October 2025 arXiv papers — page 191

Benjamin Akera, Evelyn Nafula, Patrick Walukagga, Gilbert Yiga

The development of Automatic Speech Recognition (ASR) systems for low-resource African languages remains challenging due to limited transcribed speech data. While recent advances in large multilingual models like OpenAI's Whisper offer promising pathways for low-resource ASR development, critical questions persist regarding practical deployment requirements.

Sarah M. Wagner, Jeffrey D. Scargle, Greg Madejski, Andrea Gokus

The production site and process responsible for the highly variable high-energy emission observed from blazar jets are still debated. Gravitational lenses can be used as microscopes to investigate the nature of such sources. We study the broad-band spectral properties and the high-energy variability of the gravitationally-lensed blazar PKS 1830-211, for whic

Yuhua Xu, Wei Sun, Chengpei Tang, Jiaxing Lu

Ordinary differential equation (ODE)-based diffusion models enable deterministic image synthesis, establishing a reversible mapping suitable for generative steganography. While prevailing methods strictly adhere to a standard normal prior, empirical evidence indicates that controlled deviations from this distribution reduce numerical inversion errors without

Nimrod Bratspiess, Eyal Heifetz, Leo R. M. Maas

Internal waves are an important feature of stratified fluids, both in oceanic and lake basins and in other settings. Many works have been published on the generic feature of internal wave trapping onto planar wave attractors and super-attractors in 2\&3D and the exceptional class of standing global internal wave modes. However, most of these works did not de

Wen Ye, Zhaocheng Liu, Yuwei Gui, Tingyu Yuan

Text-to-image synthesis has made remarkable progress, yet accurately interpreting complex and lengthy prompts remains challenging, often resulting in semantic inconsistencies and missing details. Existing solutions, such as fine-tuning, are model-specific and require training, while prior automatic prompt optimization (APO) approaches typically lack systemat

L. Angiuli, E. M. Mangino, L. Lorenzi

This paper focuses on systems of strongly coupled elliptic operators whose coefficients may be unbounded and are defined on a domain $\Omega \subseteq \mathbb{R}^d$. It is shown that a quasi-contractive semigroup in $L^p$-spaces can be associated with such operators for values of $p$ belonging to an interval that contains $2$ as an interior point. Then, unde

Soheil Hashtarkhani, Rezaur Rashid, Christopher L Brett, Lokesh Chinthala

Electronic health records contain inconsistently structured or free-text data, requiring efficient preprocessing to enable predictive health care models. Although artificial intelligence-driven natural language processing tools show promise for automating diagnosis classification, their comparative performance and clinical reliability require systematic eval

Maojie Xu, Argyro Sasli, Alexandra Junell, Felipe Fontinele Nunes

Time-domain surveys such as the Zwicky Transient Facility (ZTF) have opened a new frontier in the discovery and characterization of transients. While photometric light curves provide broad temporal coverage, spectroscopic observations remain crucial for physical interpretation and source classification. However, existing spectral analysis methods -- often re

Chengzhi Zhong, Fei Cheng, Qianying Liu, Yugo Murawaki

Large language models exhibit strong multilingual capabilities despite limited exposure to non-English data. Prior studies show that English-centric large language models map multilingual content into English-aligned representations at intermediate layers and then project them back into target-language token spaces in the final layer. From this observation,

Léo Touzo, Pierre Le Doussal

We study $N$ run-and-tumble particles (RTPs) in one dimension interacting via a double-well potential $W(r)=-k_0 \, r^2/2+g \, r^4/4$, which is repulsive at short interparticle distance $r$ and attractive at large distance. At large time, the system forms a bound state where the density of particles has a finite support. We focus on the determination of the

Darren Pereira, Leonardo Banchi

We present a tensor-network-based method for simulating a weakly-measured quantum circuit. In particular, we use a Markov chain to efficiently sample measurements and contract the tensor network, propagating their effect forward along the spatial direction. Applications of our algorithm include validating quantum computers (capable of mid-circuit measurement

Donald Pfaffmann, Matthias Klusch, Marcel Steinmetz

We present a novel hybrid learning-assisted planning method, named HyPlan, for solving the collision-free navigation problem for self-driving cars in partially observable traffic environments. HyPlan combines methods for multi-agent behavior prediction, deep reinforcement learning with proximal policy optimization and approximated online POMDP planning with

Syed Azer Reza, Maarij Syed

In this paper, we provide expressions for the detected optical irradiance for magneto-optic characterization of superparamagnetic nanoparticles (SPNs) in solution imparting Faraday rotation (FR) to an optical beam passing through the sample solution. For our analysis, we assume a Langevin model for SPN samples and show the presence of odd and even harmonics

G. Bueno, M. Bonehill

We propose a numerical solution to the Korteweg-de Vries (KdV) equation using a Crank-Nicolson scheme, and compare its performance to the Fast Fourier Transform method. The properties and interactions of soliton solutions are further examined. Initial conditions were varied to analyse soliton formation in the resulting system. Performing an L$^2$ error analy

Shirin Shoushtari, Yi Wang, Xiao Shi, M. Salman Asif

Out-of-distribution (OOD) detection is critical for the safe deployment of machine learning systems in safety-sensitive domains. Diffusion models have recently emerged as powerful generative models, capable of capturing complex data distributions through iterative denoising. Building on this progress, recent work has explored their potential for OOD detectio

Fangshuo Liao, Anastasios Kyrillidis

Mixture-of-Experts (MoE) architectures have emerged as a cornerstone of modern AI systems. In particular, MoEs route inputs dynamically to specialized experts whose outputs are aggregated through weighted summation. Despite their widespread application, theoretical understanding of MoE training dynamics remains limited to either separate expert-router optimi

Karsten Reichold, Ulrike Schneider

This paper derives new asymptotic results for the adaptive LASSO estimator in cointegrating regressions, allowing for uncertainty about whether the regressors are exact unit root processes. We study model selection probabilities, estimator consistency, and limiting distributions under standard and moving-parameter asymptotics. We further derive uniform conve

Benjamin Akera, Evelyn Nafula Ouma, Gilbert Yiga, Patrick Walukagga

There are more than 2000 living languages in Africa, most of which have been bypassed by advances in language technology. Current leading LLMs exhibit strong performance on a number of the most common languages (e.g. Swahili or Yoruba), but prioritise support for the languages with the most speakers first, resulting in piecemeal ability across disparate lang

Joris Dommel, Sven A. Wegner

In 2017, Hanin and Sellke showed that the class of arbitrarily deep, real-valued, feed-forward and ReLU-activated networks of width w forms a dense subset of the space of continuous functions on R^n, with respect to the topology of uniform convergence on compact sets, if and only if w>n holds. To show the necessity, a concrete counterexample function f:R^n->

KM3NeT Collaboration, O. Adriani, A. Albert, A. R. Alhebsi

The existence of an eV-scale sterile neutrino has been proposed to explain several anomalous experimental results obtained over the course of the past 25 years. The first search for such a sterile neutrino conducted with data from KM3NeT/ORCA -- a water Cherenkov neutrino telescope under construction at the bottom of the Mediterranean Sea -- is reported in t

Matthew McCormack, Andrei Teimurazov, Olga Shishkina, Moritz Linkmann

The motionless conducting state of liquid metal convection with an applied vertical magnetic field confined in a vessel with insulating side walls becomes linearly unstable to wall modes through a supercritical pitchfork bifurcation. Nevertheless, we show that the transition proceeds subcritically, with stable finite-amplitude solutions with different symmet

Prerana Khatiwada, Alejandro Ciuba, Aditya Nayak, Aakash Gautam

Social media platforms have transformed global communication and interaction, with TikTok emerging as a critical tool for education, connection, and social impact, including in contexts where infrastructural resources are limited. Amid growing political discussions about banning platforms like TikTok, such actions can create significant ripple effects, parti

Shirin Shoushtari, Edward P. Chandler, Ulugbek S. Kamilov

Poisson denoising plays a central role in photon-limited imaging applications such as microscopy, astronomy, and medical imaging. It is common to train deep learning models for denoising using the mean-squared error (MSE) loss, which corresponds to computing the posterior mean $\mathbb{E}[x \mid y]$. When the noise is Gaussian, Tweedie's formula enables appr

Shijie Sun, Jiaqin Xu, Minquan Zhou, Shenzhe Xu

The redshifted 21 cm line, arising from neutral hydrogen, offers a unique probe into the intergalactic medium and the first stars and galaxies formed in the early universe. However, detecting this signal is a challenging task because of artificial radio-frequency interference (RFI) and systematic errors such as ground effects. The interior of the Antarctic c

Aman Singh, Deepak Kapa, Suryank Joshi, Shishir Kolathaya

The optimal design of robotic actuators is a critical area of research, yet limited attention has been given to optimizing gearbox parameters and automating actuator CAD. This paper introduces COMPAct: Computational Optimization and Automated Modular Design of Planetary Actuators, a framework that systematically identifies optimal gearbox parameters for a gi

Simone Fischer-Hübner, Leonardo A. Martucci, Lejla Islami, Ala Sarah Alaqra

A rapidly growing number of cybersecurity threats and incidents demands that Swedish organisations increase their efforts to improve their cybersecurity capacities. This paper presents results from interviews and a prior survey with key representatives from enterprises and public sector organisations in the Swedish region of V\"armland in Inner Scandinavia,

Betony Adams, Francesco Petruccione

Over two decades ago, Bruce Kane proposed that spin-half phosphorus nuclei embedded in a spin-zero silicon substrate could serve as a viable platform for spin-based quantum computing. These nuclear spins exhibit remarkably long coherence times, making them ideal candidates for qubits. Despite this advantage, practical realisation of spin quantum computing re

Binyu Yang, Jinxiao Du, Junlin He, Shi An

The spatial-temporal imbalance between supply and demand in shared micro-mobility services often leads to observed demand being censored, resulting in incomplete records of the underlying real demand. This phenomenon undermines the reliability of the collected demand data and hampers downstream applications such as demand forecasting, fleet management, and m

Kalin Dimitrov

Pingmark defines a universal textual protocol for expressing spatial context through a minimal symbol: !@. Rather than embedding coordinates or using proprietary map links, Pingmark introduces a semantic trigger that compliant client applications interpret to generate a standardized resolver link of the form https://pingmark.me/lat/lon/[timestamp]. This allo

Abhishek Anand, Matthias C. Caro, Ari Karchmer, Saachi Mutreja

Quantum learning from remotely accessed quantum compute and data must address two key challenges: verifying the correctness of data and ensuring the privacy of the learner's data-collection strategies and resulting conclusions. The covert (verifiable) learning model of Canetti and Karchmer (TCC 2021) provides a framework for endowing classical learning algor

Alexandra Souly, Javier Rando, Ed Chapman, Xander Davies

Poisoning attacks can compromise the safety of large language models (LLMs) by injecting malicious documents into their training data. Existing work has studied pretraining poisoning assuming adversaries control a percentage of the training corpus. However, for large models, even small percentages translate to impractically large amounts of data. This work d

Soroosh Tayebi Arasteh, Mina Shaigan, Christiane Kuhl, Jakob Nikolas Kather

Self-supervised learning (SSL) has improved visual representation learning, but its value in chest radiography remains uncertain. DINOv3 extends earlier SSL models through Gram-anchored self-distillation and explicit high-resolution adaptation. Whether these changes improve transfer learning for chest radiograph classification has not been established. We be

Yihao Zhi, Chenghong Li, Hongjie Liao, Xihe Yang

Recent breakthroughs in video generation, powered by large-scale datasets and diffusion techniques, have shown that video diffusion models can function as implicit 4D novel view synthesizers. Nevertheless, current methods primarily concentrate on redirecting camera trajectory within the front view while struggling to generate 360-degree viewpoint changes. In

Junjie Li, Fazle Rabbi, Bo Yang, Song Wang

Although Large Language Models (LLMs) show promising solutions to automated code generation, they often produce insecure code that threatens software security. Current approaches (e.g., SafeCoder) to improve secure code generation are limited by small, imbalanced instruction-tuning datasets. In this work, we present Secure-Instruct, a novel pipeline that aut

Anand Dewansingh, Abigail Deaton, Cortland Bergman, Hengzhou Liu

A liquid-core (LiCo) dye laser was demonstrated using Rhodamine B (RhB) dissolved in glycerol as the gain medium and fluorinated ethylene propylene (FEP) tubing as the waveguide. Photoluminescence and amplified spontaneous emission (ASE) studies identified optimal RhB concentrations of 0.1 wt.% and 0.3 wt.% for low-threshold laser operation. Laser emission w

Tim D. Pearce, Torsten Löhne, Alexander V. Krivov

A key challenge in debris-disc science is that we do not know the masses of debris discs, nor the sizes of the largest debris bodies. This is because modern observations can only detect objects up to centimetre sizes, whilst larger planetesimals, which dominate disc mass, remain hidden. We must therefore use other arguments, such as dynamics, to indirectly i

Alessio Catanzaro, Diego Garlaschelli, Subodh P. Patil

Random graphs offer a useful mathematical representation of a variety of real world complex networks. Exponential random graphs, for example, are particularly suited towards generating random graphs constrained to have specified statistical moments. In this investigation, we elaborate on a generalization of the former where link probabilities are conditioned

Santiago Mazuelas

Methods for split conformal prediction leverage calibration samples to transform any prediction rule into a set-prediction rule that complies with a target coverage probability. Existing methods provide remarkably strong performance guarantees with minimal computational costs. However, they require to use calibration samples composed by labeled examples diff

Yichuan Zhang, Liangyuting Zhang, Xuning Hu, Yong Yue

Gaze input, as a modality inherently conveying user intent, offers intuitive and immersive experiences in extended reality (XR). With eye-tracking now being a standard feature in modern XR headsets, gaze has been extensively applied to tasks such as selection, text entry, and object manipulation. However, gaze based navigation despite being a fundamental int

Junkai Zeng, Xiu-Hao Deng

Noise is ubiquitous in quantum systems and is a major obstacle for the advancement of quantum information science. Noise-robust quantum control achieves high-fidelity operations by engineering the evolution path so that first-order noise contributions cancel at the final time. Such dynamical error correction typically incurs a time overhead beyond standard q

Patrick Peixuan Ye, Chen Shani, Ellen Vitercik

We introduce Bridged Clustering, a semi-supervised framework to learn predictors from any unpaired input $X$ and output $Y$ dataset. Our method first clusters $X$ and $Y$ independently, then learns a sparse, interpretable bridge between clusters using only a few paired examples. At inference, a new input $x$ is assigned to its nearest input cluster, and the

Yi Han, Enshen Zhou, Shanyu Rong, Jingkun An

Vision-Language Models (VLMs) have shown remarkable capabilities in spatial reasoning, yet they remain fundamentally limited to qualitative precision and lack the computational precision required for real-world robotics. Current approaches fail to leverage metric cues from depth sensors and camera calibration, instead reducing geometric problems to pattern r

Sareum Kim, Josie Hughes

Soft robotics increasingly relies on smart materials and innovative structures, with bistable tape springs emerging as a promising option. These structures exhibit intriguing dynamic behaviors, such as oscillation, due to their inherent bistability. This paper explores the high-speed linear amplification of motion achieved through the excitation of a looped

Masahiro Kato, Kentaro Baba, Hibiki Kaibuchi, Ryo Inokuchi

Portfolio optimization is a critical task in investment. Most existing portfolio optimization methods require information on the distribution of returns of the assets that make up the portfolio. However, such distribution information is usually unknown to investors. Various methods have been proposed to estimate distribution information, but their accuracy g

Zhaokang Liang, Shuyang Zhuang, Xiaoran Jiao, Weian Mao

This paper introduces the Single-Cell Perturbation Prediction Diffusion Model (scPPDM), the first diffusion-based framework for single-cell drug-response prediction from scRNA-seq data. scPPDM couples two condition channels, pre-perturbation state and drug with dose, in a unified latent space via non-concatenative GD-Attn. During inference, factorized classi

Adithya Sriram, Vedika Khemani, Benedikt Placke

Optimal constructions of classical LDPC codes can be obtained by choosing the Tanner graph uniformly at random among biregular graphs. We introduce a class of codes that we call ``diffusion codes'', defined by placing each edge connecting bits and checks on some graph, and acting on that graph with a random SWAP network. By tuning the depth of the SWAP netwo

Imry Ziv, Nur Lan, Emmanuel Chemla

Are large language models (LLMs) sensitive to the distinction between humanly possible and impossible languages? This question was recently used in a broader debate on whether LLMs and humans share the same innate learning biases. Previous work has answered it in the positive by comparing LLM learning curves on existing language datasets and on "impossible"

Yong-En Tian, Yu-Chien Tang, An-Zi Yen, Wen-Chih Peng

Aspect-based summarization has attracted significant attention for its ability to generate more fine-grained and user-aligned summaries. While most existing approaches assume a set of predefined aspects as input, real-world scenarios often present challenges where these given aspects may be incomplete, irrelevant, or entirely missing from the document. Users

Yixiang Zhang, Xinhao Deng, Zhongyi Gu, Yihao Chen

Large Language Models (LLMs) are increasingly deployed as agents that orchestrate tasks and integrate external tools to execute complex workflows. We demonstrate that these interactive behaviors leave distinctive fingerprints in encrypted traffic exchanged between users and LLM agents. By analyzing traffic patterns associated with agent workflows and tool in

Han Wang, Michael J. Williams, Ian Harry, Yi-Ming Hu

Space-based gravitational-wave observatories will detect the early inspiral of stellar-mass binary black holes and can track their eccentricity evolution. However, untargeted searches in the space band are computationally demanding and require relatively high detection thresholds (signal-to-noise ratio $\sim 15$). Information from ground-based detections can

Md Tawkat Islam Khondaker, Julia Harrington, Shady Shehata

Recent advancements in large language models (LLMs) have significantly transformed medical systems. However, their potential within specialized domains such as nursing remains largely underexplored. In this work, we introduce NurseLLM, the first nursing-specialized LLM tailored for multiple choice question-answering (MCQ) tasks. We develop a multi-stage data

Tianshi Zheng, Kelvin Kiu-Wai Tam, Newt Hue-Nam K. Nguyen, Baixuan Xu

Large language models are emerging as powerful tools for scientific law discovery, a foundational challenge in AI-driven science. However, existing benchmarks for this task suffer from a fundamental methodological trilemma, forcing a trade-off between scientific relevance, scalability, and resistance to memorization. Furthermore, they oversimplify discovery

Rishabh Das. Aaron Werth, Tommy Morris

Industrial control system (ICS) operations use trusted endpoints like human machine interfaces (HMIs) and workstations to relay commands to programmable logic controllers (PLCs). Because most PLCs lack layered defenses, compromise of a trusted endpoint can drive unsafe actuator commands and risk safety-critical operation. This research presents an embedded i

Yike Zhao, Simin Guo, Ziqing Yang, Shifan Han

The reasoning capabilities of Large Language Models (LLMs) play a critical role in many downstream tasks, yet depend strongly on the quality of training data. Despite various proposed data construction methods, their practical utility in real-world pipelines remains underexplored. In this work, we conduct a comprehensive analysis of open-source datasets and

Jingfei Huang, Han Tu

The ascension of social media platforms has transformed our understanding of urban environments, giving rise to nuanced variations in sentiment reaction embedded within human perception and opinion, and challenging existing multidimensional sentiment analysis approaches in urban studies. This study presents novel methodologies for identifying and elucidating

Arnaud Bodin, Christian Drouin

How do you find the integer solutions of a polynomial equation modulo an integer?

Lekang Jiang, Wenjun Sun, Stephan Goetz

Hierarchical text classification (HTC) assigns documents to multiple levels of a pre-defined taxonomy. Automated patent subject classification represents one of the hardest HTC scenarios because of domain knowledge difficulty and a huge number of labels. Prior approaches only output a flat label set, which offers little insight into the reason behind predict

Yali Kedem, Bimalendu Mahapatra, Evgeniy Boyko

Viscoelastic fluid flows in narrow non-uniform geometries are ubiquitous in various engineering applications and physiological flow systems. For such flows, one of the key interests is understanding how fluid viscoelasticity affects the flow rate-pressure drop relation, which remains not fully understood. We analyze the flow of the Oldroyd-B fluid in slowly

Tommy Rodrigues, Hervé Bouy, Sean N. Raymond, Eduardo L. Martín

Free-floating planetary-mass objects (FFPs) have been detected through direct imaging in several young, nearby star-forming regions. The properties of circumstellar disks around these objects may provide a valuable probe into their origin but are currently limited by the small sample sizes explored. We aim to perform a statistical study of the occurrence of

Junqiao Lin

In 2020, a landmark result by Ji, Natarajan, Vidick, Wright, and Yuen showed that MIP*, the class of languages that can be decided by a classical verifier interacting with multiple computationally unbounded provers sharing entanglement in the tensor product model, is equal to RE. We show that the class MIPco, a complexity class defined similarly to MIP* exce

Ali Norouzifar, Humam Kourani, Marcus Dees, Wil van der Aalst

Process discovery aims to derive process models from event logs, providing insights into operational behavior and forming a foundation for conformance checking and process improvement. However, models derived solely from event data may not accurately reflect the real process, as event logs are often incomplete or affected by noise, and domain knowledge, an i

Yeskendir Koishekenov, Aldo Lipani, Nicola Cancedda

Most efforts to improve the reasoning capabilities of large language models (LLMs) involve either scaling the number of parameters and the size of training data, or scaling inference computation by letting models generate complex chains of thought. Motivated by interpretability studies showing that the crucial computation required for reasoning tasks is conc

Fengze Xie, Xiaozhou Fan, Jacob Schuster, Yisong Yue

Fixed-wing unmanned aerial vehicles (UAVs) offer endurance and efficiency but lack low-speed agility due to highly coupled dynamics. We present an end-to-end sensing-to-control pipeline that combines bio-inspired hardware, physics-informed dynamics learning, and convex control allocation. Measuring airflow on a small airframe is difficult because near-body a

Gwenaël Richomme

The binomial notation (w u) represents the number of occurrences of the word u as a (scattered) subword in w. We first introduce and study possible uses of a geometrical interpretation of (w ab) and (w ba) when a and b are distinct letters. We then study the structure of the 2-binomial equivalence class of a binary word w (two words are 2-binomially equivale

Fermi Ma, Xinyu Tan, John Wright

We study the error correcting properties of Haar random codes, in which a $K$-dimensional code space $\boldsymbol{C} \subseteq \mathbb{C}^N$ is chosen at random from the Haar distribution. Our main result is that Haar random codes can approximately correct errors up to the quantum Hamming bound, meaning that a set of $m$ Pauli errors can be approximately cor

Carolyn Wang, Avriel Epps, Taylor Ferrari, Ra Ames

The abolitionist community faces challenges from both the carceral state and oppressive technologies which, by empowering the ruling class who have the resources to develop artificial intelligence (AI), serve to entrench societal inequities even more deeply. This paper presents a case study in participatory design with transformative and restorative justice

Jaroslav Scheinpflug, Martin Schnabl, Jakub Vošmera

We present simple explicit formulae for the change of the $g$-function, boundary state, boundary spectrum and structure constants between the endpoints of short boundary RG flows at next-to-leading order. The formulae are derived using open string field theory and tested on integrable RG flows between conformal boundary states in unitary Virasoro minimal mod

Kenneth G. Libbrecht

We describe a teaching-lab experiment that applies basic optical spectroscopy to examine the physics of semiconductor diode lasers. By using a low-power visible laser, this experiment is suitable for use in an open lab environment, where students assemble the spectrometer optics themselves as part of the project. A small holographic grating disperses light o

Jonathan J. Chipman, Oleksandr Sverdlov, Diane Uschner

Participants in clinical trials are often viewed as a unique, finite population. Yet, statistical analyses often assume that participants were randomly sampled from a larger population. Under Complete Randomization, Randomization-Based Inference (RBI; a finite population inference) and Analysis of Variance (ANOVA; a random sampling inference) provide asympto

Egor Cherepanov, Alexey K. Kovalev, Aleksandr I. Panov

Real-world robotic agents must act under partial observability and long horizons, where key cues may appear long before they affect decision making. However, most modern approaches rely solely on instantaneous information, without incorporating insights from the past. Standard recurrent or transformer models struggle with retaining and leveraging long-term d

Alexander V. Smirnov, Mao Zeng

FIRE7 is a major update to the FIRE program for integration-by-parts (IBP) reduction of Feynman integrals. A large part of improvements is related to the automatic reduction and reconstruction with the modular arithmetic approach, while the performance of the classical rational polynomial approach is also significantly increased. An improved presolve algorit

Francisco Fernández-Álvarez, José M. M. Senovilla

The existence or absence of gravitational radiation escaping from the spacetime at $\mathscr{J}$ is characterized in the presence of a cosmological constant $\Lambda$ of any sign. To that end, the properties of the asymptotic super-momentum are used. When $\Lambda=0$, the characterization is equivalent to that based on the News tensor. For $\Lambda\neq 0$, i

Arshika Lalan, Rajat Ghosh, Aditya Kolsur, Debojyoti Dutta

Recent work explores agentic inference-time techniques to perform structured, multi-step reasoning. However, stateless inference often struggles on multi-step tasks due to the absence of persistent state. Moreover, task-specific fine-tuning or instruction-tuning often achieve surface-level code generation but remain brittle on tasks requiring deeper reasonin

Sibasish Banerjee, Alexander Hock

Open topological string partition function gives rise to open Gromov-Witten invariants, open Donaldson-Thomas invariants and 3D-5D BPS indices. Utilizing the remodelling conjecture which connects topological recursion and topological string theory, in this paper we study open topological string theory for the subclass of toric Calabi-Yau threefold known as s

Jhon Manuel Portella Delgado, Ankit Goel

This paper presents a control law for stabilization and trajectory tracking of a multicopter subject to safety constraints. The proposed approach guarantees forward invariance of a prescribed safety set while ensuring smooth tracking performance. Unlike conventional control barrier function methods, the constrained control problem is transformed into an unco

Abdulrahman Ibrahim, Mariana Casari Parreira, Aram Akram Mohammed, Hawar Halshoy

Members of the Rumex genus possess allelochemical compounds that vary depending on the plant part and extract concentrations. Therefore, this study aimed to investigate the allelopathic effects of extracts from the roots, stems, and leaves of Rumex azoricus at concentrations of 0%, 25%, 50%, and 100% on the seed germination of a lettuce plant in a laboratory

Jingyu Peng, Jiansen He, Rong Lin

Finite-amplitude low-frequency Alfv\'en waves (AWs) are ubiquitous in space plasmas, where they play a key role in the transport and dissipation of energy, particularly in the heating of ions in the solar corona and solar wind. In this study, we investigate the nonlinear interaction between ions and obliquely propagating AWs. When the wave amplitude and prop

Chenfei Liao, Wensong Wang, Zichen Wen, Xu Zheng

Recent efforts to accelerate inference in Multimodal Large Language Models (MLLMs) have largely focused on visual token compression. The effectiveness of these methods is commonly evaluated by measuring the accuracy drop on existing MLLM benchmarks before and after compression. However, these benchmarks are originally designed to assess general perception an

Paulo R. de Moura, Hugerles S. Silva, Ugo S. Dias, Higo T. P. Silva

This paper studies slow, fast and opportunistic fluid antenna multiple access (FAMA) under the effect of Nakagami-m fading channels, considering the new and realistic spatial blockcorrelation model. Expressions for the outage probability (OP), based on the signal-to-interference ratio (SIR), are derived for slow FAMA. Interestingly, we provide mathematical r

Samuel Joseph Amouyal, Aya Meltzer-Asscher, Jonathan Berant

Large language models (LLMs) that fluently converse with humans are a reality - but do LLMs experience human-like processing difficulties? We systematically compare human and LLM sentence comprehension across seven challenging linguistic structures. We collect sentence comprehension data from humans and five families of state-of-the-art LLMs, varying in size

Lingcheng Kong, Jiateng Wei, Hanzhang Shen, Huan Wang

GPU kernel generation by LLMs has recently experienced rapid development, leveraging test-time scaling and reinforcement learning techniques. However, a key challenge for kernel generation is the scarcity of high-quality data, as most high-quality kernels are proprietary and not open-source. This challenge prevents us from leveraging supervised fine-tuning t

Marco Savarese, Antonio De Blasi, Carmine Zaccagnino, Giacomo Salici

Efficient energy provisioning is a fundamental requirement for modern transportation systems, making refueling path optimization a critical challenge. Existing solutions often focus either on inter-vehicle communication or intra-vehicle monitoring, leveraging Intelligent Transportation Systems, Digital Twins, and Software-Defined Internet of Vehicles with Cl

A. Andrés-Juanes, J. Agustí, R. Sett, E. S. Redchenko

The distribution of entanglement across distant qubits is a central challenge for the operation of scalable quantum computers and large-scale quantum networks. Existing approaches rely on deterministic state transfer schemes or probabilistic protocols that require active control or measurement and postselection. Here we demonstrate an alternative, fully auto

Vincent Bansaye, Alexandre Bertolino, Ayman Moussa

We study the stability of non-conservative deterministic cross diffusion models and prove that they are approximated by stochastic population models when the populations become locally large. In this model, the individuals of two species move, reproduce and die with rates sensitive to the local densities of the two species. Quantitative estimates are given a

Simon Crawford, Susan J. Sierra

Let $B = \Bbbk_q[u,v]^{C_{n+1}}$ be a Type $\mathbb{A}_n$ quantum Kleinian singularity, which is an example of a noncommutative surface singularity. This singularity is known to have a noncommutative quasi-crepant resolution $\Lambda$, which is an "algebraic" resolution of $B$. We construct a category $\mathcal{X}$ which serves as a "geometric" resolution of

Antti Koskela, Mohamed Seif, H. Vincent Poor, Andrea J. Goldsmith

We study spectral graph clustering under edge differential privacy. We propose a matrix shuffling mechanism that combines randomized edge flipping with a random permutation of the adjacency matrix. While edge flipping alone provides only a constant $\varepsilon$ guarantee as the graph grows, shuffling amplifies privacy so that the effective $\varepsilon$ ten

Jiahang Liu, Yunpeng Qi, Jiazhao Zhang, Minghan Li

Embodied Visual Tracking (EVT) is a fundamental ability that underpins practical applications, such as companion robots, guidance robots and service assistants, where continuously following moving targets is essential. Recent advances have enabled language-guided tracking in complex and unstructured scenes. However, existing approaches lack explicit spatial

Mariona Jaramillo-Civill, Peng Wu, Pau Closas

Clustered Federated Learning (CFL) improves performance under non-IID client heterogeneity by clustering clients and training one model per cluster, thereby balancing between a global model and fully personalized models. However, most CFL methods require the number of clusters K to be fixed a priori, which is impractical when the latent structure is unknown.

Yushan Xie, Huanyuan Shan, Yiping Shu, Nan Li

Strongly lensed supernovae (glSNe) provide a powerful, independent method to measure the Hubble constant, $H_{0}$, through time delays between their multiple images. The accuracy of this measurement depends critically on both the precision of time delay estimation and the robustness of lens modeling. In many current cluster-scale modeling algorithms, all mul

Sabina Sagynbayeva, Will M. Farr

Starspots trace stellar magnetic activity and influence both stellar evolution and exoplanet characterization. While occultation-based spot analyses have been applied to individual systems, comparative studies remain limited. We apply the StarryStarryProcess Bayesian surface-mapping framework to archival Kepler light curves of two planet hosts, Kepler-63 and

Sarah Cechnicka, Matthew Baugh, Weitong Zhang, Mischa Dombrowski

Recent advances in Diffusion Probabilistic Models (DPMs) have set new standards in high-quality image synthesis. Yet, controlled generation remains challenging, particularly in sensitive areas such as medical imaging. Medical images feature inherent structure such as consistent spatial arrangement, shape or texture, all of which are critical for diagnosis. H

Yaxin Wang, Linxiang Zhou, Tianfeng Feng, Hanlin Nie

A major challenge in quantum communication is addressing the negative effects of noise on channel capacity, especially for completely depolarizing channels, where information transmission is inherently impossible. The concept of indefinite causal order provides a promising solution by allowing control over the sequence in which channels are applied. We exper

Jan Fiszer, Dominika Ciupek, Maciej Malawski

Deep learning (DL) has been increasingly applied in medical imaging, however, it requires large amounts of data, which raises many challenges related to data privacy, storage, and transfer. Federated learning (FL) is a training paradigm that overcomes these issues, though its effectiveness may be reduced when dealing with non-independent and identically dist

Yibin Guo, Manuel Schneider, Takis Angelides, Karl Jansen

We present a framework for preparing quantum states from matrix product states (MPS) with open and periodic boundary conditions on quantum devices. The MPS tensors are mapped to unitary gates, which are subsequently decomposed into native gates on quantum hardware. States with periodic boundary conditions (pbc) can be represented efficiently as quantum circu

Thomas D. Cohen

The sign problem in numerical calculations of the QCD Euclidean space path integral of QCD with a chemical potential vanishes if the chemical potential is imaginary. Moreover, calculations of the partition function with imaginary chemical potentials are equivalent to calculations with Lagrange multipliers enforcing the current density. At zero temperature, L

Alireza Khalilian, Jie Fan, Mehdi Sh. Yeganeh, Joe Lo

We demonstrate a CMOS-compatible silicon-rich nitride metalens array for visible microscopy at 660 nm. Three co-planar elements provide numerical apertures of 0.54, 0.92, and 0.97, enabling within-sample NA benchmarking without objective swaps. Imaging of annular cell monolayers labeled in the AF647 ZO-1 channel shows progressive sharpening of junctional edg

Jason C. Hsu

In assessing Overall Survival (OS) in oncology studies, it is essential for the efficacy measure to be Logic-respecting, for otherwise patients may be incorrectly targeted. This paper explains, while Time Ratio (TR) is Logic-respecting, Hazard Ratio (HR) is not Logic-respecting. With Time Ratio (TR) being recommended, a smooth transitioning strategy is sugge

Tobias Rippchen, Ludovico Lami, Gerardo Adesso, Mario Berta

Sending quantum information reliably over long distances is a central challenge in quantum technology in general, and in quantum optics in particular, since most quantum communication relies on optical fibres or free-space links. Here, we address this problem by shifting the focus from the quantity of information sent to the quality of the transmission, i.e.

Yinong Chen, Wenchi Cheng, Jingqing Wang, Xiao Zheng

In emergency wireless communications (EWC) scenarios, ensuring reliable, flexible, and high-rate transmission while simultaneously maintaining seamless coverage and rapid response capabilities presents a critical technical challenge. To this end, satellite-aerial-ground integrated network (SAGIN) has emerged as a promising solution due to its comprehensive t

Dongki Jung, Jaehoon Choi, Yonghan Lee, Sungmin Eum

Monocular 3D foundation models offer an extensible solution for perception tasks, making them attractive for broader 3D vision applications. In this paper, we propose MoRe, a training-free Monocular Geometry Refinement method designed to improve cross-view consistency and achieve scale alignment. To induce inter-frame relationships, our method employs featur