December 2025 arXiv papers — page 32

Hengyi Wu, Zhenyi Wang, Heng Huang

Continual learning aims to acquire new tasks while preserving performance on previously learned ones, but most methods struggle with catastrophic forgetting. Existing approaches typically treat all layers uniformly, often trading stability for plasticity or vice versa. However, different layers naturally exhibit varying levels of uncertainty (entropy) when c

Hoang Giang Pham, Tien Mai

We study assortment and price optimization under the generalized nested logit (GNL) model, one of the most general and flexible modeling frameworks in discrete choice modeling. Despite its modeling advantages, optimization under GNL is highly challenging: even the pure assortment problem is NP-hard, and existing approaches rely on approximation schemes or ar

Alejandro Caicedo, Leonid Kolesnikov

We establish the sharpness of the percolation phase transition for a class of infinite-range weighted random connection models. The vertex set is given by a marked Poisson point process on $\mathbb{R}^d$ with intensity $\lambda>0$, where each vertex carries an independent weight. Pairs of vertices are then connected independently with a probability that depe

J. A. Rosabal

We study the partition function associated with the democratic formulation of M-theory, focusing on its global definition and quantum properties. Using a path-integral representation that makes manifest the underlying cohomological structure, we analyze the coupled system of M-theory form fields $(A_3 + A_6)$, and the background fields $(C_4 + C_7)$, as well

Sajad Ahmadi, Mohsen Akbari, Shahpoor Saeidian, Ali Motazedifard

We present a theoretical investigation of a three-level $\Lambda$-type atom driven by a strong coherent laser and a weak stochastic field exhibiting amplitude and phase fluctuations. The stochastic field is modeled as a complex Gaussian-Markovian random process with finite bandwidth to describe realistic laser noise. Using the Born-Markov and rotating-wave a

Zi-Xuan Wang, Xing-Yu Chen, Ju Chen, Gong Cheng

Mini-Extreme-Mass-Ratio Inspirals (mini-EMRIs), comprising a sub-solar exotic compact object (such as a primordial black hole or boson star) orbiting a much heavier stellar-origin or exotic compact object, represent key targets for ground-based gravitational-wave detectors to probe the early universe and the nature of dark matter. However, detecting such sys

Zi-Xuan Wang, Gong Cheng, Ju Chen, Huai-Ke Guo

Mini-extreme-mass-ratio inspirals (mini-EMRIs), composed of a stellar-mass compact object and a much lighter companion, are promising sources of continuous gravitational waves in the frequency band of ground-based interferometers such as LIGO-Virgo-KAGRA. Such systems, consisting of sub-solar-mass compact objects, provide a unique probe of exotic compact obj

Deepak, Rahul Balout, Anupam Golder, Suparna Kundu

This paper demonstrates a power analysis-based Side-Channel Analysis (SCA) attack on the SNOW-V encryption algorithm, which is a 5G mobile communication security standard candidate. Implemented on an STM32 microcontroller, power traces captured with a ChipWhisperer board were analyzed, with Test Vector Leakage Assessment (TVLA) confirming exploitable leakage

Xindi Zhang, Dechao Meng, Steven Xiao, Qi Wang

High-quality AI-powered video dubbing demands precise audio-lip synchronization, high-fidelity visual generation, and faithful preservation of identity and background. Most existing methods rely on a mask-based training strategy, where the mouth region is masked in talking-head videos, and the model learns to synthesize lip movements from corrupted inputs an

Steven Xiao, Xindi Zhang, Dechao Meng, Qi Wang

Real-time portrait animation is essential for interactive applications such as virtual assistants and live avatars, requiring high visual fidelity, temporal coherence, ultra-low latency, and responsive control from dynamic inputs like reference images and driving signals. While diffusion-based models achieve strong quality, their non-causal nature hinders st

Anchita Chetia, Nirmalya Kajuri, Chandra Prakash

Galilean Conformal Algebra (GCA) arises as a controlled nonrelativistic limit of the relativistic conformal algebra. In this paper, we initiate the study of momentum space correlation functions in two-dimensional GCA. We derive and solve momentum space Ward identities to obtain two-point and three-point functions. However, relating them to position space cor

Stefan Pilot, Murwan Siddig

This paper provides a unified framework for the problem of controlling a fleet of ride-hailing vehicles under stochastic demand. We introduce a sequential decision-making model that consolidates several problem characteristics and can be easily extended to include additional characteristics. To solve the problem, we design an efficient procedure for enumerat

Linyi Jiang, Yifei Zhu, Hao Yin, Bo Li

Recent advancements in array-camera videography enable real-time capturing of ultra-high-definition (Ultra-HD) videos, providing rich visual information in a large field of view. However, promptly processing such data using state-of-the-art transformer-based vision foundation models faces significant computational overhead in on-device computing or transmiss

Yajie Chen, Hardi Peter, Damien Przybylski, Lakshmi Pradeep Chitta

Context. Solar blowout jets are a distinct subclass of ubiquitous extreme-ultraviolet (EUV) and X-ray coronal jets. Aims. Most existing models of blowout jets prescribe an initial magnetic field configurations and apply ad-hoc changes in the photosphere to trigger the jets. In contrast, we aim for a self-consistent magneto-convective description of the jet i

Daniil Sherki, Daniil Merkulov, Alexandra Savina, Ekaterina Muravleva

We present PERELMAN (PipEline foR sciEntific Literature Meta-ANalysis), an agentic framework designed to extract specific information from a large corpus of scientific articles to support large-scale literature reviews and meta-analyses. Our central goal is to reliably transform heterogeneous article content into a unified, machine-readable representation. P

Dennis Gaitsgory, Nick Rozenblyum, Yakov Varshavsky

In this paper we record the formalism of algebro-geometric DG categories (in short AGCat) following a suggestion of V. Drinfeld. This formalism will be applied to ``real-world" problems in papers sequel to this one, [GRV2] and [GRV3].

Kyrylo Ochkan, Michael Wissmann, Louis Veyrat, Lixuan Tai

Multi-terminal topological devices are a new generation of electronic devices with quantized properties robust against imperfections. In magnetic topological insulators, dissipationless edge states give functional devices in zero magnetic field, of interest for quantum metrology (resistance standard) or topological electronics (Chern networks). Here we show

Chao Wu, Ming-Lei Xiao, Jiang-Hao Yu, Yu-Hui Zheng

We compute the complete one-loop renormalization group equations (RGEs) for all the four-fermion operators at dimension-8 Standard Model Effective Field Theory (SMEFT). We adopt the on-shell framework, where the RGEs are obtained from the unitarity cuts of the bubble integrals. To construct a consistent set of RGEs without redundancy, we utilize the Young Te

Hohyun Lee, Reza Pakdaman Zangabad, Chulyong Kim, Victor Menezes

A growing class of ultrasound-mediated diagnostic and therapeutic technologies, including sonoporation and blood-brain barrier modulation, relies on microbubble contrast agents, where precise control of microbubble dynamics governs biological responses, efficiency, and safety. However, quantitative monitoring of microbubble oscillations in the stable, weakly

Prasoon Saurabh

Since the 2019 redefinition of the SI units, precision metrology has sought to anchor all physical quantities to fundamental constants and integer invariants. While the optical frequency comb revolutionized timekeeping by discretizing the continuum of light into countable teeth, and the Quantum Hall Effect standardized resistance via topological invariants,

Alexandr V. Korchemnyi, Anatoly O. Onishchenko, Eva A. Bakaeva, Alexey K. Kovalev

Embodied agents tasked with complex scenarios, whether in real or simulated environments, rely heavily on robust planning capabilities. When instructions are formulated in natural language, large language models (LLMs) equipped with extensive linguistic knowledge can play this role. However, to effectively exploit the ability of such models to handle linguis

Zishuo Wang, Xinyu Zhang, Zhuonan Liu, Tomohito Kawabata

Socially compliant navigation requires robots to move safely and appropriately in human-centered environments by respecting social norms. However, social norms are often ambiguous, and in a single scenario, multiple actions may be equally acceptable. Most existing methods simplify this problem by assuming a single correct action, which limits their ability t

Ole Fenske, Maximilian Popko, Sebastian Bader, Thomas Kirste

Current probabilistic programming languages and tools tightly couple model representations with specific inference algorithms, preventing experimentation with novel representations or mixed discrete-continuous models. We introduce a factor abstraction with five fundamental operations that serve as a universal interface for manipulating factors regardless of

Shizhe He, Avanika Narayan, Ishan S. Khare, Scott W. Linderman

Agentic language model (LM) systems power modern applications like "Deep Research" and "Claude Code," and leverage multi-LM architectures to overcome context limitations. Beneath their apparent diversity lies a recurring pattern: smaller "compressor" LMs (that can even run locally) distill raw context into compact text that is then consumed by larger "predic

S. V. Evdokimov, A. M. Gorin, V. I. Izucheev, Yu. V. Kharlov

The results of measurements of the characteristics of photodetectors based on large-area SiPM matrices with a wide dynamic range are presented. The potential application of SiPM-based photodetectors for electromagnetic calorimetry in the energy range from hundreds of keV to tens of GeV is explored. The potential application of SiPM in gamma-spectroscopy is a

Yu Zhang, Xiaoqing Chen, Mingwen Zhang, Yanyan Zhang

Involving deterministically nonlinear photoresponse in on-chip photodetector is intriguing to develop sophisticated functions in photonic integrated circuits, such as in-sensor computing and optoelectronic mixing, though the corresponding devices are still lack of sufficient investigation. Here, we demonstrate an on-chip quadratically nonlinear photodetector

Abd Ullah Khan, Adnan Shahid, Haejoon Jung, Hyundong Shin

Space-air-ground-integrated network (SAGIN)-enabled multiconnectivity (MC) is emerging as a key enabler for next-generation networks, enabling users to simultaneously utilize multiple links across multi-layer non-terrestrial networks (NTN) and multi-radio access technology (multi-RAT) terrestrial networks (TN). However, the heterogeneity of TN and NTN introd

Shengminjie Chen, Ziyang Li, Hongyi Zhou, Jialin Zhang

In this work, we develop a framework aiming at designing quantum algorithms for combinatorial optimization problems while providing theoretical guarantees on their approximation ratios. The principal innovative aspect of our work is the construction of a time-dependent Lyapunov function that naturally induces a controlled Schr\"odinger evolution with a time

Rui Ke, Jiahui Xu, Shenghao Yang, Kuang Wang

Theme detection is a fundamental task in user-centric dialogue systems, aiming to identify the latent topic of each utterance without relying on predefined schemas. Unlike intent induction, which operates within fixed label spaces, theme detection requires cross-dialogue consistency and alignment with personalized user preferences, posing significant challen

Jintao Chen, Junjun Hu, Haochen Bai, Minghua Luo

Embodied navigation in open, dynamic environments demands accurate foresight of how the world will evolve and how actions will unfold over time. We propose AstraNav-World, an end-to-end world model that jointly reasons about future visual states and action sequences within a unified probabilistic framework. Our framework integrates a diffusion-based video ge

Reza Pirmoradian, Elham Sadoogh, Maryam Teymouri, Negar Abolqasemi-Azad

We investigate signatures of quantum chaos within Ising spin chains subjected to transverse and longitudinal fields, incorporating both local (nearest-neighbor) and non-local (long-range) couplings. While local Ising models may exhibit integrable or chaotic dynamics contingent on interaction strengths and field parameters, systems with non-local interactions

Michaela Levi-Richter, Reuth Mirsky, Oren Glickman

``Bring me a plate.'' For domestic service robots, this simple command reveals a complex challenge: inferring where everyday items are stored, often out of sight in drawers, cabinets, or closets. Despite advances in vision and manipulation, robots still lack the commonsense reasoning needed to complete this task. We introduce the Stored Household Item Challe

Y. V. Kalyuzhnyi, P. T. Cummings

We develop a multidensity formulation of the Ornstein-Zernike equation with Percus-Yevick closure for hard spheres with anisotropic surface adhesion of tetrahedral quadrupolar-like symmetry. An analytical solution is obtained using the invariant expansion method combined with Baxter's factorization technique. Structural properties are evaluated using both th

Yuyi Zhang, Boyu Tang, Tianjie Ju, Sufeng Duan

Latent tokens are gaining attention for enhancing reasoning in large language models (LLMs), yet their internal mechanisms remain unclear. This paper examines the problem from a reliability perspective, uncovering fundamental weaknesses: latent tokens function as uninterpretable placeholders rather than encoding faithful reasoning. While resistant to perturb

Zhan Chen, Zile Guo, Enze Zhu, Peirong Zhang

Video prediction is plagued by a fundamental trilemma: achieving high-resolution and perceptual quality typically comes at the cost of real-time speed, hindering its use in latency-critical applications. This challenge is most acute for autonomous UAVs in dense urban environments, where foreseeing events from high-resolution imagery is non-negotiable for saf

Md. Rakibul Islam, Most. Sharmin Sultana Samu, Md. Zahid Hossain, Farhad Uz Zaman

Large language models (LLMs) can produce text that closely resembles human writing. This capability raises concerns about misuse, including disinformation and content manipulation. Detecting AI-generated text is essential to maintain authenticity and prevent malicious applications. Existing research has addressed detection in multiple languages, but the Beng

Jing Han, Binwei Yan, Tianyu Guo, Zheyuan Bai

Despite recent advancements of fine-tuning large language models (LLMs) to facilitate agent tasks, parameter-efficient fine-tuning (PEFT) methodologies for agent remain largely unexplored. In this paper, we introduce three key strategies for PEFT in agent tasks: 1) Inspired by the increasingly dominant Reason+Action paradigm, we first decompose the capabilit

Zheng Yin, Chengjian Li, Xiangbo Shu, Meiqi Cao

Comprehensively and flexibly capturing the complex spatio-temporal dependencies of human motion is critical for multi-person motion prediction. Existing methods grapple with two primary limitations: i) Inflexible spatiotemporal representation due to reliance on positional encodings for capturing spatiotemporal information. ii) High computational costs stemmi

Shuchang Pan, Siddharth Banerjee, Dhruv Hebbar, Siddhant Patel

Human conversation is organized by an implicit chain of thoughts that manifests as timed speech acts. Capturing this causal pathway is key to building natural full-duplex interactive systems. We introduce a framework that enables reasoning over conversational behaviors by modeling this process as causal inference within a Graph-of-Thoughts (GoT). Our approac

J. M. Mangum, L. L. Bonilla, A. Torrente, P. M. Thibado

This study is presented in a series of two papers. The first paper is an analytical study. This is the second paper, and here we numerically study the thermal energy harvesting capability of two electronic circuits. The first circuit consists of a diode and capacitor in series. We solve the time-dependent Fokker-Planck equation and show the capacitor initial

Qingyue Cao, Bo Jin, Changwei Gong, Xin Tong

Graph anomaly detection technology has broad applications in financial fraud and risk control. However, existing graph anomaly detection methods often face significant challenges when dealing with complex and variable abnormal patterns, as anomalous nodes are often disguised and mixed with normal nodes, leading to the coexistence of homophily and heterophily

L. L. Bonilla, A. Torrente, J. M. Mangum, P. M. Thibado

Nonlinear elements in a rectifying circuit can be used to harvest energy from thermal fluctuations either steadily or transitorily. We study an energy harvesting system comprising a small variable capacitor (e.g., free standing graphene) wired to two diodes and two storage capacitors that may be kept at different temperatures (or at a single one) and use two

Most. Sharmin Sultana Samu, Md. Rakibul Islam, Md. Zahid Hossain, Md. Kamrozzaman Bhuiyan

The rapid growth of speech synthesis and voice conversion systems has made deepfake audio a major security concern. Bengali deepfake detection remains largely unexplored. In this work, we study automatic detection of Bengali audio deepfakes using the BanglaFake dataset. We evaluate zeroshot inference with several pretrained models. These include Wav2Vec2-XLS

Nan Chen, Xiaotian Dai, Tong Cheng, Alan Burns

Emerging real-time applications have driven the transition to multicore embedded systems, where tasks must share resources due to functional demands and limited availability. These resources, whether local or global, are protected within critical sections to prevent race conditions, with locking protocols ensuring both exclusive access and timing requirement

Xueying Sun, Ting Yan, Binyan Jiang

We explore the edge-flipping mechanism, a type of input perturbation, to release the directed graph under edge-local differential privacy. By using the noisy bi-degree sequence from the output graph, we construct the moment equations to estimate the unknown parameters in the $p_0$ model, which is an exponential family distribution with the bi-degree sequence

Eranga Bandara, Tharaka Hewa, Ross Gore, Sachin Shetty

Agentic AI represents a major shift in how autonomous systems reason, plan, and execute multi-step tasks through the coordination of Large Language Models (LLMs), Vision Language Models (VLMs), tools, and external services. While these systems enable powerful new capabilities, increasing autonomy introduces critical challenges related to explainability, acco

A V Uday Kiran Kandala

This work introduces a unified raster domain steganographic framework, termed as the Glyph Perturbation Cardinality (GPC) framework, capable of embedding heterogeneous data such as text, images, audio, and video directly into the pixel space of rendered textual glyphs. Unlike linguistic or structural text based steganography, the proposed method operates exc

Johanne Elise Vembe, Marcin Krotkiewski, Magnar Bjørgve, Morten Førre

Modern heterogeneous high-performance computing (HPC) systems powered by advanced graphics processing unit (GPU) architectures enable accelerating computing with unprecedented performance and scalability. Here, we present a GPU-accelerated solver for the three-dimensional (3D) time-dependent Dirac equation optimized for distributed HPC systems. The solver na

J. Yang, H. Otomo, Hongli Fan, Guangyuan Sun

Multi-component fluid flow simulations in multi-scale porous structures often involve regions that are under-resolved at practical computational resolutions. Accurately capturing the contributions from these unresolved regions is critical. Previous studies proposed a model to account for viscous and capillary forces in under-resolved regions, showing permeab

Md. Zahid Hossain, Most. Sharmin Sultana Samu, Md. Kamrozzaman Bhuiyan, Farhad Uz Zaman

The fast evolution of generative models has heightened the demand for reliable detection of AI-generated images. To tackle this challenge, we introduce FUSE, a hybrid system that combines spectral features extracted through Fast Fourier Transform with semantic features obtained from the CLIP's Vision encoder. The features are fused into a joint representatio

Md. Rakibul Islam, Md. Kamrozzaman Bhuiyan, Safwan Muntasir, Arifur Rahman Jawad

Handwritten Text Recognition (HTR) is a well-established research area. In contrast, Handwritten Text Generation (HTG) is an emerging field with significant potential. This task is challenging due to the variation in individual handwriting styles. A large and diverse dataset is required to generate realistic handwritten text. However, such datasets are diffi

Li Yang, Yuting Liu

Accurate segmentation of macular edema, a hallmark pathological feature in vision-threatening conditions such as age-related macular degeneration and diabetic macular edema, is essential for clinical diagnosis and management. To overcome the challenges of segmenting fluid regions in optical coherence tomography (OCT) images-notably ambiguous boundaries and c

Albert Barreiro, Roger Marí, Rafael Redondo, Gloria Haro

Recent advances in digitization technologies have transformed the preservation and dissemination of cultural heritage. In this vein, Neural Radiance Fields (NeRF) have emerged as a leading technology for 3D digitization, delivering representations with exceptional realism. However, existing methods struggle to accurately model anisotropic specular surfaces,

Huan Li, Longjun Luo, Yuling Shi, Xiaodong Gu

Visual Geometry Grounded Transformer (VGGT) delivers state-of-the-art feed-forward 3D reconstruction, yet its global self-attention layer suffers from a drastic collapse phenomenon when the input sequence exceeds a few hundred frames: attention matrices rapidly become near rank-one, token geometry degenerates to an almost one-dimensional subspace, and recons

Jiancheng Jiang, Xuejun Jiang, Hongxia Jin

Current transfer learning methods for high-dimensional linear regression assume feature alignment across domains, restricting their applicability to semantically matched features. In many real-world scenarios, however, distinct features in the target and source domains can play similar predictive roles, creating a form of cross-semantic similarity. To levera

Sergey D. Korolkov, Igor I. Baliukin, Merav Opher

Global ideal magnetohydrodynamic models of the heliosphere typically predict a greatly exaggerated magnetic field pile-up in the inner heliosheath (IHS), the region between the termination shock and heliopause. However, Voyager 1 and 2 observations show only a gradual increase throughout this region. This mismatch is largely attributed to the simplified assu

Yoshiki Fukusumi

We propose a formula for the transformation law of anyons in topologically ordered phases or topological quantum field theories (TQFTs) through a gapped or symmetry-preserving domain wall. Our formalism is based on the ring homomorphism between the $\mathbb{C}$-linear commutative fusion rings, also known as symmetry topological field theories (SymTFTs). The

Haochen Lv, Yan Lin, Shengnan Guo, Xiaowei Mao

Accurate traffic flow forecasting is crucial for intelligent transportation services such as navigation and ride-hailing. In such applications, uncertainty estimation in forecasting is important because it helps evaluate traffic risk levels, assess forecast reliability, and provide timely warnings. As a result, probabilistic traffic flow forecasting (PTFF) h

Md Motaleb Hossen Manik, Md Zabirul Islam, Ge Wang

Modern vision--language models (VLMs) are increasingly used to interpret and generate educational content, yet their semantic outputs remain challenging to verify, reproduce, and audit over time. Inconsistencies across model families, inference settings, and computing environments undermine the reliability of AI-generated instructional material, particularly

Yuntian Bo, Tao Zhou, Zechao Li, Haofeng Zhang

Cross-domain few-shot medical image segmentation (CD-FSMIS) offers a promising and data-efficient solution for medical applications where annotations are severely scarce and multimodal analysis is required. However, existing methods typically filter out domain-specific information to improve generalization, which inadvertently limits cross-domain performance

K. Nihongi, T. Kida, Y. Narumi, Y. Etoh

We present a combined experimental and theoretical study of the triangular-lattice quantum antiferromagnet CsFeCl$_3$ under high magnetic fields and high pressure. Pulsed-field magnetization for the magnetic field along the symmetric $c$ direction at ambient pressure reveals a magnetization process from a nonmagnetic singlet ground state with a nearly linear

Tian Li, Bo Lin, Shangwen Wang, Yusong Tan

Retrieval-Augmented Code Generation (RACG) is increasingly adopted to enhance Large Language Models for software development, yet its security implications remain dangerously underexplored. This paper conducts the first systematic exploration of a critical and stealthy threat: backdoor attacks targeting the retriever component, which represents a significant

Haonan Chen, Jiayu Wang, Huayao Li, Xunkai Duan

MnBi2Te4 is a versatile platform for exploring diverse topological quantum states, yet its potential is hampered by intrinsic antisite defects. While Sb substitution has been employed to tune the Fermi level towards the charge neutral point, it exacerbates the formation of Mn-Sb antisite defects. Here, we address this challenge by combining first-principles

Xin-Xiang Ju, Wen-Bin Pan, Ya-Wen Sun, Yang Zhao

We identify a non-negative and upper-bounded entanglement signal in holography which is defined as a combination of entanglement wedge cross sections (EWCS) for a tripartite mixed state $ABE$: $\mathrm{EI}_{\Delta}(A:B|E) = \mathrm{EWCS}(A:EB) + \mathrm{EWCS}(B:EA) - \mathrm{EWCS}(E:AB)$. This quantity is an analogue of conditional mutual information (CMI) a

Alain Schumacher, Takuji Nishimura, Makoto Matsumoto

The Mersenne Twister MT19937 pseudorandom number generator, introduced by the last two authors in 1998, is still widely used. It passes all existing statistical tests, except for the linear complexity test, which measures the ratio of the even-odd of the number of 1's among specific bits (and hence should not be important for most applications). Harase repor

Pakorn Ueareeworakul, Shuman Liu, Jinghao Feng, Ling Hu

As global e-commerce rapidly expands into emerging markets, the lack of high-quality semantic representations for low-resource languages has become a decisive bottleneck for retrieval, recommendation, and search systems. In this work, we present Compass-Embedding v4, a high-efficiency multilingual embedding framework specifically optimized for Southeast Asia

Angshul Majumdar

Generative adversarial networks (GANs) are widely used for distribution learning, yet their classical formulations remain theoretically fragile, with ill-posed objectives, unstable training dynamics, and limited interpretability. In this work, we introduce \emph{Dictionary-Transform Generative Adversarial Networks} (DT-GAN), a fully model-based adversarial f

Xu-Lin Dong, Wei-Kang Gao, Yi-Qing Guo, Shu-Wang Cui

Under ideal conditions, the theory of shock acceleration for cosmic rays predicts that different elements should exhibit strictly identical spectral indices when accelerated to the same rigidity (R). However, recent high-precision measurements of elemental energy spectra have definitively established the existence of variations in spectral indices across dif

Shuo Cao, Jiayang Li, Xiaohui Li, Yuandong Pu

Multimodal large language models (MLLMs) have achieved remarkable progress in visual understanding tasks such as visual grounding, segmentation, and captioning. However, their ability to perceive perceptual-level image features remains limited. In this work, we present UniPercept-Bench, a unified framework for perceptual-level image understanding across thre

Tsubasa Kondo, Hidehiro Kaneda, Shinki Oyabu, Takuma Kokusho

Interstellar hydrocarbon dust containing aromatic and aliphatic hydrocarbons, like polycyclic aromatic hydrocarbons (PAHs), is believed to be processed by various factors including UV radiation fields and mechanical shocks in the galactic environments. We systematically investigate the processing of hydrocarbon dust, especially the likely causes for the vari

Jalal Khan

Recently, a plethora of machine learning (ML) and deep learning (DL) algorithms have been proposed to achieve the efficiency, safety, and reliability of autonomous vehicles (AVs). The AVs use a perception system to detect, localize, and identify other vehicles, pedestrians, and road signs to perform safe navigation and decision-making. In this paper, we comp

Sebastian Forster, Gramoz Goranci, Ali Momeni

There has been a surge of interest in spectral hypergraph sparsification, a natural generalization of spectral sparsification for graphs. In this paper, we present a simple fully dynamic algorithm for maintaining spectral hypergraph sparsifiers of \textit{directed} hypergraphs. Our algorithm achieves a near-optimal size of $O(n^2 / \varepsilon ^2 \log ^7 m)$

Subramanyam Sahoo, Jared Junkin

Deepfake detection models have achieved high accuracy in identifying synthetic media, but their decision processes remain largely opaque. In this paper we present a mechanistic interpretability framework for deepfake detection applied to a vision-language model. Our approach combines a sparse autoencoder (SAE) analysis of internal network representations wit

Lei Zhang, Han Yan, Jinhai Zhang, Xian Chen

The Moon has been regarded as a natural Weber bar capable of amplifying gravitational waves (GWs) for detecting events across a wide range of frequencies. However, accurately determining the amplification effects remains challenging due to the absence of 3D numerical simulation methods. In this study, we develop a high-order 3D finite element method (spectra

C. L. Liu, Baoqing Sun, D. L. Zhou

The conditions for transforming pure entangled states under local operations and classical communication (LOCC) are well understood. A natural question then arises: Can we determine the transformation conditions for mixed entangled states under LOCC based on the properties of their pure-state ensembles? While much effort has been devoted to this issue, in th

Nizar El Idrissi, Hicham Zoubeir

We propose in this short note a prime numbers-based method for constructing probability measures on infinite-dimensional Banach spaces annihilating all finite-dimensional subspaces, supplementing the methods of construction of Gaussian measures and infinite-product-type probability measures. This new method confirms that probability measures with this proper

Aditya Mitra, Sibi Chakkaravarthy Sethuraman

Passwordless authentication has revolutionized the way we authenticate across various websites and services. FIDO2 Passkeys, is one of the most-widely adopted standards of passwordless authentication that promises phishing-resistance. However, like any other authentication system, passkeys require the user details to be saved on a centralized server, also kn

Prasoon Saurabh

Standard spectroscopic protocols model the dynamics of open quantum systems as a superposition of isolated, exponentially decaying eigenmodes. This paradigm fails fundamentally at Exceptional Points, where the eigenbasis collapses and the response becomes dominated by non-diagonalizable Jordan blocks. We resolve this ambiguity by introducing a geometric fram

Angshul Majumdar

Probabilistic Coalition Structure Generation (PCSG) is NP-hard and can be recast as an $l_0$-type sparse recovery problem by representing coalition structures as sparse coefficient vectors over a coalition-incidence design. A natural question is whether standard sparse methods, such as $l_1$ relaxations and greedy pursuits, can reliably recover the optimal c

Shicong Liu, Xianghao Yu, Shenghui Song, Khaled B. Letaief

Recent advancements in large-scale position-reconfigurable antennas have opened up new dimensions to effectively utilize the spatial degrees of freedom (DoFs) of wireless channels. However, the deployment of existing antenna placement schemes is primarily hindered by their limited scalability and frequently overlooked near-field effects in large-scale antenn

Ece Öner, Cenk Denktaş

Standard Spiking Neural Network (SNN) models typically neglect metabolic constraints, treating neurons as energetically unconstrained components. We bridge this gap by implementing a conductance-based leaky integrate-and-fire (gLIF) microcircuit (N=5,000) in Brian2, using temperature-dependent Q10 scaling to as a biophysically grounded proxy to couple metabo

Aarav Shah, Kanabar Jay, Maxim Khlopov, Oem Trivedi

Reheating is essential for transforming the cold, vacuum dominated Universe at the end of inflation into the hot thermal bath required by the Standard Model. In many well motivated inflationary models, however, the inflaton has no direct couplings to other fields, raising the question of how the Universe becomes repopulated with particles. We address this qu

Angshul Majumdar

We study the classical coalition structure generation (CSG) problem and compare the anytime behavior of three algorithmic paradigms: dynamic programming (DP), MILP branch-and-bound, and sparse relaxations based on greedy or $l_1$-type methods. Under a simple random "sparse synergy" model for coalition values, we prove that sparse relaxations recover coalitio

Yingying Yu, Yashu Liu, Jiaxuan Li, Xin Li

The Coons volume provides a classical approach for constructing three-dimensional parametric mappings via boundary surface interpolation and is widely employed in volumetric mesh generation, computer-aided geometric design, and isogeometric analysis. However, due to curvature variations and continuity limitations of the boundary surfaces, the Jacobian determ

Shin Sun, Daniel Bhatti, Shaobo Gao, David Elkouss

Quantum repeaters enable the generation of reliable entanglement across long distances despite the underlying channel noise. Nevertheless, realizing quantum repeaters poses a difficult engineering challenge due to various device constraints and design tradeoffs. Herein, we propose and analyze an efficient hybrid quantum repeater design that integrates atom-b

Zikun Guo, Adeyinka P. Adedigba, Rammohan Mallipeddi, Heoncheol Lee

Multi-robot path planning is a fundamental yet challenging problem due to its combinatorial complexity and the need to balance global efficiency with fair task allocation among robots. Traditional swarm intelligence methods, although effective on small instances, often converge prematurely and struggle to scale to complex environments. In this work, we prese

Liuyang Bai, Weiyi Lu, Li Guo

Speech codecs are traditionally optimized for waveform fidelity, allocating bits to preserve acoustic detail even when much of it can be inferred from linguistic structure. This leads to inefficient compression and suboptimal performance on downstream recognition tasks. We propose SemDAC, a semantic-aware neural audio codec that leverages semantic codebooks

Zi Wang, Mingkai Huang, Zhang Shi, Hongjie Hu

Multimodal cardiovascular magnetic resonance (CMR) imaging provides comprehensive and non-invasive insights into cardiovascular disease (CVD) diagnosis and underlying mechanisms. Despite decades of advancements, its widespread clinical adoption remains constrained by prolonged scan times, inconsistent image quality, and heterogeneity across medical environme

Arunkumar V, Nivethitha S, Sharan Srinivas, Gangadharan G. R

A central question for the future of work is whether person centered management can survive when algorithms take on managerial roles. Standard tools often miss what is happening because worker responses to algorithmic systems are rarely linear. We use a Double Machine Learning framework to estimate a moderated mediation model without imposing restrictive fun

Mo Chen

Coronary calcification creates blooming artifacts in Computed Tomography Angiography (CTA), severely hampering the diagnosis of lumen stenosis. While Deep Convolutional Neural Networks (DCNNs) like Dense-Unet have shown promise in removing these artifacts via inpainting, they often require large labeled datasets which are scarce in the medical domain. Inspir

Xiao Liu, Junchen Jin, Yanjie Zhao, Zhixuan Xing

Multimodal Unsupervised Anomaly Detection (UAD) is critical for quality assurance in smart manufacturing, particularly in complex processes like robotic welding. However, existing methods often suffer from process-logic blindness, treating process modalities (e.g., real-time video, audio, and sensors) and result modalities (e.g., post-weld images) as symmetr

ATM Mizanur Rahman, Sharifa Sultana

Platform laborers play an indispensable yet hidden role in building and sustaining AI systems. Drawing on an eight-month ethnography of Bangladesh's platform labor industry and inspired by Gray and Suri, we conceptualize Ghostcrafting AI to describe how workers materially enable AI while remaining invisible or erased from recognition. Workers pursue platform

Maximilian Weichart

Monte Carlo Tree Search (MCTS) has profoundly influenced reinforcement learning (RL) by integrating planning and learning in tasks requiring long-horizon reasoning, exemplified by the AlphaZero family of algorithms. Central to MCTS is the search strategy, governed by a tree policy based on an upper confidence bound (UCB) applied to trees (UCT). A key factor

Yanyan Song, Yaping Mao

For edge-ordered graphs $G^{\prec}$ and $H^{\prec}$, the size edge-ordered Ramsey number $\hat{r}_{\text{edge}}(G^{\prec}, H^{\prec})$ is defined as the smallest integer $m$ for which there exists an edge-ordered graph $F^{\prec}$ (with underlying graph $F$) having $m$ edges, such that every $2$-coloring of the edges of $F^{\prec}$ contains a monochromatic e

M. Halilsoy, S. Habib Mazharimousavi

By virtue of harmonic maps on two-dimensional spheres (S$^{2}$), a topological quantization in spacetime is proposed. The discrete character of all physical quantities follows naturally. A Schwarzschild black hole, non-black hole and wormhole based geometries are considered in which a quantum hair becomes effective. A thermometer or curvature-detecting devic

Yi Zhang, Apurav Tambe, Zhao Pan

The maximum volume ($\Omega$) of a droplet that can remain attached to a horizontal fiber defines the stability limit of droplet-fiber interactions, phenomena common in nature and critical to diverse engineering applications. Existing predictive models for $\Omega$ show limitations in accurately capturing the dependence of $\Omega$ on fiber size and wettabil

Haruka Nagamori, Kazuhiko Nishimura

This paper investigates the structural transformation of the Democratic Republic of the Congo (DRC) tin market induced by the U.S. Dodd-Frank Act. Focusing on the breakdown of the pricing mechanism, we estimate the price elasticity of export demand from 2010 to October 2022 using a structural identification strategy that overcomes the lack of reliable unit v

Mahyar Afshinmehr, Arash Ashuri, Pouria Mahmoudkhan, Kurt Mehlhorn

We study the fair allocation of indivisible goods among agents, with a focus on limiting envy. A central open question in this area is the existence of EFX allocations-allocations in which any envy of any agent i towards any agent j vanishes upon the removal of any single good from j's bundle. Establishing the existence of such allocations has proven notorio

Zhiwang Zhou, Yuandong Pu, Xuming He, Yidi Liu

Weather modeling requires both accurate prediction and mechanistic interpretation, yet existing methods treat these goals in isolation, separating generation from understanding. To address this gap, we present Omni-Weather, the first multimodal foundation model that unifies weather generation and understanding within a single architecture. Omni-Weather integ

Renping Zhou, Zanlin Ni, Tianyi Chen, Zeyu Liu

Recently, Masked Diffusion Models (MDMs) have shown promising potential across vision, language, and cross-modal generation. However, a notable discrepancy exists between their training and inference procedures. In particular, MDM inference is a multi-step, iterative process governed not only by the model itself but also by various schedules that dictate the