April 2026 arXiv papers — page 45

Zhuoqi Lyu, Qing Ke

Optical chemical structure recognition (OCSR) translates molecular images into machine-readable representations like SMILES strings or molecular graphs, but remains challenging in real-world documents due to inexhaustible variations in chemical structures, shorthand conventions, and visual noise. Most existing deep-learning-based approaches rely on teacher f

Suphannee Sivakorn, Nuttaya Rujiratanapat, Yotsapat Ruangpaisarn, Chanond Duangpayap

The aviation industry faces significant vulnerabilities from both physical and cybersecurity threats, highlighting the urgent need for enhanced cybersecurity measures amid increasingly sophisticated attacks. This paper systematically reviews emerging threats at airports, analyzing real-world incidents and relevant literature while mapping risks to the MITRE

Eric A. Galapon

We reconsider the problem of regularizing the divergent series $\sum_{n=1}^{\infty}n^{\alpha}$ for $\operatorname{Re}\alpha>-1$, and offer a regularization prescription that yields the Riemann zeta regularization as a special case. The development of the regularization is framed as a two-step problem. The first step is prescribing a regularization of the div

Imranul Ashrafi, Inigo Jauregi Unanue, Massimo Piccardi

Test-time alignment methods offer a promising alternative to fine-tuning by steering the outputs of large language models (LLMs) at inference time with lightweight interventions on their internal representations. Recently, a prominent and effective approach, RE-Control (Kong et al., 2024), has proposed leveraging an external value function trained over the L

Weihao Li, Hongjin Zhao, Gao Zhu, Ge-Peng Ji

Wildfires are an escalating global concern due to the devastating impacts on the environment, economy, and human health, with notable incidents such as the 2019-2020 Australian bushfires and the 2025 California wildfires underscoring the severity of these events. AI-enabled camera-based smoke detection has emerged as a promising approach for the rapid detect

Haosen Li, Wenshuo Chen, Lei Wang, Shaofeng Liang

Text-to-image diffusion models have achieved remarkable generative capabilities, yet accurately aligning complex textual prompts with synthesized layouts remains an ongoing challenge. In these models, the initial Gaussian noise acts as a critical structural seed dictating the macroscopic layout. Recent online optimization and search methods attempt to refine

Haoxuan Zhang, Ruochi Li, Yang Zhang, Zhenni Liang

The rapid proliferation of Generative AI necessitates rigorous documentation standards for transparency and governance. However, manual creation of Model and Data Cards is not scalable, while automated approaches lack large-scale, high-fidelity benchmarks for systematic evaluation. We introduce MetaGAI, a comprehensive benchmark comprising 2,541 verified doc

Jinkai Cui, Kaiwen Song, Chumeng Niu, Juyong Zhang

Rasterization based methods have recently enabled high-quality novel view synthesis at real-time rates, but their underlying volumetric primitives do not expose a direct, globally consistent surface representation, leaving sur face extraction to heuristic post-processing. In contrast, implicit signed dis tance field (SDF) methods provide well-defined surface

Haosen Li, Wenshuo Chen, Shaofeng Liang, Lei Wang

Diffusion models have achieved unprecedented success in text-aligned generation, largely driven by Classifier-Free Guidance (CFG). However, standard CFG operates strictly on instantaneous gradients, omitting the intrinsic curvature of the data manifold. Recent methods like Zigzag-sampling (Z-Sampling) explicitly traverse multi-step forward-backward trajector

Fred Sun

This work introduces a novel quantum algorithm for gradient-based edge detection that operates entirely within the quantum circuit model. Grayscale images are encoded using the Novel Enhanced Quantum Representation (NEQR), allowing exact arithmetic on pixel intensities. Directional gradients are computed by generating superpositions of neighboring pixels via

Weiming Huang, Hao Sun, Junting Chen

Unified 2D and 3D radio map construction supports network planning, wireless digital twins, and unmanned aerial vehicle (UAV) applications. In urban environments, blockage, reflection, and diffraction make accurate construction expensive for physics-based solvers. Autoregressive next-token prediction offers a single sequential formulation that can cover both

Jingni Huang, Peter Bloodsworth

Short-term human pose prediction plays a crucial role in interactive systems, assistive robots, and emotion-aware human-computer interaction[1-3]. While current trajectory prediction models primarily rely on geometric motion cues, they often overlook the underlying emotional signals influencing human motion dynamics[4-5]. This paper investigates whether faci

Tatpong Katanyukul

Targeting entailment model checking, a recent study has pioneered an idea of Eigenmarking search, an improvement over Grover search using extra qubits. The extra qubits condition the quantum state evolution such that the answer states (if exist) are always in the minority. The minority criteria is essential to Grover probability-amplitude amplification and c

Yiqun Zhang, Hao Li, Zihan Wang, Shi Feng

Multi-turn, long-horizon tasks are increasingly common for large language models (LLMs), but solving them typically requires many sequential model invocations, accumulating substantial inference costs. Here, we study cost-aware multi-turn LLM routing: selecting which model to invoke at each turn from a model pool, given a fixed cost budget. We propose MTRout

Nitish Vikas Deshpande, Joseph Carlson, Siyun Yang, Mohamed Akrout

To enable larger apertures in multipleinput multipleoutput MIMO systems the trihybrid MIMO architecture offers a promising lowcost and lowpower solution by introducing reconfigurable antennas as a third layer of precoding on top of conventional digital and analog processing In this paper we develop a unified signal processing framework for trihybrid MIMO tha

Sifan Wang, Shawn Koohy, Yiping Lu, Paris Perdikaris

Physics-informed neural networks (PINNs) provide a promising machine learning framework for solving partial differential equations, but their training often breaks down on challenging problems, sometimes converging to physically incorrect solutions despite achieving small residual losses. This failure, we argue, is not merely an optimization difficulty. Rath

Alfred C. K. Farris

We present a simple method to obtain optimal posterior distributions and improve the quality of Bayesian inference with reduced human and computational effort. Bayes' Theorem is reformulated in the language of statistical mechanics, wherein an improved posterior -- referred to as a tempered posterior -- is defined analogously to a canonical probability distr

Yannick Kluth

We suggest a non-minimal renormalization scheme based on dimensional regularization that naturally incorporates threshold effects of heavy particles. By renormalizing couplings and masses to subtract all poles in $d \geq 4$, the resulting scheme is mass-dependent and circumvents shortcomings of mass-independent schemes like minimal subtraction. At the same t

Binghao Yang, Guangzong Chen

The foundation of cognitive flexibility and higher-order intelligence lies in the functional structure and activity of brain networks, which can be dynamically configured by both external environments and internal states. However, decoding these dynamics from high-dimensional neural data remains a challenge. In this study, we propose a computational framewor

Lei Wang, Ying Zhang, Di Shi, Fei Ding

Accurately characterizing wind power uncertainty under icing and post-disaster conditions remains a critical challenge for resilient power system operation. To address this issue, this paper proposes a physics-aware large language model (LLM) framework for probabilistic wind power scenario generation under extreme icing conditions. The proposed framework int

Khouloud Gaaloul, Zaid Ghazal, Madhu Latha Pulimi, Sam Emmanuel Kathiravan

Safety specifications in cyber-physical systems (CPS) capture the operational conditions the system must satisfy to operate safely within its intended environment. As operating environments evolve, operational rules must be continuously refined to preserve consistency with observed system behavior during simulation-based verification and validation. Revising

Yongsen Pan, Yuxin Chen, Zheng Hu, Xu Yuan

Modern recommendation systems involve massive catalogs of multimodal items, where scalable item identification must balance compactness, semantic fidelity, and downstream effectiveness. Semantic IDs (SIDs) address this need by representing items as short discrete token sequences derived from multimodal signals, providing a compact interface for retrieval, ra

Huaisong Yong, Binyu Zhao

We present the first analytic theory with elegant and closed-form analytical solutions to explore the cosolvency effect in polymer brushes, where polymer chains that are poorly soluble in two pure solvents become fully soluble in certain mixtures thereof. This effect is key to designing stimulus-responsive smart materials but has not previously been addresse

Durga Keerthi Mandarapu, Isaac Fuksman, Artem Pelenitsyn, Gilbert Bernstein

Discrete Collision Detection (DCD) is a fundamental task in several domains including particle-based physics simulations. Efficient DCD uses indexing structures such as Bounding Volume Hierarchy (BVH), but accelerating irregular BVH traversals demands meticulous efforts to achieve performance. Modern GPUs feature Ray Tracing (RT) architecture that provides h

Ziyu Wang, Fei Lei, Dezun Dong

Multi-plane architectures have become increasingly prevalent in the Fat-Tree networks of AI data centers. By leveraging multiple ports on a single network interface card (NIC) or multiple NICs within a scale-up domain, each port or NIC is allocated to an independent network plane, thereby provisioning the overall system with multiple network planes. However,

Chen Zeng, Jiahui Wang, Qiao Wang

Existing theory suggests that Kolmogorov-Arnold Networks (KANs) can overcome the spectral bias commonly observed in neural networks under the assumption that inputs are statistically independent. However, this assumption does not hold in time series forecasting (TSF), where inputs are lagged observations with strong temporal autocorrelation. Through theoreti

Maxie Dion Schmidt

We revisit several hybrid multiplicative-to-additive type functions from a recent preprint article. These functions, $g(n)$ with Dirichlet generating function (DGF) $\zeta(s)^{-1} (1+P(s))^{-1}$ for $\Re(s) > 1$ where $P(s) = \sum_p p^{-s}$ is the prime zeta function, $|g(n)| = \lambda(n) g(n)$ with DGF $\zeta(2s)^{-1}(1-P(s))^{-1}$, and $C_{\Omega}(n)$ with

Ameer Mohammed, Aydin Abadi, Jaffer Mahdi

Publicly verifiable delegation is a well-known problem involving a user who wishes to outsource a resource-intensive computational task to a more powerful but potentially untrusted server such that any other party is able to efficiently check the veracity of the computation's result. This problem has been extensively studied in the classical domain where the

Muhammad Aimal Rehman, Zhili Lu, Chi-Kuang Yeh

Retrieval-augmented generation (RAG) combines document retrieval with large language models to produce responses grounded in external evidence. While several R packages support core components of RAG workflows, integrated evaluation of RAG systems in R remains limited and is often conducted through Python-based tools, most notably the RAG assessment (RAGAS)

Teng Li, Stephen Wu, Yong Huang, James L. Beck

The health condition of components in civil infrastructures can be described by various discrete states according to their performance degradation. Inferring these states from measurable responses is typically an ill-posed inverse problem. Although Bayesian methods are well-suited to tackle such problems, computing the posterior probability density function

Xinwei Dong, Jiyang Li, Jiabin Xie, Yang Yi

In high-conflict mixed-traffic scenarios involving human-driven and autonomous vehicles, most existing autonomous driving systems default to overly conservative behaviors, lack proactive interaction, and consequently suffer from limited public acceptance. To mitigate intent misunderstandings and decision failures, we present a Large Language Model based inte

Pradipta Mitra

We propose a simple, projection-based algorithm for clustering mixtures of discrete (Bernoulli) distributions. Unlike previous approaches that rely on coordinate-specific ``combinatorial projections,'' our algorithm is rotationally invariant and works by projecting samples onto approximate centers obtained via a $k$-means computation on the best rank-$k$ app

Qi Liu, Xiaohui Chen, Zhihui Zhao, Yaowen Zheng

Collusion among autonomous agents poses a critical security threat in embodied multi-agent systems (MAS), where coordinated behaviors can deviate from global objectives and lead to real-world consequences. Existing defenses, primarily based on identity control or post-hoc behavior analysis, are insufficient to address such threats in embodied settings due to

Chang Liu, Qiong Deng, Huadong Li, Liwei Yang

Taiji is a gravitational wave detection mission in space initiated by the Chinese Academy of Sciences, which will open the millihertz window through a heliocentric triangular constellation of three drag-free spacecraft. Its ultimate sensitivity is determined partly by the residual acceleration noise of the gravitational reference sensors (GRS), within which

Dong Huo, Tristan Aumentado-Armstrong, Samrudhdhi B. Rangrej, Maitreya Suin

Burst image super resolution (BISR) aims to construct a single high-resolution (HR) image by aggregating information from multiple low-resolution (LR) frames, relying on temporal redundancy and spatial coherence across the burst. While conventional methods achieve impressive results, they often struggle with complex textures and oversmoothing. Diffusion mode

Hossein Abedi, Nathan L. Harshman, Peter Schmelcher

We consider a generalization of the two-body contact interaction for nonrelativistic particles confined to a one-dimensional box, in which the interaction is decentered, i.e., the particles interact only when they are separated by a distance c. In contrast to the harmonically trapped system, this model is nonintegrable. Despite this, we demonstrate that the

Eyal Uzner, Ofer Neufeld

High harmonic generation (HHG) in solids has recently emerged as a powerful all-optical approach for probing material properties and ultrafast electron dynamics in quantum systems. It has been widely applied for studying two-dimensional and layered solids of various kinds. In these studies, the laser is usually polarized within the layered planes, where most

Boming Xia, Liming Zhu, Erdun Gao, Qinghua Lu

Uncertainty in large language model (LLM)-based systems is often studied at the level of a single model output, yet deployed LLM applications are compound systems in which uncertainty is transformed and reused across model internals, workflow stages, component boundaries, persistent state, and human or organisational processes. Without principled treatment o

Xiaoyu Ma, Qing-Hua Zhang, Cong Xu

We investigate quantum average correlations and complementarity relations based on metric-adjusted skew information. Several natural averaging procedures are considered, including complete families of mutually unbiased bases, all orthonormal bases, operator orthonormal bases, and twirling channels induced by the unitary group. All these approaches lead to th

Ganesh Parida

A search is presented for massive narrow-width resonances in the mass range of $1\text{-}4.5\,\text{TeV}$ decaying into pairs of Higgs bosons (HH), using proton-proton collision data at a center-of-mass energy of $13\,\text{TeV}$ collected with the CMS detector at the LHC during the $2016\text{-}2018$ data-taking. The data correspond to an integrated luminos

Xiaoyu Ma, Qing-Hua Zhang, Cong Xu

This paper studies the quantification and structural properties of quantum average correlation based on average coherence. Motivated by two mathematically equivalent approaches to define average coherence: one by averaging over complete sets of mutually unbiased bases, and the other by integrating over all orthogonal bases under the Haar measure, we define a

Md Abubakkar, Sajib Debnath, Md. Uzzal Mia

Accurate short-term electricity load forecasting is a cornerstone of U.S. grid reliability; however, prevailing deep learning models remain opaque, limiting operator trust during extreme weather. A unified, interpretable, physics-informed ensemble framework is proposed, integrating a Convolutional Neural Network (CNN) branch for local feature extraction and

Urban Senica, Michael A. Schreiber, Mattias Beck, Christian Jirauschek

Terahertz quantum cascade lasers (THz QCLs) are chip-scale semiconductor lasers operating in the frequency range between 1-6 THz, useful as compact sources for spectroscopy, communications, and non-destructive imaging and testing. Here, we apply low-frequency microwave modulation on a planarized THz QCL to generate ultrabroadband emission in the THz range. F

Sunyoung An, Xiaoming Huo

Polyak-Ruppert averaging yields an asymptotically normal estimator with sandwich covariance $H^{-1}SH^{-1}$, the foundation of online inference. When the gradient step is preconditioned by a data-driven matrix $P_t$, we ask how fast $P_t$ must stabilize for the central limit theorem (CLT) to remain valid. We resolve this via an exact preconditioner-isolating

Yoshitsugu Sekine

This paper is a collection of the author's computational notes on the van Hove model and contains no essentially new results. We discuss, from both the operator-algebraic perspective via the Weyl algebra and the resolvent algebra and the functional integral approach, the removal of infrared and ultraviolet cutoffs and the existence of the ground state and $\

Noriaki Ikeda

Recent developments of Batalin-Vilkovisky (BV) formalism and related geometry are reviewed. Mathematical structures of BV formalism are summarized as a Q-manifold and a QP-manifold. Lie algebras, Lie algebroids and other higher algebroids are explained as typical examples of Q- and QP-manifolds. Finally, the BV action functionals are constructed by geometric

Xiaomin Liu, Rongguo Yang, Jing Zhang, Tiancai Zhang

Flexible manipulation of quantum correlation resources enables the implementation of diverse quantum tasks based on hybrid quantum networks, where atom-magnon and optomagnonic entanglements and steerings play important roles. In this work, we propose an effective scheme to generate and manipulate quantum entanglements and steerings based on a hybrid optomagn

Ankur Malik

Graph-based anti-money laundering (AML) systems on blockchain networks can score suspicious activity at two granularity levels -- transactions or actor addresses -- yet compliance action is conducted per actor. This paper contributes an evaluation methodology for measuring how scoring granularity affects investigation queue composition under fixed review bud

Vijay Yadav

Early detection of mental health conditions, particularly stress and depression, from social media text remains a challenging open problem in computational psychiatry and natural language processing. Automated systems must contend with figurative language, implicit emotional expression, and the high noise inherent in user-generated content. Existing approach

Peng-Ju Wu

Recent late-Universe observations suggest an open Universe. If confirmed, such a departure from spatial flatness would carry profound implications for our understanding of cosmic inflation and the ultimate fate of the Universe. Motivated by this intriguing result and the release of new data, we revisit the question using baryon acoustic oscillation measureme

J. K. Katz, A. Rollett, R. J. Hemley

Metastable aluminum-alloyed $\delta$-plutonium shrinks rapidly and pure $\alpha$-plutonium swells rapidly at 4 K. At ambient temperature alloyed $\delta$-plutonium swells about $10^{-3}$ as fast as it shrinks at 4 K, but its bulk density decreases more slowly than would be inferred from the increase in its lattice parameter determined by X-ray diffraction. T

Yaling Hu, Jiani Wang, Yu-hong Dai, Xiaojiao Tong

In many applications, including Stackelberg games, machine learning, and power systems \cite{Mackay2018Selftuning,Heinrich1952The,Wang2021Bi-Level}, the decisions in a minimax optimization problem can be constrained by a solution to an optimization problem. In this paper, we introduce optimality conditions of this novel minimax bilevel optimization problem a

Sebastian Kobler, Matthew Clemson, Angela Sun, Jonathan K. Kummerfeld

Educational NLP systems are typically evaluated using engagement metrics and satisfaction surveys, which are at best a proxy for meeting pedagogical goals. We introduce six computational metrics for automated evaluation of pedagogical alignment in student-AI dialogue. We validate our metrics through analysis of 12,650 messages across 500 conversations from f

Zeming Sun

The objective of this paper is to further study the anabelian object referred to as \emph{pointed virtual curves}. Building upon previous work that investigated these fundamental-group-theoretic pullbacks of Galois sections in the genus-zero situation, we extend the central anabelian results to curves of arbitrary genus. To facilitate this generalization, we

Onur Oktay

The preduals of $W^*$-algebras are 1-Plichko spaces. A natural question arises: does every predual possess a projectional skeleton (PS) $\{P_s:s\in J\}$ such that each $P_s^*$ is a conditional expectation? In this note, we answer this question affirmatively for the preduals of the group von Neumann algebras of locally compact groups.

Mazal Bethany, Kim-Kwang Raymond Choo, Nishant Vishwamitra, Peyman Najafirad

Multi-component natural language processing (NLP) pipelines are increasingly deployed for high-stakes decisions, yet no existing adversarial method can test their robustness under realistic conditions: binary-only feedback, no gradient access, and strict query budgets. We formalize this strict black-box threat model and propose a two-agent evasion framework

Shamik Das, Debajyoti De, Sudipa Mondal

In this article, we study necessary conditions for certain square-free integers to be congruent numbers. Our method uses divisibility properties of class numbers of related imaginary quadratic fields. We first consider positive square-free integers of the form $n = p_1 p_2 \cdots p_t q,$ where each prime $p_i \equiv 5 \pmod{8}$ and $q \equiv 7 \pmod{8}$. We

Kazuya Nishimura, Ryoma Bise, Haruka Hirose, Yasuhiro Kojima

Deep learning-based nuclei segmentation and classification in pathology images typically rely on large-scale pixel-level manual annotations, which are costly and difficult to obtain across diverse tissues and staining conditions. To address this limitation, we propose a framework that leverages spatial transcriptomics (ST) data as supervision for nuclei segm

Rohith Reddy Bellibatlu, Edward Raff, Wenbin Zhang

Large language models are widely adopted as automated evaluation judges, yet the stability of their verdicts under semantically equivalent prompt rephrasings remains largely unexamined. We conduct a systematic empirical study of prompt-induced decision instability across multiple evaluation tasks and judge architectures. To facilitate this analysis, we relea

Cai-Hong Liao, Yan-Ling Li, Long Huang, Xing Xiao

High-precision quantum parameter estimation is fundamental to the advancement of quantum metrology. Although reservoir engineering provides a powerful approach to improve estimation by tailoring system-environment interactions, the role of the squeezing phase and correlations arising from the sequential utilization of the same squeezed reservoir remains inad

Audrey Cherilyn, Houman Safaai

We study the organization of channel-level importance in transformer feed-forward networks (FFNs). Using a Fisher-style loss proxy (LP) based on activation-gradient second moments, we show that loss sensitivity is concentrated in a small set of channels within each layer. In Llama-3.1-8B, the top 1% of channels per layer accounts for a median of 58.7% of LP

Yizhu Gao, Zhongzhou Chen, Min Li, Xiaoming Zhai

Generative Artificial Intelligence (GenAI) presents a governance challenge for STEM assessment. Unrestricted access can enable task outsourcing that undermines the validity of traditional assessments, while blanket prohibitions are difficult to enforce, may drive use underground, and do little to prepare students for workplaces where GenAI supported workflow

Hiroki Naito

Prior work showed that human-in-the-loop oversight becomes structurally untenable in high-loss domains once AI output velocity V exceeds human cognitive capacity C_max. The operative constraint, however, is V x L, where L is per-item cognitive load: triage, judgment, and response. These components respond asymmetrically to capability improvement. Triage cost

Thieu Khang Nguyen, Thu Huong Dang, Truong-Son Hy

The Chinese Postman Problem with load-dependent costs (CPP-LC) arises in real-world logistics and transportation systems where travel costs depend on vehicle load and energy consumption. In this work, we propose a hybrid optimization framework that integrates metaheuristic search with mathematical programming to efficiently solve CPP-LC. The proposed method

Juncai Wang

This work investigates whether the qualitative optimality conditions of the SMP can serve as guiding heuristics for designing robust tabular RL algorithms. We propose the \textbf{SDE-MC-AC} framework, which establishes a set of SMP-inspired correspondences: the potential field gradient is mapped to potential-based reward shaping, the diffusion coefficient to

James Rhodes, George Kang

Agent systems increasingly execute rather than advise. When an AI agent queries regulated data, invokes effectful tools, and mutates persistent state, correctness is not captured by whether a terminal output looks plausible. The operative questions are whether each step was authorized under a contract, whether the recorded history is tamper-evident, and whet

Robin Dey, Panyanon Viradecha

The emergence of autonomous AI agents as first-class participants in digital infrastructure marks a fundamental inflection point in the evolution of the Web. While significant research has been directed at agent behaviour and reasoning, comparatively little attention has been paid to the infrastructure those agents require to operate reliably at scale. This

Zhengheng Li, Panrui Li, Xuyang Liu, Puzhi Xia

While In-Context Learning (ICL) is extensively studied in Autoregressive (AR) LLMs, its mechanism within Diffusion Large Language Models (dLLMs) remains largely unexplored. Unlike AR models restricted by unidirectional causal masking, dLLMs intrinsically utilize bidirectional attention, offering extensive spatial flexibility for query placement. Unfortunatel

DeepSeek-AI, Anyi Xu, Bangcai Lin, Bing Xue

We present a preview version of DeepSeek-V4 series, including two strong Mixture-of-Experts (MoE) language models -- DeepSeek-V4-Pro with 1.6T parameters (49B activated) and DeepSeek-V4-Flash with 284B parameters (13B activated) -- both supporting a context length of one million tokens. DeepSeek-V4 series incorporate several key upgrades in architecture and

Guan-Ting Liu, Chao-Han Huck Yang, Chenhui Deng, Zhongzhi Yu

Translating sequential programming priors into the parallel temporal logic of hardware design remains a crucial bottleneck for large language models(LLM). To investigate this, we introduce a new error taxonomy grounded in problem solvability, inspired by cognitive theory. Our taxonomy categorizes failures into syntactic, semantic, solvable functional, and un

Zijing Shi, Meng Fang, Ling Chen

As autonomous web agents are increasingly deployed to perform real-world tasks, ensuring their safety has become a critical concern. In this work, we study web agent behavior under realistic deceptive interfaces in the e-commerce domain. We introduce WebDecept, a lightweight and configurable plugin framework that enables controlled injection of deceptive int

Faouzi El Yagoubi, Godwin Badu-Marfo, Ranwa Al Mallah

Large Language Models (LLMs) alter their privacy behavior based on the perceived identity of their interlocutor. While safety mechanisms typically prevent LLMs from releasing Personally Identifiable Information (PII) to human users, these models tend to reveal more sensitive data when addressing another AI agent. We refer to this as the \textbf{Interlocutor

Ritabrata Roy Choudhury, Arkajyoti Karmakar, Rudra Pratap Mitra

Mass gathering events are associated with critical safety incidents caused by insufficient crowd monitoring and inadequate emergency response coordination. Traditional surveillance systems lack intelligent analytics, resulting in delayed threat identification, poor resource deployment, and weak support for vulnerable individuals during dense public assemblie

Dipika Sarkar, S. N. Fathima

We derived $q$-continued fractions $X_i(q)$ of order thirty-four and continued fractions $Y_i(q)$ of order sixty-eight from a general continued fraction identity of Ramanujan, where $i=1,2,3,4,5,6,7$ and $8$. We established some theta-function identities, and one has been proved for the continued fractions $X_i(q)$ and $Y_i(q)$. Furthermore, we obtained resu

Augusto E. Lassen, Benedetta Vulcani, Jacopo Fritz, Bianca M. Poggianti

The environmental mechanisms driving unwinding spiral arms in cluster galaxies remain debated. While earlier studies attributed it mainly to gravitational interactions, recent works suggest that RPS alone can induce unwinding. We present a VLT/MUSE spatially resolved analysis to investigate the mechanisms responsible for spiral-arm unwinding in two galaxies,

Sharif Ahmed, Nasir U. Eisty

Context: Innovation thrives on scientific software, with useful code review feedback enhancing its correctness and impact. However, unlike general-purpose commercial and open-source software, the usefulness of code review feedback (CR comment) in scientific software remains largely unstudied. Objective: This paper aims to characterize the usefulness of CR co

Beyza Cinar, Maria Maleshkova

Disease progression varies with age and is influenced by underlying genetic, biochemical, and hormonal etiologies, suggesting the need for tailored monitoring, care, and medication beyond standard clinical guidelines. Specifically, in autoimmune diseases like type 1 diabetes (T1D), where patients depend on exogenous insulin to compensate for insulin deficien

Jongyoon Kim, Hojae Han, Seung-won Hwang

Recent advances in large language models (LLMs) have shown promise in formal theorem proving, yet evaluating semantic correctness remains challenging. Existing evaluations rely on indirect proxies such as lexical overlap with human-annotated proof, or expensive manual inspection. Inspired by the shift from lexical comparison to test-based evaluation in code

Dishant Sisodia, Sarika Jalan

We use finite-time Lyapunov exponent (FTLE) distributions to probe transition mechanisms in high-dimensional reservoir maps trained on low-dimensional chaotic dynamics across multiple regimes. While trained reservoirs accurately predict critical transitions and regime shifts, conventional analyses based on time series or bifurcation structure provide limited

Jialiang Wang, Yuchen Liu, Hang Xu, Kaichun Hu

The volume of scientific submissions continues to climb, outpacing the capacity of qualified human referees and stretching editorial timelines. At the same time, modern large language models (LLMs) offer impressive capabilities in summarization, fact checking, and literature triage, making the integration of AI into peer review increasingly attractive -- and

Thoshitha Gamage, Lasanthi Gamage

While the ASAM OpenSCENARIO 2.1 Domain-Specific Language (DSL) enables declarative, intent-driven authoring for Scenario-Based Testing (SBT), its integration into open-source simulators like CARLA remains limited by legacy parsers. We propose a multi-pass modern compiler architecture that translates the OpenSCENARIO 2.1 DSL directly into executable CARLA beh

Satori Tsuzuki

We derive a retained-spin micropolar hydrodynamic closure from the Boltzmann--Curtiss equation using a generalized Chapman--Enskog construction in which the local mean spin is retained as a quasi-slow variable. Starting from the one-particle kinetic balance identities and the corresponding exact coarse-grained finite-size balances for mass, linear momentum,

R. Ratvasky, T. Rostomyan, M. Ali, H. Atac

The Muon Scattering Experiment (MUSE) was developed to address the proton radius puzzle through simultaneous electron-proton and muon-proton scattering using the Paul Scherrer Institute's PiM1 secondary beamline. MUSE uses a large-solid-angle, non-magnetic spectrometer to detect beam particles scattering from a liquid hydrogen cell contained within a vac

Xue-Qing Ye, Hao Liu, Qi-Yi Wu, Chen Zhang

We use ultrafast optical spectroscopy to investigate the electronic and lattice dynamics of the charge-density wave (CDW) material 1$T$-TiSe$_2$ across various temperatures and pump fluences. We reveal a close relationship between the observed ultrafast dynamical processes and two characteristic temperatures: $T_{\rm CDW}$ ($\sim$202 K) and $T^*$ ($\sim$165

Mojtaba Shahbazi, Mehdi Sadeghi

Within the "complexity=anything" proposal of holography, the complexity growth rate (CGR) can exhibit jumps, interpreted as phase transitions. We demonstrate that the location and amplitude of these jumps are governed by the dynamics of bulk fields, which, via the fluid-gravity correspondence, map to the boundary energy-momentum tensor. The behavior

Zhicheng Song, Jinglan Xu, Chunxin Zheng, Yulin Li

Wheel-legged robots integrate leg agility on rough terrain with wheel efficiency on flat ground. However, most existing designs do not fully capitalize on the benefits of both legged and wheeled structures, which limits overall system flexibility and efficiency. We present FLORES, a novel wheel-legged robot design featuring a distinctive front-leg configurat

Regina Zhang, Zongru Li, Honggang Wen, Xiaofeng Liu

Forecasting systems in science must be accurate, physically consistent, and certifiably reliable. Most existing models address prediction, constraint enforcement, and verification separately, limiting scalability and interpretability. We introduce GeoCert, a geometric AI framework that unifies forecasting, physical reasoning, and formal verification within a

Daniel Tabach

This study asks whether the threat of AI detection changes how people write with AI, and whether other people can tell the difference. In a two-phase controlled experiment, 21 participants wrote opinion pieces on remote work using an AI chatbot. Half were randomly warned that their submission would be scanned by an AI detection tool. The other half received

Adam Rycerz

Unlike for tunneling Josephson junctions, for which the current-phase relation is given by the sine function, with the critical current ($I_c$) and normal-state resistance ($R_N$) following the relation $I_cR_N=(\pi/2)\,\Delta_0/e$ (where $\Delta_0$ is the superconducting gap and electron charge is $-e$), mesoscopic Josephson junctions show more complex curr

Sukhbir Kaur, Sukhbir Singh, Kanchan Jain, Pooja Soni

In this paper, a Mixed Data Sampling (MIDAS) model is studied when both low and high frequency variables are contaminated with measurement error. It is shown that the profile likelihood estimator becomes inconsistent in the presence of measurement error. Using the corrected score approach along with profile likelihood approach, a consistent estimator for par

Divakar Kumar Yadav, Tian Zhao

Large Language Models (LLMs) have achieved strong performance across natural language and multimodal tasks, yet their practical deployment remains constrained by inference latency and kernel launch overhead, particularly in interactive, short-sequence settings. This paper presents a hybrid runtime framework that combines Just-In-Time (JIT) compilation with C

Tanusree Sharma, Anish Krishnagiri, Lili Dudas, Ahmed Adnan

As generative voice models are rapidly advancing in both capabilities and public utilization, the unconsented collection, reuse, and synthesis of voice data are introducing new classes of privacy, security and governance risk that are poorly captured by existing, largely uniform threat models. To fill the gap, we present V.O.I.C.E, a taxonomy of voice genera

Dan Liu, Fida K. Dankar, Jennifer C. deBruyn, Amanda Ricciuto

Single-arm trials accelerate study timelines by reducing the number of patients that must be recruited for a concurrent control group. However, these designs require an alternative comparator to estimate treatment effects. One approach is to construct a virtual control arm using a machine learning (ML) model trained on external control data to predict the co

Michelle Mastrianni, Kwok Lung Fan, Yee Lam Elim Thompson, Jessie J. J. Gommers

Multiple diagnostic tests are frequently used to determine the presence of a disease condition in patients. In this paper, we use bivariate copulas to examine the properties of receiver operating characteristic (ROC) curves formed when two correlated diagnostic tests are used together to rule-out ("believe the negative") and rule-in ("believe the positive")

Kirk Bansak, Elisabeth Paulson, Dominik Rothenhäusler, Jeremy Ferwerda

Previous research has investigated the potential of refugee matching for boosting refugee outcomes, first considered by Bansak et al. (2018). This paper demonstrates the stability of counterfactual impact evaluation results in the context of refugee matching in the United States using a range of off-policy evaluation methods. In order to estimate counterfact

James Bona-Landry

In [arXiv:2409.08465], Quastel and Gu use Stein's equation and integration by parts to give a direct proof that drifted Brownian motions are stationary (modulo height shifts) for the full-line KPZ equation. In this article, we consider the open KPZ equation with boundary conditions $\partial_x h(t,0) = \partial_x h(t,1) = \alpha$ for a general real parameter

Danny Duan, Hanbaek Lyu, William Powell

We analyze the asymptotic behavior and scaling limits of large random matrices rescaled via the Sinkhorn algorithm to match prescribed row and column margins. For a random matrix with independent sub-exponential entries, we show that its Sinkhorn rescaling concentrates around the rescaling of its mean matrix, both at the level of the Schr\"odinger potentials

Sharan Ramjee

Chain-of-Thought (CoT) reasoning has emerged as a key technique for eliciting complex reasoning in Large Language Models (LLMs). Although interpretable, its dependence on natural language limits the model's expressive bandwidth. Continuous thought models address this bottleneck by reasoning in latent space rather than human-readable tokens. While they enable

Ben Hagag, William L. Anderson, Christian Schroeder de Witt, Sarah Scheffler

Multi-agent systems (MAS), composed of networks of two or more autonomous AI agents, have become increasingly popular in production deployments, yet introduce security risks that do not arise in single-agent settings. Even if individual agents exhibit robust security, architectural decisions governing their coordination can create attack surfaces that have n

Khalid Hasan, Jamil Saquer

The growing availability of online support groups has opened up new windows to study mental health through natural language processing (NLP). However, it is hindered by a lack of high-quality, well-validated datasets. Existing studies have a tendency to build task-specific corpora without collecting them into widely available resources, and this makes reprod