November 2025 arXiv papers — page 63

Yao Liu, Sampad Mohanty, Elizabeth Ondula, Bhaskar Krishnamachari

Spatial task allocation in systems such as multi-robot delivery or ride-sharing requires balancing efficiency with fair service across tasks. Greedy assignment policies that match each agent to its highest-preference or lowest-cost task can maximize efficiency but often create inequities: some tasks receive disproportionately favorable service (e.g., shorter

Chenyang Jiang, Hang Zhao, Xinyu Zhang, Zhengcen Li

Dataset distillation compresses large-scale datasets into compact, highly informative synthetic data, significantly reducing storage and training costs. However, existing research primarily focuses on balanced datasets and struggles to perform under real-world long-tailed distributions. In this work, we emphasize the critical role of soft labels in long-tail

Wenxi Dai, Wujiang Xu, Pinhuan Wang, Dimitris N. Metaxas

The widespread adoption of Large Language Models (LLMs) as re-rankers is shifting recommender systems towards a user-centric paradigm. However, a significant gap remains: current re-rankers often lack mechanisms for fine-grained user control. They struggle to balance inherent user preferences with multiple attribute-based constraints, often resorting to simp

Wenjie Zhong, Xinqi Huang, Xiande Zhang

Frameproof codes are a class of secure codes introduced by Boneh and Shaw in the context of digital fingerprinting, and have been widely studied from a combinatorial point of view. In this paper, we study a quantitative extension of frameproof codes and hypergraphs, referred to as {\it quantitative frameproof codes and hypergraphs}. We give asymptotically op

Xu-Qing Liu, Hao Liu, Jian-Ying Rong

This paper presents the symmetric wave interpolation method for stable global interpolation using readily available equidistant points. Its key achievement is the integration of the practical utility of such points with the numerical stability of Chebyshev interpolation. Experimental results demonstrate that symmetric wave interpolation effectively suppresse

Yuliang Zhan, Xinyu Tang, Han Wan, Jian Li

Recently, Chain-of-Thought (CoT) reasoning has significantly enhanced the capabilities of large language models (LLMs), but Vision-Language Models (VLMs) still struggle with multi-step reasoning tasks due to limited multimodal reasoning data. To bridge this gap, researchers have explored methods to transfer CoT reasoning from LLMs to VLMs. However, existing

Zhiyuan Huang, Baichuan Yang, Zikun He, Yanhong Wu

Chemical reasoning inherently integrates visual, textual, and symbolic modalities, yet existing benchmarks rarely capture this complexity, often relying on simple image-text pairs with limited chemical semantics. As a result, the actual ability of Multimodal Large Language Models (MLLMs) to process and integrate chemically meaningful information across modal

Debashish Chakraborty, Eugene Yang, Daniel Khashabi, Dawn Lawrie

Retrieval-Augmented Generation (RAG) enhances factual grounding in large language models (LLMs) by incorporating retrieved evidence, but LLM accuracy declines when long or noisy contexts exceed the model's effective attention span. Existing pre-generation filters rely on heuristics or uncalibrated LLM confidence scores, offering no statistical control over r

Hao Wu, Lucy Shao, Toni Gui, Tsungchin Wu

Doubly robust estimators (DRE) are widely used in causal inference because they yield consistent estimators of average causal effect when at least one of the nuisance models, the propensity for treatment (exposure) or the outcome regression, is correct. However, double robustness does not extend to variance estimation; the influence-function (IF)-based varia

Lijun Shang, Yadong Yu, Wenqiang Kang, Jian Zhou

Two-dimensional (2D) materials have showed widespread applications in energy storage and conversion owning to their unique physicochemical, and electronic properties. Most of the valuable information for the materials, such as their properties and preparation methods, is included in the published research papers. However, due to the dispersion of synthe

Wen-Fan Wang, Chien-Ting Lu, Jin Ping Ng, Yi-Ting Chiu

Animation pre-production lays the foundation of an animated film by transforming initial concepts into a coherent blueprint across interdependent stages such as ideation, scripting, design, and storyboarding. While generative AI tools are increasingly adopted in this process, they remain isolated, requiring creators to juggle multiple systems without integra

Claus Kadelka

Gene regulatory networks exhibit remarkable stability, maintaining functional phenotypes despite genetic and environmental perturbations. Discrete dynamical models, such as Boolean networks, provide systems biologists with a tractable framework to explore the mathematical underpinnings of this robustness. A key mechanism conferring stability is canalization.

Yuhang Ming, Chenxin Fang, Xingyuan Yu, Fan Zhang

Recent advances in Gaussian Splatting based 3D scene representation have shown two major trends: semantics-oriented approaches that focus on high-level understanding but lack explicit 3D geometry modeling, and structure-oriented approaches that capture spatial structures yet provide limited semantic abstraction. To bridge this gap, we present CUS-GS, a compa

Amit Samaddar, S. Surendra Singh

In this work, we investigate the late-time cosmic dynamics in the framework of non-linear $f(R, L_m)$ gravity, adopting the functional form $f(R,L_m)=\frac{R}{2}+L_m^2$. To explore the dark energy behavior, we assume an oscillatory parametric equation of state, $\omega(z) = \omega_0 + b \sin[\log(1+z)]$, which allows smooth deviations from the cosmological c

Xiao-Dong Zhang, Bin-Bin Cai, Song Lin

Verifying prepared quantum states is crucial for hybrid systems whose subsystems may have different local dimensions. We present a generalized stabilizer framework and associated test that apply to general multi-qudit states, including composite-dimensional and hybrid architectures. Using only adaptive local measurements, our method verifies qutrit-qubit sta

Mengqing Zhang, Shengbin Pei, Shan Chang, Pengfei Zhang

PSR J2039-5617 is a redback millisecond pulsar binary system consisting of a compact star with a mass of 1.1-1.6 $M_\odot$ and a low-mass companion of 0.15-0.22 $M_\odot$. For this binary, we performed a timing analysis using 16 years of data from the Fermi Large Area Telescope, covering the period from 2008 August to 2024 October. Our analysis detected an o

Meghanil Sinha, S. Surendra Singh

The accelerated expansion of the Universe can be suitably attributed to the existence of the dark energy (DE). On the backdrop of this concept, this paper introduces a novel, anisotropic compact star model whose stability and structure are governed by the presence of quintessence field, defined by the parameter $ w_{Q} (-1<w_{Q}<-\frac{1}{3}) $ and which adm

Weixi Song, Zhetao Chen, Tao Xu, Xianchao Zeng

Denoising-based models, such as diffusion and flow matching, have been a critical component of robotic manipulation for their strong distribution-fitting and scaling capacity. Concurrently, several works have demonstrated that simple learning objectives, such as L1 regression, can achieve performance comparable to denoising-based methods on certain tasks, wh

Hanjiang Hong, Kai-Kit Wong, Xusheng Zhu, Hao Xu

Fluid antenna multiple access (FAMA) is an emerging technology in massive access designed to meet the demands of future wireless communication networks by naturally mitigating multiuser interference through the utilization of the fluid antenna system (FAS) at RF-chain-limited mobile device. The transition from single-active-port to multi-active-port on a sha

Moein Naseri

We investigate the fundamental limits of entanglement generation under bipartite Hamiltonian dynamics when only finite physical resources-specifically, bounded energy variance-are available. Using the relative entropy of entanglement, we derive a closed analytical expression for the instantaneous entanglement generation rate for arbitrary pure states and Ham

Ziye Zhang, Bin Pan, Zhenwei Shi

Spectral super-resolution (SSR) aims to reconstruct hyperspectral images (HSIs) from multispectral observations, with broad applications in computer vision and remote sensing. Deep learning-based methods have been widely used, but they often treat spectra as discrete vectors learned from data, rather than continuous curves constrained by physics principles,

Yi Huang, Feng Han, Wenyi Liu, Jingang Yi

Chatter is a self-excited vibration in milling that degrades surface quality and accelerates tool wear. This paper presents an adaptive process controller that suppresses chatter by leveraging machine learning-based online estimation of the Stability Lobe Diagram (SLD) and surface roughness in the process. Stability analysis is conducted using the semi-discr

Chiaki Hirose, Kenta Tanaka, Osamu Miyakawa, Takafumi Ushiba

Precise alignment sensing and control are essential for maintaining the stability of laser interferometric gravitational-wave detectors. Conventional Wave Front Sensing technique (WFS), which relies on the beat between the carrier and phase-modulated (PM) sidebands, is dominated by arm-axis signals when the carrier resonates in the full interferometer. This

Xiang Gao, Cody Hyndman

We develop an arbitrage-free deep learning framework for yield curve and bond price forecasting based on the Heath-Jarrow-Morton (HJM) term-structure model and a dynamic Nelson-Siegel parameterization of forward rates. Our approach embeds a no-arbitrage drift restriction into a neural state-space architecture by combining Kalman, extended Kalman, and particl

Junichi Harada

Long time dynamics of solutions to the 6D energy critical heat equation $u_t=\Delta u+|u|^{p-1}u$ on $\R^6\times(0,\infty)$ is investigated. It is shown that there exists a radially symmetric global solution $u(x,t)\in C([0,\infty);\dot H^1(\R^6))$ of the form \begin{align*} u(x,t) = \lambda(t)^{-\frac{n-2}{2}} {\sf Q}(\tfrac{x}{\lambda(t)}) + \text{error} (

Jorge A. Arroyo

I study how first sizable industry entries reshape local and neighboring labor markets in Puerto Rico. Using over a decade of quarterly municipality--industry data (2014Q1--2025Q1), I identify ``first sizable entries'' as large, persistent jumps in establishments, covered employment, and wage bill, and treat these as shocks to local industry presence at the

Wenyuan Li, Guang Li, Keisuke Maeda, Takahiro Ogawa

Audio-Visual Dataset Distillation aims to compress large-scale datasets into compact subsets while preserving the performance of the original data. However, conventional Distribution Matching (DM) methods struggle to capture intrinsic cross-modal alignment. Subsequent studies have attempted to introduce cross-modal matching, but two major challenges remain:

Ting Huang, Dongjian Li, Rui Yang, Zeyu Zhang

Grounding natural-language instructions into continuous control for quadruped robots remains a fundamental challenge in vision language action. Existing methods struggle to bridge high-level semantic reasoning and low-level actuation, leading to unstable grounding and weak generalization in the real world. To address these issues, we present MobileVLA-R1, a

Seulgi Jeong, Jaeil Kim

In the personalization process of large-scale text-to-image models, overfitting often occurs when learning specific subject from a limited number of images. Existing methods, such as DreamBooth, mitigate this issue through a class-specific prior-preservation loss, which requires increased computational cost during training and limits user control during infe

Pume Tuchinda, Parinthapat Pengpun, Romrawin Chumpu, Patomporn Payoungkhamdee

Vision-language models (VLMs) have achieved remarkable success across multimodal tasks, yet their substantial computational demands hinder efficient deployment. Knowledge distillation (KD) has emerged as a powerful approach for building lightweight but competitive models, with strong evidence from both language and vision domains. However, its application to

Guoyang Xia, Yifeng Ding, Fengfa Li, Lei Ren

Multimodal large language models (MLLMs) have achieved impressive performance, but high-resolution visual inputs result in long sequences of visual tokens and substantial inference latency. Reducing redundant visual tokens is critical to ease computational/memory burdens while preserving performance, enabling MLLM deployment in resource-constrained or latenc

Lynne A. Hillenbrand, Matthew J. Graham, Mansi M. Kasliwal, Josiah Purdum

We announce a recently detected outburst that is currently only a few months old, and probably of FU Orionis type. The progenitor to the outburst was an emission-line, flat-spectrum SED young stellar object located in the W5 region, though somewhat outside the main star formation action. We present optical, near-infrared, and mid-infrared lightcurves that il

Jiong Lin, Jinchen Ruan, Hod Lipson

Recent advances in generative models have produced strong results for static 3D shapes, whereas articulated 3D generation remains challenging due to action-dependent deformations and limited datasets. We introduce ArticFlow, a two-stage flow matching framework that learns a controllable velocity field from noise to target point sets under explicit action con

Ruide Cao, Zhuyun Qi, Qinyang He, Chenxi Ling

For distributed control systems, modern latency-critical applications are increasingly demanding real-time guarantees and robustness. Response-time analysis (RTA) is useful for this purpose, as it helps analyze and guarantee timing bounds. However, conventional RTA methods struggle with the state-space explosion problem, especially in non-preemptive systems

Ahmad Mohammadshirazi, Pinaki Prasad Guha Neogi, Dheeraj Kulshrestha, Rajiv Ramnath

Document Visual Question Answering (DocVQA) requires models to jointly understand textual semantics, spatial layout, and visual features. Current methods struggle with explicit spatial relationship modeling, inefficiency with high-resolution documents, multi-hop reasoning, and limited interpretability. We propose MGA-VQA, a multi-modal framework that integra

SeungYun Han, Fei Xia, Sylvain Gigan, Bruno Loureiro

Optical kernel machines offer high throughput and low latency. A nonlinear optical kernel can handle complex nonlinear data, but power consumption is typically high with the conventional nonlinear optical approach. To overcome this issue, we present an optical kernel with structural nonlinearity that can be continuously tuned at low power. It is implemented

Huiguang Zhang

The synchronous Nyquist folding generalized eigenvalue method (SNGEM) realizes full frequency/amplitude/phase estimation of multitone signals at extreme sub-Nyquist rates by jointly processing the original signals and their time derivatives. In this paper, accurate Cramer-Rao bounds for amplitude ratio parameter R=A/B=1/(2\pif) are derived for two channels w

Yusong Wu, Stephen Brade, Aleksandra Teng Ma, Tia-Jane Fowler

Most applications of generative AI involve a sequential interaction in which a person inputs a prompt and waits for a response, and where reaction time and adaptivity are not important factors. In contrast, live jamming is a collaborative interaction that requires real-time coordination and adaptation without access to the other player's future moves, while

Peishi Li, Ming Li, Rang Liu, Qian Liu

Orthogonal frequency division multiplexing (OFDM) is well-suited for integrated sensing and communications (ISAC), yet its cyclic prefix (CP) is dimensioned for communications-grade multipath and is generally insufficient for sensing. When echoes exceed the CP duration, inter-symbol and inter-carrier interference (ISI/ICI) break subcarrier orthogonality and

Zhenkun Li, Fan Ye

Suppose $K \subset S^3$ is a knot and suppose $p$ and $q$ are co-prime integers with $q\ge 1$. For any field $\mathbb{K}$, we establish a dimension formula for the framed instanton homology of knot surgeries: $$ \dim I^\sharp(S^3_{p/q}(K); \mathbb{K}) = q \cdot r_{\mathbb{K}}(K) + |p - q \cdot \nu^\sharp_{\mathbb{K}}(K)| $$ for certain integers $r_{\mathbb{K

Mukul Singh, Ananya Singha, Aishni Parab, Pronita Mehrotra

Associative thinking--the ability to connect seemingly unrelated ideas--is a foundational element of human creativity and problem-solving. This paper explores whether reinforcement learning (RL) guided by associative thinking principles can enhance a model's performance across diverse generative tasks, including story writing, code generation, and chart crea

Abdelrahman Ismael, Taner Cokyasar

Freight transportation modeling often struggles with data limitations, especially in accurately representing complex supplier selection processes and their impact on network flows. This research addresses this critical gap by developing a large-scale, calibrated agent-based model for supplier selection, complemented by a probabilistic heuristic for internati

Yunyi Zhang, Shibo Cui, Baojun Liu, Jingkai Yu

LLM applications (i.e., LLM apps) leverage the powerful capabilities of LLMs to provide users with customized services, revolutionizing traditional application development. While the increasing prevalence of LLM-powered applications provides users with unprecedented convenience, it also brings forth new security challenges. For such an emerging ecosystem, th

Jin Yang, Daniel S. Marcus, Aristeidis Sotiras

Convolutional neural networks (CNNs) and vision transformers (ViTs) are widely employed for medical image segmentation, but they are still challenged by their intrinsic characteristics. CNNs are limited from capturing varying-scaled features and global contextual information due to the employment of fixed-sized kernels. In contrast, ViTs employ self-attentio

Mukul Singh, Ananya Singha, Arjun Radhakrishna, Sumit Gulwani

We analyze reasoning in language models during task-specific fine-tuning and draws parallel between reasoning tokens--intermediate steps generated while solving problem and the human working memory. Drawing from cognitive science, we align training dynamics with the Four Stages of Competence: models initially produce incorrect outputs without reasoning, then

Masaki Taho

We study tangent spaces in the setting of diffeological spaces. Several distinct tangent functors have been introduced, each of which extends the classical tangent functor from smooth manifolds. In this paper, we construct infinitely many non-isomorphic tangent functors on diffeological spaces. We compare our constructions with existing models, including the

Robert Lund, Xueheng Shi

Single changepoint tests have become a staple check for homogeneity of a climate time series, suggesting how climate has changed should non-homogeneity be declared. This paper summarizes the most prominent single changepoint tests used in today's climate literature, relating them to one and other and unifying their presentations. Asymptotic quantiles for the

Subramanyam Sahoo, Jared Junkin

Embodied AI agents exploit reward signal flaws through reward hacking, achieving high proxy scores while failing true objectives. We introduce Mechanistically Interpretable Task Decomposition (MITD), a hierarchical transformer architecture with Planner, Coordinator, and Executor modules that detects and mitigates reward hacking. MITD decomposes tasks into in

Yin L. Xu, Guang F. Yang, Yi Sun, Hong X. Song

Matter under high pressure often exhibits attractive properties, which, unfortunately, are typically irretrievable when released to ambient conditions. Intuitively, nanostructure engineering might provide a promising route to contain high-pressure phase of materials because of the exceptional mechanical strength at nanoscale. However, there is no available t

Samuel Fernández-Menduiña, Eduardo Pavez, Antonio Ortega, Tsung-Wei Huang

Discrete trigonometric transforms (DTTs), such as the DCT-2 and the DST-7, are widely used in video codecs for their balance between coding performance and computational efficiency. In contrast, data-dependent transforms, such as the Karhunen-Lo\`eve transform (KLT) and graph-based separable transforms (GBSTs), offer better energy compaction but lack symmetr

Ethan Hartley

This study evaluates large language models as estimable classifiers and clarifies how modeling choices shape downstream measurement error. Revisiting the Economic Policy Uncertainty index, we show that contemporary classifiers substantially outperform dictionary rules, better track human audit assessments, and extend naturally to noisy historical and multili

Suk Ki Lee, Ronnie F. P. Stone, Max Gao, Wenlong Zhang

Manufacturing processes are inherently dynamic and uncertain, with varying parameters and nonlinear behaviors, making robust control essential for maintaining quality and reliability. Traditional control methods often fail under these conditions due to their reactive nature. Model Predictive Control (MPC) has emerged as a more advanced framework, leveraging

Yufeng Wang, Long Hao, Lixin Liu, Fengchao Wu

High-fidelity shock experiments were performed on copper powders with controlled porosity via improved target fabrication and assembly. Optical velocimetry and multi-channel pyrometry were used to obtain Hugoniot data, isentropic release paths, and interface temperature histories. The results validate a modified two-phase equation of state (EOS) for copper b

Federico Castillo, Daniel Duarte, Maximiliano Leyton-Álvarez, Alvaro Liendo

In this paper we describe the implementation that led to the counterexamples to the Nash blowup conjectures recently discovered by the authors. We also provide new examples of toric varieties with prescribed singularities that are not resolved by the normalized Nash blowup, including cyclic quotient singularities, toric hypersurfaces, and Q-factorial Gorenst

Xuesong Jia, Yuanjie Shi, Ziquan Liu, Yi Xu

Conformal prediction (CP) is a general framework to quantify the predictive uncertainty of machine learning models that uses a set prediction to include the true label with a valid probability. To align the uncertainty measured by CP, conformal training methods minimize the size of the prediction sets. A typical way is to use a surrogate indicator function,

Lukas Harris, Micah Roschelle, Jack Bartley, Mekhail Anwar

Lensless imaging enables exceptionally compact fluorescence sensors, advancing applications in \textit{in vivo} imaging and low-cost, point-of-care diagnostics. These sensors require a filter to block the excitation light while passing fluorescent emissions. However, conventional thin-film interference filters are sensitive to angle of incidence (AOI), compl

Qi-Dong Hao, Hao Wang, Hong-Xing Song, Xiang-Rong Chen

Natural hyperbolic materials have attracted significant interest in the field of photonics due to their unique optical properties. Based on the initial successful explorations on layered crystalline materials, hyperbolic dispersion was associated with extreme structural anisotropy, despite the rarity of natural materials exhibiting this property. Here we sho

Krista G. Schoonover, Gaurav Rawat, Emily B. Pentzer, Michael S. Dimitriyev

Block copolymer (BCP) melts play a critical role in the design of thermoplastics, owing in large part to the creation of alternating nano-scale domains of soft and stiff components. Considerable attention has been given to the short-to-intermediate time response of BCP melts, when the storage modulus is expected to dominate the viscoelastic properties. Howev

Joshua Nevin

In 2021, Broadbent and Kazmi developed a gate-teleportation-based protocol for computational indistinguishability obfuscation of quantum circuits. This protocol is efficient for Clifford+T circuits with logarithmically many T-gates, where the limiting factor in the efficiency of the protocol is the difficulty, on input a quantum circuit $C$, of the classical

Allen Roush, Devin Gonier, John Hines, Judah Goldfeder

The capacity for highly complex, evidence-based, and strategically adaptive persuasion remains a formidable great challenge for artificial intelligence. Previous work, like IBM Project Debater, focused on generating persuasive speeches in simplified and shortened debate formats intended for relatively lay audiences. We introduce DeepDebater, a novel autonomo

SunMin Moon, Jangwon Gim, Chaerin Kim, Yeeun Kim

This paper presents a comprehensive study on enhancing kiosk systems through a low-code architecture, with a focus on AI-based implementations. Modern kiosk systems are confronted with significant challenges, including a lack of integration, structural rigidity, performance bottlenecks, and the absence of collaborative frameworks. To overcome these limitatio

Hiroyoshi Nakano, Yuki Minami

Dissipation anomaly-the persistence of finite energy dissipation in the inviscid limit-is a hallmark of turbulence, sometimes regarded as the "zeroth law" of turbulent flows. Here, we demonstrate that this phenomenon is not exclusive to turbulence. Using fluctuating hydrodynamics, we show that a simple gradient-driven nonequilibrium steady state, in which a

Z. Zhang, I. Agapov, S. Gasiorowski, T. Hellert

We demonstrate that multipoint Bayesian algorithm execution can overcome fundamental computational challenges in storage ring design optimization. Dynamic (DA) and momentum (MA) optimization is a multipoint, multiobjective design task for storage rings, ultimately informing the flux of x-ray sources and luminosity of colliders. Current state-of-art black-box

Shuyuan Fan, Zhao Zhang

Global communication, such as all-reduce and allgather, is the prominent performance bottleneck in large language model (LLM) pretraining. To address this issue, we present Pier, an efficient and scalable optimizer with relaxed global communication. Pier is built upon DiLoCo, which leverages an inner optimizer within groups of processors and an outer optimiz

Zhihui Tian, Ethan Suwandi, Tomas Oppelstrup, Vasily V. Bulatov

Graph neural networks (GNN) have emerged as a promising machine learning method for microstructure simulations such as grain growth. However, accurate modeling of realistic grain boundary networks requires large simulation cells, which GNN has difficulty scaling up to. To alleviate the computational costs and memory footprint of GNN, we propose a hybrid arch

Xudong Zhang, Zhaoyu Sun, Bin Guo

The non-Hermitian skin effect (NHSE) fundamentally invalidates the conventional bulk-boundary correspondence (BBC), leading topological diagnostics into a crisis. While the non-Bloch polarization $P_{\beta}$ defined on the generalized Brillouin zone restores momentum-space topology, a direct, robust real-space bulk probe has remained elusive. We resolve this

Yu Zhang, Meng Xu, Bowei Zhou, Carter Eckel

Electric-field control of spin states offers a promising route to ultra-low-power, ultra-fast magnetization switching in spintronic devices such as magnetic tunnel junctions (MTJs). Recent progress in modulating spin-orbit interactions at the interfaces between 3d transition-metal ferromagnets and dielectric layers has underscored the role of atomic-scale he

Tian Ye, Song Fei, Lei Zhu

Diffusion transformers have recently delivered strong text-to-image generation around 1K resolution, but we show that extending them to native 4K across diverse aspect ratios exposes a tightly coupled failure mode spanning positional encoding, VAE compression, and optimization. Tackling any of these factors in isolation leaves substantial quality on the tabl

Shubham Kumar, Zhiye Tang, Shinji Saito

The dynamic slowdown in glass-forming liquids remains a central topic in condensed matter science. Here, we report a theoretical investigation of the microscopic origin of the slowdown in amorphous silica, a prototypical strong glass former with a tetrahedral network structure. Using molecular dynamics simulations, we analyze atomic jump dynamics, the elemen

Seung-Yoo Lee, Masateru Ishiguro, Hangbin Jo, Sung-Chul Yoon

Earth-like exoplanets can create resonant structures in exozodiacal dust through mean motion resonances (MMRs). These structures not only suggest the presence of such planets, but also act as potential noise sources in future mid-infrared (MIR) nulling interferometry observations. We aim to investigate how resonant structures in exozodiacal dust vary across

Vladimir Dotsenko

A derived operation is a bilinear operation on a commutative associative algebra $A$ defined intrinsically out of its product and several derivations of the product. We show that operators of left (or right) multiplications of a derived operation always satisfy a &#34;standard identity&#34; of certain order. In particular, it implies that each Rankin-Cohen b

Dibyendu Samanta, Sudeep Kumar Ghosh

The superconducting diode effect (SDE), characterized by a directional asymmetry in the critical supercurrents, typically requires external magnetic fields to break time-reversal symmetry -- posing challenges for scalability and device integration. Here, we demonstrate a field-free realization of the SDE in a helical Shiba chain proximitized by a $d$-wave al

Davide Pradovera, Ion Victor Gosea, Jan Heiland

We consider the task of data-driven identification of dynamical systems, specifically for systems whose behavior at large frequencies is non-standard, as encoded by a non-trivial relative degree of the transfer function or, alternatively, a non-trivial index of a corresponding realization as a descriptor system. We develop novel surrogate modeling strategies

Shihan Cheng, Nilesh Kulkarni, David Hyde, Dmitriy Smirnov

Fine-tuning large-scale text-to-video diffusion models to add new generative controls, such as those over physical camera parameters (e.g., shutter speed or aperture), typically requires vast, high-fidelity datasets that are difficult to acquire. In this work, we propose a data-efficient fine-tuning strategy that learns these controls from sparse, low-qualit

Chenyi Wang, Zhaowei Li, Ming F. Li, Wujie Wen

Multi-agent cooperative perception (CP) promises to overcome the inherent occlusion and range limitations of single-agent systems in autonomous driving, yet its practicality is severely constrained by limited Vehicle-to-Everything (V2X) communication bandwidth. Existing approaches attempt to improve bandwidth efficiency via compression or heuristic message s

Xavier Salleras

In this work, we present homomorphic encryption-based vaults (Haults), a permissioned privacy-preserving smart wallet protocol for VM-enabled blockchains that keeps users' balances confidential, as well as the amounts transacted to other parties. To comply with regulations, we include optional compliance features that allow specific entities (the auditors) t

Ziyue Yang, Feng Liu, Yifei Jin, Konstantinos Vandikas

This paper presents RadioGUNet, a UNet-based deep learning framework for pathloss estimation in wireless communication. Unlike other frameworks, it leverages group equivariant convolutional networks, which are known to increase the expressive capacity of a neural network by allowing the model to generalize to further classes of symmetries, such as rotations

Tony Shaska

We introduce a graded formulation of internal symbolic computation for transformers. The hidden space is endowed with a grading $V=\bigoplus_{g\in G}V_g$, and symbolic operations are realized as typed block maps (morphisms) $\phi_{h\leftarrow g}:V_g\to V_h$ that are activated selectively by a differentiable routing policy. A self-supervised \emph{graded util

Yujiang Pu, Zhanbo Huang, Vishnu Boddeti, Yu Kong

Generating visual instructions in a given context is essential for developing interactive world simulators. While prior works address this problem through either text-guided image manipulation or video prediction, these tasks are typically treated in isolation. This separation reveals a fundamental issue: image manipulation methods overlook how actions unfol

Jai Arora, Sirui Lu, Devansh Jain, Tianfan Xu

Tensor compilers, essential for generating efficient code for deep learning models across various applications, employ tensor graph rewrites as one of the key optimizations. These rewrites optimize tensor computational graphs with the expectation of preserving semantics for tensors of arbitrary rank and size. Despite this expectation, to the best of our know

Maciej Doniec, Katarzyna Sznajd-Weron, Federico Vazquez

Negative information often exerts a disproportionately strong impact on human decision-making, a phenomenon known as the negativity bias. In behavioral economics, this effect is formally captured by Prospect Theory, which posits that losses loom larger than equivalent gains. For example, a single negative product review can outweigh numerous positive ones, r

Edwin Sundberg, Thea Ekmark, Workneh Yilma Ayele

RESTful APIs are central in developing interoperable, modular, and maintainable software systems in enterprises today. Also, it is essential to support system evolution, service interoperability, and governance across organizational boundaries to ensure good quality and consistency of these APIs. However, evaluating API design quality, which is part of non-f

Meenakshi Mittal, Rishi Khare, Mihran Miroyan, Chancharik Mitra

With the growing use of Large Language Model (LLM)-based Question-Answering (QA) systems in education, it is critical to evaluate their performance across individual pipeline components. In this work, we introduce {\model}, a modular function-calling LLM pipeline, and present a comprehensive evaluation along three key axes: function calling strategies, retri

Yunsheng Bai, Haoxing Ren

Debugging is the dominant cost in modern hardware verification, where assertion failures are among the most frequent and expensive to resolve. While Large Language Models (LLMs) show promise, they often fail to capture the precise, reusable expertise that engineers apply, leading to inaccurate responses. We propose GROVE, a hierarchical knowledge management

Michael Efroimsky, Michaela Walterova, Yeva Gevorgyan, Amirhossein Bagheri

It is usually taken for granted that mutual synchronisation of a tidal two-body system is attained through tidal recession, assuming the reduced Hill sphere is not reached. However, synchronisation can be achieved also via tidal approach, provided the Roche limit is not crossed. For each of the two scenarios, we derive the condition under which the evolving

Oliver Cheng, Zosia Adamska, Michael P. Brenner, Megan C. Engel

Free energy landscapes encode the kinetics, intermediates, and transition states that govern molecular processes and are thus a key target of single biomolecule research. Typical approaches to deriving optimal, error-minimizing, non-equilibrium driving protocols for estimating these landscapes require a priori knowledge of the landscape. Here, we present an

Roberto de A. Capistrano Filho, Ailton Nascimento

This manuscript presents the results of stabilization for the Zakharov--Kuznetsov equation, a two-dimensional Korteweg--de Vries-type equation. We provide rigorous proofs using two different approaches, showing that when a damping mechanism and an internal delay term (anti-damping) are introduced, the solutions of the Zakharov--Kuznetsov equation exhibit bot

Akhil Singampalli, Sudeep Pasricha

Indoor localization using machine learning has gained traction due to the growing demand for location-based services. However, its long-term reliability is hindered by hardware/software variations across mobile devices, which shift the model's input distribution to create domain shifts. Further, evolving indoor environments can introduce new locations over t

Guilherme J. Cavalcante, José Gabriel A. Moreira, Gabriel A. B. do Nascimento, Vincent Dong

Foundation models hold promise for specialized medical imaging tasks, though their effectiveness in breast imaging remains underexplored. This study leverages BiomedCLIP as a foundation model to address challenges in model generalization. BiomedCLIP was adapted for automated BI-RADS breast density classification using multi-modality mammographic data (synthe

Conrad D. Hougen, Karl T. Pazdernik, Alfred O. Hero

Interpretable topic modeling is essential for tracking how research interests evolve within co-author communities. In scientific corpora, where novelty is prized, identifying underrepresented niche topics is particularly important. However, contemporary models built from dense transformer embeddings tend to miss rare topics and therefore also fail to capture

Uma Maheswara Rao Epuganti, Gnana Bhaskar Tenali

We investigate the initial value problems for non-homogeneous linear differential equations whose solutions are set-valued maps taking values in the space of nonempty compact convex subsets of $\mathbb{R}^2$, denoted by $K_{c}(\mathbb{R}^2)$. The differential formulation is based on the generalized derivative that includes the Hukuhara derivative, as well as

Sam Dillavou, Jason W Rocks, Jacob F Wycoff, Andrea J Liu

An important component of the success of large AI models is double descent, in which networks avoid overfitting as they grow relative to the amount of training data, instead improving their performance on unseen data. Here we demonstrate double descent in a decentralized analog network of self-adjusting resistive elements. This system trains itself and perfo

Pranay Meshram, Yash Turkar, Kartikeya Singh, Praveen Raj Masilamani

Volumetric learning underpins many 3D vision tasks such as completion, reconstruction, and mesh generation, yet training objectives still rely on Chamfer Distance (CD) or Earth Mover's Distance (EMD), which fail to balance recall and precision. We propose Quality-Aware Loss (QAL), a drop-in replacement for CD/EMD that combines a coverage-weighted nearest-nei

Ziyun Chen, Spencer Compton, Daniel Kane, Jerry Li

In list-decodable learning, we are given a set of data points such that an $\alpha$-fraction of these points come from a nice distribution $D$, for some small $\alpha \ll 1$, and the goal is to output a short list of candidate solutions, such that at least one element of this list recovers some non-trivial information about $D$. By now, there is a large body

Shangjie Guo, Corneliu Buda, Nathan Wiebe

Estimating vibrational entropy is a significant challenge in thermodynamics and statistical mechanics due to its reliance on quantum mechanical properties. This paper introduces a quantum algorithm designed to estimate vibrational entropy via energy derivatives. Our approach block encodes the exact expression for the second derivative of the energy and uses

Tony Wong, Colin B. Macdonald, Byungjoon Lee

We generalize the closest point method (CPM) to solve surface partial differential equations with general boundary conditions. The proposed extrapolation method provides a unified framework for treating a broad class of inhomogeneous Neumann and Robin boundary conditions within the framework of CPM. The accuracy and robustness of the method are demonstrated

Jae Woo Lee, Kyeongsoo Hong, Min-Ji Jeong, Jang-Ho Park

TIC 322208686 is known to be a detached binary that exhibits two types of variability: pulsation and eclipse. We present the physical properties of the target star using the short-cadence TESS data from sectors 24, 57, and 58, and our echelle spectra that show the presence of a tertiary companion. The spectral analysis led to the triple-lined radial velociti

Aishwarya Mandyam, Kalyani Limaye, Barbara E. Engelhardt, Emily Alsentzer

Off-policy evaluation (OPE) estimates the value of a contextual bandit policy prior to deployment. As such, OPE plays a critical role in ensuring safety in high-stakes domains such as healthcare. However, standard OPE approaches are limited by the size and coverage of the behavior dataset. While previous work has explored using expert-labeled counterfactual

Peter Hedström, Victor Lamelas Cubero, Jón Sigurdsson, Viktor Österberg

Machine learning (ML) has emerged as a powerful tool for accelerating the computational design and production of materials. In materials science, ML has primarily supported large-scale discovery of novel compounds using first-principles data and digital twin applications for optimizing manufacturing processes. However, applying general-purpose ML frameworks

Younggeun Kim, Jordan Gué, Changhao Xu, Diego Blas

Monochromatic high-frequency gravitational waves (HFGW) provide a distinctive probe of new physics scenarios, most notably axion clouds around rotating black holes formed via superradiance. We reanalyzed data from the CAPP-12T MC (multi-cell) axion haloscope experiment [Phys. Rev. Lett. 133,051802 (2024)]. The study covers a continuous $2\,$MHz frequency spa