March 2026 arXiv papers — page 31

Zhonggang Li, Geert Leus, Raj Thilak Rajan

Conventional affine formation control (AFC) empowers a network of agents with flexible but collective motions - a potential which has not yet been exploited for large-scale swarms. One of the key bottlenecks lies in the design of an interaction graph, characterized by the Laplacian-like stress matrix. Efficient and scalable design solutions often yield subop

Francisco Delgado-Vences, José Julián Pavón-Español, Arelly Ornelas

This study addresses the inverse problem of parameter estimation for Stochastic Differential Equations (SDEs) by minimizing a regularized discrepancy functional via Stochastic Gradient Descent (SGD). To achieve computational efficiency, we leverage the Wiener Chaos Expansion (WCE), a spectral decomposition technique that projects the stochastic solution onto

Zhenya Zang, Xingda Li, David Day Uei Li

This work presents a lightweight super-resolution (LiteSR) neural network for depth and intensity images acquired from a consumer-grade single-photon avalanche diode (SPAD) array with a 48x32 spatial resolution. The proposed framework reconstructs high-resolution (HR) images of size 256x256. Both synthetic and real datasets are used for performance evaluatio

Menglian Zhou, Arno Charton, Emily Blanchard, Lawrence Cai

Body Mass Index (BMI) is a widely accessible but imprecise proxy of cardiometabolic health. While assessing true body composition is superior, gold-standard methods like Dual-Energy X-ray Absorptiometry (DXA) are not scalable. We address this gap by developing and validating "PhotoScan," a method to estimate body composition from smartphone imagery. We pretr

Linus Härenstam-Nielsen, Dmitrii Pozdeev, Thomas Dagès, Nikita Araslanov

Reconstructing complete 3D shapes from incomplete or noisy observations is a fundamentally ill-posed problem that requires balancing measurement consistency with shape plausibility. Existing methods for shape reconstruction can achieve strong geometric fidelity in ideal conditions but fail under realistic conditions with incomplete measurements or noise. At

Halley Young

We present a framework in which program analysis -- type checking, bug finding, and equivalence verification -- is organized as computing the \v{C}ech cohomology of a semantic presheaf over a program's site category. The presheaf assigns refinement-type information to observation sites and restricts it along data-flow morphisms. The cohomology group $H^{0}$

Jiaming Li, Zhijia Liang, Weikai Chen, Lin Ma

Fine-grained open-vocabulary object detection (FG-OVD) aims to detect novel object categories described by attribute-rich texts. While existing open-vocabulary detectors show promise at the base-category level, they underperform in fine-grained settings due to the semantic entanglement of subjects and attributes in pretrained vision-language model (VLM) embe

Astitva Srivastava, Hsiao-yu Chen, Ryan Goldade, Philipp Herholz

Recent advances in digital avatar technology have enabled the generation of compelling virtual characters, but deploying these avatars on compute-constrained devices poses significant challenges for achieving realistic garment deformations. While physics-based simulations yield accurate results, they are computationally prohibitive for real-time applications

Hao Li, Long Yin Chung, Jack Goler, Ryan Zhang

Underwater robotic grasping is difficult due to degraded, highly variable imagery and the expense of collecting diverse underwater demonstrations. We introduce a system that (i) autonomously collects successful underwater grasp demonstrations via a self-supervised data collection pipeline and (ii) transfers grasp knowledge from on-land human demonstrations t

Zahra Safdari Fesaghandis, Suman Kalyan Maity

Online hate speech threatens online civility, particularly in low-resource and multilingual environments. Counter-narratives offer a promising solution by promoting constructive responses to hate speech. However, automatic counter-narrative generation is hindered by the lack of high-quality data for low-resource languages like Persian. To bridge this gap, we

Brendah Nansereko, Marcel Wolbers, James R. Carpenter, Jonathan W. Bartlett

When treatment policy estimands are of interest, clinical trials often attempt to collect patient data after intercurrent events (ICEs), although such data are often limited. Retrieved dropout imputation methods, which use pre-ICE and available post-ICE data to impute missing post-ICE outcomes, are commonly applied but often yield treatment effect estimates

Hridhaan Banerjee, Soren Brown, June Cagan, Auguste H. Gezalyan

Higher-order Voronoi diagrams and Delaunay mosaics in polygonal metrics have only recently been studied, yet no tools exist for visualizing them. We introduce a tool that fills this gap, providing dynamic interactive software for visualizing higher-order Voronoi diagrams and Delaunay mosaics along with clustering and tools for exploring overlap and outer reg

Rahul Soni

Recent reasoning-focused language models such as DeepSeek R1 and OpenAI o1 have demonstrated strong performance on structured reasoning benchmarks including GSM8K, MATH, and multi-hop question answering tasks. However, their performance remains highly sensitive to prompt formulation, and designing effective prompts is typically a manual and iterative process

Stefano Palmieri

Nontrivial combinatory algebras with S and K must be infinite. Associativity is incompatible with combining a classifier and a retraction pair in a finite extensional magma. These obstructions exclude several standard settings from the finite extensional framework studied here, most notably nontrivial finite S+K-style combinatory algebras and associative str

E. M. Freeburg

Large language models produce em dashes at varying rates, and the observation that some models "overuse" them has become one of the most widely discussed markers of AI-generated text. Yet no mechanistic account of this pattern exists, and the parallel observation that LLMs default to markdown-formatted output has never been connected to it. We propose that t

Vadim Zaliva, Yannick Zakowski, Ilia Zaichuk, Valerii Huhnin

This paper presents the design of HELIX, an end-to-end verified code generation system with a focus on the intersection of high-performance and high-assurance numerical computing. The code generation can be fine-tuned to generate efficient code for a broad set of computer architectures while providing formal guarantees of the correctness of such generated co

Yeping Jin, Jiaming Hu, Ioannis Ch. Paschalidis

Large Language Models (LLMs) tend to respond correctly to prompts that align well with the data they were trained and fine-tuned on. Yet, small shifts in wording, format, or language can trigger surprisingly large failures, especially on multi-step reasoning problems. To address this problem, we propose a Distributionally Robust Token Optimization (DRTO) app

Ahmed Adel Mahmoud, Gabrielle Tournaire, Sven Bachmann, Steven Rayan

Fault-tolerant measurement-based quantum computing (MBQC) provides a compelling framework for fault-tolerant quantum computation, in which quantum information is processed through single-qubit measurements on a three-dimensional entangled resource known as cluster state. To date, this resource has been predominantly studied on Euclidean lattices, most notabl

Tanmay Mukherjee, Neil Gautam, Nikhil Kadivar, Elizabeth M. Fugate

Computational models of cardiac structure and function are increasingly central to the development of subject-specific cardiac digital twins, enabling improved characterization of contractile dysfunction, pathological remodeling, and electrical abnormalities. A critical prerequisite for these models is the accurate reconstruction of three-dimensional (3D) ca

Waris Quamer, Mu-Ruei Tseng, Ghady Nasrallah, Ricardo Gutierrez-Osuna

Speaker anonymization (SA) systems modify timbre while leaving regional or non-native accents intact, which is problematic because accents can narrow the anonymity set. To address this issue, we present PHONOS, a streaming module for real-time SA that neutralizes non-native accent to sound native-like. Our approach pre-generates golden speaker utterances tha

Shaoliang Yang, Jun Wang, Yunsheng Wang

We present AutoSiMP, an autonomous pipeline that transforms a natural-language structural problem description into a validated, binary topology without manual configuration. The pipeline comprises five modules: (1) an LLM-based configurator that parses a plain-English prompt into a validated specification of geometry, supports, loads, passive regions, and me

Xiao Tan, Rahal Nanayakkara, Paulo Tabuada, Aaron D. Ames

State estimation uncertainty is prevalent in real-world applications, hindering the application of safety-critical control. Existing methods address this by strengthening a Control Barrier Function (CBF) condition either to handle actuation errors induced by state uncertainty, or to enforce stricter, more conservative sufficient conditions. In this work, we

Youssef Ahmed, Arnob Ghosh, Chih-Chun Wang, Ness B. Shroff

For status update systems operating over unreliable energy-constrained wireless channels, we address Weaver's long-standing Level-C question: do my packets actually improve the plant's behavior? Each fresh sample carries a stochastic expiration time -- governed by the plant's instability dynamics -- after which the information becomes useless for control. Ca

Aditi Mallavarapu, Rohan Khandare, Mokshagna Kadiyala, Neelesh Yaddanapudi

Systematic reviews provide comprehensive syntheses of research fields. As a result, systematic reviews often emphasize synthesizing across the large bodies of literature rather than just describing the studies from which the conclusions were drawn. This risks an incomplete description of the sample - encouraging overgeneralization of the findings, obscuring

Irvin Steve Cardenas, Marcus Anthony Arnett, Natalie Catherine Yeo, Lucky Sah

Foundation models can endow robots with open-ended reasoning, language understanding, and adaptive planning, yet connecting a model to a physical robot today requires bespoke integration that couples perception, actuation, and safety to a single model and platform. We present ROSClaw, a model-agnostic executive layer that integrates the OpenClaw agent runtim

Nikil Ravi, Kexing Ying, Vasilii Nesterov, Rayan Krishnan

We present FormalProofBench, a private benchmark designed to evaluate whether AI models can produce formally verified mathematical proofs at the graduate level. Each task pairs a natural-language problem with a Lean~4 formal statement, and a model must output a Lean proof accepted by the Lean 4 checker. FormalProofBench targets advanced undergraduate and gra

Raj Harshit Srirangam, Leonard Jung, Rohith Poola, Michael Everett

Autonomous robots commonly aim to complete a nominal behavior while minimizing a cost; this leaves them vulnerable to failure or unplanned scenarios, where a backup or contingency plan to a safe set is needed to avoid a total mission failure. This is formalized as a trajectory optimization problem over the nominal cost with a safety constraint: from any poin

Marco Garcia Noceda, Matthew T Noakes, Andrew FigPope, Daniel E Mattox

T cells are a critical component of the adaptive immune system, playing a role in infectious disease, autoimmunity, and cancer. T cell function is mediated by the T cell receptor (TCR) protein, a highly diverse receptor targeting specific peptides presented by the major histocompatibility complex (pMHCs). Predicting the specificity of TCRs for their cognate

Ruicheng Ao, Siyang Gao, David Simchi-Levi

This technical note studies the reliability limits of LLM-based multi-agent planning as a delegated decision problem. We model the LLM-based multi-agent architecture as a finite acyclic decision network in which multiple stages process shared model-context information, communicate through language interfaces with limited capacity, and may invoke human review

Remington Mallett

All varieties of dreaming remain a mystery. Lucid dreams in particular, or those characterized by awareness of the dream, are notoriously difficult to study. Their scarce prevalence and resistance to deliberate induction make it difficult to obtain a sizeable corpus of lucid dream reports. The consequent lack of clarity around lucid dream phenomenology has l

Tony Menzo, Alexander Roman, George T. Fleming, Sergei Gleyzer

We present Diagrammatica, a symbolic computation extension to the HEPTAPOD agentic framework, which enables LLM agents to plan and execute multi-step theoretical calculations. Symbolic computation poses a distinctive reliability challenge for LLM agents, as correctness is governed by implicit mathematical conventions that are not encoded in a form that can b

Bas Cornelissen

Arvo P\"art is one of the most popular contemporary composers, known for his highly original tintinnabuli style. Works in this style are typically composed according to precise procedures and have even been described as algorithmic compositions. To understand how algorithmic P\"art's music exactly is, this paper presents an analysis by synthesis: it proposes

Bas Cornelissen

This paper develops a framework for conceptualizing, visualizing, and measuring regularities in rhythmic data. I propose to think about rhythmic data in terms of interval segments: fixed-length groups of consecutive intervals, which can be decomposed into a duration and a pattern (the ratios between the intervals). This simple conceptual framework unifies th

Weixing Zhang, Mario Herb, Martin Armbruster, Bowen Jiang

Despite Domain-Driven Design's proven value in managing complex business logic, a fundamental semantic expressiveness gap persists between generic modeling languages and tactical DDD patterns, causing continuous divergence between design intent and implementation. We envision a constraint-based tactical modeling environment that transforms abstract architect

Anosh Joseph, David Schaich

We present new results from our lattice investigations of maximally supersymmetric Yang--Mills theory in three dimensions, focusing on its nonperturbative phase diagram. Using a lattice formulation that preserves part of the supersymmetry algebra at finite lattice spacing, we study the spatial deconfinement transition, which holography relates to the transit

Mujtaba Hussain Mirza, Antonio D'Orazio, Odelia Melamed, Iacopo Masi

Despite the rapid progress in multimodal models and Large Visual-Language Models (LVLM), they remain highly susceptible to adversarial perturbations, raising serious concerns about their reliability in real-world use. While adversarial training has become the leading paradigm for building models that are robust to adversarial attacks, Test-Time Transformatio

Saunak Kumar Panda, Tong Li, Ruiqi Liu, Yisha Xiang

Reinforcement learning algorithms have been widely used for decision-making tasks in various domains. However, the performance of these algorithms can be impacted by high variance and instability, particularly in environments with noise or sparse rewards. In this paper, we propose a framework to perform statistical online inference for a sample-averaged Q-le

Ruyi Pan, Yinqiu He, Jun Young Park

Understanding the interplay between high-dimensional data from different views is essential in biomedical research, particularly in fields such as genomics, neuroimaging and biobank-scale studies involving high-dimensional features. Existing statistical tests for the association between two random vectors often do not fully capture dependencies between views

Tjeerd Jan Heeringa

Reproducing kernel Hilbert spaces are uniquely characterized by their kernel, but reproducing kernel Banach spaces (RKBS) are not. However, a characterization of which RKBS admit a given kernel as reproducing kernel is lacking. This work provides such a characterization for the well-known Bessel potential / Mat\`ern kernel, a widely used covariance kernel fo

Omar Pedraza, L. A. López, L. O. Téllez Tovar

In this contribution, we investigate the scattering and absorption sections of the improved Schwarzschild black hole. The differential scattering section is analysed using three complementary approaches: the classical approximation, the semi-classical approximation, and the partial wave technique. We show that, while the classical scattering section exhibits

Rasel Ahmed Bhuiyan, Parisa Farmanifard, Renu Sharma, Andrey Kuehlkamp

Post-mortem iris recognition brings both hope to the forensic community (a short-term but accurate and fast means of verifying identity) as well as concerns to society (its potential illicit use in post-mortem impersonation). These hopes and concerns have grown along with the volume of research in post-mortem iris recognition. Barriers to further progress in

Bryan Shaddy, Haitong Qin, Brianna Binder, James Haley

This study presents a probabilistic surrogate model for localized wildfire spread based on a conditional flow matching algorithm. The approach models fire progression as a stochastic process by learning the conditional distribution of fire arrival times given the current fire state along with environmental and atmospheric inputs. Model inputs include current

Isaac Legred, Nicolas Yunes

We reformulate the stellar structure equations in the language of dynamical systems and show that the maximum mass of stellar sequences arises from the existence of a fixed point in the relativistic regime. In an appropriate representation of the Tolman-Oppenheimer-Volkoff equations, this fixed point becomes manifest and is directly associated with the turno

P. Darc, C. R. Bom, A. Santos, S. Panda

The conditions under which binary black hole (BBH) mergers embedded in active galactic nucleus (AGN) disks produce detectable optical counterparts remain poorly constrained observationally. We report multi-epoch optical imaging and spectroscopic follow-up of S240413p, an O4 BBH candidate with 98\% classification confidence, obtained with the T80-South telesc

Abigail Kelly, Ramchandra Rimal, Arpan Sainju

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by atypical brain connectivity. One of the crucial steps in addressing ASD is its early detection. This study introduces a novel computational framework that employs an Attention-Based Graph Convolutional Network, referred to as the GATGraphClassifier, for detecting ASD. We utiliz

Menachem Stern, Adam G. Frim, Raúl Candás, Andrea J. Liu

We generalize the theory of supervised contrastive learning, previously applied to physical systems at equilibrium or steady state, to systems following any dynamics described by coupled ordinary differential equations. We show that if physical dynamics break time reversal symmetry, gradient descent on a cost function embodying the desired behavior cannot be

Alex Fallin, Nathaniel Gorski, Tripti Agarwal, Bei Wang

Many scientific codes and instruments generate large amounts of floating-point data at high rates that must be compressed before they can be stored. Typically, only lossy compression algorithms deliver high-enough compression ratios. However, many of them provide only point-wise error bounds and do not preserve topological aspects of the data such as the rel

Tashreef Muhammad, Tahsin Ahmed, Meherun Farzana, Md. Mahmudul Hasan

Accurate short-term forecasting of agricultural commodity prices is critical for food security planning and smallholder income stabilisation in developing economies, yet machine-learning-ready datasets for this purpose remain scarce in South Asia. This paper makes two contributions. First, we introduce AgriPriceBD, a benchmark dataset of 1,779 daily retail m

Anweshika Pattanayak, Amir Shee, Abhishek Chaudhuri, Debasish Chaudhuri

The interplay of chirality, self-propulsion, and spatial confinement generates striking non-equilibrium fluctuations whose higher-order statistics carry information about the dynamics and shape of the position distribution. Here, we present an exact analytical framework, based on a Laplace-transform solution of the Fokker-Planck equation, for the transient d

Thales Azevedo, Henrique Boschi-Filho

The powerful techniques of holographic quantum chromodynamics (QCD) can be employed in the investigation of glueballs -- composite particles made solely of gluons, the strong nuclear force mediators. In particular, the so-called hardwall model yields predictions for the values of the masses of various glueball states, which are related to the solutions of th

Talha Azaz, Raza Ahmad, Md Saiful Islam, Douglas Thain

Notebooks provide an author-friendly environment for iterative development, modular execution, and easy sharing. Distributed workflows are increasingly being authored and executed in notebooks, yet sharing and reproducing them remains challenging. Even small code or parameter changes often force full end-to-end re-execution of the distributed workflow, limit

Panagiotis Rigas, George Ioannakis, Ioannis Emiris

We introduce VoroFields, a hierarchical neural-field framework for approximating generalized Voronoi diagrams of finite geometric site sets in low-dimensional domains under arbitrary evaluable point-to-site distances. Instead of constructing the diagram combinatorially, VoroFields learns a continuous, differentiable surrogate whose maximizer structure induce

Gokularam Muthukrishnan, Anshoo Tandon

Differentially private $K$-means clustering enables releasing cluster centers derived from a dataset while protecting the privacy of the individuals. Non-interactive clustering techniques based on privatized histograms are attractive because the released data synopsis can be reused for other downstream tasks without additional privacy loss. The choice of the

Jonas Bergström, Thomas Wennink

We have written a computer program that implements Deligne's pullback and pushforward weight spectral sequences to compute the weight graded pieces of the rational cohomology of moduli spaces of pointed smooth curves (as well as curves of compact type and curves with rational tails) in cases where the cohomology groups appearing in the boundary stratificatio

I. Agnes Felicia Roy, Kuo Yuan Hwa, Aravindan Santhan, Slava V Rotkin

The increasing prevalence of pharmaceutical contaminants within the aquatic environment has generated considerable environmental concerns, especially regarding persistent anticancer medications like the kinase inhibitor sorafenib (SRF), which are inadequately eliminated by standard degradation methods. A heterointerface-engineered MoS2/WS2, 2D/2D layered nan

Serhii D. Koval, Alex Bihlo, Roman O. Popovych

We carry out an extended symmetry analysis of the multi-layer quasi-geostrophic problem. This model is given by a system of an arbitrary number of coupled barotropic vorticity equations. Conservation laws and a Hamiltonian structure for the general case of the model are correctly described for the first time. Using original methods, we compute the maximal Li

Nathan Wriedt, Joe McGlone, Davide Orlandini, Siddharth Rajan

Gallium oxide is an ultra-wide bandgap semiconductor with exceptional properties for power electronics and UV-C optoelectronics, but its behavior under illumination remains poorly understood. In this work, we investigate how optically generated self-trapped holes influence electrostatics and current conduction in gallium oxide devices. Using a vertical Schot

D. Gaitsgory, N. Rozenblyum, Y. Varshavsky

We use the formalism of the (2-category) AGCat, developed in [GRV], and the operation of higher categorical trace to (re)derive a number of results in the Deligne-Lusztig theory.

Prajakta Surve, Shaunak D. Bopardikar, Daigo Shishika, Dipankar Maity

We consider a hide-and-seek game between a Hider and a Seeker over a finite set of locations. The Hider chooses one location to conceal a stationary treasure, while the Seeker visits the locations sequentially along a route. As the search progresses, the Hider observes a prefix of the Seeker's route. After observing this information, the Hider has the option

Sarah Mostow, Daniel Xiang

A cornerstone of the multiple testing literature is the Benjamini-Hochberg (BH) procedure, which guarantees control of the FDR when $p$-values are independent or positively dependent. While BH controls the average quality of rejections, it does not provide guarantees for individual discoveries, particularly those near the rejection threshold, which are more

Atish Agarwala

The trend towards larger training setups has brought a renewed interest in partially asynchronous two-phase optimizers which optimize locally and then synchronize across workers. Additionally, recent work suggests that the one-worker version of one of these algorithms, DiLoCo, shows promising results as a (synchronous) optimizer. Motivated by these studies w

Emilie Panek, Alexander Roman, Gaurav Shukla, Leonardo Pagliaro

The expansion of exoplanet observations has created a need for flexible, accessible, and user-friendly workflows. Transmission spectroscopy has become a key technique for probing atmospheric composition of transiting exoplanets. The analyses of these data require the combination of archival queries, literature search, the use of radiative transfer models, an

Gulengul Mermer, Mustafa Furkan Aksu, Gozde Ozsezer, Sevki Cetinkalp

Diabetic foot ulcers (DFU) are one of the serious complications of diabetes that can lead to amputations and high healthcare costs. Regular monitoring and early diagnosis are critical for reducing the clinical burden and the risk of amputation. The aim of this study is to investigate the impact of using multimodal images on deep learning models for the class

Rabia Caglayan, Louise Desplat, Sergey Nikolaev, Fatima Ibrahim

We theoretically study the effect of in- and out-of-plane Dzyaloshinskii-Moriya interaction (DMI) on the magnetic ground states of two-dimensional (2D) Fe$_3$GeXTe (X=Te, Se, S) monolayers, where X=Se, S correspond to antisymmetric Janus structures with nonvanishing in-plane DMI. We perform atomistic spin simulations with the extended Heisenberg Hamiltonian

Dong Ho Lee, Jingqi Li, Lasse Peters, Georgios Bakirtzis

Games of ordered preference (GOOPs) model multi-player equilibrium problems in which each player maintains a distinct hierarchy of strictly prioritized objectives. Existing approaches solve GOOPs by deriving and enforcing the necessary optimality conditions that characterize lexicographically constrained Nash equilibria through a single-level reformulation.

Fabrizio De Santis, Gyunam Park, Wil M. P. van der Aalst, Francesco Zanichelli

Existing approaches for predictive process monitoring are sub-symbolic, meaning that they learn correlations between descriptive features and a target feature fully based on data, e.g., predicting the surgical needs of a patient based on historical events and biometrics. However, such approaches fail to incorporate domain-specific process constraints (knowle

Brian Kyanjo, Talea L. Mayo, Alexander A. Robel

ICESEE (ICE Sheet statE and parameter Estimator) is a Python-based, open-source data assimilation framework designed for seamless integration with ice sheet and Earth system models. It implements a parallel Ensemble Kalman Filter (EnKF) with full MPI support for scalable assimilation in state and parameter spaces. ICESEE uses a matrix-free update scheme from

Shanglin Wu, Yuyang Luo, Yueqing Liang, Kaiwen Shi

Large language model (LLM) multi-agent systems can scale along two distinct dimensions: by increasing the number of agents and by improving through accumulated experience over time. Although prior work has studied these dimensions separately, their interaction under realistic cost constraints remains unclear. In this paper, we introduce a conceptual scaling

Osvaldo Ferreira, Eduardo S. Fraga, Jürgen Schaffner-Bielich

Large lepton flavor asymmetries at the QCD epoch could generate a pion condensation phase in the early Universe. For large enough tau lepton flavor asymmetries, the speed of sound can exceed the conformal value, leaving a distinctive imprint on the low-frequency gravitational wave (GW) spectrum from causal sources. Beyond probing the formation of a pion cond

Zhenhao Li, Zheng Liu, Seunghyun Lee, Amin Fadaeinejad

Appearance-based gaze estimation (AGE) has achieved remarkable performance in constrained settings, yet we reveal a significant generalization gap where existing AGE models often fail in practical, unconstrained scenarios, particularly those involving facial wearables and poor lighting conditions. We attribute this failure to two core factors: limited image

Fabrizio De Santis, Gyunam Park, Francesco Zanichelli

Predictive modeling on sequential event data is critical for fraud detection and healthcare monitoring. Existing data-driven approaches learn correlations from historical data but fail to incorporate domain-specific sequential constraints and logical rules governing event relationships, limiting accuracy and regulatory compliance. For example, healthcare pro

Christine T. Cheng, Will Rosenbaum

In the Stable Roommates Problem (SR), a set of $2n$ agents rank one another in a linear order. The goal is to find a matching that is stable: one that has no pair of agents who mutually prefer each other over their assigned partners. We consider the problem of finding an optimal stable matching. Agents associate weights with each of their potential partners,

Yinghao Wang, Cheng Wang

Large language model (LLM) multi-agent coding systems typically fix agent capabilities at design time. We study an alternative setting, earned autonomy, in which a coding agent starts with zero pre-defined functions and incrementally builds a reusable function library through lightweight human feedback on visual output alone. We evaluate this setup in a Blen

Rahim Kargar

We develop a rigorous variational theory for the modular variable-exponent modulus of curve families in two symmetric geometries: annuli $A(r_1,r_2)\subset\mathbb{R}^n$ with radial exponent $p(x)=q_0(|x|)$, and cylinders $\mathcal C=D\times(0,L)$ with axial exponent $p(x&#39;,t)=η(t)$, under the assumption $1<p^-\le p^+<\infty$. For annuli, spherical averagi

Taminul Islam, Abdellah Lakhssassi, Toqi Tahamid Sarker, Mohamed Embaby

Quantifying exhaled CO2 from free-roaming cattle is both a direct indicator of rumen metabolic state and a prerequisite for farm-scale carbon accounting, yet no existing system can deliver continuous, spatially resolved measurements without physical confinement or contact. We present TRACE (Thermal Recognition Attentive-Framework for CO2 Emissions from Lives

Eric L. Grinberg

We propose an interpretation of, and approach to, Helly's theorem that can be included quite early in the undergraduate curriculum. At the same time, the approach connects with contemporary models of data privacy and with sampling methods used in epidemiology. The presentation is intended to be accessible to teachers and their students.

David Gentile, James M. Murphy

The optimal transportation problem defines a geometry of probability measures which leads to a definition for weighted averages (barycenters) of measures, finding application in the machine learning and computer vision communities as a signal processing tool. Here, we implement a barycentric coding model for measures which are supported on a graph, a context

Felix Haas, Sebastian P. Bayerl

This paper presents a multi-label stuttering detection system trained on multi-corpus, multilingual data in English, German, and Mandarin.By leveraging annotated stuttering data from three languages and four corpora, the model captures language-independent characteristics of stuttering, enabling robust detection across linguistic contexts. Experimental resul

Yuyang Ji, Yixuan Shen, Shengjie Zhu, Yu Kong

We present BioCoach, a biomechanics-grounded vision--language framework for fitness coaching from streaming video. BioCoach fuses visual appearance and 3D skeletal kinematics, through a novel three-stage pipeline: an exercise-specific degree-of-freedom selector that focuses analysis on salient joints; a structured biomechanical context that pairs individuali

Pijush Patra, Ludovico T. Giorgini, J. S. Wettlaufer

Understanding the interactions between ice sheets and global climate forcings over geological timescales is essential for projecting their future. Previous studies have highlighted the role of ice dynamics and climate interactions in establishing the 100,000-year glacial cycles, particularly regarding the growth of the North American ice sheet. Researchers h

Hongyi Chen, Robert Neel, Cheng Ouyang

Using sharp global heat kernel bounds and geodesic comparison geometry, we show that the Dalang condition for well-posedness of the parabolic Anderson model with measure-valued initial conditions, first introduced on Euclidean space, holds on general compact Riemannian manifolds. We furthermore establish upper and lower moment bounds for all such solutions,

Yue Yu, Guanyu Hu

In professional sports analytics, evaluating the relationship between accumulated workload and injury risk is a central objective. However, naive survival models applied to NBA game-log data consistently yield a paradox: players who recently logged heavy minutes appear less likely to sustain an injury. We demonstrate that this counterintuitive result is an a

Laura Pedrouzo-Rodriguez, Luis F. Gomez, Ruben Tolosana, Ruben Vera-Rodriguez

Recent advances in photorealistic avatar generation have enabled highly realistic talking-head avatars, raising security concerns regarding identity impersonation in AI-mediated communication. To advance in this challenging problem, the task of avatar fingerprinting aims to determine whether two avatar videos are driven by the same human operator or not. How

Klaus Ziegler, Tim Heine, Sabine Tornow

Measurements can be used to monitor the evolution of quantum systems and can give rise to quantized return statistics. It is known that the mean return time is quantized for strong monitoring through the winding number of the monitored quantum state. We discuss that under coherent weak monitoring, implemented via ancilla coupling, the mean return time of a q

Nikita Lvov, Alexander Van Werde

We give the first specific conjectures on how frequently graphs satisfy sufficient conditions for being uniquely characterized by spectral information. These conjectures arise from a theoretical framework that we developed based on abstract-algebraic random matrix statistics. Specifically, we rephrase conditions from the literature in terms of Z[x]-modules a

Snehaa Reddy, Jayaprakash Katual, Satish Mulleti

Machine learning models often struggle to generalize across domains with varying data distributions, such as differing noise levels, leading to degraded performance. Traditional strategies like personalized training, which trains separate models per domain, and joint training, which uses a single model for all domains, have significant limitations in flexibi

Jenny S Wang, Aliya Saperstein, Emma Pierson

Free-text survey responses can provide nuance often missed by structured questions, but remain difficult to statistically analyze. To address this, we introduce In Your Own Words, a computational framework for exploratory analyses of free-text survey data that identifies structured, interpretable themes in free-text responses, facilitating systematic analysi

Xinyu Yang, Haozheng Yu, Yihong Sun, Bharath Hariharan

Interactive video segmentation often requires many user interventions for robust performance in challenging scenarios (e.g., occlusions, object separations, camouflage, etc.). Yet, even state-of-the-art models like SAM2 use corrections only for immediate fixes without learning from this feedback, leading to inefficient, repetitive user effort. To address thi

Wilman Arturo Gomez, Carlos Esteban Posada

Since the beginning of this century the Colombian monetary authority has conducted monetary policy under a strategy based on setting targets for interest rate and inflation, while allowing the exchange rate of the U.S. dollar in domestic currency to float freely. This paper takes that strategy into account in order to explain inflation. Our econometric resul

Hongrui Chen, Dat Quoc Ha, Josephine V. Carstensen, Faez Ahmed

Topology optimization can generate high-performance structures, but designers often need to revise the resulting topology in ways that reflect fabrication preferences, structural intuition, or downstream design constraints. In particular, they may wish to explicitly control interpretable structural characteristics such as member thickness, characteristic mem

Dennis Gaitsgory, Vincent Lafforgue, Sam Raskin

We introduce and study the filtration on the space of automorphic functions (in the everywhere unramified situation for the function field case) obtained by transferring the filtration on the spectral side of the classical Langlands conjecture, induced by coherent singular support. We propose a number of conjectures that tie this filtration (which, by design

Alejandro Rosas Díaz, Alfredo Raya, C. A. Vaquera Araujo, S. Hernández-Ortiz

The phase diagram of Quantum Chromodynamics (QCD) remains a central topic in high-energy physics. At high temperature and low baryochemical potential, the chiral transition is experimentally observed and theoretically explored to be a smooth crossover, while at high densities, a first-order phase transition is theoretically expected in lack of direct experim

Randy C. Hoover, Jacob James, Paul May, Kyle Caudle

Parametric models deployed in non-stationary environments degrade as the underlying data distribution evolves over time (a phenomenon known as temporal domain drift). In the current work, we present KOMET (Koopman Operator identification of Model parameter Evolution under Temporal drift), a model-agnostic, data-driven framework that treats the sequence of tr

Ruoxi Shang, Dan Marshall, Edward Cutrell, Denae Ford

AI agents that communicate on behalf of individuals need to capture how each person actually communicates, yet current approaches either require costly per-person fine-tuning, produce generic outputs from shallow persona descriptions, or optimize preferences without modeling communication style. We present ASPECT (Automated Social Psychometric Evaluation of

Nikolaos M. Matzakos, Chrisovalantis Sfyrakis

Physics-Informed Neural Networks (PINNs) and Neural Ordinary Differential Equations (NODEs) represent two distinct machine learning frameworks for modeling nonlinear neuronal dynamics. This study systematically evaluates their performance on the two-dimensional Morris-Lecar model across three canonical bifurcation regimes: Hopf, Saddle-Node on Limit Cycle, a

Jianfeng Lin, Viktor F. Majewski, Jacek Rzemieniecki

We prove that every Cayley fibration of a compact torsion-free Spin(7)-manifold with full holonomy must have singular fibers. This confirms a long-standing expectation in the study of calibrated fibrations with exceptional holonomy. Our argument first uses the rigidity of the Spin(7)-structure to reduce any hypothetical nonsingular Cayley fibration to two po

Daniel Benniah John

Sixth-generation (6G) wireless networks must support heterogeneous services: enhanced Mobile Broadband (eMBB) requiring 1 Tbps data rates, massive Machine-Type Communications (mMTC) supporting 10 million devices per km, and Ultra-Reliable Low-Latency Communications (URLLC) with 0.1-1 ms latency. Current resource allocation suffers from three limitations: (1)

Swadesh M. Mahajan, David R. Hatch, Zensho Yoshida, Mike Kotschenreuther

A thermodynamic model of a plasma boundary layer, characterized by enhanced temperature contrasts is proposed. The theory is constructed to determine the inner boundary temperature $T_1$ for a specified outer (colder) boundary temperature $T_0$, the heat flux $F$ entering the inner boundary, and the parameters defining the layer. The system shows bifurcation

Maxwell Doyle, Benjamin Schrunk, D. L. Schlagel, Thomas A. Lograsso

We use laser-based Angle-Resolved Photoemission Spectroscopy (ARPES) to study how chemical substitution modifies the electronic properties of the Bi2(Se{1-x}Tex)3 (BiSeTe) family of topological insulators. We find that increasing the Te content lowers the chemical potential, leading to a decrease in the binding energy of the Dirac point and a reduction in th

David R. Law, Melanie Clarke

The integral-field unit (IFU) spectrometers on board the James Webb Space Telescope (JWST) undersample the nearly diffraction-limited point spread function provided by the telescope optics. This undersampling produces large oscillating spectral artifacts when the data is resampled into regularly-gridded data cubes, which poses a significant challenge for man