October 2025 arXiv papers — page 225

Zachary Ezetta, Wu-chang Feng

Agentic AI is transforming security by automating many tasks being performed manually. While initial agentic approaches employed a monolithic architecture, the Model-Context-Protocol has now enabled a remote-procedure call (RPC) paradigm to agentic applications, allowing for the flexible construction and composition of multi-function agents. This paper descr

Yaxin Shi, Abhishek Gupta, Ying Wu, Melvin Wong

Generative AI (GenAI) has achieved remarkable success across a range of domains, but its capabilities remain constrained to statistical models of finite training sets and learning based on local gradient signals. This often results in artifacts that are more derivative than genuinely generative. In contrast, Evolutionary Computation (EC) offers a search-driv

Juho Bae, Daegyeong Roh, Han-Lim Choi

This paper presents a data-driven approach for jointly learning a robust full-state observer and its robustness certificate for systems with unknown dynamics. Leveraging incremental input-to-state stability (delta ISS) notions, we jointly learn a delta ISS Lyapunov function that serves as the robustness certificate and prove practical convergence of the esti

Ruitao Wu, Yifan Zhao, Guangyao Chen, Jia Li

Few-Shot Class-Incremental Learning (FSCIL) challenges models to sequentially learn new classes from minimal examples without forgetting prior knowledge, a task complicated by the stability-plasticity dilemma and data scarcity. Current FSCIL methods often struggle with generalization due to their reliance on limited datasets. While diffusion models offer a p

Tobi David Olabiyi

We introduce and characterize, on the Banach lattice valued continuous function space, multiplication operators generating strongly continuous multiplication operator semigroups. Our characterization is the generalization of known results for the scalar-valued continuous functions $C_0(\Omega)$ vanishing at infinity, on a locally compact (Hausdorff) space $\

Mattia Scardecchia

Recent advances in self-supervised learning (SSL) have made it possible to learn general-purpose visual features that capture both the high-level semantics and the fine-grained spatial structure of images. Most notably, the recent DINOv2 has established a new state of the art by surpassing weakly supervised methods (WSL) like OpenCLIP on most benchmarks. In

Adel Javanmard, Baharan Mirzasoleiman, Vahab Mirrokni

Test-time scaling improves the reasoning capabilities of large language models (LLMs) by allocating extra compute to generate longer Chains-of-Thoughts (CoTs). This enables models to tackle more complex problem by breaking them down into additional steps, backtracking, and correcting mistakes. Despite its strong performance--demonstrated by OpenAI's o1 and D

Yucheng Wang, Mohamed Ragab, Yubo Hou, Zhenghua Chen

Remaining Useful Life (RUL) prediction for turbofan engines plays a vital role in predictive maintenance, ensuring operational safety and efficiency in aviation. Although data-driven approaches using machine learning and deep learning have shown potential, they face challenges such as limited data and distribution shifts caused by varying operating condition

Srinivasan Srimathy

Given a $(0,p)$-mixed characteristic complete discrete valued field $\mathcal{K}$ we define a class of finite field extensions called \emph{pseudo-perfect} extensions such that the natural restriction map on the mod-$p$ Milnor $K$-groups is trivial for all $p\neq 2$. This implies that pseudo-perfect extensions split every element in $H^i(\mathcal{K},\mu_p^{\

Gargi Sen, Debaprasad Maity, Santabrata Das

We study low angular momentum, advective accretion flows around a Kerr black hole within the framework of general relativistic magnetohydrodynamics (GRMHD) in the steady state. By solving the full set of GRMHD equations, we aim to provide a comprehensive understanding of the behavior of magnetized plasma in the strong gravity regime near a rotating black hol

Wei-Lung Mao, Chun-Chi Wang, Po-Heng Chou, Kai-Chun Liu

The rising aging population has increased the importance of fall detection (FD) systems as an assistive technology, where deep learning techniques are widely applied to enhance accuracy. FD systems typically use edge devices (EDs) worn by individuals to collect real-time data, which are transmitted to a cloud center (CC) or processed locally. However, this a

Yuguang Liu, Yiyun Miao, Luyao Xia

Multi-task learning (MTL) has emerged as a successful strategy in industrial-scale recommender systems, offering significant advantages such as capturing diverse users' interests and accurately detecting different behaviors like ``click" or ``dwell time". However, negative transfer and the seesaw phenomenon pose challenges to MTL models due to the complex an

S. E. Chorfi, F. Et-tahri, L. Maniar, M. Yamamoto

We investigate forward and backward problems associated with abstract time-fractional Schr\"odinger equations $\mathrm{i}^\nu \partial_t^\alpha u(t) + A u(t)=0$, $\alpha \in (0,1)\cup (1,2)$ and $\nu\in\{1,\alpha\}$, where $A$ is a self-adjoint operator with compact resolvent on a Hilbert space $H$. This kind of equation, which incorporates the Caputo time-f

Shafeef Omar, Majid Khadiv

We present a unified framework for multi-task locomotion and manipulation policy learning grounded in a contact-explicit representation. Instead of designing different policies for different tasks, our approach unifies the definition of a task through a sequence of contact goals--desired contact positions, timings, and active end-effectors. This enables leve

Alexander V. Mantzaris

This paper asks whether the Hierarchical Reasoning Model (HRM) with the two Transformer-style modules $(f_L,f_H)$, one step (DEQ-style) training, deep supervision, Rotary Position Embeddings, and RMSNorm can serve as a practical image classifier. It is evaluated on MNIST, CIFAR-10, and CIFAR-100 under a deliberately raw regime: no data augmentation, identica

Sina Alemohammad, Zhangyang Wang, Richard G. Baraniuk

Scaling generative AI models is bottlenecked by the scarcity of high-quality training data. The ease of synthesizing from a generative model suggests using (unverified) synthetic data to augment a limited corpus of real data for the purpose of fine-tuning in the hope of improving performance. Unfortunately, however, the resulting positive feedback loop leads

Yong Oh Lee, Byeonghun Bang, Sejun Oh

As online education platforms continue to expand, there is a growing need for assessment methods that not only measure answer accuracy but also capture the depth of students' cognitive processes in alignment with curriculum objectives. This study proposes and evaluates a rubric-based assessment framework powered by a large language model (LLM) for measuring

Tuo Wu, Kwai-Man Luk, Jie Tang, Kai-Kit Wong

Fluid antenna systems (FAS) are emerging as a transformative enabler for sixth-generation (6G) wireless communications, providing unprecedented spatial diversity through dynamic reconfiguration of antenna ports. However, the inherent spatial correlation among ports poses significant challenges for accurate analysis. Conventional models such as Jakes are anal

William Harold Nesse, Cooper John Hutchinson

We present a result concerning the mean value of orbits emerging from Hopf bifurcations. We then apply this result to identify a new phenomenon termed {\it oscillation-induced gain modulation}. A Hopf bifurcation of a system $\dot{x} = f(x; \alpha)$ with parameter $\alpha$ is characterized by the emergence of a limit cycle with an amplitude increasing from z

Kehinde O. Aina, Sehoon Ha

We address the challenge of coordinating multiple robots in narrow and confined environments, where congestion and interference often hinder collective task performance. Drawing inspiration from insect colonies, which achieve robust coordination through stigmergy -- modifying and interpreting environmental traces -- we propose a Stigmergic Multi-Agent Deep R

Faliu Yi, Sherif Abdelfattah, Wei Huang, Adrian Brown

Manual identification of visual bugs in video games is a resource-intensive and costly process, often demanding specialized domain knowledge. While supervised visual bug detection models offer a promising solution, their reliance on extensive labeled datasets presents a significant challenge due to the infrequent occurrence of such bugs. To overcome this lim

Marlen Kolbe, Jose Abraham Torres Alavez, Ruth Mottram, Marwan Katurji

Since about 2000, the total mass of the Antarctic Ice Sheet (AIS) has declined at a near-linear rate, increasing global sea levels. Since 2016, however, satellite gravimetry data reveal a slowdown in net AIS mass loss and a net mass gain since 2020, despite increases in dynamically-driven ice loss by discharge from outlet glaciers. Here we use a suite of rea

Anvith Pabba, Simin Chen, Alex Mathai, Anindya Chakraborty

Large Language Models (LLMs) have recently shown strong potential in automatic program repair (APR), especially in repository-level settings where the goal is to generate patches based on natural language issue descriptions, large codebases, and regression tests. However, despite their promise, current LLM-based APR techniques often struggle to produce corre

Yujie Chen, Antik Chakraborty, Anindya Bhadra

Bayesian inference for doubly-intractable pairwise exponential graphical models typically involves variations of the exchange algorithm or approximate Markov chain Monte Carlo (MCMC) samplers. However, existing methods for both classes of algorithms require either perfect samplers or sequential samplers for complex models, which are often either not availabl

Patricia Lan, Sherry S. Huang, Chitresh Bhushan, Xinzeng Wang

The purpose of this study is to propose a brain tissue-selective, optimized slice-by-slice B0 field shimming for high-resolution brain diffusion MRI. We incorporated actual gradient fields of X, Y, and Z gradient coils in the calculation of the shimming coefficients in dynamic slice-by-slice B0 field shimming to minimize B0 field inhomogeneity (i.e., Delta B

Vinaya K. Kavatamane, Natalia C. Carvalho, Ahmas El-Hamamsy, Elham Zohari

A key challenge for quantum photonic technologies based on spin qubits is the creation of optically active defects in photonic resonators. Several of the most promising defects for quantum applications are hosted in diamond, and are commonly created through ion implantation and annealing at high temperatures and high vacuum. However, the impact of annealing

R. B. dos Santos, A. C Vieira, R. F. D. N. Vieira

$G$ be a finite group and $A$ a $G$-graded algebra over a field $F$ of characteristic zero. We characterize the varieties of $G$-graded algebras such that the multiplicities $m_{\langle \lambda \rangle}$ appering in the $\langle n \rangle $-cocharacters of $A$ are bounded by a constant, in terms of $G$-identities. If $A$ is endowed with a graded involution $

Lin Yao, Da Yang, James P. C. Duncan, Ashesh Chattopadhyay

The Madden-Julian oscillation (MJO) is a planetary-scale, intraseasonal tropical rainfall phenomenon crucial for global weather and climate; however, its dynamics and predictability remain poorly understood. Here, we leverage deep learning (DL) to investigate the sources of MJO predictability, motivated by a central difference in MJO theories: which spatial

John Krueger, Justin Tatch Moore

We prove that for every Aronzsajn line A and every Countryman line C, there is a proper forcing extension in which A contains an isomorphic copy of either C or its converse C*. As a corollary, we obtain answers to several related questions asked by the second author in the literature: if there is an inaccessible cardinal, then there is a proper forcing exten

Aaron Burnham-Schmidt, Nicolle González

We study, characterize, and enumerate the admissible pinnacle sets of nonexceptional complex reflection groups $G(m,p,n)$, which include all generalized symmetric groups $\mathbb{Z}_m \wr S_n$ as special cases. This generalizes the work of Davis--Nelson--Petersen--Tenner for symmetric groups $S_n$ and Gonz\'alez--Harris--Rojas Kirby--Smit Vega Garcia--Tenner

Alexsandr Lednov

We consider, on a temporal star graph, the problem of optimal damping a control system is considered for a generalized pantograph equation, which is a neutral-type equation with a time-proportional delay. The delay in the system propagates through the internal vertex of the graph. We study the variational problem of minimizing the energy functional, taking i

Haoran Li, Chenhan Xiao, Muhao Guo, Yang Weng

Learning dynamics is essential for model-based control and Reinforcement Learning in engineering systems, such as robotics and power systems. However, limited system measurements, such as those from low-resolution sensors, demand sample-efficient learning. Symmetry provides a powerful inductive bias by characterizing equivariant relations in system states to

Tim Bary, Tiffanie Godelaine, Axel Abels, Benoît Macq

Accurate ground truth estimation in medical screening programs often relies on coalitions of experts and peer second opinions. Algorithms that efficiently aggregate noisy annotations can enhance screening workflows, particularly when data arrive continuously and expert proficiency is initially unknown. However, existing algorithms do not meet the requirement

Hanlong Chen, Cagatay Isil, Tianyi Gan, Mona Jarrahi

Image projection systems must be efficient in data storage, computation and transmission while maintaining a large space-bandwidth-product (SBP) at their output. Here, we introduce a hybrid image projection system that achieves extended depth-of-field (DOF) with improved resolution, combining a convolutional neural network (CNN)-based digital encoder with an

Farough Parvizi, Soma Heydari, Milad Solbi, Kayoomars Karami

Within the framework of loop quantum cosmology (LQC), we investigate the effect of inverse volume corrections on the low scale spontaneously broken supersymmetric (SB SUSY) and exponential inflationary potentials. The LQC modifications to the Friedmann equations and cosmological perturbation parameters are employed to assess the observational viability of th

Sarah C. Lotspeich, Abbey Collins, Brian J. Wells, Ashish K. Khanna

Objective: Electronic health records (EHR) data are prone to missingness and errors. Previously, we devised an "enriched" chart review protocol where a "roadmap" of auxiliary diagnoses (anchors) was used to recover missing values in EHR data (e.g., a diagnosis of impaired glycemic control might imply that a missing hemoglobin A1c value would

Liya Huang, Georgios Tzounas

This letter introduces a formal duality between discrete-time and quantized-state numerical methods. We interpret quantized state system (QSS) methods as integration schemes applied to a dual form of the system model, where time is seen as a state-dependent variable. This perspective enables the definition of novel QSS-based schemes inspired by classical tim

Raaghav Malik, Satpreet H. Singh, Sonja Johnson-Yu, Nathan Wu

Larval zebrafish hunting provides a tractable setting to study how ecological and energetic constraints shape adaptive behavior in both biological brains and artificial agents. Here we develop a minimal agent-based model, training recurrent policies with deep reinforcement learning in a bout-based zebrafish simulator. Despite its simplicity, the model reprod

Suman Majumder, Indranil Sahoo

Per- and polyfluoroalkyl substances (PFAS) are persistent environmental pollutants of major public health concern due to their resistance to degradation, widespread presence, and potential health risks. Analyzing PFAS in groundwater is challenging due to left-censoring and strong spatial dependence. Although PFAS levels are influenced by sociodemographic, in

Hiroyuki Tezuka, Yuki Sato

Partial differential equations (PDEs) are central to computational electromagnetics (CEM) and photonic design, but classical solvers face high costs for large or complex structures. Quantum Hamiltonian simulation provides a framework to encode PDEs into unitary time evolution and has potential for scalable electromagnetic analysis. We formulate Maxwell's

William L. Blair

We define Hardy classes of bicomplex-valued functions on the complex unit disk which solve bicomplex versions of the Beltrami and related equations. Using representations in terms of their complex-valued counterparts, we show these bicomplex-valued functions recover the boundary behavior associated with the classic holomorphic Hardy spaces. This work general

Alexandre Mösching, Housen Li, Axel Munk

Hidden Markov models (HMMs) are characterized by an unobservable Markov chain and an observable process -- a noisy version of the hidden chain. Decoding the original signal from the noisy observations is one of the main goals in nearly all HMM based data analyses. Existing decoding algorithms such as Viterbi and the pointwise maximum a posteriori (PMAP) algo

Ikram Belmadani, Parisa Nazari Hashemi, Thomas Sebbag, Benoit Favre

This work presents our participation in the EvalLLM 2025 challenge on biomedical Named Entity Recognition (NER) and health event extraction in French (few-shot setting). For NER, we propose three approaches combining large language models (LLMs), annotation guidelines, synthetic data, and post-processing: (1) in-context learning (ICL) with GPT-4.1, incorpora

Bongseok Kim, Jiahao Zhang, Guang Lin

Deep learning has gained attention for solving PDEs, but the black-box nature of neural networks hinders precise enforcement of boundary conditions. To address this, we propose a boundary condition-guaranteed evolutionary Kolmogorov-Arnold Network (KAN) with radial basis functions (BEKAN). In BEKAN, we propose three distinct and combinable approaches for inc

Rhine Samajdar, Haonan Zhou, R. N. Bhatt

High-spin states hold significant promise for classical and quantum information storage and emerging magnetic memory technologies. Here, we present a systematic framework for engineering such high-spin magnetic states in dopant clusters formed from substitutional impurities in semiconductors. In single-valley materials such as gallium arsenide, impurity stat

Mehmet Onurcan Kaya, Desmond Elliott, Dim P. Papadopoulos

Small Vision-Language Models (VLMs) provide a computationally efficient alternative to larger models, at the cost of weaker generalization abilities and downstream task performance. These shortcomings could be addressed by test-time scaling techniques, but existing methods are typically computationally demanding, contradicting the resource-efficient design g

Lucas Qiu, Daniel Stockel, James Kraynik, Katie Lau

This study investigates the impact of time since fire on bird community composition in Southern California chaparral ecosystems. We surveyed avian richness and abundance across 14 sites representing a 0 to 25 year post-fire chronosequence in Los Angeles County. Sites burned within the last five years supported fewer species, primarily dominated by generalist

Subhrajit Sinha, Parvathi Kooloth

We propose a novel framework to analyze symmetry breaking in dynamical systems through the lens of entropy and information transfer. Information transfer quantifies the directional exchange of entropy between observables, allowing us to anticipate the onset of symmetry breaking. For local symmetry breakings, namely, local Spontaneous Symmetry Breaking (SSB)

Burak Karabulut, Carlo Manna, Chris Develder

Fault detection in power distribution grids is critical for ensuring system reliability and preventing costly outages. Moreover, fault detection methodologies should remain robust to evolving grid topologies caused by factors such as reconfigurations, equipment failures, and Distributed Energy Resource (DER) integration. Current data-driven state-of-the-art

Mayimunah Nagayi, Alice Khan, Tamryn Frank, Rina Swart

This study evaluates four open-source Optical Character Recognition (OCR) systems which are Tesseract, EasyOCR, PaddleOCR, and TrOCR on real world food packaging images. The aim is to assess their ability to extract ingredient lists and nutrition facts panels. Accurate OCR for packaging is important for compliance and nutrition monitoring but is challenging

Mohammad Mohaiminul Islam, Thijs P. Kuipers, Sharvaree Vadgama, Coen de Vente

Generative models for sequential data often struggle with sparsely sampled and high-dimensional trajectories, typically reducing the learning of dynamics to pairwise transitions. We propose Interpolative Multi-Marginal Flow Matching (IMMFM), a framework that learns continuous stochastic dynamics jointly consistent with multiple observed time points. IMMFM em

Claudia Takyi Ankomah, Livingstone Eli Ayivor, Ireneaus Nyame, Leslie Wambo

Brain tumors, particularly gliomas, pose significant chall-enges due to their complex growth patterns, infiltrative nature, and the variability in brain structure across individuals, which makes accurate diagnosis and monitoring difficult. Deep learning models have been developed to accurately delineate these tumors. However, most of these models were traine

Fatmazohra Rezkellah, Ramzi Dakhmouche

With the increasing adoption of Large Language Models (LLMs), more customization is needed to ensure privacy-preserving and safe generation. We address this objective from two critical aspects: unlearning of sensitive information and robustness to jail-breaking attacks. We investigate various constrained optimization formulations that address both aspects in

Ashwin Prabu, Nhat Thanh Tran, Guofa Zhou, Jack Xin

A variety of models have been developed to forecast dengue cases to date. However, it remains a challenge to predict major dengue outbreaks that need timely public warnings the most. In this paper, we introduce CrossLag, an environmentally informed attention that allows for the incorporation of lagging endogenous signals behind the significant events in the

Cory Brynds, Parker McLeod, Lauren Caccamise, Asmita Pal

Privacy-preserving machine learning has become an important long-term pursuit in this era of artificial intelligence (AI). Fully Homomorphic Encryption (FHE) is a uniquely promising solution, offering provable privacy and security guarantees. Unfortunately, computational cost is impeding its mass adoption. Modern solutions are up to six orders of magnitude s

Jithin D. George, Willa Brenneis, Vinod K. Sangwan, Dilara Meli

Electrochemical Impedance Spectroscopy (EIS) is a non-invasive technique widely used for understanding charge transfer and charge transport processes in electrochemical systems and devices. Standard approaches for the interpretation of EIS data involve starting with a hypothetical circuit model for the physical processes in the device based on experience/int

Abhishek Hegade K. R., Charles F. Gammie, Nicolás Yunes

We model the motion of a small compact object on a nearly circular orbit around a spinning supermassive black hole, which is also interacting with a thin equatorial accretion-disk surrounding the latter, through tools from self-force and Hamiltonian perturbation theory. We provide an analytical and relativistically-accurate formalism to calculate the rate of

Adam Filipek

The Transformer architecture has become the de facto standard for Large Language Models (LLMs), demonstrating remarkable capabilities in language understanding and generation. However, its application in conversational AI is fundamentally constrained by its stateless nature and the quadratic computational complexity ($O(L^2)$) with respect to sequence length

Suman Mondal

Introduced by Sheekey in 2016, the study of scattered polynomials over a finite field $\mathbb{F}_{q^n}$ has been increasing regarding the classification of those that are exceptional, i.e., polynomials which are scattered over infinite field extensions, are limited to the cases where their index $t$ is small, or a prime number larger than the q-degree k of

Shen Chang, Renran Tian, Nicole Adams, Nan Kong

Rapid naloxone delivery via drones offers a promising solution for responding to opioid overdose emergencies (OOEs), by extending lifesaving interventions to medically untrained bystanders before emergency medical services (EMS) arrive. Recognizing the critical role of bystander situational awareness (SA) in human-autonomy teaming (HAT), we address a key res

Nicholas Frontiere, J. D. Emberson, Michael Buehlmann, Esteban M. Rangel

Resolving the most fundamental questions in cosmology requires simulations that match the scale, fidelity, and physical complexity demanded by next-generation sky surveys. To achieve the realism needed for this critical scientific partnership, detailed gas dynamics, along with a host of astrophysical effects, must be treated self-consistently with gravity fo

Joseph P. T. Templet, Spencer Fretwell, Andrew Marino, Robin Cantor

The Beryllium Electron capture in Superconducting Tunnel junctions (BeEST) experiment uses superconducting tunnel junction (STJ) sensors to search for physics beyond the standard model (BSM) with recoil spectroscopy of the $\mathbf{^7}$Be EC decay into $\mathbf{^7}$Li. A pulsed UV laser is used to calibrate the STJs throughout the experiment with $\sim$20 me

Peiran Quan, Zifan Gu, Zhuo Zhao, Qin Zhou

Foundation models (FMs) have transformed computational pathology by providing powerful, general-purpose feature extractors. However, adapting and benchmarking individual FMs for specific diagnostic tasks is often time-consuming and resource-intensive, especially given their scale and diversity. To address this challenge, we introduce Group-Aggregative Select

Jing Guo, Fuliang Lu, Heping Zhang

A connected graph $G$ with a perfect matching is said to be $k$-extendable for integers $k$, $1 \leq k\leq \frac{|V(G)|}{2}-1$, if any matching in $G$ of size $k$ is contained in a perfect matching of $G$. A $k$-extendable graph is minimal if the deletion of any edge results in a graph that is not $k$-extendable. In 1994, Plummer proved that every $k$-extend

Hasibur Rahman, Hanan Salam

Although large language models (LLMs) are increasingly implicated in interpersonal and societal decision-making, their ability to navigate explicit conflicts between legitimately different cultural value systems remains largely unexamined. Existing benchmarks predominantly target cultural knowledge (CulturalBench), value prediction (WorldValuesBench), or sin

Peter Alvaro, Rebecca Isaacs, Rupak Majumdar, Kiran-Kumar Muniswamy-Reddy

Many large-scale software systems demonstrate metastable failures. In this class of failures, a stressor such as a temporary spike in workload causes the system performance to drop and, subsequently, the system performance continues to remain low even when the stressor is removed. These failures have been reported by many large corporations and considered to

Yann Bellec

Dream2Image is the world's first dataset combining EEG signals, dream transcriptions, and AI-generated images. Based on 38 participants and more than 31 hours of dream EEG recordings, it contains 129 samples offering: the final seconds of brain activity preceding awakening (T-15, T-30, T-60, T-120), raw reports of dream experiences, and an approximate visual

Kento Nishigomi, Yu Yi, Souren Adhikary, Kazuhito Tsukagoshi

We theoretically study the strain effects on the electronic properties of cobalt-based benzenehexathiol (CoBHT) coordination nanosheets using first-principles calculations. Two distinct crystal structures, high-density structure (HDS) and low-density structure (LDS), are explored. Our results reveal that HDS behaves as a metal, while LDS exhibits semiconduct

Carina Veil, Moritz Flaschel, Ellen Kuhl

Soft robots, inspired by elephant trunks or octopus arms, offer extraordinary flexibility to bend, twist, and elongate in ways that rigid robots cannot. However, their motion planning remains a challenge, especially in cluttered environments with obstacles, due to their highly nonlinear and infinite-dimensional kinematics. Here, we present a graph-based path

Pantelis E. Eleftheriou, Aris Papadopoulos

The `global' Zarankiewicz problem for hypergraphs asks for an upper bound on the number of edges of a finite $r$-hypergraph $V$ in terms of the number $|V|$ of its vertices, assuming the edge relation is induced by a fixed $K_{k, \dots, k}$-free $r$-hypergraph $E$, for some $k\in\mathbb N$. In [4], such bounds of size $O(|V|^{r-1})$ were achieved for a semil

Sixten Norelius, Aaron O. Feldman, Mac Schwager

We propose SketchPlan, a diffusion-based planner that interprets 2D hand-drawn sketches over depth images to generate 3D flight paths for drone navigation. SketchPlan comprises two components: a SketchAdapter that learns to map the human sketches to projected 2D paths, and DiffPath, a diffusion model that infers 3D trajectories from 2D projections and a firs

Ieva Bagdonaviciute, Vibhav Vineet

While recent Vision-Language Models (VLMs) have achieved impressive progress, it remains difficult to determine why they succeed or fail on complex reasoning tasks. Traditional benchmarks evaluate what models can answer correctly, not why they succeed or fail. In this work, we perform a failure-mode analysis of six frontier VLMs on three physics-based benchm

Yuji Takubo, Daniele Gammelli, Marco Pavone, Simone D'Amico

Spacecraft rendezvous enables on-orbit servicing, debris removal, and crewed docking, forming the foundation for a scalable space economy. Designing such missions requires rapid exploration of the tradespace between control cost and flight time across multiple candidate targets. However, multi-objective optimization in this setting is challenging, as the und

Evandros Kaklamanos, Kristjana Kristinsdottir, Jonathan Huang, Dustin Carlson

Endoscopic procedures such as esophagogastroduodenoscopy (EGD) and colonoscopy play a critical role in diagnosing and managing gastrointestinal (GI) disorders. However, the documentation burden associated with these procedures place significant strain on gastroenterologists, contributing to inefficiencies in clinical workflows and physician burnout. To addre

Andrew Halterman, Katherine A. Keith

Generative large language models (LLMs) are now used extensively for text classification in computational social science (CSS). In this work, focus on the steps before and after LLM prompting -- conceptualization of concepts to be classified and using LLM predictions in downstream statistical inference -- which we argue have been overlooked in much of LLM-er

Manuel Schwonberg, Hanno Gottschalk

The generalization of deep neural networks to unknown domains is a major challenge despite their tremendous progress in recent years. For this reason, the dynamic area of domain generalization (DG) has emerged. In contrast to unsupervised domain adaptation, there is no access to or knowledge about the target domains, and DG methods aim to generalize across m

Anoop Nagesh Koushik, Harry van der Graaf, Kevin Ravensberg, Rene M. Wanders

Rasnik is a three-point optical displacement sensor originally developed for particle detector alignment in high-energy physics experiments, including the muon chambers of L3 at LEP and ATLAS at the LHC. The system has evolved from four-quadrant photodiodes to CMOS pixel sensors with custom ChessField coded masks, enabling absolute position measurement with

Francesco Anna Mele, Ludovico Lami

Quantum data hiding is the existence of pairs of bipartite quantum states that are (almost) perfectly distinguishable with global measurements, yet close to indistinguishable when only measurements implementable with local operations and classical communication are allowed. Remarkably, data hiding states can also be chosen to be separable, meaning that secre

Rebecca Carter, M. Ram Murty

After a brief review of the key theorems concerning recurrent sequences, we give an explicit computation of the inverse of the Vandermonde matrix. This will then be used to derive sub-exponential decay error terms in the ergodic theorem of Markov chains. Finally, we apply these results to give estimates for the diameters of directed graphs.

Zhaohan Meng, Zaiqiao Meng, Siwei Liu, Hao Xu

Multi-turn medical question answering (QA) aims to model realistic clinical diagnosis, where a doctor gathers patient information across multiple turns of conversation. Existing multi-turn medical conversation systems have shown promising progress, but they often rely on accumulated conversation histories as memory, leaving clinical evidence fragmented acros

Faruk Alpay, Hamdi Alakkad

This paper introduces Truth-Aware Decoding (TAD), a verification-oriented decoding scheme that aligns neural language generation with knowledge bases. Situated in the tradition of probabilistic program semantics for sequence models, TAD augments modern instruction-tuned systems with a lattice of semantic guards that operate at decode time. Our contributions

William Anderson, Seung Whan Chung, Youngsoo Choi

Accurate numerical solutions of partial differential equations are essential in many scientific fields but often require computationally expensive solvers, motivating reduced-order models (ROMs). Latent Space Dynamics Identification (LaSDI) is a data-driven ROM framework that combines autoencoders with equation discovery to learn interpretable latent dynamic

Zekai Liang, Kazuya Miyata, Xiao Liang, Florian Richter

Accurate camera-to-robot calibration is essential for any vision-based robotic control system and especially critical in minimally invasive surgical robots, where instruments conduct precise micro-manipulations. However, MIS robots have long kinematic chains and partial visibility of their degrees of freedom in the camera, which introduces challenges for con

Yujing Lu, Patrick Breheny

Confounding can lead to spurious associations. Typically, one must observe confounders in order to adjust for them, but in high-dimensional settings, recent research has shown that it becomes possible to adjust even for unobserved confounders. The methods for carrying out these adjustments, however, have not been thoroughly investigated. In this study, we de

Chenhaoyue Wang, Carlos J. Garcia-Cervera, Amartya S. Banerjee

Bloch oscillations (BOs) describe the coherent oscillatory motion of electrons in a periodic lattice under a constant external electric field. Deviations from pure harmonic wave packet motion or irregular Bloch oscillations can occur due to Zener tunneling (Landau-Zener Transitions or LZTs), with oscillation frequencies closely tied to interband coupling str

Ahmed Alajrami, Xingwei Tan, Nikolaos Aletras

Instruction-tuning plays a vital role in enhancing the task-solving abilities of large language models (LLMs), improving their usability in generating helpful responses on various tasks. However, previous work has demonstrated that they are sensitive to minor variations in instruction phrasing. In this paper, we explore whether introducing perturbations in i

Sayan Ghosh, Shahzaib Saqib Warraich, Dhruv Tarsadiya, Gregory Yauney

Language models can be sampled multiple times to access the distribution underlying their responses, but existing methods cannot efficiently synthesize rich epistemic signals across different long-form responses. We introduce Consensus Graphs (ConGrs), a flexible DAG-based data structure that represents shared information, as well as semantic variation in a

Alex Smith, Priya Patel, Hu Guo, Marco Ruiz

First-time patients undergoing diagnostic computed tomography (CT) scans often experience significant anxiety and uncertainty, which can negatively impact scan results and patient well-being. We present an immersive mixed reality (MR) simulator designed to prepare adult patients for their first CT scan, aiming to improve both emotional and physical preparedn

Bharti Meena, Joanna Skubisz, Harshit Rajgarhia, Nand Dave

As Large Language Models (LLMs) gain wider adoption, ensuring their reliable handling of Personally Identifiable Information (PII) across diverse regulatory contexts has become essential. This work introduces a scalable multilingual data curation framework designed for high-quality PII annotation across 13 underrepresented locales, covering approximately 336

Jonathan Mack, Shy Genel

Mergers are believed to play a pivotal role in galaxy evolution, and measuring the galaxy merger fraction is a longstanding goal of both observational and theoretical studies. In this work, we extend the consideration of the merger fraction from the standard measure of binary mergers, namely those comprising two merging galaxies, to multiple mergers, namely

C. Scarlett, E. Fischbach, B. Freeman, J. J. Coy

This paper reports on results from an experiment designed to search for exotic particles interacting with nuclear matter. These particles could be created through the Primakoff coupling between photons and an external magnetic field. Theory suggests this coupling leads to the production of weakly interacting particles (e.g. axions) that are important to unde

Yu Wang, Guanyu Han, Jan-Philipp Koester, Hans Wenzel

Mode-locked lasers (MLLs) are essential for a wide range of photonic applications, such as frequency metrology, biological imaging, and high-bandwidth coherent communications. The growing demand for compact and scalable photonic systems is driving the development of MLLs on various integrated photonics material platforms. Along these lines, developing MLLs o

Ali Elahi

In specialized domains, humans often compare new problems against similar examples, highlight nuances, and draw conclusions instead of analyzing information in isolation. When applying reasoning in specialized contexts with LLMs on top of a RAG, the pipeline can capture contextually relevant information, but it is not designed to retrieve comparable cases or

Vladimir V. Berdnikov, Alexander Somov

In experimental nuclear physics (NP), high-precision electromagnetic calorimetry typically requires a good energy resolution and linear photosensor response on the level of (1-2)% over a full dynamic range of the detector. The beam of secondary leptons at the Jefferson Lab experimental complex, provided by the Hall D pair spectrometer (PS), is an optimal fac

Alexis Derumigny, Niels Horsman, Dorota Kurowicka

The pair-copula Bayesian Networks (PCBN) are graphical models composed of a directed acyclic graph (DAG) that represents (conditional) independence in a joint distribution. The nodes of the DAG are associated with marginal densities, and arcs are assigned with bivariate (conditional) copulas following a prescribed collection of parental orders. The choice of

Boyang Chen, Mohd Tasleem Khan, George Goussetis, Mathini Sellathurai

Convolutional Neural Networks (CNNs) achieve remarkable accuracy in vision tasks, yet their computational complexity challenges low-power edge deployment. In this work, we present COMET, a framework of CNN models that employ efficient hardware offset-binary coding (OBC) techniques to enable co-optimization of performance and resource utilization. The approac

Lianghuan Huang, Sagnik Anupam, Insup Lee, Shuo Li

Reinforcement learning (RL) has emerged as a promising strategy for finetuning small language models (SLMs) to solve targeted tasks such as math and coding. However, RL algorithms tend to be resource-intensive, taking a significant amount of time to train. We propose RAPID, a novel RL algorithm that can substantially reduce the running time of RL. Our key in

Toby Drinkall

As military organisations consider integrating large language models (LLMs) into command and control (C2) systems for planning and decision support, understanding their behavioural tendencies is critical. This study develops a benchmarking framework for evaluating aspects of legal and moral risk in targeting behaviour by comparing LLMs acting as agents in mu

Taha M. Mahmoud, Naima Kaabouch

The rapid growth of the Internet of Things (IoT) has expanded opportunities for innovation but also increased exposure to botnet-driven cyberattacks. Conventional detection methods often struggle with scalability, privacy, and adaptability in resource-constrained IoT environments. To address these challenges, we present a lightweight and privacy-preserving b

Mia S. Tackney, Jonathan W. Bartlett, Elizabeth Williamson, Kim May Lee

The use of flexible machine-learning (ML) models to generate imputations of missing data within the framework of Multiple Imputation (MI) has recently gained traction, particularly in observational settings. For randomised controlled trials (RCTs), it is unclear whether ML approaches to MI provide valid inference, and whether they outperform parametric MI ap