October 2025 arXiv papers — page 217

Shoulong Li, Liang Liang, Liang Ma

Einstein-bumblebee gravity is one of the simplest vector-tensor theories that realizes spontaneous Lorentz symmetry breaking. In this work, we first construct an exact dyonic Reissner-Nordstr\"om-like black hole solution in four dimensions, carrying both electric and magnetic charges and admitting general topological horizons. We then study its thermodynamic

Jahidul Arafat, Fariha Tasmin, Sanjaya Poudel

Modern distributed systems demand low-latency, fault-tolerant event processing that exceeds traditional messaging architecture limits. While frameworks including Apache Kafka, RabbitMQ, Apache Pulsar, NATS JetStream, and serverless event buses have matured significantly, no unified comparative study evaluates them holistically under standardized conditions.

Masakazu Teragaito

In the SnapPy census, there are 9 asymmetric L-space knots. It is known that each of them admits exactly two quasi-alternating surgeries with the aid of a computer. The purpose of this article is to confirm these surgeries by the Montesinos trick.

Binyu Lu, Matthias Frey, Stark Draper, Jingge Zhu

Memristor crossbars enable vector-matrix multiplication (VMM), and are promising for low-power applications. However, it can be difficult to write the memristor conductance values exactly. To improve the accuracy of VMM, we propose a scheme based on low-rank matrix approximation. Specifically, singular value decomposition (SVD) is first applied to obtain a l

Xuyang Guo, Zekai Huang, Zhenmei Shi, Zhao Song

Vision-Language Models (VLMs) have become a central focus of today's AI community, owing to their impressive abilities gained from training on large-scale vision-language data from the Web. These models have demonstrated strong performance across diverse tasks, including image understanding, video understanding, complex visual reasoning, and embodied AI. Des

Mihail Cocos

We study a class of affine manifolds equipped with a flat affine connection $\nabla$ and a global Riemannian metric $g$ that is diagonal in local affine coordinates. These structures are closely related to \emph{Hessian manifolds}, where the metric locally arises as the Hessian of a smooth potential. For example, the Hopf manifold $(\mathbb{R}^{n+1}\setminus

Marc Cavazza

In this work, we explore the relevance of textual semantics to Large Language Models (LLMs), extending previous insights into the connection between distributional semantics and structural semantics. We investigate whether LLM-generated texts preserve semantic isotopies. We design a story continuation experiment using 10,000 ROCStories prompts completed by f

Lakshmy Priya Ajayakumar, David J. Durden, Aksshay Nandakumar Regeni, Mingcai Xie

The rapid decay of target signal strength with distance from the sensor presents a key challenge in nanoscale magnetic sensing with nitrogen-vacancy (NV) centers in diamond, limiting both sensitivity and spatial resolution. Here we introduce a strategy to overcome this limitation by using radical anions formed from rhodamine-derived dyes as reporter spins lo

Alexander M. Fichtl, Jeremias Bohn, Josefin Kelber, Edoardo Mosca

Transformers have dominated sequence processing tasks for the past seven years -- most notably language modeling. However, the inherent quadratic complexity of their attention mechanism remains a significant bottleneck as context length increases. This paper surveys recent efforts to overcome this bottleneck, including advances in (sub-quadratic) attention v

Leonardo Banchi, Dominic Branford, Chetan Waghela

Gradient-based optimization is a key ingredient of variational quantum algorithms, with applications ranging from quantum machine learning to quantum chemistry and simulation. The parameter-shift rule provides a hardware-friendly method for evaluating gradients of expectation values with respect to circuit parameters, but its applicability is limited to circ

Elvar Atlason, Simon Guest

Polyhedra are generically rigid, but can be made to flex under certain symmetry conditions. We generalise Raoul Bricard's 1897 method for making flexible octahedra to construct an infinite family of flexible polyhedra with self-intersections. Removing an edge from any of these models gives a crinkle, and these can be used to create flexible polyhedra wit

Lucas Queiroz Arakaki, Douglas Novaes, Paulo Santana

We investigate planar piecewise-smooth vector fields with a discontinuity line, focusing on the bifurcation of crossing limit cycles that arise when one of the vector fields is translated along the discontinuity set. We establish topological conditions under which such bifurcations occur and, under additional generic hypotheses, derive precise asymptotic exp

Florian List, Oliver Hahn, Thomas Flöss, Lukas Winkler

The mildly non-linear regime of cosmic structure formation holds much of the information that upcoming large-scale structure surveys aim to exploit, making fast and accurate predictions on these scales essential. We present the $N$-body module of DISCO-DJ (DIfferentiable Simulations for COsmology - Done with Jax), designed to deliver high-fidelity, GPU-accel

Yury Zabegaev, Inga Berre, Eirik Keilegavlen

Modeling multiphysics processes in porous media requires preconditioned iterative linear solvers to enable efficient simulations at industry-relevant scales. These solvers are typically composed of sub-algorithms that target individual physical processes. Various options are available for each algorithm, with the corresponding ranges of numerical parameters.

Gabriele Giacometti, Daniele Rizzo, Dario Zappala

We investigate the perturbative structure of the proper time renormalization group flow in scalar and Yang-Mills theories. Although the PT flow does not belong to the class of exact functional renormalization group equations, we show that it correctly reproduces the universal coefficients of the $β$-functions at one and two loops. For the ${\rm O(N)}$ scalar

Erin Chambers, Tim Ophelders, Anna Schenfisch, Julia Sollberger

A fixed set of vertices in the plane may have multiple planar straight-line triangulations in which the degree of each vertex is the same. As such, the degree information does not completely determine the triangulation. We show that even if we know, for each vertex, the number of neighbors in each of the four cardinal directions, the triangulation is not com

Dong Qiu, Xiang Xu, Yeqiong Ye, Ting Zhou

This paper addresses the inverse problem of simultaneously recovering multiple unknown parameters for semilinear wave equations from boundary measurements. We consider an initial-boundary value problem for a wave equation with a general semilinear term and an internal source. The inverse problem is to determine the nonlinear coefficients (potentials), the so

Tomoyuki Kaneko, Shuhei Yamashita

2048 is a stochastic single-player game involving 16 cells on a 4 by 4 grid, where a player chooses a direction among up, down, left, and right to obtain a score by merging two tiles with the same number located in neighboring cells along the chosen direction. This paper presents that a variant 2048-4x3 12 cells on a 4 by 3 board, one row smaller than the or

Yue Que, Yingyi Zhang, Xiangyu Zhao, Chen Ma

Graph-based recommender systems leverage neighborhood aggregation to generate node representations, which is highly sensitive to popularity bias, resulting in an echo effect during information propagation. Existing graph-based debiasing solutions refine the aggregation process with attempts such as edge reconstruction or weight adjustment. However, these met

Théo Dupuy, Binbin Xu, Stéphane Perrey, Jacky Montmain

Uncertainty quantification has received considerable interest in recent works in Machine Learning. In particular, Conformal Prediction (CP) gains ground in this field. For the case of time series, Online Conformal Prediction (OCP) becomes an option to address the problem of data distribution shift over time. Indeed, the idea of OCP is to update a threshold o

Lauren E. Cook, Timothy J. H. Hele

Accurate and efficient simulation of nonadiabatic dynamics is highly desirable for understanding charge and energy transfer in complex systems. A key criterion for obtaining an accurate method is conservation of the Quantum Boltzmann Distribution (QBD). For a single surface, Matsubara dynamics is known to conserve the QBD, as a consequence of truncating the

Lutz Hammer, Tilman Kißlinger, Margareta Wagner, Reinhard B. Neder

Heating a long-range ordered adsorbate phase beyond its stability temperature does not necessarily result in a disordered phase, it can also break up into heavily fluctuating ordered domains. Temporal and/or spatial averaging over these fluctuations may give the impression of both a wrong periodicity and a false local symmetry. This can happen even below liq

Christopher J. Greyson-Gaito, Sabrina H. Streipert, Gail S. K. Wolkowicz

Although maturation delays are frequently included in population models, researchers rarely account for mortality between birth and maturity. Previous discrete population models have included mortality of immature individuals during the maturation delay finding that increasing the delay decreases the equilibrium population size, eventually leading to extinct

Harsimranjit Kaur, Kuldeep Suthar

The ultracold atoms are an ideal platform to implement atomtronics and Josephson junctions analogous to superconducting circuits. The collective modes of a Bose gas split by a potential barrier have been known. However, the role of barriers on the collective excitation spectra of ultracold atomic mixtures has not been examined. Here, we examine the low-lying

Johannes Linn

In this paper power saving bounds for general Kloosterman sums for all Weyl elements for $\mathrm{GL}_n$ for $n>2$ are proven, improving the trivial bound by Dąbrowski and Reeder. This is achieved by representing the sums in an explicit way as exponential sums and bounding these through applications of the Weil bound.

Gábor Damásdi, Zichao Dong, Manfred Scheucher, Ji Zeng

In this paper, we consider saturation problems related to the celebrated Erdős--Szekeres convex polygon problem. For each $n \ge 7$, we construct a planar point set of size $(7/8) \cdot 2^{n-2}$ which is saturated for convex $n$-gons. That is, the set contains no $n$ points in convex position while the addition of any new point creates such a configuration.

Charles L. Wang, Keir Dorchen, Peter Jin

We develop a learning-theoretic framework for analyzing self-improving agents by decomposing self-modification into five axes. Within this framework, we prove a sharp boundary: under standard i.i.d. assumptions, distribution-free PAC learnability is preserved if and only if the policy-reachable family remains uniformly capacity-bounded. If reachable capacity

Buyun Liang, Liangzu Peng, Jinqi Luo, Darshan Thaker

Large Language Models (LLMs) are increasingly deployed in high-risk domains. However, state-of-the-art LLMs often exhibit hallucinations, raising serious concerns about their reliability. Prior work has explored adversarial attacks to elicit hallucinations in LLMs, but these methods often rely on unrealistic prompts, either by inserting nonsensical tokens or

Van Nguyen, Surya Nepal, Xingliang Yuan, Tingmin Wu

Software vulnerabilities (SVs) pose a critical threat to safety-critical systems, driving the adoption of AI-based approaches such as machine learning and deep learning for software vulnerability detection. Despite promising results, most existing methods are limited to a single programming language. This is problematic given the multilingual nature of moder

Bastian Jäckl, Jiří Kruchina, Lucas Joos, Daniel A. Keim

Multimodal deep-learning models power interactive video retrieval by ranking keyframes in response to textual queries. Despite these advances, users must still browse ranked candidates manually to locate a target. Keyframe arrangement within the search grid highly affects browsing effectiveness and user efficiency, yet remains underexplored. We report a stud

Karin Wittmann W., Leandro H. Ymai, Genessi Sá Neto, Angela Foerster

The ability to precisely control and predict the evolution of quantum states is a fundamental requirement for advancing quantum technologies. Here, we develop tunable atomic routing protocols based on an integrable model of dipolar bosons confined in a four-well potential with a star-shaped configuration. By adjusting the system parameters, we identify a har

Ankit Vadehra, Bill Johnson, Gene Saunders, Pascal Poupart

Text editing can involve several iterations of revision. Incorporating an efficient Grammar Error Correction (GEC) tool in the initial correction round can significantly impact further human editing effort and final text quality. This raises an interesting question to quantify GEC Tool usability: How much effort can the GEC Tool save users? We present the fi

Amit Acharya, Siladitya Pal

A model of dissipative micromagnetics coupled to (visco-)elasticity is explored, following the procedures of the Ericksen-Leslie theory of nematic liquid crystals allowing for angular momentum due to magnetization. An outcome is the Landau-Lifshitz-Gilbert theory coupled to material spin. A further power-less augmentation to the angular momentum of the theor

Faisal Hamman, Chenyang Zhu, Anoop Kumar, Xujun Peng

RAG systems are increasingly deployed in high-stakes domains where users expect outputs to be consistent across semantically equivalent queries. However, existing systems often exhibit significant inconsistencies due to variability in both the retriever and generator (LLM), undermining trust and reliability. In this work, we focus on information consistency,

Xuehai He, Shijie Zhou, Thivyanth Venkateswaran, Kaizhi Zheng

World models that support controllable and editable spatiotemporal environments are valuable for robotics, enabling scalable training data, repro ducible evaluation, and flexible task design. While recent text-to-video models generate realistic dynam ics, they are constrained to 2D views and offer limited interaction. We introduce MorphoSim, a language guide

Faye Jackson

Given a genus $g$ smooth Lefschetz fibration $\pi : M \to S^2$ with singular locus $\Delta \subseteq S^2$, we describe the subgroup $\operatorname{Br}(\pi)$ of the spherical braid group $\operatorname{Mod}(S^2,\Delta)$ consisting of braids admitting a lift to a fiber-preserving diffeomorphism of $M$. We develop general methods for showing that the index $[\o

Adrian Odenweller, Falko Ueckerdt, Johannes Hampp, Ivan Ramirez

The rapid expansion of low-cost renewable electricity combined with end-use electrification in transport, industry, and buildings offers a promising path to deep decarbonisation. However, aligning variable supply with demand requires strategies for daily and seasonal balancing. Existing models either lack the wide scope required for long-term transition path

Marc Chamberland, Karl Dilcher

A curious identity of Bunyakovsky (1882), made more widely known by P\'olya and Szeg{\H o} in their ``Problems and Theorems in Analysis", gives an evaluation of a sum of the floor function of square roots involving primes $p\equiv 1\pmod{4}$. We evaluate this sum also in the case $p\equiv 3\pmod{4}$, obtaining an identity in terms of the class number of the

Shakson Isaac, Yentl Collin, Chirag Patel

Continuous glucose monitoring (CGM) generates dense data streams critical for diabetes management, but most used forecasting models lack interpretability for clinical use. We present SSM-CGM, a Mamba-based neural state-space forecasting model that integrates CGM and wearable activity signals from the AI-READI cohort. SSM-CGM improves short-term accuracy over

Byung Kyu Na, Stefan Possanner, Xin Wang

We present a structure-preserving discretization of the hybrid magnetohydrodynamics (MHD)-driftkinetic system for simulations of low-frequency wave-particle interactions. The model equations are derived from a variational principle, assuring energetically consistent couplings between MHD fluids and driftkinetic particles. The spatial discretization is based

Vrinda Malhotra, Jiaman Li, Nandini Pisupati

We present AgentZero++, an agent-based model that integrates cognitive, emotional, and social mechanisms to simulate decentralized collective violence in spatially distributed systems. Building on Epstein's Agent\_Zero framework, we extend the original model with eight behavioral enhancements: age-based impulse control; memory-based risk estimation; affect-c

Adam Ballew, Jingbo Wang, Shaogang Ren

Bayesian Optimization (BO) has been widely used to efficiently optimize expensive black-box functions with limited evaluations. In this paper, we investigate the use of BO for prompt engineering to enhance text classification with Large Language Models (LLMs). We employ an LLM-powered Gaussian Process (GP) as the surrogate model to estimate the performance o

Qian Meng, Zhe Chen, Bo Zhang

Variable-ratio matching is a flexible alternative to conventional $1$-to-$k$ matching for designing observational studies that emulate a target randomized controlled trial (RCT). To achieve fine balance -- that is, matching treated and control groups to have the same marginal distribution on selected covariates -- conventional approaches typically partition

Masahiro Kamiyama, Shashwat Rathkanthiwar, Cristyan Quiñones-García, Seiji Mita

The neutral $(V_N-3Mg_{Ga})^0$ complex was found to be the primary compensator in Mg-doped, N-polar GaN. The experimental data showed a sharp drop in hole concentration once [Mg] exceeded ~$10^{19} cm^{-3}$. Temperature-dependent Hall measurements, in conjunction with a charge balance model, revealed that the carrier loss was due to a drastic reduction in ac

V. S. Raghu Parupudi

Reference-free metrics like self-perplexity are strongly biased against creative text generation. We propose the Confidence Score (CS), derived from a model's output probability distribution, as a less biased alternative. Experiments on gpt-4o-mini show that while fluency-based metrics prefer novel responses in 0\% of cases on 99 creative prompts, our CS doe

Mateen Ahmed Abbasi, Petri Ihantola, Tommi Mikkonen, Niko Mäkitalo

Requirement Engineering (RE) is the foundation of successful software development. In RE, the goal is to ensure that implemented systems satisfy stakeholder needs through rigorous requirements elicitation, validation, and evaluation processes. Despite its critical role, RE continues to face persistent challenges, such as ambiguity, conflicting stakeholder ne

Josh A. Taylor, Alejandro D. Domínguez-García

Distance relays detect faults on transmission lines. They face uncertainty from the fault's location and resistance, as well as the current from the line's remote terminal. In this paper, we aggregate this uncertainty with the Minkowski sum. This allows us to explicitly model the power grid surrounding the relay's line, and in turn accommodate any mix of syn

Xinshuai Dong, Ignavier Ng, Haoyue Dai, Jiaqi Sun

Identifying the structure of a partially observed causal system is essential to various scientific fields. Recent advances have focused on constraint-based causal discovery to solve this problem, and yet in practice these methods often face challenges related to multiple testing and error propagation. These issues could be mitigated by a score-based method a

Jianglin Lu, Hailing Wang, Yi Xu, Yizhou Wang

Foundation models learn highly transferable representations through large-scale pretraining on diverse data. An increasing body of research indicates that these representations exhibit a remarkable degree of similarity across architectures and modalities. In this survey, we investigate the representation potentials of foundation models, defined as the latent

Jiarui Li, Zixiang Yin, Zhengming Ding, Samuel J. Landry

T cell receptor (TCR) recognition of peptide-MHC (pMHC) complexes is a central component of adaptive immunity, with implications for vaccine design, cancer immunotherapy, and autoimmune disease. While recent advances in machine learning have improved prediction of TCR-pMHC binding, the most effective approaches are black-box transformer models that cannot pr

Abdulrahman Tamim

Neural network training is typically viewed as gradient descent on a loss surface. We propose a fundamentally different perspective: learning is a structure-preserving transformation (a functor L) between the space of network parameters (Param) and the space of learned representations (Rep). This categorical framework reveals that different training runs pro

Akshay Mittal, Vinay Venkatesh, Krishna Kandi, Shalini Sudarshan

The effectiveness of single-model sequential recommendation architectures, while scalable, is often limited when catering to "power users" in sparse or niche domains. Our previous research, PinnerFormerLite, addressed this by using a fixed weighted loss to prioritize specific domains. However, this approach can be sub-optimal, as a single, uniform weight may

V. S. Raghu Parupudi

A central question in artificial intelligence is the extent to which machine learning models comprehend mathematics. To address this, we propose a novel framework for measuring mathematical reasoning that moves beyond standard benchmarks to diagnose specific failure points. Our method first generates structured, step-by-step reasoning from gpt-3.5-turbo on t

Tejal Patwardhan, Rachel Dias, Elizabeth Proehl, Grace Kim

We introduce GDPval, a benchmark evaluating AI model capabilities on real-world economically valuable tasks. GDPval covers the majority of U.S. Bureau of Labor Statistics Work Activities for 44 occupations across the top 9 sectors contributing to U.S. GDP (Gross Domestic Product). Tasks are constructed from the representative work of industry professionals w

Hadi Nekoei, Aman Jaiswal, Patrice Bechard, Oleh Shliazhko

Large language model (LLM) agents perform well in sequential decision-making tasks, but improving them on unfamiliar domains often requires costly online interactions or fine-tuning on large expert datasets. These strategies are impractical for closed-source models and expensive for open-source ones, with risks of catastrophic forgetting. Offline trajectorie

Ignacio Gustin, Chang Woo Kim, Ignacio Franco

This paper extends the previously reported theory of dissipation pathways [J. Chem. Phys. 160, 214111 (2024)] to incorporate off-diagonal subsystem-bath coupling, which is often required to model molecular systems where the environment directly influences transitions and couplings between subsystem states. We systematically derive master equations for both p

Naimeng Ye, Arnav Ahuja, Georgios Liargkovas, Yunan Lu

AI agents are increasingly deployed in complex, interactive environments, yet their runtime remains a major bottleneck for training, evaluation, and real-world use. Typical agent behavior unfolds sequentially, with each action requiring an API call that can incur substantial latency. For example, a game of chess between two state-of-the-art agents can take h

Thach A. Nguyen, Kaitlyn S. Yasumura, Duy V. Tran, Trung V. Phan

Concave mirrors are fundamental optical elements, yet some easily observed behaviors are rarely addressed in standard textbooks, such as the formation of multiple reflected images. Here we investigate self-imaging -- where the observer is also the observed object -- using a concave cylindrical mirror. We predict the number of self-images visible from differe

Bernadette Hahn, Gael Rigaud, Richard Schmähl

Limited-angle computerized tomography stands for one of the most difficult challenges in imaging. Although it opens the way to faster data acquisition in industry and less dangerous scans in medicine, standard approaches, such as the filtered backprojection (FBP) algorithm or the widely used total-variation functional, often produce various artefacts that hi

Shashank Mangla, Chris Hokamp, Jack Boylan, Demian Gholipour Ghalandari

We design and implement NegotiationGym, an API and user interface for configuring and running multi-agent social simulations focused upon negotiation and cooperation. The NegotiationGym codebase offers a user-friendly, configuration-driven API that enables easy design and customization of simulation scenarios. Agent-level utility functions encode optimizatio

Nicola Lorenz, Max Pitz

Halin conjectured that a graph has a normal spanning tree if and only if every minor of it has countable colouring number. This has recently been proven by the second author. In this paper, we strengthen this result by establishing the following local version of it: Given a prescribed set of vertices $U$ in a connected graph $G$, there is a normal tree in $G

Christopher Klugmann, Daniel Kondermann

Human-generated categorical annotations frequently produce empirical response distributions (soft labels) that reflect ambiguity rather than simple annotator error. We introduce an ambiguity measure that maps a discrete response distribution to a scalar in the unit interval, designed to quantify aleatoric uncertainty in categorical tasks. The measure bears a

Yuhao Luo, Yuang Zhang, Kehua Chen, Xinyu Zheng

Accurate pedestrian trajectory prediction is crucial for ensuring safety and efficiency in autonomous driving and human-robot interaction scenarios. Earlier studies primarily utilized sufficient observational data to predict future trajectories. However, in real-world scenarios, such as pedestrians suddenly emerging from blind spots, sufficient observational

Morita Tarvirdians, Senthil Chandrasegaran, Hayley Hung, Catholijn M. Jonker

When making significant life decisions, people increasingly turn to conversational AI tools, such as large language models (LLMs). However, LLMs often steer users toward solutions, limiting metacognitive awareness of their own decision-making. In this paper, we shift the focus in decision support from solution-orientation to reflective activity, coining the

Hyunjun Kim, Sejong Kim

We introduce MacroBench, a code-first benchmark that evaluates whether LLMs can synthesize reusable browser-automation programs (macros) from natural-language goals by reading HTML/DOM and emitting Selenium. MacroBench instantiates seven self-hosted sites covering 681 tasks across interaction complexity and targeting difficulty. Our end-to-end protocol valid

Ryosuke Hirata

From the work of X. S. Lin and Z. Wang, it follows that degree two knot invariant admits a decomposition into the sum of a Gauss diagram count and a term involving Arnold invariants. In this paper we establish an analogous description for Milnor's triple linking number - likewise of degree two - showing that it can be represented in terms of counts of certai

Zs. Iszály, A. Husztek, B. Mehmeti, Z. Erdélyi

Magnetic hyperthermia with metallic nanoparticles is a therapeutic strategy that relies on heating cancer cells to levels sufficient to damage or destroy them. After injection, the nanoparticles accumulate in tumor tissues, where they transfer energy from the applied time-dependent magnetic field to the surrounding medium, thereby increasing the local temper

Yutong Huang, Zhiyuan Guo, Yiying Zhang

Far-memory systems, where applications store less-active data in more energy-efficient memory media, are increasingly adopted by data centers. However, applications are bottlenecked by on-demand data fetching from far- to local-memory. We present Memix, a far-memory system that embodies a deep-learning-system co-design for efficient and accurate prefetching,

Minsu Kim, Walid Saad, Doru Calin

In 6G wireless networks, multi-modal ML models can be leveraged to enable situation-aware network decisions in dynamic environments. However, trained ML models often fail to generalize under domain shifts when training and test data distributions are different because they often focus on modality-specific spurious features. In practical wireless systems, dom

Shahine Bouabid, Andre Nogueira Souza, Raffaele Ferrari

Policy targets evolve faster than the Coupled Model Intercomparison Project cycles, complicating adaptation and mitigation planning that must often contend with outdated projections. Climate model output emulators address this gap by offering inexpensive surrogates that can rapidly explore alternative futures while staying close to Earth System Model (ESM) b

Anoushka Harit, Zhongtian Sun, Jongmin Yu

We propose the Causal Sphere Hypergraph Transformer (CSHT), a novel architecture for interpretable financial time-series forecasting that unifies \emph{Granger-causal hypergraph structure}, \emph{Riemannian geometry}, and \emph{causally masked Transformer attention}. CSHT models the directional influence of financial news and sentiment on asset returns by ex

Abetharan Antony, Robert Kingham, Stefan Mijin, Marty Marinak

Accurately incorporating non-local transport into radiation-hydrodynamics codes, and indeed any fluid system, has long been elusive. To date, a simplified and accurate theory that can be easily integrated has not been available. This limitation affects modeling in inertial confinement fusion and magnetic confinement fusion systems, among others, where non-lo

Osman Bicer, Ali D. Kara, Serdar Yuksel

In this paper, for Markov decision processes (MDPs) with unbounded state spaces we present refined upper bounds presented in [Kara et. al. JMLR'23] on finite model approximation errors via optimizing the quantizers used for finite model approximations. We also consider implications on quantizer design for quantized Q-learning and empirical model learning, an

Apurva Badithela, David Snyder, Lihan Zha, Joseph Mikhail

Rapid progress in imitation learning, foundation models, and large-scale datasets has led to robot manipulation policies that generalize to a wide-range of tasks and environments. However, rigorous evaluation of these policies remains a challenge. Typically in practice, robot policies are often evaluated on a small number of hardware trials without any stati

Stephen McCrory, Romeo Orsolino, Dhruv Thanki, Luigi Penco

Teleoperation is a powerful method to generate reference motions and enable humanoid robots to perform a broad range of tasks. However, teleoperation becomes challenging when using hand contacts and non-coplanar surfaces, often leading to motor torque saturation or loss of stability through slipping. We propose a centroidal stability-based retargeting method

Aisha Alansari, Hamzah Luqman

Large language models (LLMs) have transformed natural language processing, achieving remarkable performance across diverse tasks. However, their impressive fluency often comes at the cost of producing false or fabricated information, a phenomenon known as hallucination. Hallucination refers to the generation of content by an LLM that is fluent and syntactica

Mario Villagran

The choice of protest tactics in a social movement has often been analyzed based on the demands, participants, and internal characteristics of the movement. However, recent evidence highlights the context or setting in which the demonstration takes place as another key element in the process; Using structural equation modeling, studies have shown a link betw

João H. Andrade, Jeffrey S. Case, Paolo Piccione, Juncheng Wei

We show that there are infinitely many pairwise nonhomothetic, complete, periodic metrics with constant scalar curvature that are conformal to the round metric on $S^n\setminus S^k$, where $k < \frac{n-2}{2}$. These metrics are obtained by pulling back Yamabe metrics defined on products of $S^{n-k-1}$ and compact hyperbolic $(k+1)$-manifolds. Our main result

Shao-cheng Hou, Si-wen Li

Using the gauge-gravity duality, we study the electromagnetic instability of vacuum with instantons in holographic plasma. The model we employ is the D(-1)-D3 brane system in which the D(-1)-branes correspond to the instantons in holography. To take into account the flavored quarks, the coincident probe D7-branes as flavors are embedded into the bulk geometr

Anindya Sundar Das, Kangjie Chen, Monowar Bhuyan

Pre-trained language models have achieved remarkable success across a wide range of natural language processing (NLP) tasks, particularly when fine-tuned on large, domain-relevant datasets. However, they remain vulnerable to backdoor attacks, where adversaries embed malicious behaviors using trigger patterns in the training data. These triggers remain dorman

Nahshon Mokua Obiri, Kristof Van Laerhoven

Indoor long range wide area network (LoRaWAN) propagation is shaped by structural and time-varying environmental factors, which limit single-slope log-distance models and the standard log-normal shadowing assumption. We propose an environment-conditioned path loss framework that augments a log-distance multi-wall baseline with co-recorded environmental covar

Anthony Carbery, Zane Kun Li, Yixuan Pang, Po-Lam Yung

Let $\Gamma$ be a compact patch of a well-curved $C^{n+1}$ curve in $\mathbb{R}^n$ with induced Lebesgue measure ${\rm d} \lambda$, and let $g \mapsto \widehat{g \,{\rm d}\lambda}$ be the Fourier extension operator for $\Gamma$. Then we have, for arbitrary non-negative weights $w$, \begin{equation*} \int_{B_R} |\widehat{g \,{\rm d}\lambda}|^2w \leq C_{n,a} R

Md. Saiful Bari Siddiqui, Anupam Debashis Roy

The 2024 July Revolution in Bangladesh represents a landmark event in the study of civil resistance. This study investigates the central paradox of the success of this student-led civilian uprising: how state violence, intended to quell dissent, ultimately fueled the movement's victory. We employ a mixed-methods approach. First, we develop a qualitative narr

Tobias Vogl, Viktor Ivády, Isaac J. Luxmoore, Hannah L. Stern

Atomic defects in solid-state materials are building blocks for future quantum technologies, such as quantum communication networks, computers, and sensors. Until recently, a handful of defects in a small selection of host materials have been possible candidates. Recent developments have revealed that hexagonal boron nitride, a wide-bandgap two-dimensional m

Tim S. G. van Eck, Pieter Kleer, Johan S. H. van Leeuwaarden

When selling many goods with independent valuations, we develop a distributionally robust framework, consisting of a two-player game between seller and nature. The seller has only limited knowledge about the value distribution. The seller selects a revenue-maximizing mechanism, after which nature chooses a revenue-minimizing distribution from all distributio

Harshil Vejendla

Forecasting chaotic systems is a cornerstone challenge in many scientific fields, complicated by the exponential amplification of even infinitesimal prediction errors. Modern machine learning approaches often falter due to two opposing pitfalls: over-specializing on a single, well-known chaotic system (e.g., Lorenz-63), which limits generalizability, or indi

G. Niklas Noren, Eva-Lisa Meldau, Johan Ellenius

Many high-stakes AI applications target low-prevalence events, where apparent accuracy can conceal limited real-world value. Relevant AI models range from expert-defined rules and traditional machine learning to generative LLMs constrained for classification. We outline key considerations for critical appraisal of AI in rare-event recognition, including prob

Daniel Tan, Anders Woodruff, Niels Warncke, Arun Jose

Language model finetuning often results in learning undesirable traits in combination with desired ones. To address this, we propose inoculation prompting: modifying finetuning data by prepending a short system-prompt instruction that deliberately elicits the undesirable trait. At test time, we evaluate without the instruction; inoculated models have much lo

Christian Limberg, Fares Schulz, Zhe Zhang, Stefan Weinzierl

This paper presents a novel approach to neural instrument sound synthesis using a two-stage semi-supervised learning framework capable of generating pitch-accurate, high-quality music samples from an expressive timbre latent space. Existing approaches that achieve sufficient quality for music production often rely on high-dimensional latent representations t

Mathieu Laï-king, Patrick Paroubek

Reporting quality is an important topic in clinical trial research articles, as it can impact clinical decisions. In this article, we test the ability of large language models to assess the reporting quality of this type of article using the Consolidated Standards of Reporting Trials (CONSORT). We create CONSORT-QA, an evaluation corpus from two studies on a

Abhiram Natarajan, Adam Sheffer

We generalize Pach and de Zeeuw's bound for distinct distances between points on two curves, from algebraic curves to Pfaffian curves. Pfaffian curves include those that can be defined by any combination of elementary functions, including exponential and logarithmic functions, rational and irrational powers, trigonometric functions and their inverses, integr

Jiacheng Wu

Arterial aneurysm (Fig.1) is a bulb-shape local expansion of human arteries, the rupture of which is a leading cause of morbidity and mortality in US. Therefore, the prediction of arterial aneurysm rupture is of great significance for aneurysm management and treatment selection. The prediction of aneurysm rupture depends on the analysis of the time series of

Cristian Lenart, Rui Xiong, Changlong Zhong

The main goal of this paper is to extend two fundamental combinatorial results in Schubert calculus on flag manifolds from equivariant cohomology and $K$-theory to equivariant elliptic cohomology. The foundations of elliptic Schubert calculus were laid in a few relatively recent papers by Rim\'anyi, Weber, and Kumar. They include the recursive construction o

Elliott Liu, Linus Tang, Jessica Wan

We study long $r$-twins in random words and permutations. Motivated by questions posed in works of Dudek-Grytczuk-Ruci\'nski, we obtain the following. For a uniform word in $[k]^n$ we prove sharp one-sided tail bounds showing that the maximum $r$-power length (the longest contiguous block that can be partitioned into $r$ identical subblocks) is concentrated

Gaia Carenini, Samuel Coulomb

Let $k,r \geq 2$ be two integers. We consider the problem of partitioning the hyperedge set of an $r$-uniform hypergraph $H$ into the minimum number $\chi_k'(H)$ of edge-disjoint subhypergraphs in which every vertex has either degree $0$ or degree congruent to $1$ modulo $k$. For a random hypergraph $H$ drawn from the binomial model $\mathbf{H}(n,p,r)$, with

Lan Feng, Yang Gao, Eloi Zablocki, Quanyi Li

Imitation learning for end-to-end driving trains policies only on expert demonstrations. Once deployed in a closed loop, such policies lack recovery data: small mistakes cannot be corrected and quickly compound into failures. A promising direction is to generate alternative viewpoints and trajectories beyond the logged path. Prior work explores photorealisti

Jiajun Wu, Swaleh Zaidi, Braden Teitge, Henry Leung

Electronic health records (EHRs) contain extensive unstructured clinical data that can overwhelm emergency physicians trying to identify critical information. We present a two-stage summarization system that runs entirely on embedded devices, enabling offline clinical summarization while preserving patient privacy. In our approach, a dual-device architecture

Divyajyoti, Isobel M. Romero-Shaw, Vaishak Prasad, Kaushik Paul

While the majority of gravitational wave (GW) events observed by the LIGO and Virgo detectors are consistent with mergers of binary black holes (BBHs) on quasi-circular orbits, some events are also consistent with non-zero orbital eccentricity, indicating that the binaries could have formed via dynamical interactions. Moreover, there may be GW events which s

Nghiem T. Diep, Hien Dang, Tuan Truong, Tan Dinh

Parameter-efficient fine-tuning (PEFT) methods have become the standard paradigm for adapting large-scale models. Among these techniques, Weight-Decomposed Low-Rank Adaptation (DoRA) has been shown to improve both the learning capacity and training stability of the Low-Rank Adaptation (LoRA) method by explicitly decomposing pre-trained weights into magnitude

José Elias Abrão, Daan Weltens, Rhodri Mansell, Sebastiaan van Dijken

The use of spin waves in magnetic thin films at cryogenic temperatures has long been hindered by the lack of a suitable material platform. Yttrium iron garnet (YIG) is the leading candidate, yet it is typically grown on gadolinium gallium garnet (GGG) substrates, which develop a large paramagnetic moment at low temperatures. This substrate effect limits spin