arXiv papers from March 2026

Shuyang Gong, Brice Huang, Shuangping Li, Mark Sellke

We study the binary perceptron, a random constraint satisfaction problem that asks to find a Boolean vector in the intersection of independently chosen random halfspaces. A striking feature of this model is that at every positive constraint density, it is expected that a $1-o_N(1)$ fraction of solutions are \emph{strongly isolated}, i.e. separated from all o

Yuki Yasuda, Tobias Bischoff

Multiscale spatial structure complicates temporal prediction because small-scale spatial fluctuations influence large-scale evolution, yet resolving all scales is often intractable. Standard diffusion models do not address this problem effectively since they apply uniform decay across all Fourier modes. We propose Predictor-Driven Diffusion, a framework that

Vishal Chakraborty, Youri Kaminsky, Arnav Abhijit Dhariya, Sharad Mehrotra

Deletion is a fundamental database operation, yet modern systems often fail to provide the privacy guarantee that users expect from it. A deleted value may disappear from query results and even from physical storage, yet remain inferable from dependencies, derived data, or traces exposed by the deletion event itself. Meaningful deletion, therefore, requires

Eranga H. Gamage, Zhen Zhang, Subhadip Pradhan, Ajay Kumar

We report the synthesis and magnetic characterization of WFeB and identify it as a metallic d-wave altermagnet representative of a broader TiNiSi-type family. Neutron diffraction, M\"ossbauer spectroscopy, and magnetometry establish a collinear altermagnetic ordering confirmed by first-principles calculations. The electronic structure shows a nonrelativistic

Aengus Lynch

Autonomous AI agents are being deployed with filesystem access, email control, and multi-step planning. This thesis contributes to four open problems in AI safety: understanding dangerous internal computations, removing dangerous behaviors once embedded, testing for vulnerabilities before deployment, and predicting when models will act against deployers. ACD

Suraj Kath, Sanket Badhe, Preet Shah, Ashwin Sampathkumar

Online abuse has grown increasingly complex, spanning toxic language, harassment, manipulation, and fraudulent behavior. Traditional machine-learning approaches dependent on static classifiers and labor-intensive labeling struggle to keep pace with evolving threat patterns and nuanced policy requirements. Large Language Models introduce new capabilities for

Teruyoshi Nobukawa, Shunsuke Murai, Ryo Higashida, Yuta Yamaguchi

The undesired zeroth-order diffraction (ZOD) arising from imperfections in diffractive optical elements (DOEs) degrades the quality of target optical wavefronts. Herein, we propose a zeroth-order-free holographic reconstruction method using a nanoimprinted nonlocal metasurface. By judiciously designing the metasurface structure and its angular selectivity ba

Zhiquan Zhang, Melkior Ornik

Autonomous motion planning under unknown nonlinear dynamics requires learning system properties while navigating toward a target. In this work, we develop a hierarchical planning-control framework that enables online motion synthesis with limited prior system knowledge. The state space is partitioned into polytopes and approximates the unknown nonlinear syst

Syed Eqbal Alam, Zhan Shu

We develop algorithms for collaborative control of AI agents and critics in a multi-actor, multi-critic federated multi-agent system. Each AI agent and critic has access to classical machine learning or generative AI foundation models. The AI agents and critics collaborate with a central server to complete multimodal tasks such as fault detection, severity,

Ashutosh Vijay Kotwal

The CDF II experiment at the Fermilab Tevatron used a drift chamber to measure the momenta of charged particles. We present a model for the response of the drift chamber to the curvature of a charged particle's trajectory. Constraints on the model parameters are obtained from cosmic-ray data and from information published by CDF in the context of the W boson

Jinghan Yao, Kaushik Kandadi, Bharath Ramesh, Hari Subramoni

Modern GPU-based high-performance computing clusters offer unprecedented communication bandwidth through heterogeneous intra-node interconnects and inter-node networks. However, despite this high aggregate bandwidth, many real-world communication patterns fail to fully utilize the available hardware. Traffic skew often leads to situations where a small subse

Bardia Azizian, Ivan V. Bajic

The rapid progress of large Vision-Language Models (VLMs) has enabled a wide range of applications, such as image understanding and Visual Question Answering (VQA). Query images are often uploaded to the cloud, where VLMs are typically hosted, hence efficient image compression becomes crucial. However, traditional human-centric codecs are suboptimal in this

Thomas Manuel Rost

Automated species classification from underwater imagery is bottlenecked by the cost of expert annotation, and supervised models trained on one dataset rarely transfer to new conditions. We investigate whether a simple classifier operating on frozen foundation model embeddings can close this gap. Using frozen DINOv3 ViT-B/16 embeddings with no fine-tuning, w

Youngwook Do, Yuxi Wu, Gregory D. Abowd, Sauvik Das

Sensor-based interactive systems -- e.g., "smart" speakers, webcams, and RFID tags -- allow us to embed computational functionality into physical environments. They also expose users to real and perceived privacy risks: users know that device manufacturers, app developers, and malicious third parties want to collect and monetize their personal data, which fu

Mordecai Waegell

Superdeterminism has received recent attention as a possible path toward a locally causal explanation of the entanglement correlations that appear in experimental tests of Bell's theorem. While the term `superdeterminism' was coined by Bell to refer to restrictions on the free will of experimenters, it was not rigorously defined until recently. It has now be

Anurag Kumar, Raghuveer Peri, Jon Burnsky, Alexandru Nelus

Multimodal large-language models (MLLMs) often experience degraded safety alignment when harmful queries exploit cross-modal interactions. Models aligned on text alone show a higher rate of successful attacks when extended to two or more modalities. In this work, we propose a simple conditional decoding strategy, CASA (Classification Augmented with Safety At

Mohammadreza Kamaldar

This paper presents a state- and control-dependent moving-horizon estimation (SCD-MHE) algorithm for nonlinear discrete-time systems. Within this framework, a pseudo-linear representation of nonlinear dynamics is leveraged utilizing state- and control-dependent coefficients, where the solution to a moving-horizon estimation problem is iteratively refined. At

Fan Wu, Matthias P. Nägele, Daryush D. Mehta, Elgar Fleisch

Objective: This study aimed to evaluate which voice features can predict health deterioration in patients with chronic HF. Background: Heart failure (HF) is a chronic condition with progressive deterioration and acute decompensations, often requiring hospitalization and imposing substantial healthcare and economic burdens. Current standard-of-care (SoC) home

Huseyin Tuna Erdinc, Ipsita Bhar, Rafael Orozco, Thales Souza

Recent advances in generative networks have enabled new approaches to subsurface velocity model synthesis, offering a compelling alternative to traditional methods such as Full Waveform Inversion. However, these approaches predominantly rely on the availability of large-scale datasets of high-quality, geologically realistic subsurface velocity models, which

Mohamed Serry, S. Sivaranjani, Jun Liu

Analyzing nonlinear systems with stabilizable controlled invariant sets (CISs) requires accurate estimation of their domains of stabilization (DOS) together with associated stabilizing controllers. Despite extensive research, estimating DOSs for general nonlinear systems remains challenging due to fundamental theoretical and computational limitations. In thi

Shuli Jiang, Zhaoyang Zhang, Yi Zhang, Shuo Yang

Large language models (LLMs) exhibit strong reasoning and conversational abilities, but ensuring reliable behavior in multi-turn interactions remains challenging. In many real-world applications, agents must succeed in one-shot settings where retries are impossible. Existing approaches either rely on reflection or post-hoc evaluation, which require additiona

Jonathan Shelby

CubeSats have democratised access to space for universities, start-ups and emerging space nations, but the same design decisions that reduce cost and complexity introduce distinctive cybersecurity risks. Existing risk assessment frameworksNIST SP 800-37/53 [1, 2], ISO/IEC 27001/27005 [3, 4] and supply-chain guidance such as NIST SP 800-161 [5]assume abundant

Song-Ze Zhong, Xian-Gai Deng, Xu-Guang Huang, Yu-Gang Ma

We study the generation and space-time evolution of fluid acceleration in heavy-ion collisions using AMPT and UrQMD transport models combined with a Gaussian smearing method. The peak proper acceleration reaches several hundred MeV, with mild model dependence. Transverse acceleration points outward and is strongest at the fireball boundary due to steep press

Kailiang Wu

High-order finite volume and discontinuous Galerkin methods are often stabilized by separate nonlinear devices for admissibility, entropy control, and oscillation suppression. This separation hides a simple geometric fact: all three act on the same cellwise candidate state. We propose a general framework (termed EPO) unifying fully discrete entropy stability

Elaheh Sanoubari, Neil Fernandes, Keith Rebello, Alicia Pan

This paper presents REMind, an innovative educational robot-mediated role-play game designed to support anti-bullying bystander intervention among children. REMind invites players to observe a bullying scenario enacted by social robots, reflect on the perspectives of the characters, and rehearse defending strategies by puppeteering a robotic avatar. We evalu

Nanthit Temkulkiat, Chaiyong Ragkhitwetsagul, Morakot Choetkiertikul, Ruksit Rojpaisarnkit

The rapid adoption of AI coding agents is fundamentally shifting software developers' roles from code authors to code reviewers. While developers spend a significant portion of their time reading and comprehending code, the linguistic proficiency and complexity of the Python code generated by these agents remain largely unexplored. This study investigates th

Italo Felix Santos, Gilson Antonio Giraldi, Heron Werner Junior

We propose SANA-I2I, a text-free high-resolution image-to-image generation framework that extends the SANA family by removing textual conditioning entirely. In contrast to SanaControlNet, which combines text and image-based control, SANA-I2I relies exclusively on paired source-target images to learn a conditional flow-matching model in latent space. The mode

Florian Beyer, David Garfinkle, James Isenberg, Todd A. Oliynyk

It has been shown that, in spacetime dimensions $n\geq 3$, that the Kasner-scalar field solutions to the Einstein-scalar fields equations with potential $V_0 e^{-s \phi}$, where $s<s_c=\sqrt{\frac{8(n-1)}{n-2}}$ and $V_0\in \mathbb{R}$, are nonlinearly stable to the past and terminate at a quiescent big bang singularity over the full range of sub-critical Ka

Bahar Jahani, Matsanga Leyila Kaseka, Marta Kersten-Oertel, Yiming Xiao

Stroke remains a leading cause of mortality and disability worldwide, requiring rapid and informed clinical decision-making. A solid spatial understanding of cerebrovascular anatomy and vascular territories in relation to stroke symptoms and severity is critical for timely clinical decision and patient care. However, this knowledge is typically conveyed thro

Vijay Patel, Sarah Chase, Frank S. Tsung, John P. Palastro

Generalized local and multi-dimensional conservation laws of action, energy, momentum, and angular momentum are derived for stimulated Raman (SRS) and Brillouin backscattering (SBS) in a density gradient within the paraxial ray approximation. A Lagrangian density is found that reproduces the well known envelope equations for SRS and SBS in density gradients

V. I. Yukalov, E. P. Yukalova

Virial expansions are the series in powers of density assumed to be small. However, the equations of state require to consider finite densities for which virial expansions, as a rule, diverge. In order to extrapolate a virial expansion to the values, where this expansion diverges, one uses summation methods. The most often used method is the Pad\'{e} summati

Hochan Son, Xiaofeng Lin, Jason Ni, Guang Cheng

Deep generative models for tabular data (GANs, diffusion models, and LLM-based generators) exhibit highly non-uniform behavior across datasets; the best-performing synthesizer family depends strongly on distributional stressors such as long-tailed marginals, high-cardinality categorical, Zipfian imbalance, and small-sample regimes. This brittleness makes pra

Sahil Kumar, Namrataben Patel, Honggang Wang, Youshan Zhang

MambaVoiceCloning (MVC) asks whether the conditioning path of diffusion-based TTS can be made fully SSM-only at inference, removing all attention and explicit RNN-style recurrence layers across text, rhythm, and prosody, while preserving or improving quality under controlled conditions. MVC combines a gated bidirectional Mamba text encoder, a Temporal Bi-Mam

Elliot Murphy

Biolinguistics is the interdisciplinary scientific study of the biological foundations, evolution, and genetic basis of human language. It treats language as an innate biological organ or faculty of the mind, rather than a cultural tool, and it challenges a behaviorist conception of human language acquisition as being based on stimulus-response associations.

Candace Bethea, Charanya Ravi

We introduce the degree and local degree in equivariant motivic homotopy theory for the purpose of studying equivariant enumerative problems over general fields. Given a finite, tame group scheme $G$ over a field $k$ and an equivariant motivic ring spectrum $E_G$, we define the equivariant motivic degree and a corresponding local degree of a relatively $E_G$

Ting-Ran Liu, Xiangwei Guo, Sajid Husain, Maya Ramesh

The coexisting rhombohedral-like (R', MA) and tetragonal-like (T', MC) monoclinic phases in compressively strained bismuth ferrite thin films exhibit exceptional piezoelectric and magnetic properties. While previous studies have largely focused on probing the morphotropic phase boundaries (MPBs) comprising ordered R'/T' twins, their self-organizing structure

Yanliang Huang, Peng Xie, Zhen Zhang, Wenyuan Wu

Safety-critical control of piecewise affine (PWA) systems under bounded additive disturbances requires guarantees not for individual states but for entire state sets simultaneously: a single control action must steer every state in the set toward a target, even as sets crossing mode boundaries split and evolve under distinct affine dynamics. Certifying such

WaiChing Sun

When three-dimensional bodies contain thin features, non-trivial topology, or scan-derived surfaces, volumetric meshing can become the dominant bottleneck in simulation workflows. We replace this step with a learned geometric representation: overlapping volumetric coordinate charts, each equipped with a neural decoder and Jacobian, trained from point-cloud o

Ujjwal Jain

Accurate segmentation of cardiac structures in cardiovascular magnetic resonance (CMR) images is essential for reliable diagnosis and treatment of cardiovascular diseases. However, manual segmentation remains time-consuming and suffers from significant inter-observer variability. Recent advances in deep learning, particularly foundation models such as the Se

Gaurav Rajesh Parikh, Angikar Ghosal

We formally introduce a improvisational wordplay game called Connections to explore reasoning capabilities of AI agents. Playing Connections combines skills in knowledge retrieval, summarization and awareness of cognitive states of other agents. We show how the game serves as a good benchmark for social intelligence abilities of language model based agents t

Yanliang Huang, Peng Xie, Wenyuan Wu, Zhuoqi Zeng

We present a data-driven framework for reachability analysis of nonlinear dynamical systems that requires no explicit model. A denoising diffusion probabilistic model learns the time-evolving state distribution of a dynamical system from trajectory data alone. The predicted reachable set takes the form of a sublevel set of a nonconformity score derived from

Alejandro Ciuba, Zheng YY Li, Aakash Gautam

For immigrants, language preservation is crucial to maintain their identity, but the process of immigration can put a strain on a community's ability to do so. We interviewed eight Nepali immigrants to understand barriers to language preservation across sociopolitical contexts in Nepal and immigrant life in the United States. Participants described strong mo

Mark Dranias, Adam Whitley

Large language models (LLMs) are increasingly embedded in computer science education through AI-assisted programming tools, yet such workflows often exhibit objective drift, in which locally plausible outputs diverge from stated task specifications. Existing instructional responses frequently emphasize tool-specific prompting practices, limiting durability a

Md Rakib Hossain Misu, Iris Ma, Cristina V. Lopes

Formal specifications play a central role in ensuring software reliability and correctness. However, automatically synthesizing high-quality formal specifications remains a challenging task, often requiring domain expertise. Recent work has applied large language models to generate specifications in Java Modeling Language (JML), reporting high verification p

Hongyuan Liu, Qinli Yang, Wen Li, Zhong Zhang

Vision-Language Models (VLMs) such as CLIP learn a shared embedding space for images and text, yet their representations remain geometrically separated, a phenomenon known as the modality gap. This gap limits tasks requiring cross-modal interchangeability, such as captioning and joint clustering. Existing post-processing approaches can partially improve cros

Albert S. Berahas, Frank E. Curtis, Lara Zebiane

An algorithm is proposed, analyzed, and tested for minimizing locally Lipschitz objective functions that may be nonconvex and/or nonsmooth. The algorithm, which is built upon the gradient-sampling methodology, is designed specifically for cases when objective function and generalized gradient values might be subject to bounded uncontrollable errors. Similarl

Simone Betteti, Luca Laurenti

Energy-based models (EBMs) implement inference as gradient descent on a learned Lyapunov function, yielding interpretable, structure-preserving alternatives to black-box neural ODEs and aligning naturally with physical AI. Yet their use in system identification remains limited, and existing architectures lack formal stability guarantees that globally preclud

Deepank Singh, Anurag Nihal, Vedhus Hoskere

Unsupervised segmentation approaches have increasingly leveraged foundation models (FM) to improve salient object discovery. However, these methods often falter in scenes with complex, multi-component morphologies, where fine-grained structural detail is indispensable. Many state-of-the-art unsupervised segmentation pipelines rely on mask discovery approache

Samer Abdulkarim, Evan Boyd, Karl Bridi, Alec Tufenkjian

UML state machine design is a critical process in software engineering. Traditionally, state machines are manually crafted by experienced engineers based on natural language requirements-a time-consuming and error-prone procedure. Many automated approaches exist but they require structured NL requirements. In this paper, we investigate the capabilities of cu

Karthikeyan Sankaralingam

The end of Moore's Law and Dennard scaling has fundamentally changed the economics of computer architecture. With transistor scaling delivering diminishing returns, architectural innovation is now the primary - and perhaps only - remaining lever for performance improvement. However, we argue that human-driven architecture research is fundamentally ill-suited

Igor Burban, Wassilij Gnedin

In 1970, Gelfand posed the problem of classifying the indecomposable objects in a representation category equivalent to the principal block of Harish-Chandra modules for $\mathsf{SL}_2(\mathbb{R})$; explicit solutions were obtained by Bondarenko, and, independently, Crawley-Boevey. In this article, we give a complete answer to Gelfand's problem from a derive

Manuel Pita

Cellular automata generate spatially extended, temporally persistent emergent structures from local update rules. No general method derives the mechanisms of that generation from the rule itself; existing tools reconstruct structure from observed dynamics. This paper shows that the look-up table contains a readable causal architecture and introduces a forwar

Murat Kurtand Selçuk Çakmak, Azmi Gençten

In this study, we propose an efficient quantum multiplication approach based on a QFT-assisted parallelized addition scheme. The multiplication stage is implemented using a structure composed entirely of Toffoli gates, which generate partial products. In the second stage, these partial results are accumulated using a QFT-based adder. Unlike conventional QFT-

Ivor van der Hoog, Henrik Reinstädtler, Eva Rotenberg

We present a new fully dynamic algorithm for maintaining convex hulls under insertions and deletions while supporting geometric queries. Our approach combines the logarithmic method with a deletion-only convex hull data structure, achieving amortised update times of $O(\log n \log \log n)$ and query times of $O(\log^2 n)$. We provide a robust and non-trivial

Iason Efraimidis, Rodrigo Hernández

The harmonic inner radius $\sigma_H(\Omega)$ of a planar domain $\Omega$ is the largest constant with which a univalence criterion via the Schwarzian derivative holds for harmonic mappings. We show that $\sigma_H(\Omega)\leq\sigma_H(\mathbb{D})\leq 3/2$ for the unit disk $\mathbb{D}$ and for every domain $\Omega$ that omits an open set. This is an analogue o

Xi Chen, Yuhao Li, Mihalis Yannakakis

We give an $O(\log^2 n)$-query algorithm for finding a Tarski fixed point over the $4$-dimensional lattice $[n]^4$, matching the $\Omega(\log^2 n)$ lower bound of [EPRY20]. Additionally, our algorithm yields an ${O(\log^{\lceil (k-1)/3\rceil+1} n)}$-query algorithm for any constant $k$, improving the previous best upper bound ${O(\log^{\lceil (k-1)/2\rceil+1

Xinpeng Li, Bolin Lai, Hardy Chen, Shijian Deng

We introduce Omni-MMSI, a new task that requires comprehensive social interaction understanding from raw audio, vision, and speech input. The task involves perceiving identity-attributed social cues (e.g., who is speaking what) and reasoning about the social interaction (e.g., whom the speaker refers to). This task is essential for developing AI assistants t

Efe Gürel, Abuzer Gündüz

Let $A \bowtie^{f,g} (J,J')$ be the bi--amalgamation of a commutative ring $A$ with $(B,C)$ along the ideals $(J,J')$ with respect to the ring homomorphisms $(f,g)$. In this article, we study the basic homological properties of the bi--amalgamated algebra construction. We first calculate the dimension and depth of the bi--amalgamated algebra under fairly gen

Kirill Borodin, Vasiliy Kudryavtsev, Maxim Maslov, Nikita Vasiliev

We investigate multi-stage pretraining for prosody modeling in diffusion-based TTS. A speaker-conditioned dual-stream encoder is trained with masked language modeling followed by SigLIP-style cross-modal contrastive learning using mixed-phoneme batches, with an additional same-phoneme refinement stage studied separately. We evaluate intrinsic text-audio retr

Edoardo Zorzi, Francesco Taioli, Yiming Wang, Marco Cristani

We propose Question-Asking Navigation (QAsk-Nav), the first reproducible benchmark for Collaborative Instance Object Navigation (CoIN) that enables an explicit, separate assessment of embodied navigation and collaborative question asking. CoIN tasks an embodied agent with reaching a target specified in free-form natural language under partial observability,

Eloghosa Ikponmwoba, Opeoluwa Owoyele

The computational cost of stiff chemical kinetics remains a dominant bottleneck in reacting-flow simulation, yet hybrid integration strategies are typically driven by hand-tuned heuristics or supervised predictors that make myopic decisions from instantaneous local state. We introduce a constrained reinforcement learning (RL) framework that autonomously sele

Ayoub Louaye Bouaziz, Lokmane Chebouba

Cross-hospital failure in chest X-ray models is often attributed to domain shift, yet most work assumes invariance without measuring it. This paper studies how to measure site leakage directly and how that measurement changes conclusions about transfer methods. We study multi-site self-supervised learning (SSL) and feature-level adversarial site confusion fo

Z. E. Musielak, J. L. Fry, G. W. Kanan

Complex scalar fields described by a novel Klein-Gordon equation derived from gauge and group theories are considered at the Schwarzschild's black hole singularities. It is shown that the field is well-behaved in the vicinity of these singularities and that its value reaches zero at both singularities. The obtained results also demonstrate that the field for

David S. Dean, Haim Diamant

Transport coefficients and dielectric relaxation in liquids are often treated as distinct manifestations of molecular dynamics. We show that, in polar liquids, orientational dipolar fluctuations generate a substantial contribution to the shear viscosity that can be expressed in terms of dielectric response parameters. Using a Green-Kubo approach formulated i

Zaifu Zhan, Mengyuan Cui, Rui Zhang

Large language models (LLMs) have achieved strong performance on medical question answering (medical QA), and chain-of-thought (CoT) prompting has further improved results by eliciting explicit intermediate reasoning; meanwhile, self-reflective (self-corrective) prompting has been widely claimed to enhance model reliability by prompting LLMs to critique and

Lam M. Nguyen, Dzung T. Phan, Jayant Kalagnanam

Shuffling strategies for stochastic gradient descent (SGD), including incremental gradient, shuffle-once, and random reshuffling, are supported by rigorous convergence analyses for arbitrary within-epoch permutations. In particular, random reshuffling is known to improve optimization constants relative to cyclic and shuffle-once schemes. However, existing th

Filip J. Kucia, Anirban Chakraborty, Anna Wróblewska

Despite growing interest in using Large Language Models (LLMs) for educational assessment, it remains unclear how closely they align with human scoring. We present a systematic evaluation of instruction-tuned LLMs across three open essay-scoring datasets (ASAP 2.0, ELLIPSE, and DREsS) that cover both holistic and analytic scoring. We analyze agreement with h

Cormac Guerin, Frank Guerin

Tool-calling autonomous agents based on large language models using ReAct exhibit three limitations: serial latency, quadratic context growth, and vulnerability to prompt injection and hallucination. Recent work moves towards separating planning from execution but in each case the model remains coupled to the execution mechanics. We introduce a system-level

Md Mirajul Islam, Rajesh Debnath, Adittya Soukarjya Saha, Min Chi

While apprenticeship learning has shown promise for inducing effective pedagogical policies directly from student interactions in e-learning environments, most existing approaches rely on optimal or near-optimal expert demonstrations under a fixed reward. Real-world student interactions, however, are often inherently imperfect and evolving: students explore,

Chayce Hughes, Huub de Jong

We show that an expanding toral endomorphism in dimension 2 admits a smooth (in fact linear) Markov partition if and only if some power of the corresponding integer matrix is diagonalizable with integer eigenvalues. We exhibit examples of qualitatively different smoothness behavior, and highlight the existence of a hybrid type of smoothness in dimension 2. F

Chuyi Dai, Witold Pedrycz, Suping Xu, Ding Liu

Informed Machine Learning has emerged as a viable generalization of Machine Learning (ML) by building a unified conceptual and algorithmic setting for constructing models on a unified basis of knowledge and data. Physics-informed ML involving physics equations is one of the developments within Informed Machine Learning. This study proposes a novel direction

Robert W. Gray, Lynnclaire Dennis, Louis H. Kauffman

This work concerns how the three-dimensional polyhedral Mereon structure (the 120 polyhedron) is the precise projection from four-space of the 600-cell, an analogue in four-dimensional space of a regular solid. The 600-cell is made from 120 copies of a dodecahedron that are fitted together so that each dodecahedral face is matched to the face of another dode

William Clark, Leonardo Colombo, Anthony Bloch

Impulsive mechanical systems exhibit discontinuous jumps in their state, and when such jumps are triggered by spatial events, the geometry of the impact surface carries information about the controllability of the hybrid dynamics. For mechanical systems defined on principal $G$-bundles, two qualitatively distinct types of impacts arise: interior impacts, ass

Xinmiao Zhao, Han Yan, Xian Chen

Accurately modeling the inspiral-merger-ringdown (IMR) signal of coalescing compact objects is essential for the test of general relativity. However, it is known that astrophysical environments can distort gravitational-wave (GW) signal and, if ignored, may bias parameter estimation or even our understanding of gravity. Previous studies suggest that various

Sonae Hadama, Andrew Rout

In this paper, we introduce a renormalisation procedure for the density associated with the system of nonlinear Schr\"odinger equations (NLSS) on a circle. We show that this renormalised density satisfies better orthonormal Strichartz estimates than the non-renormalised density, which was considered in Nakamura (2020). We leave as a conjecture the optimal ra

Dirk HR Spennemann

This paper investigates how generative AI produces and propagates hallucinated academic references, focusing on the recurring non-existent citation 'Education Governance and Datafication' attributed to Ben Williamson and Nelli Piattoeva. Drawing on 137 accessible source papers identified through Google Scholar and Google searches, the study analyses the stru

Chloe Scholten, Elyssa M. McMaster, Adam M. Saunders, Michael E. Kim

In cases of prevalent diseases and disorders, such as Prenatal Alcohol Exposure (PAE), multi-site data collection allows for increased study samples. However, multi-site studies introduce additional variability through heterogeneous collection materials, such as scanner and acquisition protocols, which confound with biologically relevant signals. Neuroscient

Mohamed Abouagour, Atharva Shah, Eleftherios Garyfallidis

Diffusion MRI microstructure fitting is nonconvex and often performed voxelwise, which limits fiber peak recovery in narrow crossings. This work introduces PRISM, a differentiable analysis-by-synthesis framework that fits an explicit multi-compartment forward model end-to-end over spatial patches. The model combines cerebrospinal fluid (CSF), gray matter, up

Ha Na Cho

Single-agent large language model (LLM) systems struggle to simultaneously support diverse conversational functions and maintain safety in behavioral health communication. We propose a safety-aware, role-orchestrated multi-agent LLM framework designed to simulate supportive behavioral health dialogue through coordinated, role-differentiated agents. Conversat

Pawin Taechoyotin, Daniel E. Acuna

Most automated peer review systems rely on textual manuscript content alone, leaving visual elements such as figures and external scholarly signals underutilized. We introduce REM-CTX, a reinforcement-learning system that incorporates auxiliary context into the review generation process via correspondence-aware reward functions. REM-CTX trains an 8B-paramete

Marcus Bluhm, Yuki Fujimoto, Marlene Nahrgang

We extend the theoretical formulation of Quarkyonic Matter within the IdylliQ model framework proposed in [Y. Fujimoto et al., Phys. Rev. Lett. 132, 112701 (2024) [1]] for zero temperature to non-zero temperatures. To this end, we develop a consistent statistical mechanics and grand canonical ensemble description of Quarkyonic Matter as a quantum system subj

Elyssa M. McMaster, Gaurav Rudravaram, Michael E. Kim, Trent M. Schwartz

The HEALthy Brain and Childhood Development (HBCD) Study is an ongoing longitudinal initiative to understand population-level brain maturation; however, large-scale studies must overcome site-related variance and preserve biologically relevant signal. In addition to diffusion-weighted magnetic resonance imaging images, the HBCD dataset offers analysis-ready

Brendan R. Hogan, Xiwen Chen, James T. Wilson, Kashif Rasul

We present AlphaLab, an autonomous research harness that leverages frontier LLM agentic capabilities to automate the full experimental cycle in quantitative, computation-intensive domains. Given only a dataset and a natural-language objective, AlphaLab proceeds through three phases without human intervention: (1) it adapts to the domain and explores the data

Viviane Pons, Loïc Le Mogne

We study the $(q,t)$-enumeration of triangular Dyck paths considered by Bergeron and Mazin. To do so, we introduce the notion of triangular and sim-sym tableaux and the deficit statistic which is a new interpretation of the dinv. We use it to obtain new results and proofs on triangular $2$-partitions and an interesting conjecture for a certain lattice interv

Ibrahim Sadiek, Aleksandr A. Balashov, Adrian Hjältén, Michael Rey

Quantitative spectroscopic detection of dibromomethane, CH$_2$Br$_2$, for environmental monitoring, workplace safety, and exoplanetary studies is limited by the lack of accurate absorption cross-section data and rigorous spectroscopic models. We report the first high-resolution (6.3 MHz point spacing) absorption cross-section of CH$_2$Br$_2$ in the 1180-1210

Nicholas Kuang, Vanessa Scalon, Ji Yu

The modern deep learning field is a scale-centric one. Larger models have been shown to consistently perform better than smaller models of similar architecture. In many sub-domains of biomedical research, however, the model scaling is bottlenecked by the amount of available training data, and the high cost associated with generating and validating additional

Antonín Jarolím, Martin Fajčík

Document retrieval identifies relevant documents but does not provide fine-grained evidence cues, such as specific relevant spans. A possible solution is to apply an LLM after retrieval; however, this introduces significant computational overhead and limits practical deployment. We propose FGR-ColBERT, a modification of ColBERT retrieval model that integrate

Gabriel Turinici

Algorithms for the Multi-Armed Bandit (MAB) problem play a central role in sequential decision-making and have been extensively explored both theoretically and numerically. While most classical approaches aim to identify the arm with the highest expected reward, we focus on a risk-aware setting where the goal is to select the arm with the lowest variance, fa

Oleg Alekseev

We study a weighted renormalization of the mixed Hessian of the dispersionless Toda $\tau$-function associated with polynomial conformal maps. The starting point is an explicit logarithmic-kernel representation, which yields a decomposition of the Hessian into symmetry blocks and reduces the spectral analysis to the inverse-map generating function $U(x;\zeta

Nishat Raihan, Christian Newman, Marcos Zampieri

The world's 7,000+ languages vary widely in the availability of resources for NLP, motivating efforts to systematically categorize them by their degree of resourcefulness (Joshi et al., 2020). A similar disparity exists among programming languages (PLs); however, no resource-tier taxonomy has been established for code. As large language models (LLMs) grow in

H. Gökçen Güner, Francois Barthelat, John D. Clayton, Carlos Mora-Corral

Hard magnetic soft materials -- soft polymers embedded with hard magnetic particles -- are modeled using continuum magnetomechanical formulations in which the deformation and the magnetization field are the primary kinematic variables. A recent question in such formulations is whether the Cauchy stress is symmetric, which is directly related to frame invaria

Thomas Hofweber, Andreas Sudmann, Evangelos Pournaras

Present practice of deciding on regulation faces numerous problems that make adopted regulations static, unexplained, unduly influenced by powerful interest groups, and stained with a perception of illegitimacy. These well-known problems with the regulatory process can lead to injustice and have substantial negative effects on society and democracy. We discu

Yoann Boget, Pablo Strasser, Alexandros Kalousis

Denoising-based models, including diffusion and flow matching, have led to substantial advances in graph generation. Despite this progress, such models remain constrained by two fundamental limitations: a computational cost that scales quadratically with the number of nodes and a large number of function evaluations required during generation. In this work,

Jinghan Yao, Sam Adé Jacobs, Walid Krichene, Masahiro Tanaka

Long-context decoding in LLMs is IO-bound: each token re-reads an ever-growing KV cache. Prior accelerations cut bytes via compression, which lowers fidelity, or selection/eviction, which restricts what remains accessible, and both can degrade delayed recall and long-form generation. We introduce MAC-Attention, a fidelity- and access-preserving alternative t

Wenhao Wang, Aditya Saraf, Lioba Heimbach, Kushal Babel

On high-throughput, low-fee blockchains, a qualitatively new form of maximal extractable value (MEV) has emerged: searchers submit large volumes of speculative transactions, whose profitability is resolved only at execution time. We refer to this as spam MEV. On major rollups, it can at times consume more than half of block gas, even though only a small frac

Harim Song, Donghoi Kim, Chinkyo Kim

GaN polarity inversion and the associated inversion domain boundaries (IDBs) are frequently observed during lateral overgrowth and are often discussed in terms of the small energetic spread among competing IDB structures predicted by first-principles calculations. In circular mask openings, \(\{11\bar{2}0\}\)-aligned IDBs have previously been explained by ge

Heun Mo Yoo, Tanner M. Janda, Connor Nasseraddin, Jason R. Petta

The orbital, spin and valley degrees of freedom in silicon quantum dots support many modes of spin qubit operation. However, it is generally challenging to obtain information about the energy level spectrum over large ranges of parameter space. We demonstrate a form of spectroscopy that is capable of mapping the energy level structure of a double quantum dot

Dmitry Budker, Tim Chupp, Klaus Kirch, W. Mike Snow

For a century, spin has been an indispensable probe of the fundamental laws of nature. A reflection on the role of spin in shaping modern physics is presented, from the early days of quantum mechanics to the latest precision tests of the Standard Model. The significance of magnetic and electric dipole moments in testing CP and CPT symmetries is surveyed, alo

Kun Qin, Carsten Trinitis

Intrinsic nonlinearity in FPGA-based time-to-digital converters (TDCs) is often treated as a calibration issue and evaluated mainly through post-correction metrics. In quantum key distribution (QKD), however, raw delay-line nonuniformity can affect coincidence timing and thereby influence accidental-coincidence rate and Quantum Bit Error Rate (QBER). This pa

Tanay Gondil

Large language models are trained to refuse harmful requests, but can they accurately predict when they will refuse before responding? We investigate this question through a systematic study where models first predict their refusal behavior, then respond in a fresh context. Across 3754 datapoints spanning 300 requests, we evaluate four frontier models: Claud