November 2025 arXiv papers — page 133

Divyanshu Saxena, Rishikesh Maurya, Xiaoxuan Ou, Gagan Somashekar

The rapid adoption of AI agents across domains has made systematic evaluation crucial for ensuring their usefulness and successful production deployment. Evaluation of AI agents typically involves using a fixed set of benchmarks and computing multiple evaluation metrics for the agent. While sufficient for simple coding tasks, these benchmarks fall short for

Yanjiao Yang, Daniel Suen, Yen-Chi Chen

The masking-one-out (MOO) procedure, masking an observed entry and comparing it versus its imputed values, is a very common procedure for comparing imputation models. We study the optimum of this procedure and generalize it to a missing data assumption and establish the corresponding semi-parametric efficiency theory. However, MOO is a measure of prediction

Xurui Li, Feng Xue, Yu Zhou

Zero-shot anomaly classification (AC) and segmentation (AS) methods aim to identify and outline defects without using any labeled samples. In this paper, we reveal a key property that is overlooked by existing methods: normal image patches across industrial products typically find many other similar patches, not only in 2D appearance but also in 3D shapes, w

Wencong Wu, Xiuwei Zhang, Hanlin Yin, Shun Dai

Visible-infrared object detection has gained sufficient attention due to its detection performance in low light, fog, and rain conditions. However, visible and infrared modalities captured by different sensors exist the information imbalance problem in complex scenarios, which can cause inadequate cross-modal fusion, resulting in degraded detection performan

Jinhong Jeong, Sunghyun Lee, Jaeyoung Lee, Seonah Han

Sound symbolism is a linguistic concept that refers to non-arbitrary associations between phonetic forms and their meanings. We suggest that this can be a compelling probe into how Multimodal Large Language Models (MLLMs) interpret auditory information in human languages. We investigate MLLMs' performance on phonetic iconicity across textual (orthographic an

Sebastian Bleecke, Abhijit Biswas, David I. Ketcheson, Hendrik Ranocha

We study the hyperbolic approximation of the Benjamin-Bona-Mahony (BBM) equation proposed recently by Gavrilyuk and Shyue (2022). We develop asymptotic-preserving numerical methods using implicit-explicit (additive) Runge-Kutta methods that are implicit in the stiff linear part. The new discretization of the hyperbolization conserves important invariants con

Javokhir Sharipov, Tursunali Xamidov, Qiang Wu, Sanjar Shaymatov

In this work, we investigate the dynamics of periodic orbits and the properties of accretion disks around a Schwarzschild-like black hole (BH) immersed in a King-type dark matter (DM) halo. Our analysis focuses on how the presence of the King DM halo influences both the behavior of periodic orbits and the radiative characteristics of the accretion disk. We b

Panjing Wu, Gaofei Zhang

Gluing is a cut and paste construction where the dynamics of a map in a given domain is replaced by a different one, under the condition that the two agree along the gluing curve. Here we consider two polynomials with a finite super-attracting fixed point of the same degree. We prove that any two such non-renormalizable polynomials can be glued into a ration

José Reyes Castillo, Saúl Aguilar Salazar, Diego Mauricio Gomez Coral

Balloon- and space-borne cosmic-ray experiments employ plastic scintillators read out by silicon photomultipliers (SiPMs) to achieve picosecond-level time resolutions for triggering and particle identification. The performance of these systems can be affected by temperature variations encountered in flight. In this work, a time-of-flight (TOF) prototype cons

Xinran Yang, Shuichang Lai, Jiangjing Lyu, Hongjie Li

Generating high-fidelity 3D contents remains a fundamental challenge due to the complexity of representing arbitrary topologies-such as open surfaces and intricate internal structures-while preserving geometric details. Prevailing methods based on signed distance fields (SDFs) are hampered by costly watertight preprocessing and struggle with non-manifold geo

Yuechi Zhou, Yi Su, Jianxin Zhang, Juntao Li

Large language models (LLMs) have demonstrated strong capabilities in processing long contexts, enabling them to tackle tasks involving long textual inputs such as multi-turn conversations, legal documents, or retrieved documents in Retrieval-Augmented Generation (RAG) systems. However, despite their ability to handle long sequences, the resulting decoding l

Lucrezia Cossetti, Lorenzo D'Arca

In this paper, we provide suitable characterisations of pairs of weights $(V,W),$ known as Bessel pairs, that ensure the validity of weighted Hardy-type inequalities. The abstract approach adopted here makes it possible to establish such inequalities also going beyond the classical Euclidean setting and also within a more general $L^p$ framework. As a byprod

Ziheng Li, Hengyi Cai, Xiaochi Wei, Yuchen Li

While large language models (LLMs) demonstrate emerging reasoning capabilities, current inference-time expansion methods incur prohibitive computational costs by exhaustive sampling. Through analyzing decoding trajectories, we observe that most next-token predictions align well with the golden output, except for a few critical tokens that lead to deviations.

Xiaolong Wei, Yuehu Dong, Xingliang Wang, Xingyu Zhang

Existing tool-augmented large language models (LLMs) encounter significant challenges when processing complex queries. Current frameworks such as ReAct are prone to local optimization traps due to their reliance on incremental decision-making processes. To address these limitations, we propose a novel Planner-centric Plan-Execute paradigm that fundamentally

Yotam Kenneth-Mordoch, Robert Krauthgamer

All-Pairs Minimum Cut (APMC) is a fundamental graph problem that asks to find a minimum $s,t$-cut for every pair of vertices $s,t$. A recent line of work on fast algorithms for APMC has culminated with a reduction of APMC to $\mathrm{polylog}(n)$-many max-flow computations. But unfortunately, no fast algorithms are currently known for exact max-flow in sever

Feiyang Jia, Caiyan Jia, Ailin Liu, Shaoqing Xu

As a critical task in autonomous driving perception systems, 3D object detection is used to identify and track key objects, such as vehicles and pedestrians. However, detecting distant, small, or occluded objects (hard instances) remains a challenge, which directly compromises the safety of autonomous driving systems. We observe that existing multi-modal 3D

Ziyu Gan, Heming Jiao

In [1], Caffarelli-Charro introduced a fractional Monge-Amp\`{e}re operator. Later, Wu [17] generalized it to a fractional analogue of $k$-Hessian operators and proved the strict ellipticity for $k=2$. In this paper, we introduce a fractional analogue of general Hessian operators and prove the stability. We also show that the fractional analogue $k$-Hessian

Vijay Keswani, Cyrus Cousins, Breanna Nguyen, Vincent Conitzer

Alignment methods in moral domains seek to elicit moral preferences of human stakeholders and incorporate them into AI. This presupposes moral preferences as static targets, but such preferences often evolve over time. Proper alignment of AI to dynamic human preferences should ideally account for "legitimate" changes to moral reasoning, while ignoring change

Ruichu Cai, Xiaokai Huang, Wei Chen, Zijian Li

Inferring causal relationships from observed data is an important task, yet it becomes challenging when the data is subject to various external interferences. Most of these interferences are the additional effects of external factors on observed variables. Since these external factors are often unknown, we introduce latent variables to represent these unobse

Tao Jiang, Zichuan Lin, Lihe Li, Yi-Chen Li

Large transformer models, trained on diverse datasets, have demonstrated impressive few-shot performance on previously unseen tasks without requiring parameter updates. This capability has also been explored in Reinforcement Learning (RL), where agents interact with the environment to retrieve context and maximize cumulative rewards, showcasing strong adapta

Jiangshu Du, Wenpeng Yin, Philip Yu

The quadratic complexity of standard self-attention severely limits the application of Transformer-based models to long-context tasks. While efficient Transformer variants exist, they often require architectural changes and costly pre-training from scratch. To circumvent this, we propose ScaleFormer(Span Representation Cumulation for Long-Context Transformer

Yan Wang, Rangrong Ma, Kaifeng Shen, Zebo Tang

Machine learning techniques are increasingly being applied in high-energy nuclear physics data analysis thanks to their outstanding performance. One key challenge in such applications is the construction of training samples that can accurately represent real data. Training samples are typically generated through detector simulations, but discrepancies betwee

Risha Surana, Qinyuan Ye, Swabha Swayamdipta

Emergency responders managing hazardous material HAZMAT incidents face critical, time-sensitive decisions, manually navigating extensive chemical guidelines. We investigate whether today's language models can assist responders by rapidly and reliably understanding critical information, identifying hazards, and providing recommendations. We introduce the Chem

Noam Koren, Ralf J. J. Mackenbach, Ruud J. G. van Sloun, Kira Radinsky

Neural operators have emerged as a promising paradigm for learning solution operators of partial differential equa- tions (PDEs) directly from data. Existing methods, such as those based on Fourier or graph techniques, make strong as- sumptions about the structure of the kernel integral opera- tor, assumptions which may limit expressivity. We present SVD-NO,

Ayush Sahu, Richa Arya, Sergio E. Jorás, Karim H. Seleim

Warm inflation is a well-motivated and generalized framework of inflation, describing a coupled inflaton-radiation bath. In this work, we investigate a warm inflation model with a quartic potential and a composite dissipation coefficient $\Upsilon(\phi, T) = C_1 \frac{T^3}{M_{\text{Pl}}^2} + C_2 \frac{T^3}{\phi^2}.$ The two terms in $\Upsilon$ dominate at di

Farzan Saeedi, Sanaz Keshvari, Nasser Shoeibi

This paper encompasses an in-depth examination of Retinopathy of Prematurity (ROP) diagnosis, employing advanced deep learning methodologies. Our focus centers on refining and evaluating CNN-based approaches for precise and efficient ROP detection. We navigate the complexities of dataset curation, preprocessing strategies, and model architecture, aligning wi

Chaofan Zhu, Xiaobing Rui, Zhixiao Wang

Imbalanced node classification is a critical challenge in graph learning, where most existing methods typically utilize Graph Neural Networks (GNNs) to learn node representations. These methods can be broadly categorized into the data-level and the algorithm-level. The former aims to synthesize minority-class nodes to mitigate quantity imbalance, while the l

Egor Davydenko, Andrei Volchenkov, Vladimir Gerasimov, Roman Gorbachev

In this paper, we propose a novel design of an electrically actuated robotic leg, called the DecARt (Decoupled Actuation Robot) Leg, aimed at performing agile locomotion. This design incorporates several new features, such as the use of a quasi-telescopic kinematic structure with rotational motors for decoupled actuation, a near-anthropomorphic leg appearanc

Kamalpreet Singh Kainth, Prathamesh Dinesh Joshi, Raj Abhijit Dandekar, Rajat Dandekar

Neural differential equations offer a powerful framework for modeling continuous-time dynamics, but forecasting stiff biophysical systems remains unreliable. Standard Neural ODEs and physics informed variants often require orders of magnitude more iterations, and even then may converge to suboptimal solutions that fail to preserve oscillatory frequency or am

Yuxin Jiang, Wei Luo, Hui Zhang, Qiyu Chen

We propose Anomagic, a zero-shot anomaly generation method that produces semantically coherent anomalies without requiring any exemplar anomalies. By unifying both visual and textual cues through a crossmodal prompt encoding scheme, Anomagic leverages rich contextual information to steer an inpainting-based generation pipeline. A subsequent contrastive refin

Mujin Choi, Maximilian Gorsky, Gunwoo Kim, Caleb McFarland

We introduce the tree-decomposition-based graph parameter Odd-Cycle-Packing-treewidth (OCP-tw) as a width parameter that asks to decompose a given graph into pieces of bounded odd cycle packing number. The parameter OCP-tw is monotone under the odd-minor-relation and we provide an analogue to the celebrated Grid Theorem of Robertson and Seymour for OCP-tw. T

Xinyi Wang, Xun Yang, Yanlong Xu, Yuchen Wu

Effective human-agent collaboration in physical environments requires understanding not only what to act upon, but also where the actionable elements are and how to interact with them. Existing approaches often operate at the object level or disjointedly handle fine-grained affordance reasoning, lacking coherent, instruction-driven grounding and reasoning. I

Divan A. Burger, Janet van Niekerk, Peter C. le Roux, Morgan J. Raath-Krüger

Continuous proportions measured on the same experimental unit often pose two challenges: interior outliers that inflate variance beyond the beta ceiling and residual dependence that invalidates independent-margin models. We introduce a Bayesian copula modeling approach that combines rectangular-beta margins, which temper interior outliers by reallocating mas

Dejin Ren, Yiling Xue, Taoran Wu, Bai Xue

Barrier certificates play an important role in verifying the safety of continuous-time systems, including autonomous driving, robotic manipulators and other critical applications. Recently, ReLU neural barrier certificates -- barrier certificates represented by the ReLU neural networks -- have attracted significant attention in the safe control community due

Jingwei Song, Wanyi Chen, Xinyuan Song, Max

Speculative decoding accelerates large language model (LLM) inference by using a lightweight draft model to propose tokens that are later verified by a stronger target model. While effective in centralized systems, its behavior in decentralized settings, where network latency often dominates compute, remains under-characterized. We present Decentralized Spec

Gyubok Lee, Woosog Chay, Edward Choi

Recent advances in Large Language Models (LLMs) have enabled the development of text-to-SQL models that allow clinicians to query structured data stored in Electronic Health Records (EHRs) using natural language. However, deploying these models for EHR question answering (QA) systems in safety-critical clinical environments remains challenging: incorrect SQL

Guofeng Meng, Li Shen, Qiuyan Zhong, Wei Wang

Large language models (LLMs) are rapidly transforming various domains, including biomedicine and healthcare, and demonstrate remarkable potential from scientific research to new drug discovery. Graph-based retrieval-augmented generation (RAG) systems, as a useful application of LLMs, can improve contextual reasoning through structured entity and relationship

Shufeng Kong, Zijie Wang, Nuan Cui, Hao Tang

Automated interpretation of medical images demands robust modeling of complex visual-semantic relationships while addressing annotation scarcity, label imbalance, and clinical plausibility constraints. We introduce MIRNet (Medical Image Reasoner Network), a novel framework that integrates self-supervised pre-training with constrained graph-based reasoning. T

Huy M. Le, Dat Tien Nguyen, Ngan T. T. Vo, Tuan D. Q. Nguyen

In today's world, emotional support is increasingly essential, yet it remains challenging for both those seeking help and those offering it. Multimodal approaches to emotional support show great promise by integrating diverse data sources to provide empathetic, contextually relevant responses, fostering more effective interactions. However, current methods h

Shahid Amin, Syed Pervez Hussnain Shah

The remarkable progress in Artificial Intelligence (AI) is foundation-ally linked to a concurrent revolution in computer architecture. As AI models, particularly Deep Neural Networks (DNNs), have grown in complexity, their massive computational demands have pushed traditional architectures to their limits. This paper provides a structured review of this co-e

Aditya Mehta, Swarnim Chaudhary, Pratik Narang, Jagat Sesh Challa

Modern generative and diffusion models produce highly realistic images that can mislead human perception and even sophisticated automated detection systems. Most detection methods operate in RGB space and thus analyze only three spectral channels. We propose HSI-Detect, a two-stage pipeline that reconstructs a 31-channel hyperspectral image from a standard R

Hasib Md Abid Bin Farid, Md Tashfiq Bin Kashem

Dual-absorber solar cells represent a promising approach to surpass the efficiency limit of single-junction devices by extending spectral absorption and minimizing thermalization losses. Among earth-abundant thin-film materials, kesterites have attracted considerable interest, however, the well-studied Cu2ZnSnS4 (CZTS) continues to face challenges related to

Nidhi Yadav, Punam Gupta, R. K. Gangele

In this paper, we investigate the transverse geometry of trans-Sasakian manifolds and present several significant findings. We analyze the Levi-Civita connection associated with the metric on the product manifold of two trans-Sasakian manifolds. We outline the conditions under which the complex structure is harmonic on the product manifold. Notably, we also

Xuancun Lu, Jiaxiang Chen, Shilin Xiao, Zizhi Jin

Vision-Language-Action (VLA) models revolutionize robotic systems by enabling end-to-end perception-to-action pipelines that integrate multiple sensory modalities, such as visual signals processed by cameras and auditory signals captured by microphones. This multi-modality integration allows VLA models to interpret complex, real-world environments using dive

Hongqin Lyu, Yonghao Wang, Jiaxin Zhou, Zhiteng Chao

Assertion-based verification (ABV) is a key approach to checking whether a logic design complies with its architectural specifications. Existing assertion generation methods based on design specifications typically produce only top-level assertions, overlooking verification needs on the implementation details in the modules at the micro-architectural level,

Yongjun Xiao, Dian Meng, Xinlei Huang, Yanran Liu

Effectively modeling multimodal spatial omics data is critical for understanding tissue complexity and underlying biological mechanisms. While spatial transcriptomics, proteomics, and epigenomics capture molecular features, they lack pathological morphological context. Integrating these omics with histopathological images is therefore essential for comprehen

Qiaoyan Peng, Qingqing Wu, Guangji Chen, Wen Chen

Rotatable intelligent reflecting surface (IRS) introduces a new spatial degree of freedom (DoF) by dynamically adjusting orientations without the need of changing its elements' positions in real time. To unleash the full potential of rotatable IRSs for wireless communications, this paper investigates the joint optimization of IRS rotation angles to maximize

Minjun Kim, Jaeri Lee, Jongjin Kim, Jeongin Yun

How can we accurately quantize a pre-trained Vision Transformer model? Quantization algorithms compress Vision Transformers (ViTs) into low-bit formats, reducing memory and computation demands with minimal accuracy degradation. However, existing methods rely on uniform precision, ignoring the diverse sensitivity of ViT components to quantization. Metric-base

Xuexun Liu, Xiaoxu Xu, Qiudan Zhang, Lin Ma

Weakly supervised 3D instance segmentation is essential for 3D scene understanding, especially as the growing scale of data and high annotation costs associated with fully supervised approaches. Existing methods primarily rely on two forms of weak supervision: one-thing-one-click annotations and bounding box annotations, both of which aim to reduce labeling

Shivam Sharma, Riya Naik, Tejas Gawas, Heramb Patil

Large Language Models (LLMs) have demonstrated remarkable capabilities in understanding and generating human-like content. This has revolutionized various sectors such as healthcare, software development, and education. In education, LLMs offer potential for personalized and interactive learning experiences, especially in regions with limited teaching resour

Greg Hather, Daniel Aranki

During online commerce, a customer will typically share his or her mailing address with a merchant to allow product delivery. This creates privacy risks for the customer, where the information may be misused, sold, or leaked by multiple merchants. While physical and virtual PO boxes can reduce the privacy risk, these solutions have associated costs that prev

Saket S. Chaturvedi, Gaurav Bagwe, Lan Zhang, Pan He

LiDAR-based 3D object detection is widely used in safety-critical systems. However, these systems remain vulnerable to backdoor attacks that embed hidden malicious behaviors during training. A key limitation of existing backdoor attacks is their lack of physical realizability, primarily due to the digital-to-physical domain gap. Digital triggers often fail i

Hui Dou, Lei Jin, Yuxuan Zhou, Jiang He

The performance of modern DBMSs such as MySQL and PostgreSQL heavily depends on the configuration of performance-critical knobs. Manual tuning these knobs is laborious and inefficient due to the complex and high-dimensional nature of the configuration space. Among the automated tuning methods, reinforcement learning (RL)-based methods have recently sought to

Yu-Shiang Huang, Yun-Yu Lee, Tzu-Hsin Chou, Che Lin

BERTScore has become a widely adopted metric for evaluating semantic similarity between natural language sentences. However, we identify a critical limitation: BERTScore exhibits low sensitivity to numerical variation, a significant weakness in finance where numerical precision directly affects meaning (e.g., distinguishing a 2% gain from a 20% loss). We int

Alireza F. Pour, Shai Ben-David

We address the general task of learning with a set of candidate models that is too large to have a uniform convergence of empirical estimates to true losses. While the common approach to such challenges is SRM (or regularization) based learning algorithms, we propose a novel learning paradigm that relies on stronger incorporation of empirical data and requir

Haoyu Li, Mingyang Han, Yu Xi, Dongxiao Wang

Flow-Matching (FM)-based zero-shot text-to-speech (TTS) systems exhibit high-quality speech synthesis and robust generalization capabilities. However, the speaker representation ability of such systems remains underexplored, primarily due to the lack of explicit speaker-specific supervision in the FM framework. To this end, we conduct an empirical analysis o

Ao Xu, Han Zhao, Weihao Cui, Quan Chen

Large language models (LLMs) are increasingly deployed under the Model-as-a-Service (MaaS) paradigm. To meet stringent quality-of-service (QoS) requirements, existing LLM serving systems disaggregate the prefill and decode phases of inference. However, decode instances often experience low GPU utilization due to their memory-bound nature and insufficient bat

Xiaohan Cai

We prove some Liouville-type theorems for positive harmonic functions on compact Riemannian manifolds with nonnegative Ricci curvature and strictly convex boundary, thereby confirming some cases of Wang's conjecture (J. Geom. Anal. 31, 2021). We further investigate Wang's conjecture on warped product manifolds and provide a partial verification of this conje

Zhongjian Miao, Hao Fu, Chen Wei

We introduce SPAN, a cross-calendar temporal reasoning benchmark, which requires LLMs to perform intra-calendar temporal reasoning and inter-calendar temporal conversion. SPAN features ten cross-calendar temporal reasoning directions, two reasoning types, and two question formats across six calendars. To enable time-variant and contamination-free evaluation,

Yoshiaki Goto, Genki Shibukawa

We describe some monotone properties of solutions to second order linear difference equations with real constant coefficients. As an application, we give a characterization of the Fibonacci numbers.

Mehdi Zafari, A. Lee Swindlehurst

Integrated Sensing and Communication (ISAC) is a key emerging 6G technology. Despite progress, ISAC still lacks scalable methods for joint AP clustering and user/target scheduling in distributed deployments under fronthaul limits. Moreover, existing ISAC solutions largely rely on centralized processing and full channel state information, limiting scalability

Duttatreya, Ipsika Mohanty, Sanjib Dey

Quantum computing's potential for exponential speedup is fundamentally limited by decoherence, a phenomenon arising from environmental interactions. Non-Hermitian quantum mechanics, particularly $PT$-symmetric systems, offers a novel framework for extending coherence times. This study examines a qubit's coherence under non-Hermitian $PT$-symmetric dynamics,

Fushuo Huo

The rapid evolution of machine learning has propelled neural networks to unprecedented success across diverse domains. In particular, multimodal learning has emerged as a transformative paradigm, leveraging complementary information from heterogeneous data streams (e.g., text, vision, audio) to advance contextual reasoning and intelligent decision-making. De

Yu-Ting Ho

This paper studies a decentralized many-to-one matching market where preferences remain uncertain during the matching process. Institutions initiate matching by sending offers, and applicants decide whether to accept upon receiving them. Since applicants learn their preferences only after receiving offers, institutions face a challenge in deciding how many o

Shiv Sundram, Akhilesh Balasingam, Nathan Zhang, Kunle Olukotun

We present Cyclotron, a framework and compiler for using recurrence equations to express streaming dataflow algorithms, which then get portably compiled to distributed topologies of interlinked processors. Our framework provides an input language of recurrences over logical tensors, which then gets lowered into an intermediate language of recurrences over lo

Hui Zhang, Daming Tian, Xiaobing Chen, Lu Chen

Quantum transport phenomena in two-dimensional electron gases (2DEGs) at oxide interfaces have garnered significant interest owing to their potential in spintronic and quantum information technologies. Here, we systematically investigate the quantum conductance corrections of spin-polarized 2DEGs formed at the interfaces between two insulating oxides, ferrom

Rutwig Campoamor-Stursberg, Danilo Latini, Ian Marquette, Junze Zhang

In this paper, we review a new approach to study subalgebra chains $\mathfrak{g} \supset \mathfrak{g}'$ in the context of nuclear physics. This approach does not rely on explicit realizations as bosons or differential operators. We rely on the enveloping algebra, the notion of commutant $C_{U(\mathfrak{g})}(\mathfrak{g}^{\prime})$ and $\mathfrak{g}^{\prime}$

Bo Li, Zhenghua Xu, Rui Xie

Multilingual Retrieval-Augmented Generation (RAG) enables large language models (LLMs) to perform knowledge-intensive tasks in multilingual settings by leveraging retrieved documents as external evidence. However, when the retrieved evidence differs in language from the user query and in-context exemplars, the model often exhibits language drift by generatin

Yanwen Luo, Xu Xu, Chao Zheng

The maximum principle for hyperbolic inversive distance circle packings on polyhedral surfaces is established,which unifies and generalizes existing maximum principles for various types of circle packings in the literature.As an application of this principle, a discrete Schwarz-Ahlfors lemma is established.Furthermore, an infinite rigidity theorem for weight

Marius Tărnăuceanu

Given a group $G$ and an automorphism $\varphi$ of $G$, two elements $x,y\in G$ are said to be $\varphi$-conjugate if $x=gy\varphi(g)^{-1}$ for some $g\in G$. The number $R(\varphi)$ of equivalence classes with respect to this relation is called the Reidemeister number of $\varphi$ and the set $\{R(\varphi)|\varphi\in {\rm Aut}(G)\}$ is called the Reidemeist

Ke Wang, Yu-Fei Wang, Bo-Chao Liu, Fei Huang

In this work, we propose a new method to detect the composition of the $X(3872)$ state. Based on a widely accepted interpretation that $X(3872)$ is a weakly bound $S$-wave molecule of the $D^{*0} \bar{D}^{0}$ (neutral) and $D^{*+} D^{-}$ (charged) configurations, the process $X(3872) \to \bar{D}^{*0} D^0$ is described by one-loop triangle diagrams with the c

Bo Li, Tian Tian, Zhenghua Xu, Hao Cheng

Dynamic retrieval-augmented generation (RAG) allows large language models (LLMs) to fetch external knowledge on demand, offering greater adaptability than static RAG. A central challenge in this setting lies in determining the optimal timing for retrieval. Existing methods often trigger retrieval based on low token-level confidence, which may lead to delayed

Saumya Shah, Zi-Yu Khoo, Abel Yang, Stéphane Bressan

This work explores using the physics-inspired AI Feynman symbolic regression algorithm to automatically rediscover a fundamental equation in astronomy -- the Equation of the Centre. Through the introduction of observational and inductive biases corresponding to the physical nature of the system through data preprocessing and search space restriction, AI Feyn

Yongdeuk Seo, Hyun-seok Min, Sungchul Choi

Scene Text Editing (STE) is the task of modifying text content in an image while preserving its visual style, such as font, color, and background. While recent diffusion-based approaches have shown improvements in visual quality, key limitations remain: lack of support for low-resource languages, domain gap between synthetic and real data, and the absence of

Sirui Liang, Pengfei Cao, Jian Zhao, Cong Huang

Parameter-Efficient finetuning (PEFT) enhances model performance on downstream tasks by updating a minimal subset of parameters. Representation finetuning (ReFT) methods further improve efficiency by freezing model weights and optimizing internal representations with fewer parameters than PEFT, outperforming PEFT on several tasks. However, ReFT exhibits a si

Po-Yen Chen, Teruyasu Mizoguchi

Electric field-induced studies, including phase transition and polarization hysteresis, for ferroelectric HfO2 at the atomic scale are critical since they can largely affect its application in ferroelectric and dielectric devices. However, conventional first-principles approaches are computationally limited in capturing large-scale atomic dynamics under real

Zitong Zhang, Hao Sun

Data-driven discovery of governing equations from data remains a fundamental challenge in nonlinear dynamics. Although sparse regression techniques have advanced system identification, they struggle with rational functions and noise sensitivity in complex mechanical systems. The Lagrangian formalism offers a promising alternative, as it typically avoids rati

Carlos Deleon, Dmitri Gavrilov, Peggy ODay, Ajith Pattammattel

We present X-AutoMap, a modular framework for autonomous X-ray fluorescence (XRF) mapping that enables chemically informed targeting of regions of interest through a correlative feature detection strategy. The system integrates classical computer vision and rule-based logic to identify features based on spatial relationships across multiple elemental maps, r

Pratik Sahu, Sashi Satpathy, Birabar Ranjit Kumar Nanda

We investigate the orbital and spin Edelstein effect(OEE and SEE) in two-dimensional Janus transition metal dichalcogenides (TMDs) of the form MXX$^\prime$ $(M = Mo,\ W,\ Nb;\ X/X^\prime = S,\ Se,\ Te)$ with the aid of density functional theory calculations and tight-binding model Hamiltonian studies. The chalcogen layers $X$ and $X^\prime$, break the mirror

Satoshi Suzuki, Shin'ya Yamaguchi, Shoichiro Takeda, Taiga Yamane

Contrastive pre-trained vision-language models, such as CLIP, demonstrate strong generalization abilities in zero-shot classification by leveraging embeddings extracted from image and text encoders. This paper aims to robustly fine-tune these vision-language models on in-distribution (ID) data without compromising their generalization abilities in out-of-dis

Tongda Xu

Many recent works utilize denoising score matching to optimize the conditional input of diffusion models. In this workshop paper, we demonstrate that such optimization breaks the equivalence between denoising score matching and exact score matching. Furthermore, we show that this bias leads to higher score norm. Additionally, we observe a similar bias when o

Heyang Ji, Lan Xue, Ufuk Beyaztas, Roger S. Zoh

Wearable devices are often used in clinical and epidemiological studies to monitor physical activity behavior and its influence on health outcomes. These devices are worn over multiple days to record activity patterns, such as step counts recorded at the minute level, resulting in multi-level, longitudinal, high-dimensional, or functional data. When monitori

Peter Røysland Aarnes, Vinay Setty

Large language models show strong performance on knowledge intensive tasks such as fact-checking and question answering, yet they often struggle with numerical reasoning. We present a systematic evaluation of state-of-the-art models for veracity prediction on numerical claims and evidence pairs using controlled perturbations, including label-flipping probes,

Dimitrios Sinodinos, Jack Yi Wei, Narges Armanfard

Tabular data is the most abundant data type in the world, powering systems in finance, healthcare, e-commerce, and beyond. As tabular datasets grow and span multiple related targets, there is an increasing need to exploit shared task information for improved multitask generalization. Multitask learning (MTL) has emerged as a powerful way to improve generaliz

Juliana Nieto-Cardenas, Erin Joy Kramer, Peter Kurto, Ethan Dickey

We present Owlgorithm, an educational platform that supports Self-Regulated Learning (SRL) in competitive programming (CP) through AI-generated reflective questions. Leveraging GPT-4o, Owlgorithm produces context-aware, metacognitive prompts tailored to individual student submissions. Integrated into a second- and third-year CP course, the system-provided re

Anisur Rahaman

We investigate perturbative quasinormal-mode (QNM) shifts of black holes arising from fractional, nonlocal modifications to the wave operator. Starting from a scalar master equation corrected by a small fractional Laplacian term $(-\Delta)^{s}$ with $0<s<1$, we derive an analytic expression for the complex frequency shift at first order in the nonlocal coupl

Nasrin Sadeghzadeh

This paper introduces a new quantity in Finsler geometry, called the generalized Berwald projective Weyl ($GB\widetilde{W}$) metric. The $C$-projective invariance of these metrics is demonstrated, and it is shown that they constitute a proper subset of the class of generalized Douglas ($GDW$) metrics. The paper also proves that all $GDW$ metrics with vanishi

Georgy Artemov, Kentaro Tomoeda

We study how school choice mechanisms shape wealth segregation in the long term by endogenizing residential choice. Families buy houses in school zones that determine admission priority, experience shocks to school preferences, and participate in one of three mechanisms: neighborhood assignment (N), Deferred Acceptance (DA), or Top Trading Cycles (TTC). Neig

Yijie Zhu, Haojie Zhou, Wanting Hong, Tailin Liu

Retrieval-augmented generation (RAG) has been extensively employed to mitigate hallucinations in large language models (LLMs). However, existing methods for multi-hop reasoning tasks often lack global planning, increasing the risk of falling into local reasoning impasses. Insufficient exploitation of retrieved content and the neglect of latent clues fail to

Chunlei Shi, Han Xu, Yinghao Li, Yi-Lin Wei

Satellite-based radar retrieval methods are widely employed to fill coverage gaps in ground-based radar systems, especially in remote areas affected by terrain blockage and limited detection range. Existing methods predominantly rely on overly simplistic spatial-domain architectures constructed from a single data source, limiting their ability to accurately

Chenxu Wu, Qingpeng Kong, Peiang Zhao, Wendi Yang

Recent advances in generative models, especially diffusion models, have significantly improved image restoration (IR) performance. However, existing problem-agnostic diffusion model-based image restoration (DMIR) methods face challenges in fully leveraging diffusion priors, resulting in suboptimal performance. In this paper, we address the limitations of cur

Noah van der Vleuten, Anthony Flores, Shray Mathur, Max Rakitin

Evaluating large language models (LLMs) for instrument control requires methods that go beyond standard, stateless algorithmic benchmarks, since the behavior of physical systems cannot be fully captured by unit tests alone. Here we introduce EnvTrace, a simulation-based method that evaluates execution traces to assess semantic code equivalence. EnvTrace is d

Iasson Karafyllis, Dionysis Theodosis, Miroslav Krstic

In this work we study an age-structured chemostat model with a renewal boundary condition and a coupled substrate equation. The model is nonlinear and consists of a hyperbolic partial differential equation and an ordinary differential equation with nonlinear, nonlocal terms appearing both in the ordinary differential equation and the boundary condition. Both

Ziqing Yin, Xuanjing Chen, Xi Zhang

The rapid proliferation of AI-generated content (AIGC) has reshaped the dynamics of digital marketing and online consumer behavior. However, predicting the diffusion trajectory and market impact of such content remains challenging due to data heterogeneity, non linear propagation mechanisms, and evolving consumer interactions. This study proposes an AI drive

Hao Zheng, Qiang Wang, Longxiang Wang, Xishi Qiu

Traditional memory management suffers from metadata overhead, architectural complexity, and stability degradation, problems intensified in cloud environments. Existing software/hardware optimizations are insufficient for cloud computing's dual demands of flexibility and low overhead. This paper presents Vmem, a memory management architecture for in-productio

Ram Munde, Heng-Ray Chuang, Raisul Islam

Nanostructured materials, critical for thermal management in semiconductor devices, exhibit a strong size dependence in thermal transport. Studying thermal resistance variation across grain boundaries is critical for designing effective thermal interface materials. Frequency-domain Thermoreflectance (FDTR)-based techniques can provide thermal resistance mapp

Ayumu Fukushi, Yoshinori Nakanishi-Ohno, Takeru Matsuda

In Wasserstein geometry, one-dimensional location-scale models are flat both intrinsically and extrinsically-that is, they are curvature-free as well as totally geodesic in the space of probability distributions. In this study, we introduce a class of one-dimensional statistical models, termed the location-scale-shape model, which generalizes several distrib

Xiangyi Wei, Haotian Zhang, Xinyi Cao, Siyu Xie

The Vision-Language-Action models (VLA) have achieved significant advances in robotic manipulation recently. However, vision-only VLA models create fundamental limitations, particularly in perceiving interactive and manipulation dynamic processes. This paper proposes Audio-VLA, a multimodal manipulation policy that leverages contact audio to perceive contact

Duc-Ly Vu, Thanh-Cong Nguyen, Minh-Khanh Vu, Ngoc-Thanh Nguyen

The increasingly sophisticated environment in which attackers operate makes software security an even greater challenge in open-source projects, where malicious packages are prevalent. Static analysis tools, such as Malcontent, are highly useful but are often incapable of dealing with obfuscated malware. Such situations lead to an unreasonably high rate of f

Mani Tofigh, Edward Guo, Weiwei Jia, Xiaoning Ding

This paper shows that cache-based optimizations are often ineffective in cloud virtual machines (VMs) due to limited visibility into and control over provisioned caches. In public clouds, CPU caches can be partitioned or shared among VMs, but a VM is unaware of cache provisioning details. Moreover, a VM cannot influence cache usage via page placement policie