October 2025 arXiv papers — page 145

Peng Wang, Tianshu Wu

Recent studies have shown that a secondary potential barrier, forming a potential well outside the event horizon, can destabilize the Quasinormal Mode (QNM) spectrum of black holes. We find that spectral instability may persist even after the potential well vanishes, giving rise to a distinct family of spectrally unstable QNMs that differ from the spectrally

Hongbo Cai, Pengjie Zhang, Yilun Guan

The remote dipole and quadrupole fields (RDF/RQF) encode information about the observable universe as seen from remote places within our past light cone. Sensitive to the superhorizon inhomogeneites, they provide a unique way to probe physics at the largest scales, bypassing the limitations of cosmic variance inherent in the primary cosmic microwave backgrou

Han Zhu, Juntao Dai, Jiaming Ji, Haoran Li

With the widespread use of multi-modal Large Language models (MLLMs), safety issues have become a growing concern. Multi-turn dialogues, which are more common in everyday interactions, pose a greater risk than single prompts; however, existing benchmarks do not adequately consider this situation. To encourage the community to focus on the safety issues of th

Xiao Yang, Dengbo He, Jiyao Wang, Kaishun Wu

Remote physiological measurement gained wide attention, while it requires collecting users' privacy-sensitive information, and existing contactless measurements still rely on labeled client data. This presents challenges when we want to further update real-world deployed models with numerous user data lacking labels. To resolve these challenges, we instantia

Fali Wang, Jihai Chen, Shuhua Yang, Ali Al-Lawati

Large language models (LLMs) have achieved remarkable progress across domains and applications but face challenges such as high fine-tuning costs, inference latency, limited edge deployability, and reliability concerns. Small language models (SLMs), with compact, efficient, and adaptable features, offer promising solutions. Building on this potential, recent

Haobin Ni, Robbert van Renesse, Greg Morrisett

Distributed system theory literature often argues for correctness using an informal, Hoare-like style of reasoning. While these arguments are intuitive, they have not all been foolproof, and whether they directly correspond to formal proofs is in question. We formally ground this kind of reasoning and connect it to standard formal approaches through language

Daisuke Kawagoe

We consider the stationary transport equation with the incoming boundary condition. We are interested in discontinuities of the solution. Under the generalized convexity condition, it is known that it has only boundary-induced discontinuities, which are discontinuities arising from discontinuous boundary data, they propagate along positive characteristic lin

Liming Ling, Huajie Su

We prove that the $N$-solitons, including breathers and multi-hump solitons, of the coupled nonlinear Schr\"odinger (CNLS) equations are nonlinearly stable in the Sobolev space $H^{N}$. Moreover, $(N_{1},N_{2})$-solitons of the coupled modified Korteweg--de Vries (CmKdV) equations are shown to be nonlinearly stable in the Sobolev space $H^{2N_{1}+N_{2}}$. Th

Ziqi Zhao, Vivek Sarin

Gaussian Process Regression (GPR) is an important type of supervised machine learning model with inherent uncertainty measure in its predictions. We propose a new framework, nuGPR, to address the well-known challenge of high computation cost associated with GPR training. Our framework includes several ideas from numerical linear algebra to reduce the amount

Zhenxin Lei, Zhangwei Gao, Changyao Tian, Erfei Cui

Generalist visual captioning goes beyond a simple appearance description task, but requires integrating a series of visual cues into a caption and handling various visual domains. In this task, current open-source models present a large performance gap with commercial ones, which limits various applications such as data synthesis. To bridge the gap, this pap

Mengyang Chen, Lingwei Wei, Wei Zhou, Songlin Hu

The spread of fake news on social media poses a serious threat to public trust and societal stability. While propagation-based methods improve fake news detection by modeling how information spreads, they often suffer from incomplete propagation data. Recent work leverages large language models (LLMs) to generate synthetic propagation, but typically overlook

Henry Worden, Mihir Chandra, Yijie Zhou, Zarif Ahmad Razin Bhuiyan

This study presents a new strategy for achieving ultralow thermal conductivity in nonporous polymer/organic filler hybrids by suppressing heat capacity through tailored atomic vibrations to enhance thermal insulation. Unlike conventional polymer/inorganic filler hybrids, these hybrids exhibit interfacial thermal resistance one to three orders of magnitude lo

David Parra, Felipe Gutierrez-Barragan, Trevor Seets, Andreas Velten

Single-photon cameras are becoming increasingly popular in time-of-flight 3D imaging because they can time-tag individual photons with extreme resolution. However, their performance is susceptible to hardware limitations, such as system bandwidth, maximum laser power, sensor data rates, and in-sensor memory and compute resources. Compressive histograms were

Tomoki Morokuma, Malte Schramm, Toshihiro Kawaguchi, Josefa Becerra González

We present the discovery of a large gradual apparent fading event in optical and near-infrared wavelengths in a quasar at z=1.767 by a factor of 20-30 (in optical) over a period of ~20 years in the observed frame. This pronounced fading trend in brightness was first identified by comparing the magnitudes measured in the Subaru/Hyper Suprime-Cam (HSC) images

Rongzhi Zhang, Liqin Ye, Yuzhao Heng, Xiang Chen

Precise attribute intensity control--generating Large Language Model (LLM) outputs with specific, user-defined attribute intensities--is crucial for AI systems adaptable to diverse user expectations. Current LLM alignment methods, however, typically provide only directional or open-ended guidance, failing to reliably achieve exact attribute intensities. We a

Zhenyu Mao, Jacky Keung, Fengji Zhang, Shuo Liu

The increasing demand for software development has driven interest in automating software engineering (SE) tasks using Large Language Models (LLMs). Recent efforts extend LLMs into multi-agent systems (MAS) that emulate collaborative development workflows, but these systems often fail due to three core deficiencies: under-specification, coordination misalign

Ziyuan Luo, Yangyi Zhao, Ka Chun Cheung, Simon See

The widespread adoption of Retrieval-Augmented Image Generation (RAIG) has raised significant concerns about the unauthorized use of private image datasets. While these systems have shown remarkable capabilities in enhancing generation quality through reference images, protecting visual datasets from unauthorized use in such systems remains a challenging pro

Yaolong Shen, Changjian Su, Rui Xiong

To a quiver with involution, we study the Coulomb branch of the 3d $\mathcal{N} = 4$ involution-fixed part of the quiver gauge theory. We show that there is an algebra homomorphism from the corresponding shifted twisted Yangian to the quantized Coulomb branch algebra. This gives a new instance of 3D mirror symmetries.

Yue Hu, Guohang Zhuang

Food image classification plays a vital role in intelligent food quality inspection, dietary assessment, and automated monitoring. However, most existing supervised models rely heavily on large labeled datasets and exhibit limited generalization to unseen food categories. To overcome these challenges, this study introduces MultiFoodChat, a dialogue-driven mu

Lipeng He, Vasisht Duddu, N. Asokan

Chatbot service providers (e.g., OpenAI) rely on tiered subscription plans to generate revenue, offering black-box access to basic models for free users and advanced models to paying subscribers. However, this approach is unprofitable and inflexible. A pay-to-unlock scheme for premium features (e.g., math, coding) offers a more sustainable alternative. Enabl

Bajian Xiang, Shuaijiang Zhao, Tingwei Guo, Wei Zou

End-to-end Large Speech Language Models (LSLMs) have demonstrated impressive conversational generation abilities, yet consistently fall short of traditional pipeline systems on semantic understanding benchmarks. In this work, we reveal through systematic experimentation that although LSLMs lose some text input performance after speech-text alignment training

Xin Zhao, Naoki Yoshinaga, Yuma Tsuta, Akiko Aizawa

Multilingual domain adaptation (ML-DA) is widely used to learn new domain knowledge across languages into large language models (LLMs). Although many methods have been proposed to improve domain adaptation, the mechanisms of multilingual knowledge acquisition, how domain knowledge is learned within a language and transferred across languages, remain underexp

Wenjie Li, Xiangyi Wang, Heng Guo, Guangwei Gao

Old-photo face restoration poses significant challenges due to compounded degradations such as breakage, fading, and severe blur. Existing pre-trained diffusion-guided methods either rely on explicit degradation priors or global statistical guidance, which struggle with localized artifacts or face color. We propose Self-Supervised Selective-Guided Diffusion

Sangho Suh, Rahul Hingorani, Bryan Wang, Tovi Grossman

The landscape of interactive systems is shifting toward dynamic, generative experiences that empower users to explore and construct knowledge in real time. Yet, timelines -- a fundamental tool for representing historical and conceptual development -- remain largely static, limiting user agency and curiosity. We introduce the concept of a generative timeline:

Akshima, Tyler Besselman, Kai-Min Chung, Siyao Guo

In permutation inversion, we are given a permutation $\pi : [N] \rightarrow [N]$, and want to prepare some advice of size $S$, such that we can efficiently invert any image in time $T$. This is a fundamental cryptographic problem with profound connections to communication complexity and circuit lower bounds. In the classical setting, a tight $ST = \tilde{\Th

Qirui Hou, Wenzhang Sun, Chang Zeng, Chunfeng Wang

Real-time, high-fidelity reconstruction of dynamic driving scenes is challenged by complex dynamics and sparse views, with prior methods struggling to balance quality and efficiency. We propose DrivingScene, an online, feed-forward framework that reconstructs 4D dynamic scenes from only two consecutive surround-view images. Our key innovation is a lightweigh

Aakash Lahoti, Tanya Marwah, Ratish Puduppully, Albert Gu

Transformer-based deep learning methods have become the standard approach for modeling diverse data such as sequences, images, and graphs. These methods rely on self-attention, which treats data as an unordered set of elements. This ignores the neighborhood structure or graph topology of the data and requires inductive biases--such as position embeddings in

Ziliang Qiu, Renfen Hu

The evaluation of LLMs' creativity represents a crucial research domain, though challenges such as data contamination and costly human assessments often impede progress. Drawing inspiration from human creativity assessment, we propose PACE, asking LLMs to generate Parallel Association Chains to Evaluate their creativity. PACE minimizes the risk of data conta

Fernando Spadea, Oshani Seneviratne

Most financial recommendation systems often fail to account for key behavioral and regulatory factors, leading to advice that is misaligned with user preferences, difficult to interpret, or unlikely to be followed. We present FLARKO (Financial Language-model for Asset Recommendation with Knowledge-graph Optimization), a novel framework that integrates Large

Lili Shen, Jian Zhang

Let $[0,1]_*$ be the unit interval $[0,1]$ equipped with a continuous t-norm $*$. It is shown that the category of $[0,1]_*$-sets is cartesian closed if, and only if, $*$ is the minimum t-norm on $[0,1]$.

Jiawei Zhan, Jun Liu, Jinlong Peng, Xiaochen Chen

With the excellent representation capabilities of Pre-Trained Models (PTMs), remarkable progress has been made in non-rehearsal Class-Incremental Learning (CIL) research. However, it remains an extremely challenging task due to three conundrums: increasingly large model complexity, non-smooth representation shift during incremental learning and inconsistency

Yang Bai, Caiyun He, Weirong Liu, Songtao Cheng

Active navigation in disordered media depends on a biased random walk interacting with environmental constraints. Using E. coli chemotactic navigation in agar gels as a model system, we reveal a fundamental trade-off between diffusive exploration and chemotactic directional bias that dictates the optimal strategy for population range expansion. Counter-intui

Lei Zhao, Daoli Zhu, Shuzhong Zhang

In this paper, we study the local linear convergence behavior of proximal-gradient (PG) descent algorithm on a parameterized gap-function reformulation of a smooth but non-monotone variational inequality problem (VIP). The aim is to solve the non-monotone VI problem without assuming the existence of a Minty-type solution. We first introduce and study various

Ziming Dai, Tuo Zhang, Fei Gao, Xingyi Cai

The growing industrial demand for customized and cost-efficient large language models (LLMs) is fueled by the rise of vertical, domain-specific tasks and the need to optimize performance under constraints such as latency and budget. Knowledge distillation, as an efficient model compression and transfer technique, offers a feasible solution. However, existing

Hao He, Zengji Tu, Yuanlei Wang, Hongyan Zhao

Spectrum manipulation is central to photonic systems, where advanced computing and sensing applications often demand highly complex spectral responses to achieve high throughput. Conventional methods for enhancing spectral complexity typically rely on cascading discrete photonic components, resulting in a complexity that scales only linearly with the number

M. R. Xie, J. G. Li, N. Keeley, N. Michel

Single-nucleon transfer reactions offer a valuable way to probe nuclear structure. We explore the effect of directly introducing overlap functions computed using the Gamow shell model (GSM) into reaction calculations, taking the $\left< ^7\mathrm{Li} \mid \protect{^6\mathrm{He}} + p \right>$ single proton overlap as a case study. By incorporating both inter-

Donghyun Lee, Alex Sima, Yuhang Li, Panos Stinis

Building on the success of transformers, Spiking Neural Networks (SNNs) have increasingly been integrated with transformer architectures, leading to spiking transformers that demonstrate promising performance on event-based vision tasks. However, despite these empirical successes, there remains limited understanding of how spiking transformers fundamentally

Pengyu Yin, Shenghai Yuan, Haozhi Cao, Xingyu Ji

We present a one-shot LiDAR global localization algorithm featuring semantic disambiguation ability based on a lightweight tri-layered scene graph. While landmark semantic registration-based methods have shown promising performance improvements in global localization compared with geometric-only methods, landmarks can be repetitive and misleading for corresp

Nadia Benakli, Nicole Froitzheim, David Martinez

A vertex $w$ in a graph $G$ is said to resolve two vertices $u$ and $v$ if $d(w,u)\neq d(w, v)$. A set $W$ of vertices is a resolving set for $G$ if every pair of distinct vertices is resolved by some vertex in $W$. The metric dimension of $G$ is the minimum cardinality of such a set. In this paper, we investigate the metric dimension of generalized theta gr

Junfeng Ni, Yixin Chen, Zhifei Yang, Yu Liu

Despite recent advances in leveraging generative prior from pre-trained diffusion models for 3D scene reconstruction, existing methods still face two critical limitations. First, due to the lack of reliable geometric supervision, they struggle to produce high-quality reconstructions even in observed regions, let alone in unobserved areas. Second, they lack e

Guang-Wei Mi, Xiaofen Huang, Shao-Ming Fei, Tinggui Zhang

We investigate certain quantumness in the vicinity of the Schwarzschild black hole by utilizing the W state. We explore the influence of the Hawking effect on the l_1-norm of quantum coherence, the first-order coherence (FOC), the concurrence-fill (CF) and the global concurrence (GC) in Schwarzschild black hole, for systems with one, two and three physically

Jianping Li, Dongyang Guo, Wenjie Li, Wei Zhao

Unlike general image deblurring that prioritizes perceptual quality, QR code deblurring focuses on ensuring successful decoding. QR codes are characterized by highly structured patterns with sharp edges, a robust prior for restoration. Yet existing deep learning methods rarely exploit these priors explicitly. To address this gap, we propose the Edge-Guided A

Joshua Roberson, Matthew B. Bayliss, M. D. Gladders, Gourav Khullar

Understanding the properties of the first generation of galaxies is an ongoing challenge in observational astrophysics. While advances in deep field observation have led to the identification of large numbers of galaxies within the Epoch of Reionization, there are very few observed galaxies at this range that are sufficiently bright for high signal-to-noise

Guozheng Ma, Lu Li, Zilin Wang, Haoyu Wang

Scaling neural networks has driven breakthrough advances in machine learning, yet this paradigm fails in deep reinforcement learning (DRL), where larger models often degrade performance due to unique optimization pathologies such as plasticity loss. While recent works show that dynamically adapting network topology during training can mitigate these issues,

Wenxu Zhou, Kaixuan Nie, Hang Du, Dong Yin

In this study, we present IL3D, a large-scale dataset meticulously designed for large language model (LLM)-driven 3D scene generation, addressing the pressing demand for diverse, high-quality training data in indoor layout design. Comprising 27,816 indoor layouts across 18 prevalent room types and a library of 29,215 high-fidelity 3D object assets, IL3D is e

Wenjie Ma, Andrei Cojocaru, Neel Kolhe, Bradley Louie

Recent advances in large language models (LLMs) for mathematical reasoning have largely focused on tasks with easily verifiable final answers while generating and verifying natural language math proofs remains an open challenge. We identify the absence of a reliable, fine-grained evaluator for LLM-generated math proofs as a critical gap. To address this, we

Heng Zhang, Tianyi Zhang, Zijun Liu, Yuling Shi

Text-attributed graphs are widely used across domains, offering rich opportunities for zero-shot learning via graph-text alignment. However, existing methods struggle with tasks requiring fine-grained pattern recognition, particularly on heterophilic graphs. Through empirical and theoretical analysis, we identify an \textbf{over-abstraction problem}: current

Xiaojian Ding, Lin Zhao, Xian Li, Xiaoying Zhu

Incomplete multi-view data, where certain views are entirely missing for some samples, poses significant challenges for traditional multi-view clustering methods. Existing deep incomplete multi-view clustering approaches often rely on static fusion strategies or two-stage pipelines, leading to suboptimal fusion results and error propagation issues. To addres

Samuel Lai, Nithyanandan Thyagarajan, O. Ivy Wong, Foivos Diakogiannis

Image reconstruction in very-long baseline interferometry operates under severely sparse aperture coverage with calibration challenges from both the participating instruments and propagation medium, which introduce the risk of biases and artefacts. Interferometric closure invariants offers calibration-independent information on the true source morphology, bu

Alex J. Best, Sander R. Dahmen, Nuno Freitas

Let $n \in \mathbb{Z}_{\geq 2}$. We study the generalized Fermat equation \[x^{13}+y^{13}=z^n, \quad x,y,z \in \mathbb{Z}, \quad \gcd(x,y,z)=1.\] Using a combination of techniques, including the modular method, classical descent, unit sieves, and Chabauty and Mordell--Weil sieve methods over number fields, we show that for $n=5$ all its solutions $(a,b,c)$ a

Lijie Ding, Jan-Michael Carrillo, Changwoo Do

We introduce ToPolyAgent, a multi-agent AI framework for performing coarse-grained molecular dynamics (MD) simulations of topological polymers through natural language instructions. By integrating large language models (LLMs) with domain-specific computational tools, ToPolyAgent supports both interactive and autonomous simulation workflows across diverse pol

Hakan Ceylan, Edoardo Sinibaldi, Sanjay Misra, Pankaj J. Pasricha

Untethered mobile milli/microrobots hold transformative potential for interventional medicine by enabling more precise and entirely non-invasive diagnosis and therapy. Realizing this promise requires bridging the gap between groundbreaking laboratory demonstrations and successful clinical integration. Despite remarkable technical progress over the past two d

Xingpei Ma, Shenneng Huang, Jiaran Cai, Yuansheng Guan

Recent advances in diffusion models have significantly improved audio-driven human video generation, surpassing traditional methods in both quality and controllability. However, existing approaches still face challenges in lip-sync accuracy, temporal coherence for long video generation, and multi-character animation. In this work, we propose a diffusion tran

Zaid Khan, Archiki Prasad, Elias Stengel-Eskin, Jaemin Cho

Symbolic world modeling requires inferring and representing an environment's transitional dynamics as an executable program. Prior work has focused on largely deterministic environments with abundant interaction data, simple mechanics, and human guidance. We address a more realistic and challenging setting, learning in a complex, stochastic environment where

Heng Zhang, Tianyi Zhang, Yuling Shi, Xiaodong Gu

Representation learning on text-attributed graphs (TAGs) integrates structural connectivity with rich textual semantics, enabling applications in diverse domains. Current methods largely rely on contrastive learning to maximize cross-modal similarity, assuming tighter coupling between graph and text representations improves transfer performance. However, our

Ruijin Sun, Xiang Guo, Andreas Ruschhaupt, Zhihai Wang

The coherent emission of multiple atoms gives rise to superradiance, a cornerstone phenomenon in quantum optics with wide-ranging applications in quantum information processing and precision metrology. Despite its importance, how the superradiant scaling with respect to the number of participating atoms can be effectively controlled remains largely unexplore

Heng Zhang, Tianyi Zhang, Yuling Shi, Xiaodong Gu

Graph foundation models represent a transformative paradigm for learning transferable representations across diverse graph domains. Recent methods leverage large language models to unify graph and text modalities into a shared representation space using contrastive learning. However, systematic evaluations reveal significant performance degradation at struct

Weixuan Li, Guang Yu, Quanjun Li, Junhua Zhou

Chaotic systems play a key role in modern image encryption due to their sensitivity to initial conditions, ergodicity, and complex dynamics. However, many existing chaos-based encryption methods suffer from vulnerabilities, such as inadequate permutation and diffusion, and suboptimal pseudorandom properties. This paper presents Kun-IE, a novel encryption fra

Benjamin W. Nelson, Celeste Wong, Matthew T. Silvestrini, Sooyoon Shin

Large language models often mishandle psychiatric emergencies, offering harmful or inappropriate advice and enabling destructive behaviors. This study evaluated the Verily behavioral health safety filter (VBHSF) on two datasets: the Verily Mental Health Crisis Dataset containing 1,800 simulated messages and the NVIDIA Aegis AI Content Safety Dataset subsette

Huy Nguyen, Christoph Treude, Patanamon Thongtanunam

Automated program comprehension underpins many software engineering tasks, from code summarisation to clone detection. Recent deep learning models achieve strong results but typically rely on source code alone, overlooking contextual information such as version history or structural relationships. This limits their ability to capture how code evolves and ope

Anna Fang, Jiayang Shi, Hriday Chhabria, Bosi Li

Despite growing interest in virtual and augmented reality (VR/AR) for mental well-being, prior work using immersive interventions to teach mental health skills has largely focused on calming or abstract settings. As a result, little is known about how realistic social simulation may better support the transfer and application of skills to in-person environme

Rabimba Karanjai, Yang Lu, Ranjith Chodavarapu, Lei Xu

The rapid advancement of large language model (LLM) technology has led to diverse applications, many of which inherently require randomness, such as stochastic decision-making, gaming, scheduling, AI agents, and cryptography-related tasks. However, the capabilities of LLMs in handling randomness, particularly in generating and utilizing random numbers effect

Yuze Sun, Wentao Luo, Yanfei Xiang, Jiancheng Pan

With the growing role of artificial intelligence in climate and weather research, efficient model training and inference are in high demand. Current models like FourCastNet and AI-GOMS depend heavily on GPUs, limiting hardware independence, especially for Chinese domestic hardware and frameworks. To address this issue, we present a framework for migrating la

Joseph W. Eatson, Richard J. Parker

The decay of Short-Lived Radioisotopes (SLRs) can be a significant source of heating early in protoplanetary systems, though how a protoplanetary disk becomes enriched with these SLRs far above the galactic background level remains an open question. Observational evidence suggests that this enrichment occurs during the period from when the disk forms to when

Sijing Xie, Dingzhu Wen, Changsheng You, Qimei Chen

Fine-tuning (FT) large language models (LLMs) is crucial for adapting general-purpose models to specific tasks, enhancing accuracy and relevance with minimal resources. To further enhance generalization ability while reducing training costs, this paper proposes Federated LoRA with Dropout (FedLoDrop), a new framework that applies dropout to the rows and colu

Pierre Fayolle, Alexandre Bône, Noëlie Debs, Mathieu Naudin

DSC-MRI perfusion is a medical imaging technique for diagnosing and prognosing brain tumors and strokes. Its analysis relies on mathematical deconvolution, but noise or motion artifacts in a clinical environment can disrupt this process, leading to incorrect estimate of perfusion parameters. Although deep learning approaches have shown promising results, the

Einar Urdshals, Edmund Lau, Jesse Hoogland, Stan van Wingerden

We study neural network compressibility by using singular learning theory to extend the minimum description length (MDL) principle to singular models like neural networks. Through extensive experiments on the Pythia suite with quantization, factorization, and other compression techniques, we find that complexity estimates based on the local learning coeffici

Junyi Xie, Jina Kim, Yao-Yi Chiang, Lingyi Zhao

Traditional anomaly detection in human mobility has primarily focused on trajectory-level analysis, identifying statistical outliers or spatiotemporal inconsistencies across aggregated movement traces. However, detecting individual-level anomalies, i.e., unusual deviations in a person's mobility behavior relative to their own historical patterns, within data

Aashish Dhawan, Divyanshu Mudgal

The major challenge in today's computer vision scenario is the availability of good quality labeled data. In a field of study like image classification, where data is of utmost importance, we need to find more reliable methods which can overcome the scarcity of data to produce results comparable to previous benchmark results. In most cases, obtaining labeled

Vijay Gopal Thirupakuzi Vangipuram, Abdul Mukit, Kaitian Zhang, Salva Salmani-Rezaie

AlScN thin films were grown via metalorganic chemical vapor deposition (MOCVD), showing controllable incorporation of scandium (Sc) into the AlN lattices. Systematic variation of growth parameters demonstrated an obvious influence on Sc incorporation, with X-ray photoelectron spectroscopy (XPS) analysis indicating Sc composition up to $\sim$13\% when (MCp)$_

Liming Ling, Xuan Sun

In this work, we primarily focus on the two-phase solutions and their stability to the focusing mKdV equation. By employing the algebro-geometric approach in combination with an effective integration method, we construct explicit two-phase solutions and their corresponding wave-functions expressed in terms of the Riemann theta function. The spectral stabilit

Zixing Lei, Sheng Yin, Yichen Xiong, Yuanzhuo Ding

Embodied decision-making enables agents to translate high-level goals into executable actions through continuous interactions within the physical world, forming a cornerstone of general-purpose embodied intelligence. Large language models (LLMs), with their general decision-making capabilities, offer a promising path to realize this potential; however, LLMs

Sangmin Jo, Jee Seok Yoon, Wootaek Jeong, Kwanseok Oh

Deep learning-based automatic sleep staging has significantly advanced in performance and plays a crucial role in the diagnosis of sleep disorders. However, those models often struggle to generalize on unseen subjects due to variability in physiological signals, resulting in degraded performance in out-of-distribution scenarios. To address this issue, domain

Sandeep Mishra, Oindrila Saha, Alan C. Bovik

Motion-preserved video editing is crucial for creators, particularly in scenarios that demand flexibility in both the structure and semantics of swapped objects. Despite its potential, this area remains underexplored. Existing diffusion-based editing methods excel in structure-preserving tasks, using dense guidance signals to ensure content integrity. While

Shangkun Weng, Wengang Yang

The coupled motion between the hydrodynamic flow and magnetic field introduces significant complexity into the structure of the magnetohydrodynamic (MHD) equations. A key factor contributing to this complexity is the presence of Alfv\'en waves, which critically influences the character of the flow and makes the problem considerably more challenging. Within t

Junyi Xie, Yuankun Jiao, Jina Kim, Yao-Yi Chiang

Inferring demographic attributes such as age, sex, or income level from human mobility patterns enables critical applications such as targeted public health interventions, equitable urban planning, and personalized transportation services. Existing mobility-based demographic inference studies heavily rely on large-scale trajectory data with demographic label

Alessandro Achille, Stefano Soatto

We describe AI agents as stochastic dynamical systems and frame the problem of learning to reason as in transductive inference: Rather than approximating the distribution of past data as in classical induction, the objective is to capture its algorithmic structure so as to reduce the time needed to solve new tasks. In this view, information from past experie

Soma Furusawa, Taisei Kato, Ryo Hayakawa, Kazunori Hayashi

In order to realize analog compressed sensing, the paper considers approximate proximal operators of the $\ell_1$ and minimax concave penalty (MCP) regularization functions. Specifically, we propose to realize the approximate functions by an electric analog circuit using forward voltage-current (V-I) characteristics of the PN-junction diodes. To confirm the

Ariel Kamen

This study presents a comparative evaluation of ten state-of-the-art large language models (LLMs) applied to unstructured text categorization using the Interactive Advertising Bureau (IAB) 2.2 hierarchical taxonomy. The analysis employed a uniform dataset of 8,660 human-annotated samples and identical zero-shot prompts to ensure methodological consistency ac

Huiming Yang, Wenzhuo Liu, Yicheng Qiao, Lei Yang

The sparse cross-modality detector offers more advantages than its counterpart, the Bird's-Eye-View (BEV) detector, particularly in terms of adaptability for downstream tasks and computational cost savings. However, existing sparse detectors overlook the quality of token representation, leaving it with a sub-optimal foreground quality and limited performance

Jun Dai, Xiaorun Wang, Kexiong Fang, Zheng Yang

The proliferation of Large Language Models (LLMs) with exponentially growing parameters is making cross-data center (DC) training an inevitable trend. However, viable strategies for extending single-DC training frameworks to multi-DC environments remain underdeveloped. We experimentally demonstrate, for the first time, a high-performance geo-distributed LLMs

Sunzhu Li, Zhiyu Lin, Shuling Yang, Jiale Zhao

Large Reasoning Models (LRMs) are powerful, but they still suffer from inefficient and off-target reasoning. Currently, training-free methods are limited to either rigid heuristics or descriptive, non-actionable analyses. In this paper, we introduce ThinkPilot, a training-free framework that automatically optimizes LRMs reasoning. It uses an evolutionary pro

Ranjith Chodavarapu, Rabimba Karanjai, Xinxin Fan, Weidong Shi

Random numbers play a vital role in many decentralized applications (dApps), such as gaming and decentralized finance (DeFi) applications. Existing random number provision mechanisms can be roughly divided into two categories, on-chain, and off-chain. On-chain approaches usually rely on the blockchain as the major input and all computations are done by block

Yiheng Chen, Lingyao Li, Zihui Ma, Qikai Hu

Effective disaster response is essential for safeguarding lives and property. Existing statistical approaches often lack semantic context, generalize poorly across events, and offer limited interpretability. While Large language models (LLMs) provide few-shot generalization, they remain text-bound and blind to geography. To bridge this gap, we introduce a Ge

Yi-Chung Chen, David I. Inouye, Jing Gao

Generative classifiers, which leverage conditional generative models for classification, have recently demonstrated desirable properties such as robustness to distribution shifts. However, recent progress in this area has been largely driven by diffusion-based models, whose substantial computational cost severely limits scalability. This exclusive focus on d

Svetlana Poznanović, Owen Cardwell, Christine Heitsch

Background: In the Nearest-Neighbor Thermodynamic Model, a standard approach for RNA secondary structure prediction, the energy of the multiloops is modeled using a linear entropic penalty governed by three branching parameters. Although these parameters are typically fixed, recent work has shown that reparametrizing the multiloop score and considering alter

Mao Hoshino

We classify semisimple left module categories over the representation category of a type A quantum group whose fusion rules arise from the maximal torus. The classification is connected to equivariant Poisson structures on compact full flag manifolds in the operator-algebraic setting, and on semisimple coadjoint orbits in the algebraic setting. We also provi

Xinxin Huang, Han Sun, Junmin Cai, Ningzhong Liu

Detecting camouflaged objects in underwater environments is crucial for marine ecological research and resource exploration. However, existing methods face two key challenges: underwater image degradation, including low contrast and color distortion, and the natural camouflage of marine organisms. Traditional image enhancement techniques struggle to restore

Yonggang Hu, Yiqing Liao, Lufeng Yang, Ke Zhang

Non-destructive characterization of lithium-ion batteries provides critical insights for optimizing performance and lifespan while preserving structural integrity. Optimizing electrolyte design in commercial LIBs requires consideration of composition, electrolyte-to-capacity ratio, spatial distribution, and associated degradation pathways. However, existing

Wenjin Xie, Tao Jia

With the rapid expansion of scientific literature, scholars increasingly demand precise and high-quality paper recommendations. Among various recommendation methodologies, graph-based approaches have garnered attention by effectively exploiting the structural characteristics inherent in scholarly networks. However, these methods often overlook the asymmetric

Jesse Atuhurra, Iqra Ali, Tomoya Iwakura, Hidetaka Kamigaito

Vision Language Models (VLMs) are pivotal for advancing perception in intelligent agents. Yet, evaluation of VLMs remains limited to predominantly English-centric benchmarks in which the image-text pairs comprise short texts. To evaluate VLM fine-grained abilities, in four languages under long-text settings, we introduce a novel multilingual benchmark VLURes

Bolei Ma, Yong Cao, Indira Sen, Anna-Carolina Haensch

Large Language Models (LLMs) are increasingly used to simulate public opinion and other social phenomena. Most current studies constrain these simulations to multiple-choice or short-answer formats for ease of scoring and comparison, but such closed designs overlook the inherently generative nature of LLMs. In this position paper, we argue that open-endednes

Ty Trusty

We introduce Coordinate Condensation, a variant of coordinate descent that accelerates physics-based simulation by augmenting local coordinate updates with a Schur-complement-based subspace correction. Recent work by Lan et al. 2025 (JGS2) uses perturbation subspaces to augment local solves to account for global coupling, but their approach introduces dampin

Zhihao Xu, Ma Luo

Bilayer meta-gratings with asymmetric structural parameters could host unidirectional guide resonances. The distribution of unidirectional guide resonances in the space of structural parameters and synthetic parameters is identified. As the incident optical beam being resonant with the unidirectional guide resonance, the Goos-H$\ddot{a}$nchen shifts of the s

Dinh-Nguyen Nguyen, Sadia Shakil, Raymond Kai-Yu Tong, Ngoc-Duy Dinh

Microwell microfluidics has been utilized for single-cell analysis to reveal heterogeneity in gene expression, signaling pathways, and phenotypic responses for identifying rare cell types, understanding disease progression, and developing more precise therapeutic strategies. However, designing microwell microfluidics is a considerably complex task, requiring

Baisub Lee, Sanghyun Byun, Mohanad Odema, Jung Guack

Deploying useful Long-Context Transformer Models (LCTMs) requires addressing two key challenges: (1) A growing memory footprint due to quadratic self-attention and linear KV-cache scaling in memory as sequence length increases; (2) the ContextRot phenomena where empirical evidence suggests that transformer architecture's performance degrades with increasing

Mohit Daga

Thin spanning trees lie at the intersection of graph theory, approximation algorithms, and combinatorial optimization. They are central to the long-standing \emph{thin tree conjecture}, which asks whether every $k$-edge-connected graph contains an $O(1/k)$-thin tree, and they underpin algorithmic breakthroughs such as the $O(\log n/\log\log n)$-approximation

Veeramani Pugazhenthi, Wei-Hsiang Chu, Junwei Lu, Jadyn N. Miyahira

The use of tiny devices capable of low-latency gesture recognition is gaining momentum in everyday human-computer interaction and especially in medical monitoring fields. Embedded solutions such as fall detection, rehabilitation tracking, and patient supervision require fast and efficient tracking of movements while avoiding unwanted false alarms. This study

Berkay Akturk, Ufuk Beyaztas, Han Lin Shang

Functional logistic regression is a popular model to capture a linear relationship between binary response and functional predictor variables. However, many methods used for parameter estimation in functional logistic regression are sensitive to outliers, which may lead to inaccurate parameter estimates and inferior classification accuracy. We propose a robu

Soohan Lim, Joonghyuk Hahn, Hyunwoo Park, Sang-Ki Ko

Current code generation evaluation measures functional correctness on well-formed inputs that satisfy all input preconditions. This paradigm has a critical limitation: task descriptions often leave these preconditions implicit, while evaluation filters out inputs that violate them. As a result, generated code may achieve high pass@k scores while failing to e