October 2025 arXiv papers — page 227

Sasanka Dowarah, Mengxin Du, Alan Zanders, Shengwang Du

The atomtronic matter-wave triple-well transistor is theoretically predicted to exhibit current gain and act as a coherent matter-wave emitter. In this work, we investigate the dynamics of an atomtronic transistor composed of a triple-well potential -- source, gate, and drain -- modeled by the time-dependent Gross-Pitaevskii equation. We systematically explo

Manuel Morales-Alvarado

Foundation models, large machine learning models trained on broad, multimodal datasets, have been gaining increasing attention in scientific applications due to their strong performance on diverse downstream tasks. Large Language Models (LLMs), a prominent instance of foundation models, have achieved remarkable success in tasks such as text and image generat

Scott Lucchini, Jiwon Jesse Han, Sapna Mishra, Andrew J. Fox

We use constrained idealized simulations of the LMC/Milky Way interaction to determine if the size of the LMC's gaseous halo (Corona) can be used to distinguish between first and second passage models $-$ an orbital trajectory for the LMC in which it has just recently approached the Milky Way for the first time (first passage), or one in which it has had a p

Donghwan Rho

Reinforcement learning with verifiable rewards (RLVR) is a promising approach for training large language models (LLMs) with stronger reasoning abilities. It has also been applied to a variety of logic puzzles. In this work, we study the Korean word-chain game using RLVR. We show that rule-derived rewards can naturally conflict, and demonstrate through exper

Scott Lucchini, Jiwon Jesse Han

Recent discoveries have shown that a population of hypervelocity stars (HVSs) originate from the Large Magellanic Cloud (LMC). We use three such HVSs as dynamical tracers to constrain the past orbit of the LMC. Since each star was ejected at a finite time in the past, it must intersect the past position of the LMC's central black hole at its ejection time. W

Ahmed Barbar, Anatoly Dymarsky, Alfred Shapere

We formulate a precise holographic duality between an ensemble of 4d $U(1)^g$ Maxwell theories living on a spin four-manifold $M_4$ and an Abelian BF-type 2-form gauge theory of level $N$, summed over all five-manifolds with boundary $M_4$. The elements of the boundary ensemble are Abelian gauge theories specified by self-dual symplectic codes over $Z_N$, th

Mark N. Costantini, Luca Mantani, James M. Moore, Valentina Schutze Sanchez

We present Colibri, an open-source Python code that provides a general and flexible tool for PDF fits. The code is built so that users can implement their own PDF model, and use the built-in functionalities of Colibri for a fast computation of observables. It grants easy access to experimental data, several error propagation methodologies, including the Hess

Arjun Srinivasan Kudinoor, Daniel Pablos, Krishna Rajagopal

We report on our investigation in arXiv:2509.08881 of how recent jet substructure measurements constrain the resolution length $L_{\rm res}$ of the quark-gluon plasma formed in heavy-ion collisions. $L_{\rm res}$ is defined such that high-energy partons within a jet shower are resolved by the medium if and only if they are separated by a distance greater tha

Ci-Siang Lin, Min-Hung Chen, Yu-Yang Sheng, Yu-Chiang Frank Wang

Multimodal Large Language Models (MLLMs) have achieved strong performance on general visual benchmarks but struggle with out-of-distribution (OOD) tasks in specialized domains such as medical imaging, where labeled data is limited and expensive. We introduce LEAML, a label-efficient adaptation framework that leverages both scarce labeled VQA samples and abun

Sebastian Gehrmann

The emergence of reinforcement learning in post-training of large language models has sparked significant interest in reward models. Reward models assess the quality of sampled model outputs to generate training signals. This task is also performed by evaluation metrics that monitor the performance of an AI model. We find that the two research areas are most

Suyuchen Wang, Tianyu Zhang, Ahmed Masry, Christopher Pal

GUI grounding, the task of mapping natural-language instructions to pixel coordinates, is crucial for autonomous agents, yet remains difficult for current VLMs. The core bottleneck is reliable patch-to-pixel mapping, which breaks when extrapolating to high-resolution displays unseen during training. Current approaches generate coordinates as text tokens dire

H. -H. Sandy Chiu, Mateusz Ruszkowski, Maria Werhahn, Christoph Pfrommer

The equipartition model is widely used to estimate magnetic field strength from synchrotron intensity in radio galaxies, yet the validity of its underlying assumptions remains uncertain. Using an Arepo simulation which incorporates a two-moment cosmic ray (CR) transport scheme and a multiphase interstellar medium, we compare magnetic fields inferred from syn

Ricardo T. Fares, Lucas C. Ribas

Randomized neural networks for representation learning have consistently achieved prominent results in texture recognition tasks, effectively combining the advantages of both traditional techniques and learning-based approaches. However, existing approaches have so far focused mainly on improving cross-information prediction, without introducing significant

Theodoros Kapourniotis, Dominik Leichtle, Luka Music, Harold Ollivier

With the advent of quantum cloud computing, the security of delegated quantum computation has become of utmost importance. While multiple statistically secure blind verification schemes in the prepare-and-send model have been proposed, none of them achieves full quantum fault-tolerance, a prerequisite for useful verification on scalable quantum computers. In

Filippo Ascolani, Giacomo Zanella

Finite mixtures are a cornerstone of Bayesian modelling, and it is well-known that sampling from the resulting posterior distribution can be a hard task. In particular, popular reversible Markov chain Monte Carlo schemes are often slow to converge when the number of observations $n$ is large. In this paper we introduce a novel and simple non-reversible sampl

Vidushi Sharma, Lee A. Collins, Alexander J. White

Pairing the accuracy of Kohn-Sham density-functional framework with the efficiency of a stochastic algorithmic approach, mixed stochastic-deterministic Density Functional Theory (mDFT) achieves a favorable computational scaling with system sizes and electronic temperatures. We employ the recently developed mDFT formalism to investigate the dynamic charge-tra

Bishal Kumar Das, Sourav Manna, Vaibhav Madhok

The emergence of classical world from underlying quantum mechanics is characterized by not only vanishing quantum correlations but also an unfolding of objectivity also known as quantum Darwinism. We show that the absence of this objectivity has a quantum advantage in cryptography and also provides the crucial missing link in efficient classical simulation o

Dong Lao, Yuxiang Zhang, Haniyeh Ehsani Oskouie, Yangchao Wu

We propose a test-time defense mechanism against adversarial attacks: imperceptible image perturbations that significantly alter the predictions of a model. Unlike existing methods that rely on feature filtering or smoothing, which can lead to information loss, we propose to "combat noise with noise" by leveraging stochastic resonance to enhance robustness w

Hongxiang Zhang, Yuan Tian, Tianyi Zhang

Large Language Models (LLMs) tend to generate a long reasoning chain when solving complex tasks. However, as the reasoning chain extends, critical intermediate steps and the original prompt will be buried in the context, receiving insufficient attention and leading to errors. In this work, we present ATAR, a novel reasoning method that leverages the inherent

Guanhua Huang, Tingqiang Xu, Mingze Wang, Qi Yi

Reinforcement Learning with Verifiable Rewards (RLVR) has propelled Large Language Models in complex reasoning, yet its scalability is often hindered by a training bottleneck where performance plateaus as policy entropy collapses, signaling a loss of exploration. Previous methods typically address this by maintaining high policy entropy, yet the precise mech

Madeline Lucey, Cecilia Mateu, Adrian Price-Whelan, David Hogg

Understanding the formation and evolutionary history of the Milky Way requires detailed mapping of its stellar components, which preserve fossil records of the Galaxy's assembly through cosmic time. RR Lyrae stars are particularly well-suited for this endeavor, as they are old, standard candle variables that probe the Galaxy's earliest formation epochs. In t

Carter Fox, Jose D. Mella, Jack Rollins, Yangchen He

Developing new multiferroics at the two-dimensional (2D) limit with energy-efficient magnetoelectric coupling can inform the interplay physics of novel orders and advance on-chip high-performance computing applications. Here we apply stacking order engineering to create a new type of 2D multiferroics, namely sliding multiferroics, based on polar hexagonal st

Al Nahian Bin Emran, Rajendra Upadhyay, Rajendra Paudyal, Lisa Donnan

In the rapidly evolving landscape of 5G technology, the adoption of cloud-based infrastructure for the deployment of 5G services has become increasingly common. Using a service-based architecture, critical 5G components, such as the Access and Mobility Management Function (AMF), Session Management Function (SMF), and User Plane Function (UPF), now run as con

Atul Singh Arora, Carl A. Miller, Mauro E. S. Morales, Jamie Sikora

Coin-flipping is a fundamental task in two-party cryptography where two remote mistrustful parties wish to generate a shared uniformly random bit. While quantum protocols promising near-perfect security exist for weak coin-flipping -- when the parties want opposing outcomes -- it has been shown that they must be inefficient in terms of their round complexity

José Cambronero, Michele Tufano, Sherry Shi, Renyao Wei

Agentic Automated Program Repair (APR) is increasingly tackling complex, repository-level bugs in industry, but ultimately these patches still need to be reviewed by a human before committing them to ensure they address the bug. Showing patches unlikely to be accepted can lead to substantial noise, wasting valuable developer time and eroding trust in automat

Talha Ahmed, Nehal Ahmed Shaikh, Hassan Mohy-ud-Din

For equitable deployment of AI tools in hospitals and healthcare facilities, we need Deep Segmentation Networks that offer high performance and can be trained on cost-effective GPUs with limited memory and large batch sizes. In this work, we propose Wave-GMS, a lightweight and efficient multi-scale generative model for medical image segmentation. Wave-GMS ha

Tianyu Fu, Zihan Min, Hanling Zhang, Jichao Yan

Multi-LLM systems harness the complementary strengths of diverse Large Language Models, achieving performance and efficiency gains that are not attainable by a single model. In existing designs, LLMs communicate through text, forcing internal representations to be transformed into output token sequences. This process both loses rich semantic information and

Jonathan W. Arenberg

This paper advances the concept maturity level (CML) of the Habitable Worlds Observatory (HWO) servicing architecture. Since servicing has occurred on other missions, this paper argues that the current CML is 2. To advance to CML 3, option spaces must be established for trade studies. We introduce the three space ages and the argument that we are on the cusp

Erin S. Lamb, Yaakov Glick, Jose Pincha, Ishu Goel

Polarization maintaining (PM), all-fiber amplifiers offer the benefits of alignment free and environmentally stable operation. To achieve high output powers, particularly in pulsed operation, it is necessary to balance the need to reduce deleterious nonlinear effects, often through the use of large mode area (LMA) fibers, with the onset of transverse mode in

J. A. Aguilar-Saavedra

Since its discovery at the Tevatron in 1995, the top quark has been extensively studied due to its unique properties. We discuss how the remarkable progress in top-quark physics has opened the possibility of using the top quark as a tool to test quantum mechanics at the energy frontier. After reviewing the motivations for exploring beyond quantum mechanics,

Kirill Trapeznikov, Paul Cummer, Pranay Pherwani, Jai Aslam

The increasing realism of synthetic speech generated by advanced text-to-speech (TTS) models, coupled with post-processing and laundering techniques, presents a significant challenge for audio forensic detection. In this paper, we introduce the SAFE (Synthetic Audio Forensics Evaluation) Challenge, a fully blind evaluation framework designed to benchmark det

Engin Başakoğlu, Yuzhao Wang

We establish local well-posedness for the hyperbolic nonlinear Schrodinger equation (HNLS) in the critical spaces. Following the approach of Killip and Visan, we derive scale-invariant Strichartz estimates for HNLS on both rational and irrational tori, thereby removing the epsilon-loss of derivative present in the hyperbolic Strichartz estimates of Bourgain

Liam Hanany

We show that infinitely many alternating groups arise as quotients of the free group of rank 2, with kernel a characteristic subgroup. We also show that such simple quotients exist of arbitrarily large Lie rank. This resolves two questions posed by arXiv:2308.14302

Yiming Zhang, Wolfgang Ridinger, David Wozabal

As renewable energy integration increases supply variability, battery energy storage systems (BESS) present a viable solution for balancing supply and demand. This paper proposes a novel approach for optimizing battery BESS participation in multiple electricity markets. We develop a joint bidding strategy that combines participation in the primary frequency

Qiang Dong, Hongxia Zhang

In this paper, we provide a new method for constructing tilting objects in a triangulated category via recollements. The $p$-cycle approach to exceptional curve processes significant advantages in constructing recollements and ladders, due to the existence of reduction/insertion functors. In order to construct tilting objects in the stable category of vector

Yuda Song, Dhruv Rohatgi, Aarti Singh, J. Andrew Bagnell

Partial observability is a notorious challenge in reinforcement learning (RL), due to the need to learn complex, history-dependent policies. Recent empirical successes have used privileged expert distillation--which leverages availability of latent state information during training (e.g., from a simulator) to learn and imitate the optimal latent, Markovian p

Cai Zhou, Chenxiao Yang, Yi Hu, Chenyu Wang

Diffusion language models, especially masked discrete diffusion models, have achieved great success recently. While there are some theoretical and primary empirical results showing the advantages of latent reasoning with looped transformers or continuous chain-of-thoughts, continuous diffusion models typically underperform their discrete counterparts. In thi

Shenjia Ding, David Flynn, Paul Harvey

The increased use of software in the operation and management of telecommunication networks has moved the industry one step closer to realizing autonomous network operation. One consequence of this shift is the significantly increased need for testing and validation before such software can be deployed. Complementing existing simulation or hardware-based app

Zixuan Yi, Sami Abu-el-Haija, Yawen Wang, Teja Vemparala

DB engines produce efficient query execution plans by relying on cost models. Practical implementations estimate cardinality of queries using heuristics, with magic numbers tuned to improve average performance on benchmarks. Empirically, estimation error significantly grows with query complexity. Alternatively, learning-based estimators offer improved accura

Imene Kerboua, Sahar Omidi Shayegan, Megh Thakkar, Xing Han Lù

Web agents powered by large language models (LLMs) must process lengthy web page observations to complete user goals; these pages often exceed tens of thousands of tokens. This saturates context limits and increases computational cost processing; moreover, processing full pages exposes agents to security risks such as prompt injection. Existing pruning strat

Dylan Herman, Guneykan Ozgul, Anuj Apte, Junhyung Lyle Kim

We present new theoretical mechanisms for quantum speedup in the global optimization of nonconvex functions, expanding the scope of quantum advantage beyond traditional tunneling-based explanations. As our main building-block, we demonstrate a rigorous correspondence between the spectral properties of Schr\"{o}dinger operators and the mixing times of classic

Yann Collet, Nick Terrell, W. Felix Handte, Danielle Rozenblit

Research techniques in the last decade have improved lossless compression ratios by significantly increasing processing time. These techniques have remained obscure because production systems require high throughput and low resource utilization. In practice, application-specific compression algorithms that leverage knowledge of the data structure and semanti

Abteen Ebrahimi, Adam Wiemerslage, Katharina von der Wense

We present NN-Rank, an algorithm for ranking source languages for cross-lingual transfer, which leverages hidden representations from multilingual models and unlabeled target-language data. We experiment with two pretrained multilingual models and two tasks: part-of-speech tagging (POS) and named entity recognition (NER). We consider 51 source languages and

Philipp Jettkant, Andreas Sojmark

We introduce a system of Brownian particles, each absorbed upon hitting an associated moving boundary. The boundaries are determined by the conditional probabilities of the particles being absorbed before some final time horizon, given the current knowledge of the system. While the particles evolve forward in time, the conditional probabilities are computed

Luca Collorone, Matteo Gioia, Massimiliano Pappa, Paolo Leoni

Intention drives human movement in complex environments, but such movement can only happen if the surrounding context supports it. Despite the intuitive nature of this mechanism, existing research has not yet provided tools to evaluate the alignment between skeletal movement (motion), intention (text), and the surrounding context (scene). In this work, we in

Toshiki Nakai, Ravi Kiran Chikkala, Lena Sophie Oberkircher, Nicholas Jennings

The 2025 Multimodal Models for Low-Resource Contexts and Social Impact (MMLoSo) Language Challenge addresses one of India's most pressing linguistic gaps: the lack of resources for its diverse low-resource languages (LRLs). In this study, we investigate whether enforcing cross-lingual similarity in specific internal layers of a decoder-only multilingual larg

Qiwei Di, Kaixuan Ji, Xuheng Li, Heyang Zhao

LLM inference often generates a batch of candidates for a prompt and selects one via strategies like majority voting or Best-of- N (BoN). For difficult tasks, this single-shot selection often underperforms. Consequently, evaluations commonly report Pass@$k$: the agent may submit up to $k$ responses, and only the best of them is used when computing regret. Mo

Junchao Huang, Xinting Hu, Boyao Han, Shaoshuai Shi

Autoregressive video diffusion models have proved effective for world modeling and interactive scene generation, with Minecraft gameplay as a representative application. To faithfully simulate play, a model must generate natural content while exploring new scenes and preserve spatial consistency when revisiting explored areas. Under limited computation budge

James Thomas, Johan Wahlström

Accurate estimation of rating of perceived exertion (RPE) can enhance resistance training through personalized feedback and injury prevention. This study investigates the application of machine learning models to estimate RPE during single-arm dumbbell bicep curls, using data from wearable inertial and electromyography (EMG) sensors. A custom dataset of 69 s

Emanuele Caputo, Nicola Cavallucci

We prove a generalization of Tyson-Wu's characterization of metric spaces biLipschitz equivalent to snowflakes to every metric space, by removing compactness, doubling and embeddability assumptions. We also characterize metric spaces that are biLipschitz equivalent to a snowflake in terms of the absence of non-trivial metric $1$-currents in every ultralimit,

W. A. Rojas C., A. Zamora V., L. F. Quijano W., Y. Beltran P

This paper presents an application of geometrothermodynamics (GTD) to the economic analysis of Bogot\'a's sports sector through the Satellite Account of Sport (CSDB). By establishing an analogy between thermodynamic systems and economic structures, we develop a mathematical framework where monetary flows behave analogously to energy, while economic entropy,

Chanyeol Choi, Yoon Kim, Yu Yu, Young Cha

Natural language processing (NLP) has been widely used in quantitative finance, but traditional methods often struggle to capture rich narratives in corporate disclosures, leaving potentially informative signals under-explored. Large language models (LLMs) offer a promising alternative due to their ability to extract nuanced semantics. In this paper, we ask

Zichen Chen, Jiefeng Chen, Sercan Ö. Arik, Misha Sra

Deep research has revolutionized data analysis, yet data scientists still devote substantial time to manually crafting visualizations, highlighting the need for robust automation from natural language queries. However, current systems struggle with complex datasets containing multiple files and iterative refinement. Existing approaches, including simple sing

Devlin Mallory

We show that if $X$ is a smooth Fano variety containing a line or a conic with respect to $-K_X$, then the Frobenius cokernel $\mathcal B_X:=\mathrm{coker}(\mathcal O_X\to F_* \mathcal O_X)$ is not antiample; using this criteria, we show that the only smooth Fano threefolds with antiample Frobenius cokernel are $\mathbb P^3$ and the quadric threefold (in cha

J. L. Rizos, J. L. Ortiz, P. J. Gutierrez, I. M. Navajas

We present a web-based application designed to simulate rotational light curves of small airless Solar System bodies under user-defined geometrical and physical conditions. The tool integrates both physical and empirical photometric models and enables users to input custom shape models, surface properties, and viewing geometries. A dedicated module also comp

Gamaliel Morales

Recently, Kulo\u{g}lu {\it et al.} \cite{Kul} introduced the higher order Horadam numbers. In this study, novel 3-parameter generalized quaternion sequences of higher order Horadam numbers, which have not been studied before, are defined by investigating the relationship between generalized quaternions, which are important mathematical objects used in physic

Denis Zavadski, Nikita Philip Tatsch, Carsten Rother

Product quantisation (PQ) is a classical method for scalable vector encoding, yet it has seen limited usage for latent representations in high-fidelity image generation. In this work, we introduce PQGAN, a quantised image autoencoder that integrates PQ into the well-known vector quantisation (VQ) framework of VQGAN. PQGAN achieves a noticeable improvement ov

Adrian Dawid

We construct a family of probability measures on the group of Hamiltonian diffeomorphisms of a closed symplectic manifold $(M,\omega)$. We show that these measures are Borel measures with respect to the topology induced by the Hofer metric. Further, we show that these measures turn any Hofer-Lipschitz function into a random variable with finite expectation.

Tidiane Camaret Ndir, Alexander Pfefferle, Robin Tibor Schirrmeister

Interactive 3D biomedical image segmentation requires efficient models that can iteratively refine predictions based on user prompts. Current foundation models either lack volumetric awareness or suffer from limited interactive capabilities. We propose a training strategy that combines dynamic volumetric prompt generation with content-aware adaptive cropping

Pavan Kumar Meena, Dibyendu Samanta, Shashank Srivastava, Poulami Manna

Ternary transition-metal germanide superconductors with nonsymmorphic symmetries offer promising platforms for symmetry-protected topological phases. In this work, we investigate ZrIrGe, which crystallizes in the nonsymmorphic TiNiSi-type structure. Electrical, magnetic, and specific heat measurements confirm bulk type-II superconductivity with a full gap an

André Longon, David Klindt, Meenakshi Khosla

The superposition hypothesis states that single neurons may participate in representing multiple features in order for the neural network to represent more features than it has neurons. In neuroscience and AI, representational alignment metrics measure the extent to which different deep neural networks (DNNs) or brains represent similar information. In this

Wanjia Zhao, Qinwei Ma, Jingzhe Shi, Shirley Wu

Benchmarks for competition-style reasoning have advanced evaluation in mathematics and programming, yet physics remains comparatively explored. Most existing physics benchmarks evaluate only final answers, which fail to capture reasoning processes, while recent stepwise methods rely on heuristic LLM-as-judge scoring or restrictive linear assumptions, limitin

Elena Grigorescu, Alice Moayyedi

The notion of code sparsification was introduced by Khanna, Putterman and Sudan (arxiv.2311.00788), as an analogue to the the more established notion of cut sparsification in graphs and hypergraphs. In particular, for $\alpha\in (0,1)$ an (unweighted) one-sided $\alpha$-sparsifier for a linear code $\mathcal{C} \subseteq \mathbb{F}_2^n$ is a subset $S\subset

Karim Sabri, Yves Gallant, Justine Devin, Kirsty Feijen

Pulsar halos are a class of extended very-high-energy (VHE) sources highlighted by the HAWC observatory towards the Geminga pulsar and PSR B0656$+$14. These VHE sources are interpreted as the inverse Compton emission from electrons and positrons diffusing in the interstellar medium at an inhibited rate, having escaped the pulsar wind nebula. Our aim is to se

Yilun Hao, Yongchao Chen, Chuchu Fan, Yang Zhang

Vision Language Models (VLMs) show strong potential for visual planning but struggle with precise spatial and long-horizon reasoning, while Planning Domain Definition Language (PDDL) planners excel at formal long-horizon planning but cannot interpret visual inputs. Recent works combine these complementary advantages by translating visual problems into PDDL.

Ha Manh Bui, Felix Parker, Kimia Ghobadi, Anqi Liu

We study the Non-Stationary Reinforcement Learning (RL) under distribution shifts in both finite-horizon episodic and infinite-horizon discounted Markov Decision Processes (MDPs). In the finite-horizon case, the transition functions may suddenly change at a particular episode. In the infinite-horizon setting, such changes can occur at an arbitrary time step

Shuaijie Yuan, Xu Zhu, Jin Yang, Yu Liu

Stokes polarimetry has been considered as an alluring platform that enables a plethora of applications ranging from single-molecule orientation to deep-space sensing. Existing polarimetry avenues, however, rely primarily on the transversely polarized field reconstruction, thus suffering from several challenges such as multiple time sequenced detections, comp

Robert Boltje, Gabriel Navarro

This paper is motivated by a strong version of Feit's conjecture, first formulated by the authors in joint work with A. Kleshchev and P. H. Tiep in 2025, concerning the conductor $c(\chi)$ of an irreducible character $\chi$ of a finite group $G$. We connect the conjecture with the following construction: For any positive integer $n$ dividing the exponent of

Cuong Chi Le, Minh V. T. Pham, Cuong Duc Van, Hoang N. Phan

Large Language Models (LLMs) achieve strong results on code tasks, but how they derive program meaning remains unclear. We argue that code communicates through two channels: structural semantics, which define formal behavior, and human-interpretable naming, which conveys intent. Removing the naming channel severely degrades intent-level tasks such as summari

Arjun Arunasalam, Madison Pickering, Z. Berkay Celik, Blase Ur

Large language models (LLMs) can underpin AI assistants that help users with everyday tasks, such as by making recommendations or performing basic computation. Despite AI assistants' promise, little is known about the implicit values these assistants display while completing subjective everyday tasks. Humans may consider values like environmentalism, charity

Georg Loho, Arnau Padrol, Germain Poullot

The study of the cone of submodular functions goes back to Jack Edmonds' seminal 1970 paper, which already highlighted the difficulty of characterizing its extreme rays. Since then, researchers from diverse fields have sought to characterize, enumerate, and bound the number of such rays. In this paper, we introduce an inductive construction that generates ne

Amotz Bar-Noy, Igor Kalinichev, David Peleg, Dror Rawitz

The Degree Realization problem requires, given a sequence $d$ of $n$ positive integers, to decide whether there exists a graph whose degrees correspond to $d$, and to construct such a graph if it exists. A more challenging variant of the problem arises when $d$ has many different realizations, and some of them may be more desirable than others. We study \emp

Michael J. Martens, Qinghua Lian, Brent R. Logan

Safety evaluation is an essential component of clinical trials. To protect study participants, these studies often implement safety stopping rules that will halt the trial if an excessive number of toxicity events occur. Existing safety monitoring methods often treat these events as binary outcomes. A strategy that instead handles these as time-to-event endp

Xuan Xu, Zhongliang Yang, Haolun Li, Beilin Chu

Topic modeling aims to produce interpretable topic representations and topic--document correspondences from corpora, but classical neural topic models (NTMs) remain constrained by limited representation assumptions and semantic abstraction ability. We study LLM-based topic modeling from both white-box and black-box perspectives. For white-box LLMs, we propos

Sierra Knavel

We prove an upper bound for the first Betti number of a nontrivial genus-$g$ Lefschetz fibration. We also show that if the monodromy of a Lefschetz fibration is transitive with respect to the mapping class group, the Lefschetz fibration is simply connected. Lastly, we discuss a potential family of indecomposable genus-2 Lefschetz fibrations with maximally no

Luuk Zonneveld, Paul Urbach, Aurèle Adam

The influence of low-spatial frequency errors of an optical component of an imaging system on the point spread function can be quantified using Zernike polynomials. High-spatial frequency errors cause strong scattering due to which the focused energy is reduced but the shape of the point spread function is mainly preserved. In contrast, the influence of mid-

Rajat Kumar Panda, Sukanta Panda, Abbas Tinwala

We revisit the Lagrangian formulation of stochastic inflation, where the path-integral approach is employed to derive the Langevin equation governing the dynamics of long-wavelength fields, in contrast to the standard method where the Langevin equation is derived directly from the equation of motion of the full quantum field. Focusing on a massless, minimall

Rafaello Sanna, William E. Byrd, Nada Amin

We present {Kanren} (read: set-Kanren), an extension to miniKanren with constraints for reasoning about sets and association lists. {Kanren} includes first-class set objects, a functionally complete family of set-theoretic constraints (including membership, union, and disjointedness), and new constraints for reasoning about association lists with shadowing a

Duanjiao Li, Yun Chen, Ying Zhang, Junwen Yao

For typical applications of UAVs in power grid scenarios, we construct the problem as planning UAV trajectories for coverage in cluttered environments. In this paper, we propose an optimal smooth coverage trajectory planning algorithm. The algorithm consists of two stages. In the front-end, a Genetic Algorithm (GA) is employed to solve the Traveling Salesman

Andreea C. Nicoara

Notions of finite type play an important role in several complex variables. The most standard notion is D'Angelo type, which measures the order of contact of holomorphic curves with the boundary of a domain in ${\mathbb C}^n$. For the $\bar \partial$-Neumann problem, however, the order of contact of the boundary of the domain with $q$-dimensional complex var

Francisco Patitucci, Ruichen Jiang, Aryan Mokhtari

A recent breakthrough in nonconvex optimization is the online-to-nonconvex conversion framework of [Cutkosky et al., 2023], which reformulates the task of finding an $\varepsilon$-first-order stationary point as an online learning problem. When both the gradient and the Hessian are Lipschitz continuous, instantiating this framework with two different online

Alberto González-Sanz, Marc Hallin, Yisha Yao

Prediction is a key issue in time series analysis. Just as classical mean regression models, classical autoregressive methods, yielding L$^2$ point-predictions, provide rather poor predictive summaries; a much more informative approach is based on quantile (auto)regression, where the whole distribution of future observations conditional on the past is consis

Irene Tenison, Anna Murphy, Charles Beauville, Lalana Kagal

Federated learning (FL) enables collaborative model training across distributed devices while preserving data privacy, but deployment on resource-constrained edge nodes remains challenging due to limited memory, energy, and communication bandwidth. Traditional synchronous and asynchronous FL approaches further suffer from straggler induced delays and slow co

Jiapeng Tang, Matthew Levine, Dor Verbin, Stephan J. Garbin

We introduce ROGR, a novel approach that reconstructs a relightable 3D model of an object captured from multiple views, driven by a generative relighting model that simulates the effects of placing the object under novel environment illuminations. Our method samples the appearance of the object under multiple lighting environments, creating a dataset that is

Ha Manh Bui, Iliana Maifeld-Carucci, Anqi Liu

We study the problem of actively learning a classifier with a low calibration error. One of the most popular Acquisition Functions (AFs) in pool-based Active Learning (AL) is querying by the model's uncertainty. However, we recognize that an uncalibrated uncertainty model on the unlabeled pool may significantly affect the AF effectiveness, leading to sub-opt

Ángela López-Cardona, Sebastián Idesis, Mireia Masias-Bruns, Sergi Abadal

Do brains and language models converge toward the same internal representations of the world? Recent years have seen a rise in studies of neural activations and model alignment. In this work, we review 25 fMRI-based studies published between 2023 and 2025 and explicitly confront their findings with two key hypotheses: (i) the Platonic Representation Hypothes

Ming Zhao, Wenhui Dong, Yang Zhang, Xiang Zheng

Spine disorders affect 619 million people globally and are a leading cause of disability, yet AI-assisted diagnosis remains limited by the lack of level-aware, multimodal datasets. Clinical decision-making for spine disorders requires sophisticated reasoning across X-ray, CT, and MRI at specific vertebral levels. However, progress has been constrained by the

Vanja Marić, Luka Paljk, Lenart Zadnik

We identify the mechanism of slow heterogeneous relaxation in quantum kinetically constrained models (KCMs) in which the potential energy strength is controlled by a coupling parameter. The regime of slow relaxation includes the large-coupling limit. By expanding around that limit, we reveal a \emph{nested hierarchy} of states that remain frozen on time scal

Claude Lobry

Everyone can see that over the last 150 years, theoretical ecology has become considerably more mathematical. But what is the nature of this phenomenon? Are mathematics applied, as in the use of statistical tests, for example, or are they involved, as in physics, where laws cannot be expressed without them? Through the history of the {\em Competitive Exclusi

A. V. Anufriev, V. N. Kovalenko

In this paper, we propose a method for numerical modeling of the nuclear matter properties within the framework of relativistic heavy-ion collisions using a holographic equation of state. Machine learning methods were applied to address the regression and optimization issues during the calibration of the relevant parameters using the LQCD results for quark m

Iñigo Parra

Previous work has shown correlations between the hidden states of large language models and fMRI brain responses, on language tasks. These correlations have been taken as evidence of the representational similarity of these models and brain states. This study tests whether these previous results are robust to several possible concerns. Specifically this stud

Muhammad Arif Hakimi Zamrai

Inverse design, which seeks to find optimal parameters for a target output, is a central challenge in engineering. Surrogate-based optimization (SBO) has become a standard approach, yet it is fundamentally structured to converge to a single-point solution, thereby limiting design space exploration and ignoring potentially valuable alternative topologies. Thi

Stevens Johnson, Varun Puram, Johnson Thomas, Acsah Konuparamban

Accurate modeling of neuronal action potential (AP) onset timing is crucial for understanding neural coding of danger signals. Traditional leaky integrate-and-fire (LIF) models, while widely used, exhibit high relative error in predicting AP onset latency, especially under strong or rapidly changing stimuli. Inspired by recent experimental findings and quant

Qi Li, Shuwen Qiu, Julien Han, Xingzi Xu

The rapid growth of e-commerce has intensified the demand for Virtual Try-On (VTO) technologies, enabling customers to realistically visualize products overlaid on their own images. Despite recent advances, existing VTO models face challenges with fine-grained detail preservation, robustness to real-world imagery, efficient sampling, image editing capabiliti

Katherine Thai, Bradley Emi, Elyas Masrour, Mohit Iyyer

A significant proportion of queries to large language models ask them to edit user-provided text, rather than generate new text from scratch. While previous work focuses on detecting fully AI-generated text, we demonstrate that AI-edited text is distinguishable from human-written and AI-generated text. First, we propose using lightweight similarity metrics t

Hima Jacob Leven Suprabha, Laxmi Nag Laxminarayan Nagesh, Ajith Nair, Alvin Reuben Amal Selvaster

The integration of Large Language Models (LLMs) into multiagent systems has opened new possibilities for collaborative reasoning and cooperation with AI agents. This paper explores different prompting methods and evaluates their effectiveness in enhancing agent collaborative behaviour and decision-making. We enhance CoELA, a framework designed for building C

Anantajit Subrahmanya, Chandrakanth Gudavalli, Connor Levenson, B. S. Manjunath

Accurately modeling human mobility is critical for urban planning, epidemiology, and traffic management. In this work, we introduce Markovian Reeb Graphs, a novel framework that transforms Reeb graphs from a descriptive analysis tool into a generative model for spatiotemporal trajectories. Our approach captures individual and population-level Patterns of Lif

Yehuda Dar

This paper uses classical high-rate quantization theory to provide new insights into mixture-of-experts (MoE) models for regression tasks. Our MoE is defined by a segmentation of the input space to regions, each with a single-parameter expert that acts as a constant predictor with zero-compute at inference. Motivated by high-rate quantization theory assumpti

Sheng-Chieh Lin, Yuanyuan Su, Iraj Vaezzadeh, William Forman

The Virgo Cluster is the nearest cool core cluster that features two well-studied sloshing cold fronts at radii of $r \approx 30$ kpc and $r \approx 90$ kpc, respectively. In this work, we present results of XMM-Newton mosaic observations of a third, southwestern, cold front at a radius of $r \approx 250$ kpc, originally discovered with Suzaku. All three col

Dhruv Rohatgi, Abhishek Shetty, Donya Saless, Yuchen Li

Test-time algorithms that combine the generative power of language models with process verifiers that assess the quality of partial generations offer a promising lever for eliciting new reasoning capabilities, but the algorithmic design space and computational scaling properties of such approaches are still opaque, and their benefits are far from apparent wh