which are defined on the concepts of Laplacian matrix, Distance matrix, Common neighbourhood matrix and Seidel matrix. The Seidel matrix of the graph $G$ is the square matrix in which $ij^{

Vishal Ramesh

Variable impedance model predictive control (MPC) formulations often treat joint stiffness as an instantaneous decision variable. The resulting feasible set strictly contains the physically realizable set under first-order actuator dynamics. We identify this as a formulation error rather than a modeling approximation, formalize the distinction between the pa

Tomon Kinugawa, Tetsuo Hyodo

We quantify the internal structure of near-threshold bound, virtual, and resonance states in systems where Coulomb and short-range interactions coexist by evaluating the compositeness. Using the Coulomb-modified effective range expansion, we derive an expression for the compositeness in terms of the eigenenergy and Coulomb effective range in the weak-binding

L. I. Mashonkina, S. A. Yakovleva

Europium plays a key role in studies of nucleosynthesis in the rapid (r-) process of neutron capture nuclear reactions and the evolution of the r-process element abundances in galaxies. We refine the method for analyzing the Eu~II lines in stellar spectra by updating the Eu~II model atom with recent data on the rate coefficients for inelastic processes in th

Mayumi Akamatsu, Hiroki Imai, Makoto Sakamoto, Maki Takeuchi

We investigate the vacuum expectation value of a complex scalar field on a two-dimensional torus with quantized magnetic flux $M$. A characteristic feature of this system is the emergence of a critical area: when the area of the torus exceeds this critical value, the vacuum expectation value becomes nonvanishing. Furthermore, any nonzero vacuum expectation v

Yu-Wei Fan

In this short note, we show that, assuming a conjecture of Arcara and Miles, a line bundle on a smooth complex projective surface admits a deformed Hermitian-Yang-Mills metric if and only if it is stable in the ``large scaling limit" with respect to a generic K\"ahler form. The same statement for toric surfaces was recently proved by Stoppa. The purpose of t

Mingchen Shao, Hang Su, Wenjie Tian, Bingshen Mu

While Large Audio Language Models (LALMs) achieve strong performance on short audio, they degrade on long-form inputs. This degradation is more severe in temporal awareness tasks, where temporal alignment becomes increasingly inaccurate as audio duration grows. We attribute these limitations to the lack of data, benchmarks, and modeling approaches tailored f

Aayushi Shrivastava, Kartik Nagpal, Sairam Jinkala, Jean-Baptiste Bouvier

Ensuring safety for black-box hybrid dynamical systems presents significant challenges due to their instantaneous state jumps and unknown explicit nonlinear dynamics. Existing solutions for strict safety constraint satisfaction, like control barrier functions (CBFs) and reachability analysis, rely on direct knowledge of the dynamics. Similarly, safe reinforc

Sunghwan Ko

Let $P$ be a compact hyperbolic Coxeter truncation polytope of dimension $d\ge 3$, and let $\Gamma$ be the orbifold fundamental group of the associated Coxeter orbifold $\mathcal{O}_P$. Let $\mathscr{G}(\Gamma,G)$ be the geometric component containing the holonomy representation in $\operatorname{Hom}(\Gamma,G)/G$. $\mathscr{G}(\Gamma,G)$ is identified with

Chattu Bhargavi, Vikash Kumar Singh, Alok Kumar Shukla

Spatial crowdsourcing (SC) enables the assignment of location-based tasks to mobile users who must travel to specific locations to perform sensing or service activities. However, SC systems often operate in strategic environments where both task requesters and task executors possess private valuation information, posing challenges for designing efficient and

Zhuding Liang, Tianyi Yan, Dubing Chen, Jiasen Zheng

Generative world models increasingly rely on 4D occupancy for realistic autonomous driving simulation. However, existing generation frameworks depend on rigid geometric conditions (e.g., explicit trajectories) or simplistic attribute-level text, failing to orchestrate complex, sequential multi-agent interactions. To address this semantic-spatiotemporal gap,

Zhanli Li, Yixuan Cao, Lvzhou Luo, Ping Luo

This paper introduces the task of analytical question answering over large, semi-structured document collections. We present MuDABench, a benchmark for multi-document analytical QA, where questions require extracting and synthesizing information across numerous documents to perform quantitative analysis. Unlike existing multi-document QA benchmarks that typi

Zhilin Fan, Deliang Wang, Penghe Chen, Yu Lu

Diagnosing student problem behaviors requires teachers to synthesize multifaceted information, identify behavioral categories, and plan intervention strategies. Although fine-tuned large language models (LLMs) can support this process through multi-turn dialogue, they rarely explain why a strategy is recommended, limiting transparency and teachers' trust. To

Yifan Guo, Jann Spiess

Human decision-makers often face choices about complex cases with many potentially relevant features, but limited bandwidth to inspect and integrate all available information. In such settings, we study algorithms that highlight a small subset of case-specific features for human consideration, rather than producing a single prediction or recommendation. We m

Yunho Kim, Quan Nguyen, Taewhan Kim, Youngjin Heo

Industrial robots are widely used in manufacturing, yet most manipulation still depends on fixed waypoint scripts that are brittle to environmental changes. Learning-based control offers a more adaptive alternative, but it remains unclear whether such methods, still mostly confined to laboratory demonstrations, can sustain hours of reliable operation, delive

Taizun Jafri, Vidya A. Chhabria

Modern ASIC design is becoming increasingly complex, driving up design costs while limiting productivity gains from existing EDA tools. Despite decades of progress, current tools rely on fixed heuristics and offer limited control via tool hyperparameters, requiring extensive manual tuning to achieve an acceptable quality of results (QoR). While prior work ha

Facundo D. Moyano, Hongwei Ge, Zhanwen Han, Beatriz Bordadágua

Asteroseismology enabled measuring the rotation rate in the deep stellar interiors of stars across several evolutionary phases, advancing the theory of angular momentum transport in single stars from the main sequence to the white dwarf phase. However, binary stellar evolution products have not yet been studied in the context of angular momentum transport co

Nguyen Duong Hoang Duy, Nguyen Trinh Dong, Vu Tuan Hai, Le Vu Trung Duong

Device-Independent Quantum Key Distribution (DIQKD) provides information-theoretic security by relying solely on the violation of Bell inequalities, eliminating the need to trust the quantum devices. However, practical implementations of DIQKD are highly sensitive to noise. Efficient error correction during the classical post-processing stage is important fo

Birendra Mandal, Joydip Chaudhuri

Capillary rise is one of the classical problems in fluid mechanics and is traditionally described by Jurin's law, which balances capillary suction against hydrostatic pressure. Here we extend this classical result to active fluids, materials that generate internal stresses through microscopic energy consumption. Using the continuum theory of active nematics,

Xiaoyun Qiu, Yang Yu, Haifeng Xu

Performance manipulation arises when agents exploit easily measurable, routine tasks to inflate observable outcomes without contributing genuine innovation or expert judgment. We formalize this phenomenon in a game-theoretic model in which agents allocate effort along two margins. Creative effort is non-routine cognitive labor whose return is complementary t

Zhancun Mu, Guangyu Zhao, Yiwu Zhong, Chi Zhang

One-step offline RL actors are attractive because they avoid backpropagating through long iterative samplers and keep inference cheap, but they still have to improve under a critic without drifting away from actions that the dataset can support. In recent one-step extraction pipelines, a strong iterative teacher provides one target action for each latent dra

Amirhossein Sojoodi, Yiltan Hassan Temucin, Amirreza Baratisedeh, Hamed Sharifian

Effective intra-node GPU communication is essential for optimizing performance in MPI-based HPC applications, especially when leveraging multiple communication paths. In this study, we propose a novel approach that integrates CUDA Graphs into the UCX framework to enhance intra-node multi-path point-to-point GPU communication. By concurrently leveraging multi

Somyajit Chakraborty

Classical robot ethics is often framed around obedience, including Asimov's laws. This framing is insufficient for contemporary AI systems, which are increasingly adaptive, generative, embodied, and embedded in physical, psychological, and social environments. This paper proposes conditional mutualism under governance as a framework for human-AI coexistence:

Xi Wang, Jie Wang, Xingchen Song, Baijun Song

While generative text-to-speech (TTS) models approach human-level quality, monolithic metrics fail to diagnose fine-grained acoustic artifacts or explain perceptual collapse. To address this, we propose TTS-PRISM, a multi-dimensional diagnostic framework for Mandarin. First, we establish a 12-dimensional schema spanning stability to advanced expressiveness.

Leah Chen, Keni Chih-Hua Wu, Boon Tat Chia, Xiuqing Xing

AI is increasingly used to accelerate engineering design by improving decision-making and shortening iteration cycles. Application to marine propeller design, however, remains challenging due to scarce training data and the lack of widely available pretrained models. We address this gap with a physics-based data generation pipeline and a generative-AI framew

Seongjun Mo, Dongming Li, Mancheon Han, Johan Jönsson

QAssemble is a pure-Python package for the quantum many-body problem. It implements various functional approaches, such as tight-binding, Hartree-Fock, and GW approximations within a unified object-oriented architecture. Each physical concept--crystal structure, Hamiltonian, Green's function, self-energy, polarizability, screened Coulomb interaction--is repr

T. P. Freitas, J. Bouvier, B. Zaire, S. H. P. Alencar

Studies of magnetospheric accretion and magnetic field topology in T Tauri stars have advanced over the years, but their applications to fully convective, very-low-mass T Tauri stars remain relatively unexplored. We aim to analyze the circumstellar environment of the very-low-mass dipper-like star JH 223 by investigating the accretion process and characteriz

Ashutosh Shukla, John J. Hasenbein, Erhan Kutanoglu

Stochastic programming models for decision-making under uncertainty often suffer from scenario scarcity, where obtaining representative samples of uncertain parameters requires expensive simulations or measurements. This work presents a framework that leverages the Normal-to-Anything (NORTA) generative model to enhance the reliability of two-stage stochastic

Chunpeng Wang, Binyan Qu, Xiaoyu Wang, Zhiqiu Xia

Digital image watermarking has advanced rapidly for copyright protection of generative AI, yet the comparatively limited progress in watermark attack techniques has broken the attack-defense balance and hindered further advances in the field. In this paper, we propose FMDiffWA, a frequency-domain modulated diffusion framework for watermark attacks. Specifica

Catherine Greenhill, Mikhail Isaev, Charles Lewis

We consider $p$-orientations, which are defined to be orientations of $d$-regular graphs such that every vertex either has in-degree $p$ or out-degree $p$. These generalise the orientations considered in Jaeger's conjecture, where $d=4p+1$. Working with random $d$-regular graphs using the small subgraph conditioning method, we prove that a $d$-regular graph

Poulami Ghosh, Anupama Chauhan, Sidhanta Sahu, Sk Kalimuddin

An excitonic insulator represents a quantum phase in which spontaneous condensation of excitons leads to novel many-body phenomena. Ta$_2$NSi$_5$ (TNSe), a layered narrow-gap semiconductor, has emerged as a model platform to probe these correlated excitonic phases and their underlying dynamics below 327 K. In this work, we investigate the nonequilibrium dyna

Mehedi Hasan Shanto, Muhammad Asaduzzaman, Alioune Ngom

User comments on online programming platforms such as Stack Overflow play a vital role in maintaining the correctness and relevance of shared code examples. However, the majority of comments express gratitude or clarification, while only a small fraction highlight actionable issues that drive meaningful edits. This paper demonstrates how agentic AI principle

Hui-Mean Foo, Yuan-chin Ivan Chang

Most clinical prediction studies are developed from retrospective cohorts and reported as if all patient information were observed at once. In practice, clinicians face a more consequential question: \emph{when is there already enough information to stop testing and act?} A later stage can produce a better-looking model and still fail to justify the added de

Jon-Paul Cacioli

Verbal confidence elicitation is widely used to extract uncertainty estimates from LLMs. We tested whether seven instruction-tuned open-weight models (3-9B parameters, four families) produce verbalised confidence that meets minimal validity criteria for item-level Type-2 discrimination under minimal numeric elicitation with greedy decoding. In a pre-register

Huifan Zhang, Yun Hu, Caizhi Sheng, Yurui Qu

This work presents ROSA, a microring-based optical neural network architecture that improves robustness and energy efficiency using an optical shift-and-add (OSA) module and a layer-wise hybrid mapping strategy. It introduces a noise-aware voltage-to-weight model considering DAC and thermal variations, and a workload-aware framework to co-optimize MRR array

Sonam Bhakat, Pushpak Banerjee, Ahmedullah Aziz, Jackson Miller

Scalable, low-dissipation memory operating below 4 K is a critical requirement for superconducting and quantum computing systems. Existing cryogenic memory technologies rely on CMOS derivatives or hybrid architectures that incur leakage, refresh overhead or limited compatibility with superconducting logic. Here we demonstrate a superconducting tunnelling mag

Amelie Read, Job Feldbrugge, Celine Boehm, Rien van de Weygaert

We apply caustic skeleton theory to the Manticore-Local simulations, which are Bayesian constrained reconstructions of the Local Universe from the 2M++ galaxy catalogue, and extract the three-dimensional multi-scale caustic skeleton of two canonical weblike structures in our Local Universe, namely the Coma Cluster and the Pisces-Perseus ridge as they represe

Harry Dong, Timofey Efimov, Megna Shah, Jeff Simmons

In spite of the utility of 3-D electron back-scattered diffraction (EBSD) microscopy, the data collection process can be time-consuming with serial-sectioning. Hence, it is natural to look at other modalities, such as polarized light (PL) data, to accelerate EBSD data collection, supplemented with shared information. Complementarily, features in chaotic PL d

Dakang Cen, Zhiyuan Li, Wenlong Zhang

In this work, we propose an observation system based on the available data which solution is one-be-one mapping to the forward problem(with the unknown initial function) solution. It implies their solutions share the same linear structure in the finite dimensional space. Theoretical results show model reduction approaches constructed for the observation syst

Ziyun Xu, Hao Wang, Meng Sun

Parallel execution has become a key approach to improving blockchain scalability, but the lack of formal semantics for smart contract languages in such settings makes rigorous reasoning difficult. Crystality is a smart contract language designed for parallel EVMs, supporting scoped state and asynchronous relay across execution engines. This paper introduces

Chunyu Qiang, Xiaopeng Wang, Kang Yin, Yuzhe Liang

Generative audio modeling has largely been fragmented into specialized tasks, text-to-speech (TTS), text-to-music (TTM), and text-to-audio (TTA), each operating under heterogeneous control paradigms. Unifying these modalities remains a fundamental challenge due to the intrinsic dissonance between structured semantic representations (speech/music) and unstruc