May 2025 arXiv papers — page 27

Matthias Beck, Thomas Kunze

If $\mathcal{P}$ is a lattice polytope (i.e., $\mathcal{P}$ is the convex hull of finitely many integer points in $\mathbb{R}^d$), Ehrhart's famous theorem (1962) asserts that the integer-point counting function $|t \mathcal{P} \cap \mathbb{Z}^d|$ is a polynomial in the integer variable $t$. Chapoton (2016) proved that, given a fixed integral form $\lambda:

Naram Mhaisen, George Iosifidis

We revisit the Follow the Regularized Leader (FTRL) framework for Online Convex Optimization (OCO) over compact sets, focusing on achieving dynamic regret guarantees. Prior work has highlighted the framework's limitations in dynamic environments due to its tendency to produce "lazy" iterates. However, building on insights showing FTRL's ability to produce "a

Joseph Sullivan, Ian Good, Samuel A. Burden, Jeffrey Ian Lipton

Energy efficiency is critical to the success of legged robotics. Efficiency is lost through wasted energy during locomotion and standing. Including elastic elements has been shown to reduce movement costs, while including breaks can reduce standing costs. However, adding separate elements for each increases the mass and complexity of a leg, reducing overall

Chahat Raj, Bowen Wei, Aylin Caliskan, Antonios Anastasopoulos

While bias in large language models (LLMs) is well-studied, similar concerns in vision-language models (VLMs) have received comparatively less attention. Existing VLM bias studies often focus on portrait-style images and gender-occupation associations, overlooking broader and more complex social stereotypes and their implied harm. This work introduces VIGNET

R. D. George, C. Vignat

This work validates and extends the method of integration by differentiation, initially introduced by A. Kempf et al., and demonstrates its compatibility with classical rules of integration. It provides applications to classical integrals, including one by Ramanujan, and extends the method to the multivariate setting. Volumes of simplexes are computed by act

V. A. Dzuba, V. V. Flambaum, G. K. Vong

Advanced theoretical techniques that combine the linearized coupled-cluster method, configuration interaction method, and perturbation theory are used to calculate energy levels, ionization potentials, electron affinities, field isotope shift, and static dipole polarizabilities of the superheavy elements Lv and Ts, along with their lighter analogs Te, I, Po,

C. S. Bhargav, Shiteng Chen, Radu Curticapean, Prateek Dwivedi

For every fixed graph $H$, it is known that homomorphism counts from $H$ and colorful $H$-subgraph counts can be determined in $O(n^{t+1})$ time on $n$-vertex input graphs $G$, where $t$ is the treewidth of $H$. On the other hand, a running time of $n^{o(t / \log t)}$ would refute the exponential-time hypothesis. Komarath, Pandey and Rahul (Algorithmica, 202

Julian Schulte, Mario Senden, Gustavo Deco, Xenia Kobeleva

Understanding how cortex, subcortex and cerebellum integrate is a major challenge for neuroscience, however, studies of the brain's structural connectivity have mostly focused on cortico-cortical links. Here, we used diffusion imaging to construct the structural connectome of the entire human brain including 360 cortical, 233 subcortical, and 125 cerebellar

B. Sharmila, Sander M. Vermeulen, Animesh Datta

Spacetime fluctuations (SFs), a common feature of different proposed gravity models, could be detected using laser interferometers. In the search for SFs, a correspondence between the expected output signals and different gravity models is needed, both for guiding the design of future interferometers, and for identifying the signal in experimental data. In t

Haidar Al-Naseri

The process of electron positron pair annihilation, driven by strong fields (Inverse Schwinger mechanism) and high-frequency waves, is studied using the Dirac Heisenberg Wigner formalism. In an electron positron plasma, the presence of a strong field leads to both pair creation and annihilation. Depending on plasma properties such as non-degeneracy and the m

Carlos A. Vargas Venegas, Daning Huang, Patrick Blonigan, JohnTencer

This work presents a physics-infused reduced-order modeling (PIROM) framework for efficient and accurate prediction of transient thermal behavior in multi-layered hypersonic thermal protection systems (TPS). The PIROM architecture integrates a reduced-physics backbone, based on the lumped-capacitance model (LCM), with data-driven correction dynamics formulat

Hamidreza Montazeri Hedesh, Moh Kamalul Wafi, Milad Siami

We study the local stability of nonlinear systems in the Lur'e form with static nonlinear feedback realized by feedforward neural networks (FFNNs). By leveraging positivity system constraints, we employ a localized variant of the Aizerman conjecture, which provides sufficient conditions for exponential stability of trajectories confined to a compact set. Usi

Jirui Qi, Shan Chen, Zidi Xiong, Raquel Fernández

Recent Large Reasoning Models (LRMs) with thinking traces have shown strong performance on English reasoning tasks. However, their ability to think in other languages is less studied. This capability is as important as answer accuracy for real world applications because users may find the reasoning trace useful for oversight only when it is expressed in thei

Sam Dillavou, Marcelo Guzman, Andrea J. Liu, Douglas J. Durian

Performing machine learning with analog signals offers advantages in speed and energy efficiency, but sensitivity to component and measurement imperfections often foils training without a system-specific companion digital model. Here we take a different perspective, accepting and characterizing these inherent imperfections and ultimately overcoming them with

Beatrice Costa, Bianca Scaparra, Xiao Wei, Hubert Riedl

The development of deterministic single photon sources emitting in the telecommunication bands is a key challenge for quantum communication and photonic quantum computing. Here, we investigate the optical properties and single-photon emission of molecular beam epitaxy grown semiconductor quantum dots emitting in the telecom O- and C- bands. The quantum dots

Moudrik Chamoux

On a deformation to the normal cone $\operatorname{DNC}(M,V)$ we show that given a distribution $u\in\mathcal{D}'(\operatorname{DNC}(M,V)\setminus V\times\mathbb{R})$ if $u$ is homogeneous of order $a$ for the zoom action, then it admits an $a$-homogeneous extension $\widetilde{u}\in\mathcal{D}'(\operatorname{DNC}(M,V))$. We describe all such extensions and

Venkata Sai Sravya Sambaturu

Software quality is critical in modern software engineering, especially in large and evolving codebases. This study analyzes the evolution of software quality metrics in five successive versions of the open-source Java testing framework TestNG. Using the static analysis tool Understand, eleven key object-oriented metrics, including cyclomatic complexity, cla

Jeffrey Carvalho, Chiran Wijesundara, Tim Thomay

The photon statistics of Spontaneous Parametric Down Conversion (SPDC) exhibit dependencies on wavelength, pump power, and coincidence time. Notably, the average photon numbers were found to asymmetrically increase with increasing pump power around the degenerate wavelength of emission. By the coupling of the detection scheme to a spectrometer, studying diff

Wen Yang, Zhixian Xie, Yiting Wang, Abhijit Tadepalli

Real-time tracking of previously unseen, highly dynamic objects in contact-rich scenes, such as during dexterous in-hand manipulation, remains a major challenge. Pure vision-based approaches often fail under heavy occlusions due to frequent contact interactions and motion blur caused by abrupt impacts. We propose Twintrack, a physics-aware perception system

Hyungki Im, Wyame Benslimane, Paul Grigas

We study an extension of contextual stochastic linear optimization (CSLO) that, in contrast to most of the existing literature, involves inequality constraints that depend on uncertain parameters predicted by a machine learning model. To handle the constraint uncertainty, we use contextual uncertainty sets constructed via methods like conformal prediction. G

Xiaoyang Zhan, Shixin Zhou, Qianqian Yang, Yixuan Zhao

This paper presents a system for autonomous semantic exploration and dense semantic target mapping of a complex unknown environment using a ground robot equipped with a LiDAR-panoramic camera suite. Existing approaches often struggle to balance collecting high-quality observations from multiple view angles and avoiding unnecessary repetitive traversal. To fi

Philippe Merle, Fabio Petrillo

Modern distributed applications increasingly rely on cloud-native platforms to abstract the complexity of deployment and scalability. As the de facto orchestration standard, Kubernetes enables this abstraction, but its declarative configuration model makes the architectural understanding difficult. Developers, operators, and architects struggle to form accur

Shams Tarek, Dipayan Saha, Sujan Kumar Saha, Farimah Farahmandi

The current landscape of system-on-chips (SoCs) security verification faces challenges due to manual, labor-intensive, and inflexible methodologies. These issues limit the scalability and effectiveness of security protocols, making bug detection at the Register-Transfer Level (RTL) difficult. This paper proposes a new framework named BugWhisperer that utiliz

Kaveen Hiniduma, Zilinghan Li, Aditya Sinha, Ravi Madduri

Privacy-Preserving Federated Learning (PPFL) is a decentralized machine learning approach where multiple clients train a model collaboratively. PPFL preserves the privacy and security of a client's data without exchanging it. However, ensuring that data at each client is of high quality and ready for federated learning (FL) is a challenge due to restricted d

N. V. Krishnendu, Aldo Perri, Sumanta Chakraborty, Alessandro Pesci

Existence of a minimal length in spacetime geometries avoids several singular situations involving quantum theory and gravity. In this work, we show that the existence of such a minimal length also affects the gravitational wave (GW) waveform of any inspiraling binary black hole (BH) system by introducing a minimum frequency, below which the BHs behave as pe

Jingyi Zhao, Claudia Archetti, Tuan Anh Pham, Thibaut Vidal

The inventory routing problem (IRP) focuses on jointly optimizing inventory and distribution operations from a supplier to retailers over multiple days. Compared to other problems from the vehicle routing family, the interrelations between inventory and routing decisions render IRP optimization more challenging and call for advanced solution techniques. A fe

Chao Liu, Ankur Dhar, Emma Snively, Mohamed Othman

The low-level RF (LLRF) systems for S-band linear accelerating structures are typically implemented with heterodyne base architectures. We have developed and characterized the next generation LLRF (NG-LLRF) based on the RF system-on-chip (RFSoC) for C-band accelerating structures, and the platform delivered the pulse-to-pulse fluctuation levels considerably

Lawrence Hollom, Lyuben Lichev, Adva Mond, Julien Portier

In this work we establish several monotonicity and decomposition results in the framework of random regular graphs. Among other results, we show that, for a wide range of parameters $d_1 \leq d_2$, there exists a coupling of $G(n,d_1)$ and $G(n,d_2)$ satisfying that $G(n,d_1) \subseteq G(n,d_2)$ with high probability, confirming a conjecture of Gao, Isaev an

Navindra D. Singh, James Leung, Ji Feng, Alma K. González-Alcalde

Most solar desalination efforts are photothermal: they evaporate water with ``black'' materials that absorb as much sunlight as possible. Such ``brine-boiling'' methods are limited by the high thermal mass of water, i.e., its capacity to store and release heat. Here, we study the light-enhanced evaporation by a hard, white, aluminum nitride wick, and propose

Stephen J. Lee, Cailinn Drouin

We present a novel framework for high-resolution forecasting of residential heating demand and non-heating electricity demand using probabilistic deep learning models. Because our models are trained on electricity consumption from a predominantly gas-heated region, the learned electricity demand patterns primarily reflect non-heating end uses such as lightin

Ilia Sucholutsky, Katherine M. Collins, Nori Jacoby, Bill D. Thompson

LLMs are already transforming the study of individual cognition, but their application to studying collective cognition has been underexplored. We lay out how LLMs may be able to address the complexity that has hindered the study of collectives and raise possible risks that warrant new methods.

Chao Liu, Ankur Dhar, Ryan Herbst, Emilio A. Nanni

In various of particle accelerator designs, amplitude and phase modulation methods are commonly applied to shape the RF pulses for implementing pulse compressors or compensating for the fluctuations introduced by the high-power RF components and beam loading effects. Phase modulations are typically implemented with additional phase shifters that require driv

Yuval David, Fabiana Fournier, Lior Limonad, Inna Skarbovsky

Causal reasoning is essential for business process interventions and improvement, requiring a clear understanding of causal relationships among activity execution times in an event log. Recent work introduced a method for discovering causal process models but lacked the ability to capture alternating causal conditions across multiple variants. This raises th

Fernando Gómez-Ortiz, Ramamoorthy Ramesh, Javier Junquera

We report the emergence of a transverse dielectric response in PbTiO$_{3}$/SrTiO$_{3}$ superlattices hosting polar vortex structures. Using second-principles simulations, we find that an electric field applied along one direction induces significant local polarization responses along orthogonal directions, with magnitudes approaching half that of the diagona

Junbo Yin, Chao Zha, Wenjia He, Chencheng Xu

Existing PLMs generate protein sequences based on a single-condition constraint from a specific modality, struggling to simultaneously satisfy multiple constraints across different modalities. In this work, we introduce CFP-Gen, a novel diffusion language model for Combinatorial Functional Protein GENeration. CFP-Gen facilitates the de novo protein design by

Md Hasibul Amin, Mohammadreza Mohammadi, Jason D. Bakos, Ramtin Zand

In this paper, we propose the CrossNAS framework, an automated approach for exploring a vast, multidimensional search space that spans various design abstraction layers-circuits, architecture, and systems-to optimize the deployment of machine learning workloads on analog processing-in-memory (PIM) systems. CrossNAS leverages the single-path one-shot weight-s

Iknoor Singh, Carolina Scarton, Kalina Bontcheva

The proliferation of online news and the increasing spread of misinformation necessitate robust methods for automatic data analysis. Narrative classification is emerging as a important task, since identifying what is being said online is critical for fact-checkers, policy markers and other professionals working on information studies. This paper presents our

Nicolas Espinosa-Dice, Yiyi Zhang, Yiding Chen, Bradley Guo

Diffusion and flow models have emerged as powerful generative approaches capable of modeling diverse and multimodal behavior. However, applying these models to offline reinforcement learning (RL) remains challenging due to the iterative nature of their noise sampling processes, making policy optimization difficult. In this paper, we introduce Scalable Offlin

Susan Liang, Dejan Markovic, Israel D. Gebru, Steven Krenn

Binaural rendering aims to synthesize binaural audio that mimics natural hearing based on a mono audio and the locations of the speaker and listener. Although many methods have been proposed to solve this problem, they struggle with rendering quality and streamable inference. Synthesizing high-quality binaural audio that is indistinguishable from real-world

Derek Weitzel, Ashton Graves, Sam Albin, Huijun Zhu

The National Research Platform (NRP) represents a distributed, multi-tenant Kubernetes-based cyberinfrastructure designed to facilitate collaborative scientific computing. Spanning over 75 locations in the U.S. and internationally, the NRP uniquely integrates varied computational resources, ranging from single nodes to extensive GPU and CPU clusters, to supp

Yupei Li, Shuaijie Shao, Manuel Milling, Björn W. Schuller

Depression is a growing concern gaining attention in both public discourse and AI research. While deep neural networks (DNNs) have been used for recognition, they still lack real-world effectiveness. Large language models (LLMs) show strong potential but require domain-specific fine-tuning and struggle with non-textual cues. Since depression is often express

Yeon-Koo Che, Andrew B. Choi

Motivated by applications such as cloud computing, gig platforms, and blockchain auctions, we study optimal selling mechanisms for dynamic markets with stochastic supply and demand. In our model, buyers with private valuations and homogeneous goods arrive stochastically and can be held in queues at a cost. The optimal mechanism pairs allocative efficiency wi

Carlota Parés-Morlans, Michelle Yi, Claire Chen, Sarah A. Wu

Tasks that involve complex interactions between objects with unknown dynamics make planning before execution difficult. These tasks require agents to iteratively improve their actions after actively exploring causes and effects in the environment. For these type of tasks, we propose Causal-PIK, a method that leverages Bayesian optimization to reason about ca

Bargav Jayaraman, Virendra J. Marathe, Hamid Mozaffari, William F. Shen

In enterprise settings, organizational data is segregated, siloed and carefully protected by elaborate access control frameworks. These access control structures can completely break down if an LLM fine-tuned on the siloed data serves requests, for downstream tasks, from individuals with disparate access privileges. We propose Permissioned LLMs (PermLLM), a

Hidenobu Matsuki, Gwangbin Bae, Andrew J. Davison

We propose the first 4D tracking and mapping method that jointly performs camera localization and non-rigid surface reconstruction via differentiable rendering. Our approach captures 4D scenes from an online stream of color images with depth measurements or predictions by jointly optimizing scene geometry, appearance, dynamics, and camera ego-motion. Althoug

Sanjoy Kundu, Shanmukha Vellamcheti, Sathyanarayanan N. Aakur

Open-world egocentric activity recognition poses a fundamental challenge due to its unconstrained nature, requiring models to infer unseen activities from an expansive, partially observed search space. We introduce ProbRes, a Probabilistic Residual search framework based on jump-diffusion that efficiently navigates this space by balancing prior-guided explor

Vladimir Bataev, Andrei Andrusenko, Lilit Grigoryan, Aleksandr Laptev

Statistical n-gram language models are widely used for context-biasing tasks in Automatic Speech Recognition (ASR). However, existing implementations lack computational efficiency due to poor parallelization, making context-biasing less appealing for industrial use. This work rethinks data structures for statistical n-gram language models to enable fast and

Jingcheng Zhou, Senlei Li, Chuangtang Wang, Hanshang Jin

Recently, unconventional spin-orbit torques (SOTs) with tunable spin generation open new pathways for designing novel magnetization control for cutting-edge spintronics innovations. A leading research thrust is to develop field-free deterministic magnetization switching for implementing scalable and energy favorable magnetic recording and storage application

Ruining Deng, Junchao Zhu, Juming Xiong, Can Cui

Continual learning is rapidly emerging as a key focus in computer vision, aiming to develop AI systems capable of continuous improvement, thereby enhancing their value and practicality in diverse real-world applications. In healthcare, continual learning holds great promise for continuously acquired digital pathology data, which is collected in hospitals on

Kornel Howil, Joanna Waczyńska, Piotr Borycki, Tadeusz Dziarmaga

Gaussian Splatting (GS) has recently emerged as an efficient representation for rendering 3D scenes from 2D images and has been extended to images, videos, and dynamic 4D content. However, applying style transfer to GS-based representations, especially beyond simple color changes, remains challenging. In this work, we introduce CLIPGaussian, the first unifie

Farrukh A. Chishtie

We present a novel, unified quaternion-complex framework for formulating the incompressible Navier-Stokes equations that reveals the geometric structure underlying viscous fluid motion and resolves the Clay Institute's Millennium Prize problem. By introducing complex coordinates $z = x + iy$ and expressing the velocity field as $F = u + iv$, we demonstrate t

Krti Tallam, Emma Miller

CaMeL (Capabilities for Machine Learning) introduces a capability-based sandbox to mitigate prompt injection attacks in large language model (LLM) agents. While effective, CaMeL assumes a trusted user prompt, omits side-channel concerns, and incurs performance tradeoffs due to its dual-LLM design. This response identifies these issues and proposes engineerin

James Beyer, Jaewon Min, Greg Muller

Given a finite set of points in general position in the plane or sphere, we count the number of ways to separate those points using two types of circles: circles through three of the points, and circles through none of the points (up to an equivalence). In each case, we show the number of circles which separate the points into subsets of size k and l is inde

Dilara Aktas, Ozgur B. Akan

Molecular communication (MC), a biologically inspired technology, enables applications in nanonetworks and the Internet of Everything (IoE), with great potential for intra-body systems such as drug delivery, health monitoring, and disease detection. This paper extends our prior work on the Flexure-FET MC receiver by integrating a competitive binding model to

Pingjun Hong, Beiduo Chen, Siyao Peng, Marie-Catherine de Marneffe

There is increasing evidence of Human Label Variation (HLV) in Natural Language Inference (NLI), where annotators assign different labels to the same premise-hypothesis pair. However, within-label variation--cases where annotators agree on the same label but provide divergent reasoning--poses an additional and mostly overlooked challenge. Several NLI dataset

Mason Faldet, Clayton Shonkwiler

Unit-norm tight frames in finite-dimensional Hilbert spaces (FUNTFs) are fundamental in signal processing, offering optimal robustness to noise and measurement loss. In this paper we introduce the Eigenlift algorithm for sampling random FUNTFs. Our approach exploits the symplectic geometry of the FUNTF space, which we characterize as a symplectic reduction o

Andrei Kozyrev, Nikita Khramov, Gleb Solovev, Anton Podkopaev

Interactive Theorem Proving was repeatedly shown to be fruitful when combined with Generative Artificial Intelligence. This paper assesses multiple approaches to Rocq generation and illuminates potential avenues for improvement. We identify retrieval-based premise selection as a central component of effective Rocq proof generation and propose a novel approac

Harvey Dam, Jonas Knochelmann, Vinu Joseph, Ganesh Gopalakrishnan

We introduce a method to reduce refusal rates of large language models (LLMs) on sensitive content without modifying model weights or prompts. Motivated by the observation that refusals in certain models were often preceded by the specific token sequence of a token marking the beginning of the chain-of-thought (CoT) block (<think>) followed by a double newli

Sandra Höltervennhoff, Jonas Ricker, Maike M. Raphael, Charlotte Schwedes

As generative AI is increasingly contributing to the spread of deceptively realistic misinformation, lawmakers have introduced regulations requiring the disclosure of AI-generated content. However, it is unclear if labels reduce the risk of users falling for AI-generated misinformation. To address this research gap, we study the effect of labels on users' pe

Mahmoud M. Samak, Osama R. Bilal

Band structures encode electronic, optical, and acoustic properties of matter and can serve as an essential tool in material discovery and design. Dispersion anomalies -- sharp, non-standard features in the frequency-wavenumber relation -- have been historically correlated with phonon-electron coupling or long-range interaction. Through a combination of expe

Alexander Herzog, Aliai Eusebi, Lorenzo Cavallaro

The performance figures of modern drift-adaptive malware classifiers appear promising, but does this translate to genuine operational reliability? The standard evaluation paradigm primarily focuses on baseline performance metrics, neglecting confidence-error alignment and operational stability. While prior works established the importance of temporal evaluat

Arthur S. Bianchessi, Yasmin C. Aguirre, Rodrigo C. Barros, Lucas S. Kupssinskü

Transformer-based language models rely on positional encoding (PE) to handle token order and support context length extrapolation. However, existing PE methods lack theoretical clarity and rely on limited evaluation metrics to substantiate their extrapolation claims. We propose the Bayesian Attention Mechanism (BAM), a theoretical framework that formulates p

Franck Gabriel, François Ged, Maria Han Veiga, Emmanuel Schertzer

Diffusion models now set the benchmark in high-fidelity generative sampling, yet they can, in principle, be prone to memorization. In this case, their learned score overfits the finite dataset so that the reverse-time SDE samples are mostly training points. In this paper, we interpret the empirical score as a noisy version of the true score and show that its

Dharambir Mahto, Prashant Yadav, Mahesh Banavar, Jim Keany

Sepsis is a life-threatening condition affecting over 48.9 million people globally and causing 11 million deaths annually. Despite medical advancements, predicting sepsis remains a challenge due to non-specific symptoms and complex pathophysiology. The SXI++ LNM is a machine learning scoring system that refines sepsis prediction by leveraging multiple algori

Liu Yuezhang, Xue-Xin Wei

Recent studies suggest that diffusion models significantly improve the empirical adversarial robustness of deep neural network models. While intuitive explanations have been proposed, the mechanisms underlying diffusion-based robustness remain largely unclear. This work aims to demystify how diffusion models improve adversarial robustness. We observe that di

Douglas R. Stinson

The "variance method" has been used to prove many classical inequalities in design theory and coding theory. The purpose of this expository note is to review and present some of these inequalities in a unified setting. I will also discuss some examples from my own research where I have employed these techniques.

Akshat Tandon, James Brown, Kenny Heitritter, Tarini Hardikar

The prediction of material degradation is an important problem to solve in many industries. Environmental conditions, such as humidity and temperature, are important drivers of degradation processes, with corrosion being one of the most prominent ones. Quantum machine learning is a promising research field but suffers from well known deficits such as barren

Pierre Brugière, Gabriel Turinici

Option pricing theory, such as the Black and Scholes (1973) model, provides an explicit solution to construct a strategy that perfectly hedges an option in a continuous-time setting. In practice, however, trading occurs in discrete time and often involves transaction costs, making the direct application of continuous-time solutions potentially suboptimal. Pr

Sasha Zotine

This article highlights the ToricHigherDirectImages package in Macaulay2. The central feature is a method for computing (higher) direct images of line bundles under surjective toric morphisms.

Maria Violaris

Can quantum theory be applied on all scales? While there are many arguments for the universality of quantum theory, this question remains a subject of debate. It is unknown how far the existence of macroscopic irreversibility can be derived from or reconciled with time-reversal symmetric quantum dynamics. Furthermore, reasoning about quantum measurements can

Chi-Hong Yuen

Tunneling ionization followed by strong-field excitation leads to important ultrafast phenomena such as charge migration and lasing. Recent theoretical developments suggest that the population of the ionic excited state can be greatly enhanced due to the complex interplay between tunneling and excitation. In this Letter, using an adiabatic approach for both

Sophia E Marx, Rajan Amit Mehta

In this survey article, we give an introduction to the notion of a 2-Segal set and prove that 2-Segal sets are equivalent to pseudomonoids in the bicategory of spans. The proof utilizes graphical techniques for 2-Segal sets and spans that should be useful in more general settings. There are procedures for obtaining an associative algebra from a 2-Segal set (

Peiling Jiang, Haijun Xia

Web-based activities span multiple webpages. However, conventional browsers with stacks of tabs cannot support operating and synthesizing large volumes of information across pages. While recent AI systems enable fully automated web browsing and information synthesis, they often diminish user agency and hinder contextual understanding. We explore how AI could

Alexander Gill, Abhilasha Ravichander, Ana Marasović

Large language models (LLMs) are increasingly used for data generation. However, creating evaluation benchmarks raises the bar for this emerging paradigm. Benchmarks must target specific phenomena, penalize exploiting shortcuts, and be challenging. Through two case studies, we investigate whether LLMs can meet these demands by generating reasoning over-text

Yisca Kares

We determine the range of the des statistic on every conjugacy class in the symmetric group $S_n$, prove that the minimum is $1$ (except for the identity class), and show that every intermediate value from $1$ to the maximum value is attained. We also demonstrate a constructive method to achieve every value in the range and discuss its combinatorial implicat

Sergey G. Chefranov, Alexander G. Chefranov

It is shown that linear instability of plane Couette flow can take place even at finite Reynolds numbers which meets with known experimental data. This new result of the linear theory of hydrodynamic stability is obtained only due by abandoning traditional assumption of the longitudinal periodicity of disturbances in the flow direction.

Chaimae El Mortajinea, Moussa Labbadib, Adnane Saoudc, Mostafa Bouzia

In this paper, we develop and analyze an integral fixed-time sliding mode control method for a scenario in which the system model is only partially known, utilizing Gaussian processes. We present two theorems on fixed-time convergence. The first theorem addresses the fully known system model, while the second considers situations where the system's drift is

Christoph Flamm, Daniel Merkle, Peter F. Stadler

Assembly theory has received considerable attention in the recent past. Here we analyze the formal framework of this model and show that assembly pathways coincide with certain minimal hyperpaths in B-hypergraphs. This makes it possible to generalize the notion of assembly to general chemical reaction systems and to make explicit the connection to rule based

Michael Klamkin, Mathieu Tanneau, Pascal Van Hentenryck

Machine Learning (ML) techniques for Optimal Power Flow (OPF) problems have recently garnered significant attention, reflecting a broader trend of leveraging ML to approximate and/or accelerate the resolution of complex optimization problems. These developments are necessitated by the increased volatility and scale in energy production for modern and future

Dongzhe Zheng

Classical constrained Hamiltonian theory assumes complete observability of system states, but in reality only partial state information is often available. This paper establishes a complete geometric theoretical framework for handling such incompletely observed systems. By introducing the concept of observation-induced fiber bundles, we naturally extend Dira

Yingming Wang, Pepa Atanasova

With the rapid development of Large Language Models (LLMs), Natural Language Explanations (NLEs) have become increasingly important for understanding model predictions. However, these explanations often fail to faithfully represent the model's actual reasoning process. While existing work has demonstrated that LLMs can self-critique and refine their initial

Paul Gessinger, Heather M. Gray, Attila Krasznahorkay, Charles Leggett

We present the current development status and progress of traccc, a GPU track reconstruction library developed in the context of the A Common Tracking Software (ACTS) project. traccc implements tracking algorithms used in high energy physics (HEP) experiments, including the Kalman filter based track finding and fitting. We benchmark the software with data si

Achim Blumensath

We study two subclasses of the class of automatic structures: automatic structures of polynomial growth and Presburger structures. We present algebraic characterisations of the groups and the equivalence structures in these two classes.

Ayush Sawarni, Sahasrajit Sarmasarkar, Vasilis Syrgkanis

This paper investigates the integration of response time data into human preference learning frameworks for more effective reward model elicitation. While binary preference data has become fundamental in fine-tuning foundation models, generative AI systems, and other large-scale models, the valuable temporal information inherent in user decision-making remai

Abdelhay Benmoussa

We establish a new identity linking Bernoulli, Stirling (first kind), and Bessel (first kind) numbers: \[ \sum_{k=0}^{n} 2^{\,n-k}\,s(n,k)\,B_k \;=\; \sum_{k=0}^{n} b(n,k)\,\frac{(-1)^k\,k!}{k+1}. \] This parallels the classical Stirling--Bernoulli relation \[ B_n = \sum_{k=0}^{n} S(n,k)\,\frac{(-1)^k\,k!}{k+1}, \] replacing $S(n,k)$ with $s(n,k)$ and $b(n,k

Linghan Zhong, Samuel Yuan, Jiyang Zhang, Yu Liu

Exceptional behavior tests (EBTs) are crucial in software development for verifying that code correctly handles unwanted events and throws appropriate exceptions. However, prior research has shown that developers often prioritize testing "happy paths", e.g., paths without unwanted events over exceptional scenarios. We present exLong, a framework that automat

Woo Hyeon Jeong, Junzhi Ye, Jongbeom Kim, Rui Xu

The anisotropy of perovskite nanoplatelets (PeNPLs) opens up many opportunities in optoelectronics, including enabling the emission of linearly polarized light. But the limited stability of PeNPLs is a pressing challenge, especially for red-emitting CsPbI$_3$. Herein, we address this limitation by alloying FA into the perovskite cuboctahedral site. Unlike Cs

Dominik Hahn, S. A. Parameswaran, Benedikt Placke

Preparing the thermal density matrix $\rho_{\beta} \propto e^{-\beta H}$ corresponding to a given Hamiltonian $H$ is a task of central interest across quantum many-body physics, and is particularly salient when attempting to study it with quantum computers. Although solved in principle by recent constructions of efficiently simulable Lindblad master equation

Anju Anand, Emrah Akyol

We develop a communication-theoretic framework for privacy-aware and resilient decision making in cyber-physical systems under misaligned objectives between the encoder and the decoder. The encoder observes two correlated signals ($X$,$\theta$) and transmits a finite-rate message $Z$ to aid a legitimate controller (the decoder) in estimating $X+\theta$, whil

Zhangyi Hu, Jiemin Wu, Hua Xu, Mingqian Liao

Irregular Multivariate Time Series (IMTS) forecasting is challenging due to the unaligned nature of multi-channel signals and the prevalence of extensive missing data. Existing methods struggle to capture reliable temporal patterns from such data due to significant missing values. While pre-trained foundation models show potential for addressing these challe

Jonghan Lim, Ilya Kovalenko

Manufacturing environments are becoming more complex and unpredictable due to factors such as demand variations and shorter product lifespans. This complexity requires real-time decision-making and adaptation to disruptions. Traditional control approaches highlight the need for advanced control strategies capable of overcoming unforeseen challenges, as they

Josiah Couch, Miao Li, Rima Arnaout, Ramy Arnaout

In the universal quest to optimize machine-learning classifiers, three factors -- model architecture, dataset size, and class balance -- have been shown to influence test-time performance but do not fully account for it. Previously, evidence was presented for an additional factor that can be referred to as dataset quality, but it was unclear whether this was

Ludvig Modin

We give a new proof of the $\hat{U}$-theorem of B\'erczi, Doran, Hawes and Kirwan on the existence of geometric quotients for actions of graded unipotent groups in terms of stacks of filtrations and gradings introduced by Halpern-Leistner. Our proof works over any affine Noetherian base, in particular it simultaneously generalizes the previous results to arb

Ba-Hien Tran, Van Minh Nguyen

Weight binarization has emerged as a promising strategy to reduce the complexity of large language models (LLMs). Existing approaches fall into post-training binarization, which is simple but causes severe performance loss, and training-aware methods, which depend on full-precision latent weights, adding complexity and limiting efficiency. We propose a novel

Zhoufaran Yang, Yan Shu, Jing Wang, Zhifei Yang

Visual texts embedded in videos carry rich semantic information, which is crucial for both holistic video understanding and fine-grained reasoning about local human actions. However, existing video understanding benchmarks largely overlook textual information, while OCR-specific benchmarks are constrained to static images, limiting their ability to capture t

Andrew Zhu, Evan Osgood, Chris Callison-Burch

Much work has been done on conversational LLM agents which directly assist human users with tasks. We present an alternative paradigm for interacting with LLM agents, which we call "overhearing agents". These overhearing agents do not actively participate in conversation -- instead, they "listen in" on human-to-human conversations and perform background task

Daniel Apai, Rory Barnes, Matthew M. Murphy, Tim Lichtenberg

The search for extraterrestrial life in the Solar System and beyond is a key science driver in astrobiology, planetary science, and astrophysics. A critical step is the identification and characterization of potential habitats, both to guide the search and to interpret its results. However, a well-accepted, self-consistent, flexible, and quantitative termino

Felipe Areces, John C. Duchi

The basic question of delineating those statistical problems that are solvable without making any assumptions on the underlying data distribution has long animated statistics and learning theory. This paper characterizes when a convex M-estimation or stochastic optimization problem is solvable in such an assumption-free setting, providing a precise dividing

Philip Quirke, Narmeen Oozeer, Chaithanya Bandi, Amir Abdullah

This position paper argues that the prevailing trajectory toward ever larger, more expensive generalist foundation models controlled by a handful of companies limits innovation and constrains progress. We challenge this approach by advocating for an "Expert Orchestration" (EO) framework as a superior alternative that democratizes LLM advancement. Our propose

Raluca Rufu, Robin M. Canup

Exoplanetary systems that contain multiple planets on short-period orbits appear to be prevalent in the current observed exoplanetary population, yet the processes that give rise to such configurations remain poorly understood. A common prior assumption is that planetary accretion commences after the infall of gas and solids to the circumstellar disk ended.