April 2026 arXiv papers — page 115

Sharookh Daruwalla, Nitin Mayande, Shreeya Verma Kathuria, Nitin Joglekar

The fundamental challenge of using Large Language Models (LLMs) for reliable, enterprise-grade analytics, such as sentiment prediction, is the conflict between the LLMs' inherent stochasticity (generative, non-deterministic nature) and the analytical requirement for consistency. The LLM inconsistency, coupled with the noisy nature of chaotic modern datasets,

Gerald A. Miller

Pohl et al. measured the energy difference between the 2P and 2S states of muonic hydrogen and used it to determine a precise value of the proton radius. The result disagreed significantly from values extracted from electronic hydrogen and elastic electron-proton scattering. This discrepancy was exciting because it indicated a breakdown of Coulomb's law. In

K. Weichman, M. VanDusen-Gross, G. Bruhaug, J. P. Palastro

For sufficiently short relativistic-intensity laser pulses, the disparity in time scales for electron and ion motion causes ions to behave as a fixed, neutralizing background. As the pulse duration or intensity is increased, ion motion becomes important, leading to instability in uniform plasmas but more complex, and potentially desirable behavior in structu

Fei Jiang, Lei Yang

This paper presents a comprehensive review of univariate process capability indices (PCIs), which are critical metrics for assessing how effectively a manufacturing process satisfies customer specifications based on a single quality characteristic. The primary objective of this review is to develop practical procedural workflows for conducting process capabi

The STAR Collaboration

We report the first measurement of one- and two-point energy correlators within jets in transversely polarized proton-proton collisions at $\sqrt{s}=200$ GeV, using the STAR detector at RHIC. These observables quantify the energy-weighted angular distribution of single hadrons and hadron pairs within jets, respectively. Sizable spin-dependent asymmetries are

Kyle Lucke, Zuzanna Krajewska-Travar, Shoukun Sun, Lu Cai

Tristructural isotropic (TRISO)-coated particle fuels undergo dimensional changes and chemical reactions during high-temperature neutron irradiation. Post-irradiation materialography helps understand processes that impact fuel performance, such as coating integrity and fission product retention. Conventionally, experts manually evaluate features in thousands

Álvaro Yángüez, Noam Avidan, Jan Kochanowski, Thomas A. Hahn

Quantum systems may contain underlying correlations which are inaccessible to computationally bounded observers. We capture this distinction through a framework that analyses bipartite states only using efficiently implementable quantum channels. This leads to a complexity-constrained max-divergence and a corresponding computational min-entropy. The latter q

Armando Coco, Alessandro Coclite, Stéphane Clain, Rui Miguel Pereira

Unfitted boundary methods are widely used to numerically solve partial differential equations (PDEs) on irregular domains, avoiding the computational burden of generating boundary-conforming grids. In the finite-difference framework, structured Cartesian grids offer advantages such as ease of implementation and efficient parallelization, while geometry is re

Tianhao Liu, Daniel Andrés Díaz-Pachón, J. Sunil Rao

Supervised No Free Lunch Theorems (NFLTs) are well studied, yet unsupervised NFLTs remain underexplored. For elliptical distributions, we prove that there exist two equally optimal, scientifically meaningful bump-hunting strategies that are exact opposites, with no universal winner. Specifically, peeling $k$ orthogonal dimensions from $\mathbb{R}^d$ ($d \ge

A. E. Petrov, A. M. Bykov

The orbital observatories PAMELA and AMS-02 have detected a significant excess in the cosmic ray (CR) positron flux at energies above several tens of GeV. The measured values exceed those expected in models of secondary origin of positrons due to inelastic collisions of CR nuclei with the interstellar matter. This excess may be due to the annihilation or dec

John B. Etnyre

These notes are an expanded version of evening talks at the 2025 Georgia International Topology Conference, and an abbreviated version of talks at Georgia Tech, which were aimed at graduate students. The hope was to indicate a common framework that has been used since the late 1980s to construct homology theories in low-dimensional topology and symplectic an

Anagha Khiste, Ganesh Tarte, Vinayak Joshi

In this paper, we are motivated by the conjectures proposed by C.~Bender \textit{et al.}, \cite{C} in 2024. We have settled the first two conjectures negatively by providing a counter example in \cite{KTJ}, whereas in this paper, we prove the third conjecture positively, which has remained an open question until now. The third conjecture is stated as if $G(S

Jonathan A. Noel

The $r$-neighbour bootstrap process on a graph $G$ begins with a set of infected vertices; subsequently, healthy vertices become infected once they have at least $r$ infected neighbours. The central extremal problem in bootstrap percolation is to determine the minimum cardinality of an initial infected set that eventually spreads to all vertices of $G$, deno

Abhishek Uppar, Omar Muhammad, Sumanth Prabhu, Deepak D'Souza

We consider the problem of verification modulo tested library contracts as a step towards automating the verification of client programs that use complex libraries. We formulate this problem as the synthesis of modular contracts for the library methods used by the client that are adequate to prove the client correct, and that also pass the scrutiny of a test

Sicong Chang, Yidan Shen, Justina Varghese, Akshay R Prabhakar

Chronic rhinosinusitis (CRS) is a common heterogeneous inflammatory disorder that causes substantial morbidity and healthcare costs. CRS is difficult to identify early from routine encounters, as symptom presentations overlap with common conditions such as allergic rhinitis, and heterogeneous phenotypes further obscure risk patterns. Prior predictive studies

Andrii Vakhnovskyi

We present a dual-radio hierarchical mesh architecture for infrastructure-free emergency communication that exploits the complementary strengths of Bluetooth Low Energy (BLE) and LoRa. Nodes equipped with both an nRF52840 (BLE 5.0 Coded PHY) and an SX1262 (LoRa sub-GHz) form local clusters via BLE advertising-based AODV routing, while dynamically elected clu

Sotirios D. Nikolopoulos

Adaptive specification search generates statistically significant backtests even under martingale-difference nulls. We introduce a falsification audit testing complete predictive workflows against synthetic reference classes, including zero-predictability environments and microstructure placebos. Workflows generating significant walk-forward evidence in thes

Ellen Lee

In astronomy, image slicer integral field units (IFUs) are often used in integral field spectrographs to simultaneously record spatial and spectral information. The majority of astronomical instruments, including integral field spectrographs, are designed using the Zemax OpticStudio optical design software. Modeling an image slicer IFU in Zemax traditionally

Yang Li, Zirui Zhang, Yang Liu, Chengzhi Mao

Current large language models reason in isolation. Although it is common to sample multiple reasoning paths in parallel, these trajectories do not interact, and often fail in the same redundant ways. We introduce LACE, a framework that transforms reasoning from a collection of independent trials into a coordinated, parallel process. By repurposing the model

Arman Mollakhani, Jerayu Tiamraj, Shu-Jie Cao, Dongning Guo

Large-scale low-Earth-orbit satellite constellations offer a promising platform for global low-latency networking, aided by faster propagation in free space than in fiber and copper. In such systems, end-to-end latency is largely determined by the inter-satellite link (ISL) topology. In particular, the network diameter, the maximum shortest path between any

Ke Zhang, Patricio Gallardo, Maziar Raissi, Sudhir Murthy

Automatic translation of natural language mathematics into faithful Lean 4 code is hindered by the fundamental dissonance between informal set-theoretic intuition and strict formal type theory. This gap often causes LLMs to hallucinate non-existent library definitions, resulting in code that fails to compile or lacks semantic fidelity. In this work, we inves

A. Chervinskaya, R. Metzler, K. E. Polovnikov

We develop a kinetic theory of cohesin-driven loop extrusion on a disordered chromatin track with transient barriers. In the stationary state, the mean loop size is shown to obey a universal law determined by the bare processivity and a renormalized obstacle density. Beyond the mean, one-sided extrusion always yields a single-exponential loop-length distribu

Shunzhi Zhang, Shichen Liao, Congying Han, Tiande Guo

We study unconstrained smooth convex optimization under stochastic first- and zeroth-order oracles subject only to finite-moment bounds, naturally admitting persistent bias and heavy-tailed noise. In this hostile environment, integrating momentum into \emph{adaptive step search} to secure acceleration poses an inherent structural challenge, because momentum

Dimitris Bertsimas, Carol Gao, Angelos G. Koulouras, Georgios Antonios Margonis

External validation is widely regarded as the gold standard for prognostic model evaluation. In this study, we challenge the assumption that successful external calibration guarantees model generalizability and propose two complementary strategies to improve transportability of prognostic models across cohorts. Using six real-world surgical cohorts from tert

Mingrui Wang, Prakash Chakraborty, Uday V. Shanbhag

In this paper, we consider two distinct challenges in the resolution of nonsmooth stochastic optimization. Of these, the first pertains to the pronounced dependence of dimension in Gaussian smoothing-enabled zeroth-order schemes, impeding applications to large-scale settings. Second, no unified analysis {exists} for smoothing-enabled stochastic zeroth-order

Behzad Djafari Rouhani, Jan Lang, Osvaldo Méndez

It is shown that if $p_n$ is a sequence of continuous, unbounded exponents on a bounded, smooth domain $\Omega\subset {\mathbb R}^n$ with $1<\inf\limits_{x\in \Omega}p_n(x)$ and $p_n\rightarrow \infty$ uniformly, then the sequence $(u_n)$ of solutions of the $p_n(\cdot)$-Laplacian converges to the viscosity solution of a suitable differential operator. The n

Dillon Jensen, Obi Nnorom, Grant Wilkins, Hugo Budd

Large-scale AI model training workloads use thousands of GPUs operating in tightly synchronized loops. During synchronous communication, start-up, shut-down, and checkpointing, GPU power consumption can swing from peak to idle within milliseconds. These large and rapid load swings endanger grid infrastructure as they induce steep power ramp rates, voltage an

Sucheng Ren, Qihang Yu, Ju He, Xiaohui Shen

Flow matching models have emerged as a powerful framework for realistic image generation by learning to reverse a corruption process that progressively adds Gaussian noise. However, because noise is injected in the latent domain, its impact on different frequency components is non-uniform. As a result, during inference, flow matching models tend to generate

Oleg Vlasovets, Fabian Schaipp, Leo Simpson, Evan Bolyen

Motivation: Statistical analysis of microbial count data derived from 16S rRNA or metagenomics sequencing poses unique challenges due to the sparse, compositional, and high-dimensional nature of the data. While QIIME 2 already provides many tools for data pre-processing and analysis, plugins for statistical regression, classification, and microbial network e

Graham M Shore

Rovibrational transitions in the hydrogen and antihydrogen molecular ions $H_2^+$ and $\overline{H}_2^-$ offer the possibility of testing Lorentz and CPT symmetry to extremely high precision, in principle attaining $O(10^{-17})$. In this paper, the third in a series, we give a comprehensive derivation of the rovibrational spectrum of $H_2^+$ and $\overline{H

Vinícius Pereira da Silva Oliveira, Danilo da Silva Borges, Erick de Moraes Franklin, Jorge Manuel Peixinho

The results of a numerical investigation of fluidized beds of spherical particles in a narrow vertical cylindrical pipe, with particular attention to the spontaneous settling along the wall, are reported. Starting from a steady fluidized state, the particles fluctuate because of fluid-particle, particle-particle, and particle-wall interactions. The particles

Longchen Niu, Gennaro Notomista

This paper introduces a safety-critical optimization-based control strategy that leverages control Lyapunov and control barrier functions to guide the spatial density of robotic swarms governed by the Fokker-Planck equation to a predefined target distribution. In contrast to traditional open-loop state-constrained optimal control strategies, the proposed app

Nikola Veselinov

Let $S = \{p_1,\dots,p_r\}$ be a finite set of distinct primes, let $\Psi_S(X)$ be the number of $S$-smooth integers not exceeding $X$, and let $F_S(X)$ be the maximum size of a subset of $M(S) \cap [1,X]$ containing no set $\{n,p_1 n,\dots,p_r n\}$. We prove that $ F_S(X)=\frac{r}{r+1}\Psi_S(X)+O_S\bigl((\log X)^{r-1}\bigr) $ as $X \to \infty$, and equivale

Dipto Das, Christelle Tessono, Syed Ishtiaque Ahmed, Shion Guha

In November 2025, the Government of Canada operationalized its commitment to transparency by releasing its first Federal AI Register. In this paper, we argue that such registers are not neutral mirrors of government activity, but active instruments of ontological design that configure the boundaries of accountability. We analyzed the Register's complete data

Anka He Chen, Jerry Hsu, Youssef Ayman, Miles Macklin

We present Divide and Truncate (DAT), a unified framework for coupling multi-physics systems through penetration-free collision handling, including rigid bodies, volumetric soft bodies, thin shells, rods, and animated objects. By partitioning the ambient space into exclusive regions and truncating displacements to remain within them, DAT guarantees penetrati

Maryam Tavassoli Sabzevari, Arif Ali Khan

Quantum computing (QC) is increasingly transitioning toward practical and industrial adoption, highlighting the need to understand how developers engage with quantum technologies. In this study, we analyze 1,404 Stack Overflow posts related to quantum computing topics, including quantum programming, tools, and algorithms, to investigate real-world developer

Sean T. Griffin, Jake Levinson, Rohini Ramadas, Rob Silversmith

The main result of this paper is a formula for the limit cycle of a 1-parameter family of subvarieties of a tropical compactification, expressed in terms of tropical intersections. Our theorem generalizes results of Dickenstein-Feichtner-Sturmfels and Katz to the case of tropical compactifications. In the second part of the paper, we apply our formula to the

Pavel Kos, Dominik S. Wild, Kristian Knakkergaard Nielsen

Understanding mechanisms for the breakdown of thermalization in closed quantum systems is a central problem in quantum many-body physics. We demonstrate strong non-ergodic behavior in the XX model on coupled chains, where domain-wall initial states retain an inhomogeneous magnetization profile for arbitrarily long times. We find that this effect arises due t

Han Zhou, Wenjun Ying

In this work, we propose a correction-function-based kernel-free boundary integral (CF-KFBI) method for solving Stokes- and Brinkman-type interface problems. We begin by recasting the original interface problem with discontinuous coefficients as boundary integral equations, in which the integral operators can be interpreted as boundary data for potential fun

David M. Berry

LLMbench is a browser-based workbench for the comparative close reading of large language model (LLM) outputs. Where existing tools for LLM comparison, such as Google PAIR's LLM Comparator are engineered for quantitative evaluation and user-rating metrics, LLMbench is oriented towards the hermeneutic practices of the digital humanities. Two model responses t

Kaleb Ben Naveed, Manveer Singh, Devansh R. Agrawal, Dimitra Panagou

Planning safe trajectories under model uncertainty is a fundamental challenge. Robust planning ensures safety by considering worst-case realizations, yet ignores uncertainty reduction and leads to overly conservative behavior. Actively reducing uncertainty on-the-fly during a nominal mission defines the dual control problem. Most approaches address this by a

Samuel Millstone, Megan Reiter, Morten Andersen, Thomas J. Haworth

A major obstacle to improving models of planet formation is understanding how the local environment influences the lifetime of the disks in which they form. The spread in observed disk lifetimes is caused by effects both observational (e.g., target selection, survey sensitivity) and physical (e.g., disk destruction by internal and external photoevaporation);

Jihye Choi, Jinsung Yoon, Long T. Le, Somesh Jha

LLM agents operating under organizational policies must comply with authorization constraints typically specified in natural language. In practice, such specifications inevitably contain ambiguities and logical or semantic gaps that cause the agent's behavior to systematically diverge from the true requirements. We ask: by letting an agent evolve its policy

Ihsan Kahveci, Timothy A. Thomas, Nathalie E. Williams, Janelle Rothfolk

Home eviction poses a significant threat to housing stability, a critical determinant of health. This study examines the relationship between eviction and health and substance use within the unhoused population of King County, Washington. Using a sample of 1,106 individuals experiencing homelessness, we employed a quasi-experimental design to compare the hea

Jingnong Qu, Ashvin Ranjan, Shane Steinert-Threlkeld

Recent breakthroughs in language models (LMs) using neural networks have raised the question: how similar are these models' processing to human language processing? Results using a framework called Brain Score (BS) -- predicting fMRI activations during reading from LM activations -- have been used to argue for a high degree of similarity. To understand this

Yubo Zhang, Luca Venturino, Xiaodong Wang

This work considers a system where a dual-function radar transmitter (source) performs direct communication with a reader while simultaneously enabling ambient backscatter communication from a tag. The source embeds its message into a coded pulse repeatedly transmitted over a frame, whereas the tag exploits the resulting environmental reverberation (clutter)

Matthew G. Baring, Jacob Casey, Sohee Chun, Ephraim Gau

We present updated hard X-ray polarization measurements of the Crab pulsar and nebula obtained with the balloon-borne polarimeter XL-Calibur in the ~19-64 keV energy range. During the flight, intermittent GPS-failure resulted in poorly constrained timing for ~38% of the Crab dataset. By implementing a new phase-recovery method that reconstructs timing during

Yukuan Zhang, Mengxin Zheng, Qian Lou

Cryptographically secure neural network inference typically relies on secure computing techniques such as Secure Multi-Party Computation (MPC), enabling cloud servers to process client inputs without decrypting them. Although prior privacy-preserving inference systems co-design network optimizations with MPC, they remain slow and costly, limiting real-world

Divya Shyamal, Marta Knežević, Lan Tran, Chanakya Ekbote

Test-time scaling (TTS) improves large language models (LLMs) by allocating additional compute at inference time. In practice, TTS is often achieved through parallel scaling: generating multiple candidate responses and selecting the best via a Best-of-N (BoN) strategy. Its effectiveness therefore hinges on the scoring function. Learned scorers such as proces

Anagha Khiste, Ganesh Tarte, Vinayak Joshi

In this paper, we derive a set of equivalent conditions for the zero-divisor graph $\Gamma(Q)$ of a poset $Q$ with $0$ to be complemented, characterizing it in terms of quasi-complemented posets. Furthermore, we prove that the notions of a complemented zero-divisor graph and a uniquely complemented zero-divisor graph coincide for any poset $Q$ with $0$. In a

Reachsak Ly, Alireza Shojaei

The communication protocols and data transfer mechanisms employed by IoT devices in smart buildings and corresponding digital twin systems predominantly rely on centralized architectures. Such centralized systems are vulnerable to single points of failure, where a malfunction can disrupt operational processes. This study introduces a blockchain-based decentr

Nicola Maggiore

We formulate a covariant version of Maxwell-like fracton electrodynamics in six dimensions using a symmetric tensor gauge field with scalar gauge symmetry $\delta A_{\mu\nu}=\partial_\mu\partial_\nu\Lambda$. This provides a relativistic setting in which the characteristic fractonic restriction on mobility follows directly from gauge invariance and the allowe

Shivendra Agrawal, Bradley Hayes

Navigating complex, densely packed environments like retail stores, warehouses, and hospitals poses a significant spatial grounding challenge for humans and embodied AI. In these spaces, dense visual features quickly become stale given the quasi-static nature of items, and long-tail semantic distributions challenge traditional computer vision. While Vision-L

Mohammad Mahdi Abootorabi, Parvin Mousavi, Purang Abolmaesumi, Evan Shelhamer

Deep networks that rely on prototypes-interpretable representations that can be related to the model input-have gained significant attention for balancing high accuracy with inherent interpretability, which makes them suitable for critical domains such as healthcare. However, these models are limited by their reliance on training data, which hampers their ro

Hongjian Zhou, Haoyu Yang, Haoxing Ren, Joaquin Matres

As AI systems scale to multi-chiplet and wafer-level architectures, the demand for ultra-high bandwidth and system scalability has outpaced the capabilities of electrical interconnects and computing units. Large-scale heterogeneous electronic-photonic integrated chiplets (EPICs) provide a promising solution, but their practical adoption is limited by the lac

Zijun Li, Aswin Kannan

We consider single and multiobjective simulation-based optimization problems. Simulation-based optimization has traditionally used both model-based and search-based methods, often in isolation. Model-based methods include trust region approaches and Bayesian optimization, while search methods include genetic algorithms and Direct Search-type techniques. In t

Eleanor M. Lin, David Jurgens

Recent developments in reasoning capabilities have enabled large language models to solve increasingly complex mathematical, symbolic, and logical tasks. Interestingly, while reasoning models are often trained to generate monolingual text, these models have also been observed to code-switch (i.e., mix languages). Prior works have either viewed code-switching

Mehdi Hosseinzadeh, Roohallah Alizadehsani, Amin Beheshti, Hamid Alinejad-Roknyd

The Internet of Medical Things (IoMT) enables intelligent healthcare services but faces challenges such as dynamic topology, energy constraints, and diverse QoS requirements. This paper proposes QQMR, a Q-learning-based QoS-aware multipath routing method for WBANs. QQMR classifies data into three priority levels and employs adaptive multi-level queuing and f

Zixuan Weng, Jinghuai Zhang, Kunlin Cai, Ying Li

Large language models (LLMs) often exhibit undesirable behaviors, such as safety violations and hallucinations. Although inference-time steering offers a cost-effective way to adjust model behavior without updating its parameters, existing methods often fail to be simultaneously effective, utility-preserving, and training-efficient due to their rigid, one-si

Jianyu Zhang

The prevalence of matter over antimatter in the observable universe remains one of the most profound puzzles in modern physics, necessitating sources of charge-parity (CP) symmetry violation beyond those incorporated in the Standard Model. While electric dipole moments (EDMs) of elementary particles serve as sensitive probes for such new physics, the hyperon

Priscila A. Tapia Presas, Lautaro Galarregui, Wilson Román Acevedo, Myriam H. Aguirre

Ferroelectric memristive devices based on hafnia are promising systems for neuromorphic electronics, yet the interplay between polarization-modulated resistive changes and defect-mediated transport often leads to complex and debated switching mechanisms. Here, we investigate this competition in epitaxial Hf$_{0.5}$Zr$_{0.5}$O$_2$/La$_{0.67}$Sr$_{0.33}$MnO$_3

Faranak Hatami, Valmor F. de Almeida

An iterative optimization algorithm with MD simulations in the loop is developed and applied to optimize Lennard-Jones (LJ) parameters specific for liquid tri-n-butyl phosphate (TBP). The optimization loop uses non-dominated sorting genetic algorithms to obtain LJ parameters that reproduce key properties such as mass density, electric dipole moment, heat of

Sarah Bedell, Nazanin Siavash, Armin Moin

Memory safety has long been a critical challenge in software engineering, particularly for legacy systems written in memory-unsafe languages such as C and C++. Rust, one of the youngest modern programming languages, offers built-in memory-safety guarantees that make it a strong candidate for secure systems development. Consequently, transpiling C/C++ code in

Jayson Steffens

We present **vstash**, a local-first document memory system that combines vector similarity search with full-text keyword matching via Reciprocal Rank Fusion (RRF) and adaptive per-query IDF weighting. All data resides in a single SQLite file using sqlite-vec for approximate nearest neighbor search and FTS5 for keyword matching. We make four primary contribu

Physical Intelligence, Bo Ai, Ali Amin, Raichelle Aniceto

We present a new robotic foundation model, called ${\pi}_{0.7}$, that can enable strong out-of-the-box performance in a wide range of scenarios. ${\pi}_{0.7}$ can follow diverse language instructions in unseen environments, including multi-stage tasks with various kitchen appliances, provide zero-shot cross-embodiment generalization, for example enabling a r

Yisheng Zhong, Sijia Liu, Zhuangdi Zhu

Large Language Models (LLMs) unlearning is crucial for removing hazardous or privacy-leaking information from the model. Practical LLM unlearning demands satisfying multiple challenging objectives simultaneously: removing undesirable knowledge, preserving general utility, avoiding over-refusal of neighboring concepts, and, crucially, ensuring robustness agai

David M. Fobes, Harsha Nagarajan, Manuel Garcia, Robert Ferrando

The distribution system restoration (DSR) problem has received considerable attention over the last decade or more. Solutions to the DSR problem identify the best set or sequence of actions to perform on a distribution circuit to restore service after a disruption. The problem is challenging from a computational perspective, with engineering constraints spec

Goffredo Chirco, Alfonso Lamberti, Patrizia Vitale

We study the Hamiltonian formulation of a gravity model obtained from a Yang--Mills theory for a one-parameter family of (A)dS Lie algebras parametrized by $\alpha$, when the family of algebras is contracted to the Poincar\'e algebra in the limit $\alpha \to 0$. We derive the canonical structure and first-class constraints and analyze the resulting algebra i

Neda Valizadeh, Robabeh Rahimi, Ramin Abolfath

{\bf Purpose}: To develop a geometry-governed diffusion framework that explains differential tissue response under FLASH ultra-high dose rate (UHDR) irradiation by explicitly accounting for structural heterogeneity and anomalous transport in biological tissues. {\bf Methods}: We formulate a generalized diffusion--reaction model on fractal substrates to descr

Max H. Cohen, Pio Ong, Pol Mestres, Aaron D. Ames

Control barrier functions (CBFs) provide a rigorous framework for designing controllers enforcing safety constraints. While CBF theory is well-developed for a finite number of safety constraints, certain applications, e.g., backup CBFs, require an infinite number of constraints. Despite the practical success of CBFs, several fundamental questions remain unan

Pernille Undrum Fathi, Irene Occhiodori, Patrick Devaney, Amberly Ricks

Nonlinear frequency conversion unlocks technologies ranging from telecommunications to quantum computation; however, weak nonlinearities and architectures that resist miniaturization currently limit devices. Here, we combine a bandstructure-engineered GaAs/AlGaAs heterostructure with a high quality factor dielectric metasurface to simultaneously tailor the i

Yang Zhou, Yash Shetye, Long Quang, Devon Super

Deploying learned multi-robot models on heterogeneous robots remains challenging due to hardware heterogeneity, communication constraints, and the lack of a unified execution stack. This paper presents NeuroMesh, a multi-domain, cross-platform, and modular decentralized neural inference framework that standardizes observation encoding, message passing, aggre

Jack T. Beerman, Tyler J. Abele, Mehdi Taghizadeh, Andrew Davis

Physics-aware recurrent convolutional networks (PARC) have demonstrated strong performance in predicting nonlinear spatiotemporal dynamics by embedding differential operators directly into the computational graph of a neural network. However, pixel-based convolutions are restricted to static, uniform Cartesian grids, making them ill-suited to following evolv

Maja Gwozdz

For the distance cost $c(x,y)=|x-y|$, the set $O(\mu,\nu)$ of $W_1$-optimal plans is generally not a singleton. Under the classical absolute-continuity hypotheses in the Euclidean case, secondary variational selection by the quadratic energy $C_2$ yields the ray-monotone $W_1$-optimal plan. We provide a counterexample to an open problem posed by Santambrogio

Yuriy Kim, Evgeny Belyaev

This paper is dedicated to lossless data compression with probability estimation using neural networks. First, we propose a probability estimation architecture based on a chain of neural predictors, so that each unit of the chain is defined as a neural network with the minimum possible number of weights, which is sufficient for efficient compression of data

Máximo Bañados, Marc Henneaux

We analyze the two-dimensional Palatini Gauss-Bonnet theory on an infinite strip (product of a finite interval with the infinite line, corresponding to ``time"). The theory has only boundary degrees of freedom. Its phase space is the cotangent bundle to the group manifold of $SL(2,\mathbf{R})$, subject to a (first-class) constraint quadratic in the momenta.

Joshua D. Ibrahim, Mahdi Taheri, Soon-Jo Chung, Fred Y. Hadaegh

This paper focuses on data-driven fault detection, identification, and recovery (FDIR) for nonlinear control-affine systems under actuator faults. We create a unified framework in the space of probability densities, rather than on individual trajectories, using fault-indexed Perron--Frobenius (PF) operators to predict the evolution of state distributions und

Yucong Liu, Zilai Si, Alexander Strang

Posterior distributions arising in ill-posed Bayesian inverse problems are often both analytically intractable and highly sensitive to parameters of the chosen prior family. We aim to understand the sensitivity of intractable posterior distributions to changes in prior assumptions by tracking how a sample representation of the posterior changes as the prior

Robert Feldt, Per Lenberg, Julian Frattini, Dhasarathy Parthasarathy

AI-based systems, currently driven largely by LLMs and tool-using agentic harnesses, are increasingly discussed as a possible threat to software engineering. Foundation models get stronger, agents can plan and act across multiple steps, and tasks such as scaffolding, routine test generation, straightforward bug fixing, and small integration work look more ex

Emily Curl, Kofi Ampomah, Md Erfan, Sayanton Dibbo

While deep learning systems are becoming increasingly prevalent in medical image analysis, their vulnerabilities to adversarial perturbations raise serious concerns for clinical deployment. These vulnerability evaluations largely rely on Attack Success Rate (ASR), a binary metric that indicates solely whether an attack is successful. However, the ASR metric

Brody Gatza, Cheng Huang

Even with the most advanced computational capabilities, high-fidelity (e.g., large-eddy) simulations of large-scale rocket engines remain far out of reach. In the current work, we develop and establish a component-based reduced-order modeling (CBROM) framework to enable accurate and efficient parametric modeling of large-scale rocket engines by geometrically

Kenneth Moore, Arsenii Sagdeev

In this note, we prove that any 2-coloring of the plane contains 4 points of the same color forming a rhombus with unit sides and non-unit diagonals, answering a question of Axenovich, Liu, and the second author.

Saleh Amareen, Arif Rahman, Sazzadur Rahaman, Amiangshu Bosu

GraphQL provides a schema-based, strongly typed query language that enables highly efficient client-server communication. This paper introduces GraphQLify, an automated framework designed to migrate existing REST APIs to GraphQL. Unlike prior approaches that rely on relational databases, resource description frameworks (RDF), or machine-parsable specificatio

Jevin Jiang, Ying Chen, Blake A. Hechtman, Fenghui Zhang

Large Language Model (LLM) deployment is increasingly shifting to cost-efficient accelerators like Google's Tensor Processing Units (TPUs), prioritizing both performance and total cost of ownership (TCO). However, existing LLM inference kernels and serving systems remain largely GPU-centric, and there is no well-established approach for efficiently mapping L

Sebastien Lleo, Wolfgang Runggaldier

We study a benchmarked risk-sensitive portfolio problem in a factor-based setting to bring together three strands of the literature: benchmarked risk-sensitive investment management, the Kuroda-Nagai change-of-measure method, and the free energy-entropy duality of Dai Pra et al. (1996). We show that the duality yields a direct solution of the benchmarked pro

Ioannis Gkeneralis

We study topological rigidity of real moment-angle manifolds associated to flag simplicial complexes. Using the cubical geometry arising from the Davis construction, we identify the universal cover with the Davis complex and deduce that it admits a CAT(0) metric. As a consequence, its fundamental group satisfies the Farrell--Jones conjecture. Applying surger

Nassima M. Bouzid, Dehao Yuan, Nam H. Nguyen, Mayana Pereira

LLM-based simulators offer a promising path for generating complex synthetic data where traditional differentially private (DP) methods struggle with high-dimensional user profiles. But can LLMs faithfully reproduce statistical distributions from DP-protected inputs? We evaluate this using PersonaLedger, an agentic financial simulator, seeded with DP synthet

Michael Wallner

A chain is defined as a directed acyclic graph (DAG) with one source and one sink, where the children are ordered and the spanning tree computed using a depth-first search is a path. Such DAGs emerge in the context of tree compression and are therefore uniquely associated with a tree. The tree size of a DAG is defined as the size of the associated tree. For

Hengky Susanto, David James Woo, Chingyi Yeung, Stephanie Wing Yan Lo-Philip

The rapid evolution of Large Language Models (LLMs) has made them powerful tools for enhancing student writing. This study explores the extent and limitations of LLMs in assisting secondary-level English as a Foreign Language (EFL) students with their writing tasks. While existing studies focus on output quality, our research examines the developmental shift

Yuxin Liu, Yiqing Dong, Wenxue Yu, Zhan Wu

Medical image denoising (MID) lacks absolutely clean images for supervision, leading to a noisy reference problem that fundamentally limits denoising performance. Existing simulated-supervised discriminative learning (SimSDL) and simulated-supervised generative learning (SimSGL) treat noisy references as clean targets, causing suboptimal convergence or refer

Fatma Terzioglu

We study mapping properties of the $k$-plane transform in Sobolev, Besov, and Triebel--Lizorkin spaces. For $1\le k\le d-1$, the $k$-plane transform integrates a function over $k$-dimensional affine planes in $\mathbb{R}^d$, yielding a function on the affine Grassmannian $\mathcal{G}_{k,d}$. First, we establish Sobolev stability estimates for compactly suppo

Yunsoo Ha, Sara Shashaani, Quoc Tran-dinh

We propose a stochastic nonconvex optimization algorithm that achieves almost sure $\tilde{\mathcal{O}}(\epsilon^{-1.5})$ iteration complexity for problems with smooth objective functions and gradients only observable with noise. The mean-zero stochastic noise is decision-dependent and has unbounded support with subexponential tail, allowing our framework to

Zhizheng Wang, Chih-Hsuan Wei, Joey Chan, Robert Leaman

Trustworthiness and transparency are essential for the clinical adoption of artificial intelligence (AI) in healthcare and biomedical research. Recent deep research systems aim to accelerate evidence-grounded scientific discovery by integrating AI agents with multi-hop information retrieval, reasoning, and synthesis. However, most existing systems lack expli

Skye Thompson, Ondrej Biza, George Konidaris

Given a demonstration, a robot should be able to generalize a skill to any object it encounters-but existing approaches to skill transfer often fail to adapt to objects with unfamiliar shapes. Motivated by examples of improved transfer from compositional modeling, we propose a method for improving transfer by decomposing objects into their constituent semant

Lars Moreels, Nicolai Bechler, Bartel Van Waeyenberge, Jonathan Leliaert

In this paper, we demonstrate how altermagnets can be simulated in the recently released micromagnetic simulation package mumax+. We have added a new magnet class for d-wave altermagnets and demonstrate how mumax+ is able to reproduce the analytical solutions for line profiles of the Néel vector and net magnetization for a Bloch domain wall. Next, we show si

Ginevra Braga, Raul Jimenez, Sabino Matarrese

We ask whether a local symmetry can organize both classical degeneracy and perturbative stability in DHOST theories. An AI-assisted search finds a candidate in a constant-disformal image of Einstein gravity. We identify it as a field-dependent diffeomorphism plus a vertical translation of a spectator scalar and extend it to every regular first-derivative map

Zhitong Gao, Parham Rezaei, Ali Cy, Mingqiao Ye

Tokenization is a key component of autoregressive (AR) generative models, converting raw data into more manageable units for modeling. Commonly, tokens describe local information, such as regions of pixels in images or word pieces in text, and AR generation predicts these tokens in a fixed order. A worthwhile question is whether token structures affect the a

Stephen Jun Villejo, Peter Diggle, Finn Lindgren, Haavard Rue

This work develops a block aggregation approach to spatial estimation and prediction when the response is observed at a coarse spatial scale, for example as counts of events in administrative areas, or blocks, while covariates are available at a finer spatial resolution, typically as raster images. Our approach specifies a linear predictor at the finer resol

Baiang Li, Wenhao Chai, Felix Heide

Large-scale visual learning is increasingly limited by training cost. Existing knowledge distillation methods transfer from a stronger teacher to a weaker student for compression or final-accuracy improvement. We instead investigate distillation to accelerate the training of strong students. We propose a generalizable plug-and-play recipe that freezes a weak

David Michael Riley, Guglielmo Scovazzi, Ioannis Stefanou

Porous media containing cracks, fractures, or internal discontinuities arise throughout subsurface geomechanics, biomechanics, and materials science. Numerical simulation of the coupled hydromechanical response is inherently challenging because the pressure and displacement fields are tightly coupled through the Biot equations, requiring stable mixed formula