October 2025 arXiv papers — page 161

Mohan Zhang, Yihua Zhang, Jinghan Jia, Zhangyang Wang

Modern large reasoning models (LRMs) exhibit impressive multi-step problem-solving via chain-of-thought (CoT) reasoning. However, this iterative thinking mechanism introduces a new vulnerability surface. We present the Deadlock Attack, a resource exhaustion method that hijacks an LRM's generative control flow by training a malicious adversarial embedding to

Yunlong Deng, Guangyi Chen, Tianpei Gu, Lingjing Kong

Vision-Language Models (VLMs) integrate visual knowledge with the analytical capabilities of Large Language Models (LLMs) through supervised visual instruction tuning, using image-question-answer triplets. However, the potential of VLMs trained without supervised instruction remains largely unexplored. This study validates that VLMs possess inherent self-ref

Ming Tan, Wei Li, Hu Tao, Hailong Ma

Open-source large language models (LLMs) have demonstrated considerable dominance over proprietary LLMs in resolving neural processing tasks, thanks to the collaborative and sharing nature. Although full access to source codes, model parameters, and training data lays the groundwork for transparency, we argue that such a full-access manner is vulnerable to M

Yanee Tangjai, Agnieszka Leszczynska, Tatphicha Promfu, Achara Seripienlert

This study evaluates the response of the IceTop tanks to low-energy air showers in the GeV to TeV energy range based on simulated and measured count rates. Correlating this response with primary cosmic rays provides a tool to study Galactic and solar cosmic-ray flux modulations, particularly for solar particle events. We present long-term behavior of the Ice

Alex Dantart

This paper presents a comprehensive governance framework for AI systems in the legal sector, designed to ensure verifiable compliance with the EU AI Act. The framework integrates a normative mapping of the regulation to technical controls, a forensic architecture for RAG/LLM systems, and an evaluation system with metrics weighted by legal risk. As a primary

Buqing Xu, Jianfeng Zhu, Yichi Zhang, Qinyi Cai

CPU simulators are vital for computer architecture research, primarily for estimating performance under different programs. This poses challenges for fast and accurate simulation of modern CPUs, especially in multi-core systems. Modern CPU peformance simulators such as GEM5 adopt the cycle-accurate and event-driven approach, which is timeconsuming to simulat

Narayan S Iyer, Bivas Bhaumik, Ram S Iyer, Satyasaran Changdar

Partial differential equations (PDEs) provide a mathematical foundation for simulating and understanding intricate behaviors in both physical sciences and engineering. With the growing capabilities of deep learning, data$-$driven approaches like Physics$-$Informed Neural Networks (PINNs) have been developed, offering a mesh$-$free, analytic type framework fo

Yu Liu, Thea Marie Dale, Emma van der Minne, Susanne Boucher

Anisotropic strain engineering in epitaxial oxide films provides new opportunities to control the antiferromagnetic and structural properties crucial for advancements of antiferromagnetic spintronics. Here we report on a (La0.7Sr0.3MnO3/LaFeO3)4 superlattice grown on (101)o DyScO3 substrate which imposes significant anisotropic in-plane strain. Reciprocal sp

Guangxin He, Shen Nie, Fengqi Zhu, Yuankang Zhao

Diffusion LLMs have attracted growing interest, with plenty of recent work emphasizing their great potential in various downstream tasks; yet the long-context behavior of diffusion LLMs remains largely uncharted. We present a case study of post-training techniques for extending the context window of diffusion LLMs (i.e., LLaDA) without retraining from scratc

Zishen Zhang, Xiangzhe Kong, Wenbing Huang, Yang Liu

Designing protein binders targeting specific sites, which requires to generate realistic and functional interaction patterns, is a fundamental challenge in drug discovery. Current structure-based generative models are limited in generating nterfaces with sufficient rationality and interpretability. In this paper, we propose Retrieval-Augmented Diffusion for

Damian Brzozowski, Yu Liu, Karola Neeleman, Magnus Nord

Two-dimensional materials have attracted growing interest due to their unique electronic properties and potential applications in spintronics. Interfacing strongly spin-orbit-coupled chalcogenides with functional oxides such as perovskites has a particularly high potential. In this work, highly textured Bi2Te3 thin films were deposited on (111) oriented SrTi

Deng Li, Jun Shao, Bohao Xing, Rong Gao

Micro-gesture recognition (MGR) targets the identification of subtle and fine-grained human motions and requires accurate modeling of both long-range and local spatiotemporal dependencies. While CNNs are effective at capturing local patterns, they struggle with long-range dependencies due to their limited receptive fields. Transformer-based models address th

A H M Rezaul Karim, Ozlem Uzuner

Medical Visual Question Answering (MedVQA) enables natural language queries over medical images to support clinical decision-making and patient care. The MEDIQA-WV 2025 shared task addressed wound-care VQA, requiring systems to generate free-text responses and structured wound attributes from images and patient queries. We present the MasonNLP system, which

Zhijian Zhou, Liuhua Peng, Xunye Tian, Feng Liu

The relative similarity testing aims to determine which of the distributions, P or Q, is closer to an anchor distribution U. Existing kernel-based approaches often test the relative similarity with a fixed kernel in a manually specified alternative hypothesis, e.g., Q is closer to U than P. Although kernel selection is known to be important to kernel-based t

Xuanjian Wang, Jian Cao, Hualin Shu, Yi Yuan

Optical clocks have extremely attractive applications in many fields, including time-frequency metrology, validation of fundamental physical principles, and relativistic geodesy. The 467 nm octupole transition in 171Yb+ ion exhibits intrinsic insensitivity to magnetic field and an ultra-long clock state lifetime of 1.6 years. In addition, the entire laser sy

A H M Rezaul Karim, Ozlem Uzuner

Medical order extraction is essential for structuring actionable clinical information, supporting decision-making, and enabling downstream applications such as documentation and workflow automation. Orders may be embedded in diverse sources, including electronic health records, discharge summaries, and multi-turn doctor-patient dialogues, and can span catego

Jingyi Wu, Junying Liang

Understanding what drives popularity is critical in today's digital service economy, where content creators compete for consumer attention. Prior studies have primarily emphasized the role of content features, yet creators often misjudge what audiences actually value. This study applies Latent Dirichlet Allocation (LDA) modeling to a large corpus of TED Talk

Huizhi Wang, Tierui Gong, Emil Björnson, Chau Yuen

Rydberg atomic quantum receivers (RAQRs) have attracted significant attention in recent years due to their ultra-high sensitivity. Although capable of precisely detecting the amplitude and phase of weak signals, conventional RAQRs face inherent limitations in accurately receiving wideband RF signals, due to the discrete nature of atomic energy levels and the

Qiran Zou, Hou Hei Lam, Wenhao Zhao, Yiming Tang

Large language models (LLMs) have sparked growing interest in machine learning research agents that can autonomously propose ideas and conduct experiments. However, existing benchmarks predominantly adopt an engineering-oriented perspective: they emphasize application-oriented tasks and evaluate primarily on final performance and computational cost, overlook

Chuang Chen, Yi Lin, Bo Wang, Jing Hu

Environmental perception systems are crucial for high-precision mapping and autonomous navigation, with LiDAR serving as a core sensor providing accurate 3D point cloud data. Efficiently processing unstructured point clouds while extracting structured semantic information remains a significant challenge. In recent years, numerous pseudo-image-based represent

Omar A. M. Abdelraouf

Integrated and tunable light sources are critical for advancing quantum nanophotonic chips in quantum computing, communications, and sensing. However, efficient and tunable emission amplification post-fabrication poses major challenges. Hybrid metasurfaces combining niobium pentoxide (Nb2O5), copper indium sulfide (CIS) quantum dots or hexagonal boron nitrid

Hongzhe Wen, R. S. M. Lau

Stablecoins have emerged as a significant component of global financial infrastructure, with aggregate market capitalization surpassing USD250 billion in 2025. Their increasing integration into payment and settlement systems has simultaneously introduced novel channels of systemic exposure, particularly liquidity risk during periods of market stress. This st

Robert Mahony, Jonathan Kelly, Stephan Weiss

Galilean symmetry is the natural symmetry of inertial motion that underpins Newtonian physics. Although rigid-body symmetry is one of the most established and fundamental tools in robotics, there appears to be no comparable treatment of Galilean symmetry for a robotics audience. In this paper, we present a robotics-tailored exposition of Galilean symmetry th

Gunho Park, Jeongin Bae, Beomseok Kwon, Byeongwook Kim

The deployment of large language models (LLMs) is increasingly constrained by memory and latency bottlenecks, motivating the need for quantization techniques that flexibly balance accuracy and efficiency. Recent work has introduced multi-precision models, which enable inference at multiple precisions within a single model depending on runtime constraints. To

Jinjin Cao, Zhiyang Chen, Zijun Wang, Liyuan Ma

Vision-Language Models (VLMs) have shown solid ability for multimodal understanding of both visual and language contexts. However, existing VLMs often face severe challenges of hallucinations, meaning that VLMs tend to generate responses that are only fluent in the language but irrelevant to images in previous contexts. To address this issue, we analyze how

Yi Ren, Xinjie Yu

Modern Internet of Things (IoT) systems generate massive, heterogeneous multivariate time series data. Accurate Multivariate Time Series Forecasting (MTSF) of such data is critical for numerous applications. However, existing methods almost universally employ a shared embedding layer that processes all channels identically, creating a representational bottle

Junhao Dong, Dejia Liu, Ruiqi Ding, Zongxing Chen

Transjugular intrahepatic portosystemic shunt (TIPS) is an established procedure for portal hypertension, but provides variable survival outcomes and frequent overt hepatic encephalopathy (OHE), indicating the necessity of accurate preoperative prognostic modeling. Current studies typically build machine learning models from preoperative CT images or clinica

Harshit Joshi, Amal Manoharan, Samriddhi Sankar Ray

The significance of small-scale forcing of particles on the carrier two-dimensional turbulent flow has been shown to influence the spectral scaling properties of the carrier fluid. We investigate possible consequences of such two-way coupling in a turbulent suspension of inertial particles through one- and two-point Eulerian and Lagrangian statistics. In par

Chen Zhong, Yuxuan Yang, Xinyue Zhang, Ruohan Ma

Medical image segmentation annotation suffers from inter-rater variability (IRV) due to differences in annotators' expertise and the inherent blurriness of medical images. Standard approaches that simply average expert labels are flawed, as they discard the valuable clinical uncertainty revealed in disagreements. We introduce a fundamentally new approach wit

Hongjie Zheng, Zesheng Shi, Ping Yi

Autonomous agents utilizing Large Language Models (LLMs) have demonstrated remarkable capabilities in isolated medical tasks like diagnosis and image analysis, but struggle with integrated clinical workflows that connect diagnostic reasoning and medication decisions. We identify a core limitation: existing medical AI systems process tasks in isolation withou

Zongyi Lyu, Songqiang Chen, Zhenlan Ji, Liwen Wang

Multi-agent systems (MASs) have emerged as a promising paradigm for automated code generation, demonstrating impressive performance on established benchmarks. Despite their prosperous development, the fundamental mechanisms underlying their robustness remain poorly understood, raising critical concerns for real-world deployment. This paper conducts a systema

Prawaal Sharma, Poonam Goyal, Navneet Goyal, Vidisha Sharma

Digital communication has become the cornerstone of modern interaction, enabling rapid, accessible, and interactive exchanges. However, individuals with lower academic literacy often face significant barriers, exacerbating the "digital divide". In this work, we introduce a novel, universal ideographic metalanguage designed as an innovative communication fram

Chenke Zhang, Qing Cui, Jinze Hu, Erfei Yue

Let $G$ be a graph and $\mathcal{F}$ be a family of graphs. We say a graph $G$ is $\mathcal{F}$-saturated if $G$ does not contain any member in $\mathcal{F}$ and for any $e\in E(\overline{G})$, $G+e$ creates a copy of some member in $ \mathcal{F}$. The saturation number of $\mathcal{F}$ is the minimum number of edges of an $\mathcal{F}$-saturated graphs with

Shaobo Wang, Cong Wang, Wenjie Fu, Yue Min

As the demand for comprehensive evaluations of diverse model capabilities steadily increases, benchmark suites have correspondingly grown significantly in scale. Despite notable advances in redundancy reduction and subset-level performance prediction, a systematic framework that effectively integrates these methods to ensure both prediction accuracy and rank

Tai Le-Gia, Ahn Jaehyun

Zero-shot image anomaly classification (AC) and segmentation (AS) are vital for industrial quality control, detecting defects without prior training data. Existing representation-based methods compare patch features with nearest neighbors in unlabeled test images but struggle with consistent anomalies -- similar defects recurring across multiple images -- re

Jiayu Ding, Xulin Chen, Garrett E. Katz, Zhenyu Gan

Quadrupedal robots exhibit a wide range of viable gaits, but generating specific footfall sequences often requires laborious expert tuning of numerous variables, such as touch-down and lift-off events and holonomic constraints for each leg. This paper presents a unified reinforcement learning framework for generating versatile quadrupedal gaits by leveraging

Peng Fan, Wenping Wang, Fei Deng

The mismatch of speech length and text length poses a challenge in automatic speech recognition (ASR). In previous research, various approaches have been employed to align text with speech, including the utilization of Connectionist Temporal Classification (CTC). In earlier work, a key frame mechanism (KFDS) was introduced, utilizing intermediate CTC outputs

Utsav Maskey, Mark Dras, Usman Naseem

Safety alignment in large language models (LLMs) induces over-refusals -- where LLMs decline benign requests due to aggressive safety filters. We analyze this phenomenon in retrieval-augmented generation (RAG), where both the query intent and retrieved context properties influence refusal behavior. We construct RagRefuse, a domain-stratified benchmark spanni

Keisuke Fujii, Kazushi Tsutsui, Yu Teshima, Makoto Itoh

Simulators of animal movements play a valuable role in studying behavior. Advances in imitation learning for robotics have expanded possibilities for reproducing human and animal movements. A key challenge for realistic multi-animal simulation in biology is bridging the gap between unknown real-world transition models and their simulated counterparts. Becaus

P Sangeerth, David Smith Sundarsingh, Bhabani Shankar Dey, Pushpak Jagtap

Incremental stability is a property of dynamical systems that ensures the convergence of trajectories with respect to each other rather than a fixed equilibrium point or a fixed trajectory. In this paper, we introduce a related stability notion called incremental input-to-state practical stability ({\delta}-ISpS), ensuring safety guarantees. We also present

Ricardo Alonzo Fernández Salguero

The 2008 global financial crisis marked the beginning of a decade dominated by fiscal austerity policies in much of the developed world. This paper presents a qualitative narrative review of an extensive collection of academic literature to synthesize evidence on the multifaceted effects of austerity. Following a thematic approach inspired by PRISMA guidelin

Alexei Zhedanov

We introduce and study a special family of polynomials orthogonal on the unit circle (OPUC). These OPUC satisfy a mirror symmetry property of their Verblunsky coefficients. Several equivalent conditions for the OPUC to be mirror symmetric are presented. Corresponding unitary CMV matrices satisfy simple algebraic relations similar to relations for persymmetri

Masoud Makrehchi

We analyze a reversed-supervision strategy that searches over labelings of a large unlabeled set \(B\) to minimize error on a small labeled set \(A\). The search space is \(2^n\), and the resulting complexity remains exponential even under large constant-factor speedups (e.g., quantum or massively parallel hardware). Consequently, arbitrarily fast -- but not

Mohd Aariyan Khan, Hemant Rathi, Dibakar Roychowdhury

We compute holographic DC conductivity associated with the Taub-NUT-$AdS_4$ black holes following the probe D-brane approach. In particular, we examine the effects of frame dragging on charge transport in both low and high temperature regimes. Our analysis reveals that in the low temperature regime, the conductivity is sensitive to the presence of the Misner

Jingyi Chen, Zhimeng Guo, Jiyun Chun, Pichao Wang

Understanding emotion from speech requires sensitivity to both lexical and acoustic cues. However, it remains unclear whether large audio language models (LALMs) genuinely process acoustic information or rely primarily on lexical content. We present LISTEN (Lexical vs. Acoustic Speech Test for Emotion in Narratives), a controlled benchmark designed to disent

Hong-Bo Li, Yacheng Kang, Ren-Xin Xu

Rapidly rotating neutron stars (NSs) are promising targets for continuous gravitational-wave (CGW) searches with current and next-generation ground-based GW detectors. In this work, we present the first study of thermal deformations in super-Eddington magnetized NSs with column accretion, where magnetic fields induce anisotropic heat conduction that leads to

Pei Yu Chang, Vishnu Renganathan, Qadeer Ahmed

This paper presents a hybrid control framework with a risk-budgeted monitor for safety-certified autonomous driving. A sliding-window monitor tracks insufficient barrier residuals and triggers switching from a relaxed control barrier function (R-CBF) to a more conservative conditional value-at-risk CBF (CVaR-CBF) when the safety margin deteriorates. Two real

Zhichen Zeng, Qi Yu, Xiao Lin, Ruizhong Qiu

Different large language models (LLMs) exhibit diverse strengths and weaknesses, and LLM ensemble serves as a promising approach to integrate their complementary capabilities. Despite substantial progress in improving ensemble quality, limited attention has been paid to the robustness of ensembles against potential erroneous signals, which often arise from h

Oem Trivedi

Building on initial work on the Thermodynamic Split Conjecture (TSC), which posits that black hole and cosmological horizon thermodynamics are generically inequivalent, we examine the consequences of that split for the Gibbons Hawking temperature and its role across cosmology. We consider many key results in both early and late universe cosmology and show th

Alex Ayoub, Samuel Robertson, Dawen Liang, Harald Steck

Matrix factorization is a widely used approach for top-N recommendation and collaborative filtering. When implemented on implicit feedback data (such as clicks), a common heuristic is to upweight the observed interactions. This strategy has been shown to improve performance for certain algorithms. In this paper, we conduct a systematic study of various weigh

Peiyuan Xu, Lu Chen, Guohao Li, Yang Han

The measurements of baryon acoustic oscillation by the Dark Energy Spectroscopic Instrument Data Release 2 indicate that dark energy may be dynamical with a time-varying equation of state. This has challenged the core assumptions of the $\Lambda$CDM model and aroused widespread discussion. Existing work has achieved fruitful results in the dark energy models

Tuowei Wang, Kun Li, Zixu Hao, Donglin Bai

The adaptation of pre-trained large language models (LLMs) to diverse downstream tasks via fine-tuning is critical for numerous applications. However, the inefficiency of parameter-efficient fine-tuning (PEFT) techniques presents significant challenges in terms of time investments and operational costs. In this paper, we first introduce a nuanced form of spa

Shuixin Li, Jiecheng Chen, Qingtang Jiang, Jian Lu

In nature, signals often appear in the form of the superposition of multiple non-stationary signals. The overlap of signal components in the time-frequency domain poses a significant challenge for signal analysis. One approach to addressing this problem is to introduce an additional chirprate parameter and use the chirplet transform (CT) to elevate the two-d

Minali Grover, Ajay Sharma

This paper explores the influence of inheritance rights on women' empowerment in India. We employ the quasi-natural experiment framework wherein; five states amended the Hindu Succession Act (HSA) from 1976 to 1994 before it was federally amended in 2005. Further, we apply difference-in-difference (DID) strategy and consider triangulation approach to identif

Ming Gong

Brownian motion in terms of Lifson and Jackson (LJ) formula has been widely explored in periodic systems and it has been believed for a long time that the LJ formula only applies to periodic potentials. Recently we show that for the following Brownian motion $\gamma \dot{x} = -U'(x) + \xi$, where $U(x)$ is the quasi-periodic potential, the effective diffusio

Kangjian Zhu, Haobo Jiang, Yigong Zhang, Jianjun Qian

We propose MonoSE(3)-Diffusion, a monocular SE(3) diffusion framework that formulates markerless, image-based robot pose estimation as a conditional denoising diffusion process. The framework consists of two processes: a visibility-constrained diffusion process for diverse pose augmentation and a timestep-aware reverse process for progressive pose refinement

Zixiang Xu, Menghui Zhou, Jun Qi, Xuanhan Fan

Alzheimer's Disease (AD) is the most prevalent neurodegenerative disorder in aging populations, posing a significant and escalating burden on global healthcare systems. While Multi-Tusk Learning (MTL) has emerged as a powerful computational paradigm for modeling longitudinal AD data, existing frameworks do not account for the time-varying nature of feature c

Zhichen Zeng, Mengyue Hang, Xiaolong Liu, Xiaoyi Liu

Deep models have driven significant advances in click-through rate (CTR) prediction. While vertical scaling via layer stacking improves model expressiveness, the layer-by-layer sequential computation poses challenges to efficient scaling. Conversely, horizontal scaling through Mixture of Experts (MoE) achieves efficient scaling by activating a small subset o

Arash Beikmohammadi, Andrei A. Bulatov

The Promise Constraint Satisfaction Problem (PCSP for short) is a generalization of the well-studied Constraint Satisfaction Problem (CSP). The PCSP has its roots in such classic problems as the Approximate Graph Coloring and the $(1+\varepsilon)$-Satisfiability problems. The area received much attention recently with multiple approaches developed to design

Xue Chen, Shengtang Huang, Xin Li

We study explicit constructions of min-wise hash families and their extension to $k$-min-wise hash families. Informally, a min-wise hash family guarantees that for any fixed subset $X\subseteq[N]$, every element in $X$ has an equal chance to have the smallest value among all elements in $X$; a $k$-min-wise hash family guarantees this for every subset of size

Wenfei Liu, Renjie Lyu

Building on the Atiyah--Singer holomorphic Lefschetz fixed-point theorem, we define ramification modules associated to the fixed loci of a finite group acting on a compact complex manifold. This allows us to generalize the Chevalley--Weil formula for compact Riemann surfaces to higher dimensions. More precisely, let $G$ be a finite group acting on a compact

Sergio Da Silva, Aniya Stewart

In this article, we explore the use of universal Gr\"obner bases in public-key cryptography by proposing a key establishment protocol that is resistant to quantum attacks. By utilizing a universal Gr\"obner basis $\mathcal{U}_I$ of a polynomial ideal $I$ as a private key, this protocol leverages the computational disparity between generating the universal Gr

Shivangi Rathore, S. Surendra Singh

We perform the dynamical system analysis of the Locally Rotationally Symmetric (LRS) Bianchi type-I cosmological model in f(T) gravity in the presence of energy interaction . A cosmologically viable form of $f(T)$ is chosen (where $T$ is the torsion scalar in teleparallelism) in the background of homogenous and anisotropic. For our model, we take $f(T) = T+\

Taehyun Kim, Dimitri A. Gadotti, Myeong-gu Park, Yun Hee Lee

Dark gaps, low surface brightness regions along the bar minor axis, are expected to form as a consequence of secular evolution in barred galaxies. Although several studies have proposed links between dark gap locations and dynamical resonances, the results remain inconclusive. Using DESI Legacy Imaging Survey data, we find that approximately 61% of barred ga

Suyang Xi, Chenxi Yang, Hong Ding, Yiqing Ni

Multimodal large language models (MLLMs) often fail in fine-grained visual question answering, producing hallucinations about object identities, positions, and relations because textual queries are not explicitly anchored to visual referents. Retrieval-augmented generation (RAG) alleviates some errors, but it fails to align with human-like processing at both

Sara Dragutinović, Andrew M. Saxe, Aaditya K. Singh

The remarkable ability of transformers to learn new concepts solely by reading examples within the input prompt, termed in-context learning (ICL), is a crucial aspect of intelligent behavior. Here, we focus on understanding the learning algorithm transformers use to learn from context. Existing theoretical work, often based on simplifying assumptions, has pr

Carlos Gabriel Valenzuela Ruiz

We present a comparison map between the uberhomology of a simplicial complex $\mathcal{K}$ and the double homology of its associated moment-angle complex $\mathcal{Z}_{\mathcal{K}}$. We show these two homology theories differ at three bidegrees, which depend on whether the complex $K$ is neighbourly or not.

Ehsan Heidari, Alireza Kaviani, Masoud Seddighin, AmirMohammad Shahrezaei

The maximin share ($\textsf{MMS}$) is the most prominent share-based fairness notion in the fair allocation of indivisible goods. Recent years have seen significant efforts to improve the approximation guarantees for $\textsf{MMS}$ for different valuation classes, particularly for additive valuations. For the additive setting, it has been shown that for some

Jyotirmay Nag Setu, Kevin Desai, John Quarles

With the rapid advancement of virtual reality (VR) technology, its adoption across domains such as healthcare, education, and entertainment has grown significantly. However, the persistent issue of cybersickness, marked by symptoms resembling motion sickness, continues to hinder widespread acceptance of VR. While recent research has explored multimodal deep

Junbin Yuan, Brady Moon, Muqing Cao, Sebastian Scherer

Achieving persistent tracking of multiple dynamic targets over a large spatial area poses significant challenges for a single-robot system with constrained sensing capabilities. As the robot moves to track different targets, the ones outside the field of view accumulate uncertainty, making them progressively harder to track. An effective path planning algori

Lin-Qing Song, Hai-Qing Zhou

In this study, we discuss some general critical properties of bound states with one-boson-exchange potential. For simplicity, we first take a system with two identical scalar particles as an example. The interaction between these two scalar particles is described by the exchange of another massive scalar meson under the instantaneous approximation, which res

Mamadou K. Keita, Christopher Homan, Sebastien Diarra

We introduce the Rule-to-Tag (R2T) framework, a hybrid approach that integrates a multi-tiered system of linguistic rules directly into a neural network's training objective. R2T's novelty lies in its adaptive loss function, which includes a regularization term that teaches the model to handle out-of-vocabulary (OOV) words with principled uncertainty. We fra

Weiwei Sun, Keyi Kong, Xinyu Ma, Shuaiqiang Wang

Generative retrieval (GR) reformulates information retrieval (IR) by framing it as the generation of document identifiers (docids), thereby enabling end-to-end optimization and seamless integration with generative language models (LMs). Despite notable progress under supervised training, GR still struggles to generalize to zero-shot IR scenarios, which are p

Shao-Yuan Huang, Hsiu-Yu Wu

Let p1, p2,..., pn be distinct prime numbers, and let Nn be their product. We prove that, for any positive integer L that is divisible by the least common multiple of p1 minus one, p2 minus one, and so on, and for integers a1, a2,..., an satisfying that each ai is relatively prime to Nn and shares the same prime factor pi, a certain congruence relation holds

Zhao-Yang Wang, Zhimin Shao, Anirudh Nanduri, Basudha Pal

Gait recognition is an important biometric for human identification at a distance, particularly under low-resolution or unconstrained environments. Current works typically focus on either 2D representations (e.g., silhouettes and skeletons) or 3D representations (e.g., meshes and SMPLs), but relying on a single modality often fails to capture the full geomet

Kannon Hossain, Roger Sidje, Fahad Mostafa

The recent advancements in mathematical modeling of biochemical systems have generated increased interest in sensitivity analysis methodologies. There are two primary approaches for analyzing these mathematical models: the stochastic approach, which employs chemical master equations (CME), and the deterministic approach, which utilizes ordinary differential

Federica Bologna, Tiffany Pan, Matthew Wilkens, Yue Guo

Evaluating multi-paragraph clinical question answering (QA) systems is resource-intensive and challenging: accurate judgments require medical expertise and achieving consistent human judgments over multi-paragraph text is difficult. We introduce CQA-Eval, an evaluation framework and set of evaluation recommendations for limited-resource and high-expertise se

Chin-Hung Teng, Ben-Jian Dong

Image feature matching plays a vital role in many computer vision tasks. Although many image feature detection and matching techniques have been proposed over the past few decades, it is still time-consuming to match feature points in two images, especially for images with a large number of detected features. Feature spatial order can estimate the probabilit

Saurabh Khanna

The technological revolution of the Internet has digitized the social, economic, political, and cultural activities of billions of humans. While researchers have been paying due attention to concerns of misinformation and bias, these obscure a much less researched and equally insidious problem - that of uncritically consuming incomplete information. The prob

Shao-Yuan Huang

In this paper, we study the exact multiplicity and bifurcation curves of positive solutions for the semipositone problem defined on the interval from minus one to one, with zero boundary conditions at both ends. The function f is twice continuously differentiable on the positive real line, and there exist two positive numbers such that f is positive between

Ilias Mitrai

In this paper, we propose symbolic decision trees as surrogate models for approximating model predictive control laws. The proposed approach learns simultaneously the partition of the input domain (splitting logic) as well as local nonlinear expressions for predicting the control action leading to interpretable piecewise nonlinear control laws. The local non

Hui Xu

Rust is a memory-safe programming language that disallows undefined behavior. Its safety guarantees have been extensively examined by the community through empirical studies, which has led to its remarkable success. However, unsafe code remains a critical concern in Rust. By reviewing the safety design of Rust and analyzing real-world Rust projects, this pap

Siddartha Devic, Charlotte Peale, Arwen Bradley, Sinead Williamson

Uncertainty quantification for LLMs is a key research direction towards addressing hallucination and other issues that limit their reliable deployment. In this work, we show that reasoning trace length is a simple and useful confidence estimator in large reasoning models. Through comprehensive experiments across multiple models, datasets, and prompts, we sho

Yi-Hsuan Lin

We extend monotonicity-based inversion methods to an inverse coefficient problem for the isotropic nonlocal elliptic equation \[ (-\nabla \cdot \sigma \nabla)^s u = 0 \quad \text{in } \Omega \subset \mathbb{R}^n, \] where $0 < s < 1$, $n \geq 3$, and $\Omega$ is a bounded open set. We establish a monotonicity relation between the leading coefficient $\sigma$

Shaolun Liu, Sina Marefat, Omar Tsai, Yu Chen

GraphQL's flexible query model and nested data dependencies expose APIs to complex, context-dependent vulnerabilities that are difficult to uncover using conventional testing tools. Existing fuzzers either rely on random payload generation or rigid mutation heuristics, failing to adapt to the dynamic structures of GraphQL schemas and responses. We present Pr

Zhao-Yang Wang, Jieneng Chen, Jiang Liu, Yuxiang Guo

Gait recognition, a fundamental biometric technology, leverages unique walking patterns for individual identification, typically using 2D representations such as silhouettes or skeletons. However, these methods often struggle with viewpoint variations, occlusions, and noise. Multi-modal approaches that incorporate 3D body shape information offer improved rob

Monirul Shaikh, Aleksander L. Wysocki

The interplay among structural, electronic, and magnetic properties of Fe adatoms on the surface of two-dimensional ferroelectric {\alpha}-In$_2$Se$_3$ is investigated using first-principles electronic structure calculations, with a focus on how these properties are modulated by the direction of the electric polarization of the substrate. We identify two com

Yuito Ito, Tomoaki Niiyama, Tetsuya Asai, Gouhei Tanaka

The detection of ultrafast optical and radio-frequency (RF) signals is crucial for applications ranging from high-speed communications to advanced sensing. However, conventional detectors are fundamentally constrained by their intrinsic bandwidth, limiting accurate broadband signal measurement. Here, we show that a neuromorphic photonic processing approach c

Alice Lin

Using integral $p$-adic Hodge theory, Kato and Koshikawa define a generalization of the Faltings height of an abelian variety to motives defined over a number field. Assuming the adelic Mumford-Tate conjecture, we prove a finiteness property for heights in the isogeny class of a motive, where the isogenous motives are not required to be defined over the same

Jiachi Zhao, Zehong Wang, Yamei Liao, Chuxu Zhang

Graph generation is a fundamental problem in graph learning with broad applications across Web-scale systems, knowledge graphs, and scientific domains such as drug and material discovery. Recent approaches leverage diffusion models for step-by-step generation, yet unconditional diffusion offers little control over desired properties, often leading to unstabl

Yue Gu, Zhihao Du, Ying Shi, Jiqing Han

Fine-tuning generic ASR models with large-scale synthetic personal data can enhance the personalization of ASR models, but it introduces challenges in adapting to synthetic personal data without forgetting real knowledge, and in adapting to personal data without forgetting generic knowledge. Considering that the functionally invariant path (FIP) framework en

Harshal D. Kaushik, Roshni Anna Jacob, Souma Chowdhury, Jie Zhang

Restoring power distribution systems after extreme events such as tornadoes presents significant logistical and computational challenges. The complexity arises from the need to coordinate multiple repair crews under uncertainty, manage interdependent infrastructure failures, and respect strict sequencing and routing constraints. Existing methods often rely o

Geunyeong Jeong, Juoh Sun, Seonghee Lee, Harksoo Kim

Large Language Models store extensive factual knowledge acquired during large-scale pre-training. However, this knowledge is inherently static, reflecting only the state of the world at the time of training. Knowledge editing has emerged as a promising solution for updating outdated or incorrect facts without full retraining. However, most existing locate-an

Kai Zhang, Xinyuan Zhang, Ejaz Ahmed, Hongda Jiang

Accurate recall from large scale memories remains a core challenge for memory augmented AI assistants performing question answering (QA), especially in similarity dense scenarios where existing methods mainly rely on semantic distance to the query for retrieval. Inspired by how humans link information associatively, we propose AssoMem, a novel framework cons

Wenxiang Guo, Changhao Pan, Zhiyuan Zhu, Xintong Hu

Humans rely on multisensory integration to perceive spatial environments, where auditory cues enable sound source localization in three-dimensional space. Despite the critical role of spatial audio in immersive technologies such as VR/AR, most existing multimodal datasets provide only monaural audio, which limits the development of spatial audio generation a

Xinlong Chen, Yue Ding, Weihong Lin, Jingyun Hua

Audiovisual video captioning aims to generate semantically rich descriptions with temporal alignment between visual and auditory events, thereby benefiting both video understanding and generation. In this paper, we present AVoCaDO, a powerful audiovisual video captioner driven by the temporal orchestration between audio and visual modalities. We propose a tw

Man Yin Cheung, Mona Berciu, Kyle Monkman

We consider a class of non-unitary operations that is naturally implemented via a combination of projective measurement and unitary operators. We implement these operations without using measurements by coupling the system to an infinite set of ancilla states and time-evolving with a single time-independent Hamiltonian. The infinite ancilla enables the main

Ipsita Datta, Yuan Yao

We adapt "Obstruction Bundle Gluing (OBG)" techniques from Hutchings and Taubes (arxiv: 0701300, 0705.2074) to Morse theory. We consider Morse function-metric pairs with gradient flowlines that have nontrivial yet well-controlled cokernels (i.e., the gradient flowlines are not transversely cut out). We investigate (i) whether these nontransverse gradient flo

Jan Pedersen, Kevin Chalmers

Correct concurrent behaviour is important in understanding how components will act within certain conditions. In this work. we analyse the behaviour of shared communicating channels within a coorporatively scheduled runtime. We use the refinement checking and modelling tool FDR to develop both specifications of how such shared channels should behave and mode

Max Sokolich, Yanda Yang, Subrahmanyam Cherukumilli, Fatma Ceren Kirmizitas

This paper presents MicroRoboScope, a portable, compact, and versatile microrobotic experimentation platform designed for real-time, closed-loop control of both magnetic and acoustic microrobots. The system integrates an embedded computer, microscope, power supplies, and control circuitry into a single, low-cost and fully integrated apparatus. Custom control