November 2025 arXiv papers — page 106

Antonio David Bastida Zamora, Ljubomir Budinski, Valtteri Lahtinen, Pierre Sagaut

This article presents a novel encoding for quantum Lattice Boltzmann method algorithm using Carleman linearization. In contrast to previous articles \cite{Sanavio2024LatticeBC,sanavio2025carleman}, the encoding used allows for local collision rules while keeping a higher probability to obtain the right result, which is of the order of $10^{-2}$. The algorith

Jungin Lee

In this paper, we determine the sharp threshold for universality of cokernels of random matrices over finite fields. More precisely, we prove the following: given any constant $c>1$, let $(A(n))_{n \ge 1}$ be a sequence of random $n \times n$ matrices over $\mathbb{F}_p$ such that, for all sufficiently large $n$, the entries of $A(n)$ are independent and tak

Zhenyu Mao, Jacky Keung, Yicheng Sun, Yifei Wang

Context: Responsibility gaps, long-recognized challenges in socio-technical systems where accountability becomes diffuse or ambiguous, have become increasingly pronounced in GenAI-enabled software. The generative and adaptive nature complicates how human oversight and responsibility are specified, delegated, and traced. Existing requirements engineering (RE)

Yukun Chen, Xiangdi Fu, Zhaofeng Lin, Yanqi Qiu

We establish the Salem properties for the uncovered sets in the celebrated Dvoretzky random coverings of the unit circle.

Zhi-Gang Wang, Yang Liu

In this work, we construct the color $\bar{\mathbf{3}}\bar{\mathbf{3}}\bar{\mathbf{3}}$ type local five-quark currents with the light quarks $qss$ in the flavor octet, and study the $qssc\bar{c}$ pentaquark states via the QCD sum rules in a comprehensive way, and we emphasize that we achieve two light-flavor octets. We obtain the mass spectrum of the hidden-

Frank Gilson

We analyse the logical complexity and absoluteness of natural statements about Ulam sequences, with particular emphasis on the rigidity phenomena introduced by Hinman, Kuca, Schlesinger and Sheydvasser for the family $U(1,n)$. For each pair of coprime integers $a<b$ we view the associated Ulam sequence $U(a,b)$ as a recursive subset of $\mathbb{N}$ and consi

Reeshoon Sayera, Akash Kumar, Sirshapan Mitra, Prudvi Kamtam

Appearance-based gait recognition have achieved strong performance on controlled datasets, yet systematic evaluation of its robustness to real-world corruptions and silhouette variability remains lacking. We present RobustGait, a framework for fine-grained robustness evaluation of appearance-based gait recognition systems. RobustGait evaluation spans four di

Arijit Das, Minh-Binh Tran

Building on recent developments in numerical schemes designed to capture energy cascades for 3-wave kinetic equations~\cite{das2024numerical, walton2022deep, walton2023numerical, walton2024numerical}, we construct in this work a finite-volume algorithm for a significantly more complex wave kinetic equation whose collision operator incorporates both 3-wave an

Zhenghua Li, Hang Chen, Zihao Sun, Kai Li

Accurate segmentation of neural structures in Electron Microscopy (EM) images is paramount for neuroscience. However, this task is challenged by intricate morphologies, low signal-to-noise ratios, and scarce annotations, limiting the accuracy and generalization of existing methods. To address these challenges, we seek to leverage the priors learned by visual

Mohit Meena, Yash Punjabi, Abhishek A, Vishal Sharma

Graph Neural Networks (GNNs) have emerged as powerful tools for learning over graph-structured data, yet recent studies have shown that their performance gains are beginning to plateau. In many cases, well-established models such as GCN and GAT, when appropriately tuned, can match or even exceed the performance of more complex, state-of-the-art architectures

Zhaoxuan Wang, Weichen Kang, Yutian Han, Lingyuan Zhao

Human Immunodeficiency Virus (HIV) has posed a major global health challenge for decades, and forecasting HIV diagnoses continues to be a critical area of research. However, capturing the complex spatial and temporal dependencies of HIV transmission remains challenging. Conventional Message Passing Neural Network (MPNN) models rely on a fixed binary adjacenc

Vladimír Macko, Vladimír Boža

Sparse Matrix-Vector Multiplication (SpMV) is a fundamental operation in the inference of sparse Large Language Models (LLMs). Because existing SpMV methods perform poorly under the low and unstructured sparsity (30-90%) commonly observed in pruned LLMs, unstructured pruning provided only limited memory reduction and speedup. We propose MACKO-SpMV, a GPU-opt

Duo Yi

Online decision systems routinely operate under delayed feedback and order-sensitive (noncommutative) dynamics: actions affect which observations arrive, and in what sequence. Taking a Bregman divergence $D_\Phi$ as the loss benchmark, we prove that the excess benchmark loss admits a structured lower bound $L \ge L_{\mathrm{ideal}} + g_1(\lambda) + g_2(\vare

Kunle Adegoke, Robert Frontczak, Karol Gryszka

In this paper, we continue our investigation of double sums where the inner sum is binomial but incomplete. We prove many new results for these types of double sums associated with binomial transform pairs. As applications we deduce new identities for double sums involving special numbers like Bernoulli numbers, Fibonacci numbers, harmonic numbers, Catalan n

Anatoly S. Miroshnichenko, Sergey V. Zharikov, Nadezhda L. Vaidman, Serik A. Khokhlov

The B[e] phenomenon discovered nearly 50 years ago features the presence of forbidden emission lines due to extended and dense circumstellar gas and large IR excesses due to the radiation from circumstellar dust in a wide variety of objects from pre-main-sequence stars to Planetary Nebulae. It also shows up in a small group of supergiants that includes Lumin

Satyanarayan Pati

Dense retrieval models have become a standard for state-of-the-art information retrieval. However, their high-dimensional, high-precision (float32) vector embeddings create significant storage and memory challenges for real-world deployment. To address this, we conduct a rigorous empirical study on the BEIR SciFact benchmark, evaluating the trade-offs betwee

Xin Huang, Shengwei Zhou

We present a new algorithm that achieves a $\frac{7}{9}$-approximation for the maximin share (MMS) allocation of indivisible goods under additive valuations, improving the current best ratio of $\frac{10}{13}$ (Heidari et al., SODA 2026). Building on a new analytical framework, we further obtain an FPTAS that achieves a $\frac{7}{9}-\varepsilon$ approximatio

Ruixin Liu, Zejian Yuan

Monocular 3D lane detection is challenged by aleatoric uncertainty arising from inherent observation noise. Existing methods rely on simplified geometric assumptions, such as independent point predictions or global planar modeling, failing to capture structural variations and aleatoric uncertainty in real-world scenarios. In this paper, we propose MonoUnc, a

Bo Fang, Yuxin Song, Qiangqiang Wu, Haoyuan Sun

Complex video reasoning remains a significant challenge for Multimodal Large Language Models (MLLMs), as current R1-based methodologies often prioritize text-centric reasoning derived from text-based and image-based developments. In video tasks, such strategies frequently underutilize rich visual information, leading to potential shortcut learning and increa

Akira Tamamori

Kernel-based learning methods can dramatically increase the storage capacity of Hopfield networks, yet the dynamical mechanisms behind this enhancement remain poorly understood. We address this gap by combining a geometric characterization of the attractor landscape with the spectral theory of kernel machines. Using a novel metric, Pinnacle Sharpness, we emp

Yunhun Nam, Jaehyung Kim, Jongheon Jeong

Language models (LMs) are often adapted through supervised fine-tuning (SFT) to specialize their capabilities for downstream tasks. However, in typical scenarios where the fine-tuning data is limited, e.g., compared to pre-training, SFT can lead LMs to overfit, causing them to rely on spurious patterns within the target task or to compromise other broadly us

Wei Chao

Conventional Leptogenesis mechanism, which provides compelling explanation to the origin of the baryon asymmetry of the universe (BAU), assumes the absence of hypermagnetic field in the early universe, thereby disregard the implications of hyper gauge field helicity, that have been thoroughly studied in the magnetogenesis mechanism. In this paper, we address

Jiaqiang Jiang, Wenfeng Xu, Jing Fan, Rui Yan

Brain-inspired spiking neural networks (SNNs) are recognized as a promising avenue for achieving efficient, low-energy neuromorphic computing. Direct training of SNNs typically relies on surrogate gradient (SG) learning to estimate derivatives of non-differentiable spiking activity. However, during training, the distribution of neuronal membrane potentials v

Antoine Ledent, Mun Chong Soo, Nong Minh Hieu

We study a matrix completion problem where both the ground truth $R$ matrix and the unknown sampling distribution $P$ over observed entries are low-rank matrices, and \textit{share a common subspace}. We assume that a large amount $M$ of \textit{unlabeled} data drawn from the sampling distribution $P$ is available, together with a small amount $N$ of labeled

Yong Li, Hui Cheng

Practical global path planning is critical for commercializing cleaning robots working in semi-structured environments. In the literature, global path planning methods for free space usually focus on path length and neglect the traffic rule constraints of the environments, which leads to high-frequency re-planning and increases collision risks. In contrast,

Yan Gong, Jianli Lu, Yongsheng Gao, Jie Zhao

Indoor semantic segmentation is fundamental to computer vision and robotics, supporting applications such as autonomous navigation, augmented reality, and smart environments. Although RGB-D fusion leverages complementary appearance and geometric cues, existing methods often depend on computationally intensive cross-attention mechanisms and insufficiently mod

Ichiro Matsuda, Komichi Takezawa, Katsuhito Muroi, Kensuke Katori

LLMs can act as an impartial other, drawing on vast knowledge, or as personalized self-reflecting user prompts. These personalized LLMs, or Digital Humans, occupy an intermediate position between self and other. This research explores the dynamic of self and other mediated by these Digital Humans. Using a Research Through Design approach, nine junior and sen

Mayur Abhisheki, Prasanta Kumar Das

In this work, we study the early universe inflation and the post-inflation reheating era employing an inverse tangent potential of the form $V=V_0 \cdot[tan^{-1}(\frac{\kappa \phi}{m_p})]^2$, where $\kappa$ is a free parameter of the potential and $m_p$ is the reduced Planck mass. We derive the slow roll parameters, the number of e-folds(N), the scalar spect

Xinyuan Zhou, Yi Lei, Xiaoyu Zhou, Jingyi Sun

Large Language Models (LLMs) have shown significant promise in automated theorem proving, yet progress is often constrained by the scarcity of diverse and high-quality formal language data. To address this issue, we introduce Spark-Prover-X1, a 7B parameter model trained via an three-stage framework designed to unlock the reasoning potential of more accessib

Yong Li, Hui Cheng

Paths generated by A* and other graph-search-based planners are widely used in the robotic field. Due to the restricted node-expansion directions, the resulting paths are usually not the shortest. Besides, unnecessary heading changes, or zig-zag patterns, exist even when no obstacle is nearby, which is inconsistent with the human intuition that the path segm

Miaomiao Cai, Min Hou, Lei Chen, Le Wu

Collaborative Filtering~(CF) plays a crucial role in modern recommender systems, leveraging historical user-item interactions to provide personalized suggestions. However, CF-based methods often encounter biases due to imbalances in training data. This phenomenon makes CF-based methods tend to prioritize recommending popular items and performing unsatisfacto

Nabadwip Sarkar, Debabrata Pramanik, Lata Mahato

In this paper, we investigate the uniqueness problem of entire functions that share an entire function with their higher-order difference operators. We obtain two results that confirm the conjectures posed by Liu and Laine \cite{LL1} and by Zhang et al. \cite{ZKL1}, respectively. In addition, we present several relevant examples to further illustrate and sup

Kasun Wickramasinghe, Nisansa de Silva

Sans a dwindling number of monolingual embedding studies originating predominantly from the low-resource domains, it is evident that multilingual embedding has become the de facto choice due to its adaptability to the usage of code-mixed languages, granting the ability to process multilingual documents in a language-agnostic manner, as well as removing the d

Bo Peng, Yu Zhang

Open quantum systems exhibit dynamics ranging from unitary evolution to irreversible dissipation. While the Gorini--Kossakowski--Sudarshan--Lindblad (GKSL) equation uniquely characterizes Markovian CPTP evolution, many physical platforms display non-Markovian features such as algebraic relaxation and coherence backflow. Fractional calculus provides a natural

Dahyun Chung, Donghyun Shin, Yujin Sung, Seunggi Moon

Contrastive Language-Image Pre-training (CLIP) has demonstrated strong generalization across a wide range of visual tasks by leveraging large-scale English-image pairs. However, its extension to low-resource languages remains limited due to the scarcity of high-quality multilingual image-text data. Existing multilingual vision-language models exhibit consist

Zeyuan Wang, Da Li, Yulin Chen, Ye Shi

We introduce a one-step generative policy for offline reinforcement learning that maps noise directly to actions via a residual reformulation of MeanFlow, making it compatible with Q-learning. While one-step Gaussian policies enable fast inference, they struggle to capture complex, multimodal action distributions. Existing flow-based methods improve expressi

Rahul Misra, Manuela L. Bujorianu, Rafał Wisniewski

We propose a reinforcement learning (RL) framework for multi-objective decision-making, where the agent seeks to optimize a vector of rewards rather than a single scalar value. The objective is to ensure that the time-averaged reward vector converges asymptotically to a predefined target set. Since standard RL algorithms operate on scalar rewards, we introdu

Valter Uotila, Cong Yu, Bo Zhao

Quantum computing is an emerging computational paradigm with the potential to outperform classical computers in solving a variety of problems. To achieve this, quantum programs are typically represented as quantum circuits, which must be optimized and adapted for target hardware through quantum circuit compilation. We introduce ZX-DB, a data-driven system th

Sheng Liu, Yuanzhi Liang, Jiepeng Wang, Sidan Du

We present Uni-Inter, a unified framework for human motion generation that supports a wide range of interaction scenarios: including human-human, human-object, and human-scene-within a single, task-agnostic architecture. In contrast to existing methods that rely on task-specific designs and exhibit limited generalization, Uni-Inter introduces the Unified Int

Weihua Wang, Yubo Cui, Xiangru Lin, Zhiheng Li

Vision-based 3D Semantic Scene Completion (SSC) has received growing attention due to its potential in autonomous driving. While most existing approaches follow an ego-centric paradigm by aggregating and diffusing features over the entire scene, they often overlook fine-grained object-level details, leading to semantic and geometric ambiguities, especially i

Stephen Catsamas, Sarah Caddy, Michele Trenti, Benjamin Metha

In this work we investigate the potential of a thermal infrared (IR) space telescope to remotely characterize the component temperatures of a satellite. With the rapid increase in the number of objects launched in recent years, the ability to detect, track, identify and determine the intent of satellites has become of increasing importance. Spectral modeling

Declan Jackson, William Keating, George Cameron, Micah Hill-Smith

Existing language model evaluations primarily measure general capabilities, yet reliable use of these models across a range of domains demands factual accuracy and recognition of knowledge gaps. We introduce AA-Omniscience, a benchmark designed to measure both factual recall and knowledge calibration across 6,000 questions. Questions are derived from authori

Masayuki Kimura, Juan F. R. Archilla, Yusuke Doi, Víctor J. Sánchez-Morcillo

In this work, we study a space-time modulated electro-mechanical system, consisting of an array of coupled cantilevers with their on-site potential provided by electromagnets driven by AC currents. Model equations are derived, and the effect of the modulation on the dispersion bands is examined. The theory of breather existence and stability is extended to i

Sadegh Mahdavi, Branislav Kisacanin, Shubham Toshniwal, Wei Du

Large language models have achieved remarkable success on final-answer mathematical problems, largely due to the ease of applying reinforcement learning with verifiable rewards. However, the reasoning underlying these solutions is often flawed. Advancing to rigorous proof-based mathematics requires reliable proof verification capabilities. We begin by analyz

Charles Fefferman, Jonathan Marty, Kevin Ren

We consider the problem of reconstructing the intrinsic geometry of a manifold from noisy pairwise distance observations. Specifically, let $M$ denote a diameter 1 d-dimensional manifold and $\mu$ a probability measure on $M$ that is mutually absolutely continuous with the volume measure. Suppose $X_1,\dots,X_N$ are i.i.d. samples of $\mu$ and we observe noi

Aneesh Sivasankaran, Laura Blecha, Paul Torrey, Luke Zoltan Kelley

We study fast nuclear winds driven by Active Galactic Nucleus (AGN) feedback in merging galaxies using high-resolution hydrodynamics simulations. We use Stars and MUltiphase Gas in GaLaxiEs (SMUGGLE) to explicitly model the multiphase interstellar medium (ISM) and employ sub-grid dynamical friction for massive black holes (BHs). Furthermore, we use a super-L

Jiacheng Wang, Yejun Zeng, Jinyang Guo, Yuqing Ma

Despite the growing interest in Small Language Models (SLMs) as resource-efficient alternatives to Large Language Models (LLMs), their deployment on edge devices remains challenging due to unresolved efficiency gaps in model compression. While quantization has proven effective for LLMs, its applicability to SLMs is significantly underexplored, with critical

Eshani Patel, Yisong Yue, Geeling Chau

General-purpose foundation models for neural time series can help accelerate neuroscientific discoveries and enable applications such as brain computer interfaces (BCIs). A key component in scaling these models is population-level representation learning, which leverages information across channels to capture spatial as well as temporal structure. Population

Sachin Vashistha, Aryan Bibhuti, Atharva Naik, Martin Tutek

Real-world conversations are rich with pragmatic elements, such as entity mentions, references, and implicatures. Understanding such nuances is a requirement for successful natural communication, and often requires building a local world model which encodes such elements and captures the dynamics of their evolving states. However, it is not well-understood w

Zheyuan Hu, Chieh-Hsin Lai, Ge Wu, Yuki Mitsufuji

MeanFlow (MF) is a diffusion-motivated generative model that enables efficient few-step generation by learning long jumps directly from noise to data. In practice, it is often used as a latent MF by leveraging the pre-trained Stable Diffusion variational autoencoder (SD-VAE) for high-dimensional data modeling. However, MF training remains computationally dem

S Sairam, Sara Girdhar, Shivam Soni

The R-Learner is a powerful, theoretically-grounded framework for estimating heterogeneous treatment effects, prized for its robustness to nuisance model errors. However, its application to network data, where causal heterogeneity is often graph-dependent, presents a critical challenge to its core assumption of a well-specified final-stage model. In this pap

Steven M. Girvin, Leo Radzihovsky

Motivated by recent experimental breakthroughs toward a realization of a solid-state Thorium-229 nuclear clock, we review the technology, basic physics motivation, and limitations of the present generation of atomic clocks. We then discuss prospects for a new generation of clocks based on an anomalous low-energy 8.4 eV nuclear transition in Th-229, with an e

Subramanyam Sahoo

Reward design is central to reinforcement learning from human feedback (RLHF) and alignment research. In this work, we propose a unified framework to study hard, continuous, and hybrid reward structures for fine-tuning large language models (LLMs) on mathematical reasoning tasks. Using Qwen3-4B with LoRA fine-tuning on the GSM8K dataset, we formalize and emp

King-Man Tam, Satoshi Ikehata, Yuta Asano, Zhaoyi An

Universal Photometric Stereo is a promising approach for recovering surface normals without strict lighting assumptions. However, it struggles when multi-illumination cues are unreliable, such as under biased lighting or in shadows or self-occluded regions of complex in-the-wild scenes. We propose GeoUniPS, a universal photometric stereo network that integra

Solon P. Pissis

In the classical longest palindromic substring (LPS) problem, we are given a string $S$ of length $n$, and the task is to output a longest palindromic substring in $S$. Gilbert, Hajiaghayi, Saleh, and Seddighin [SPAA 2023] showed how to solve the LPS problem in the Massively Parallel Computation (MPC) model in $\mathcal{O}(1)$ rounds using $\mathcal{\widetil

Zhen-Qing Chen, Xicheng Zhang

In this paper, we investigate Harnack estimates for weak solutions to the following nonlocal equation: $$ \partial_t u = \Delta^{\alpha/2} u + b \cdot \nabla u + f, $$ where $\Delta^{\alpha/2}$ denotes the fractional Laplacian, $b$ is a divergence-free vector field in a critical or supercritical regularity regime, and $f$ is a distribution in a fractional So

Qingsen Ma, Chen Zou, Dianyun Wang, Jia Wang

Under extremely low-light conditions, novel view synthesis (NVS) faces severe degradation in terms of geometry, color consistency, and radiometric stability. Standard 3D Gaussian Splatting (3DGS) pipelines fail when applied directly to underexposed inputs, as independent enhancement across views causes illumination inconsistencies and geometric distortion. T

Jeongwhan Choi, Seungjun Park, Sumin Park, Sung-Bae Cho

Graph Neural Networks (GNNs) have emerged as powerful tools for learning on graph-structured data, but often struggle to balance local and global information. While graph Transformers aim to address this by enabling long-range interactions, they often overlook the inherent locality and efficiency of Message Passing Neural Networks (MPNNs). We propose a new c

Yong Liu, Keyang Ye, Tianjia Shao, Kun Zhou

We propose Transmission-Reflection Gaussians (TR-Gaussians), a novel 3D-Gaussian-based representation for high-fidelity rendering of planar transmission and reflection, which are ubiquitous in indoor scenes. Our method combines 3D Gaussians with learnable reflection planes that explicitly model the glass planes with view-dependent reflectance strengths. Real

Jongho Park, Kazuya Takahashi, Kenji Toma, Kazuhiro Hada

Relativistic jets from supermassive black holes are expected to be magnetically launched and guided, with magnetic energy systematically converted to bulk kinetic energy throughout an extended acceleration-collimation zone (ACZ). A key prediction of magnetohydrodynamic (MHD) models is a transition from poloidally dominated fields near the engine to toroidall

Yiyang Zhao, Huiyu Bai, Xuejiao Zhao

Alignment of large language models (LLMs) with human preferences typically relies on supervised reward models or external judges that demand abundant annotations. However, in fields that rely on professional knowledge, such as medicine and law, such large-scale preference labels are often unachievable. In this paper, we propose a generative entropy-guided pr

Fangzhi Li, Zhichu Ren, Cunhua Pan, Hong Ren

To enhance the performance of aerial-ground networks, this paper proposes an integrated sensing and communication (ISAC) framework for multi-UAV systems. In our model, ground base stations (BSs) cooperatively serve multiple unmanned aerial vehicles (UAVs), employing a dynamic time-division strategy where beam scanning for sensing precedes data communication

Wenqian Ye, Di Wang, Guangtao Zheng, Bohan Liu

Large vision-language models, such as CLIP, have shown strong zero-shot classification performance by aligning images and text in a shared embedding space. However, CLIP models often develop multimodal spurious biases, which is the undesirable tendency to rely on spurious features. For example, CLIP may infer object types in images based on frequently co-occ

Jiasheng Li, Xiaoyun Lv, Shoujun Xu

Let $G=(V(G),E(G)) $ be a graph with vertex set $V(G)$ and edge set $E(G)$. An even factor of $G$ is a spanning subgraph $F$ such that every vertex in $F$ has a nonzero even degree. Note that $\delta(G)\geq 2$ is a trivial necessary condition for a graph to have an even factor, where \( \delta(G) \) is the minimum degree of \( G \). In this paper, for a conn

Shota Ampuku, Yasuhiro Yamaguchi, Masayasu Harada

A central question in exotic-hadron physics is their internal structure whether these states are loosely bound hadronic molecules or compact multiquark configurations. To shed light on this issue, we develop a model that incorporates mixing between hadronic-molecular and compact multiquark components. We then apply this framework to the specific case of the

Weiying Shen, Hao Yu, Yu Dong, Pan Liu

Real-time crash detection is essential for developing proactive safety management strategy and enhancing overall traffic efficiency. To address the limitations associated with trajectory acquisition and vehicle tracking, road segment maps recording the individual-level traffic dynamic data were directly served in crash detection. A novel two-stage trajectory

Jihun Park, Kyoungmin Lee, Jongmin Gim, Hyeonseo Jo

We present Infinite-Story, a training-free framework for consistent text-to-image (T2I) generation tailored for multi-prompt storytelling scenarios. Built upon a scale-wise autoregressive model, our method addresses two key challenges in consistent T2I generation: identity inconsistency and style inconsistency. To overcome these issues, we introduce three co

Pengcheng Shi, Jiawei Chen, Jiaqi Liu, Xinglin Zhang

We introduce Medal S, a medical segmentation foundation model that supports native-resolution spatial and textual prompts within an end-to-end trainable framework. Unlike text-only methods lacking spatial awareness, Medal S achieves channel-wise alignment between volumetric prompts and text embeddings, mitigating inaccuracies from resolution mismatches. By p

Lingxuan Kong, Yumin Zhang, Chenkun Wang, Yaoyuan Vincent Tan

Identifying variables associated with clinical endpoints is of much interest in clinical trials. With the rapid growth of cell and gene therapy (CGT) and therapeutics for ultra-rare diseases, there is an urgent need for statistical methods that can detect meaningful associations under severe sample-size constraints. Motivated by data-borrowing strategies for

Harrison H. Li, Medhanie Irgau, Nabil Janmohamed, Karen Solveig Rieckmann

Precise estimation and uncertainty quantification for average crop yields are critical for agricultural monitoring and decision making. Existing data collection methods, such as crop cuts in randomly sampled fields at harvest time, are relatively time-consuming. Thus, we propose an approach based on prediction-powered inference (PPI) to supplement these crop

Zewei Chang, Zheng-Peng Duan, Jianxing Zhang, Chun-Le Guo

Image retouching aims to enhance visual quality while aligning with users' personalized aesthetic preferences. To address the challenge of balancing controllability and subjectivity, we propose a unified diffusion-based image retouching framework called PerTouch. Our method supports semantic-level image retouching while maintaining global aesthetics. Using p

Enze Hou, Xiaoyang Wang, Han Wang

Lead (Pb) is a typical low-melting-point ductile metal and serves as an important model material in the study of dynamic responses. Under shock-wave loading, its dynamic mechanical behavior comprises two key phenomena: plastic deformation and shock induced phase transitions. The underlying mechanisms of these processes are still poorly understood. Revealing

Debjit Basu

In this article we study conditions under which weight one Koszul cohomology vanishes on projective varieties. As corollary of more general results, we obtain statements on the so-called property (M_q) reflecting on the higher syzygies of minimal surfaces and higher dimensional projective varieties. By considering both properties (M_q) and (N_p), we gain a s

Longfei Chen, Ruibin Yan, Taiyu Wong, Yiyang Chen

Smart contracts are commonly audited through static analysis to explore vulnerabilities. However, static approaches typically produce heterogeneous findings rather than reproducible, executable proof-of-concept (PoC) test cases, leading to costly and ad hoc manual validation. Large language models (LLMs) offer a promising way to translate audit reports into

Lintong Zhang, Kang Yin, Seong-Whan Lee

In the domain of non-generative visual counterfactual explanations (CE), traditional techniques frequently involve the substitution of sections within a query image with corresponding sections from distractor images. Such methods have historically overlooked the semantic relevance of the replacement regions to the target object, thereby impairing the model's

Zeyu Shi, Ziming Wang, Tianyu Chen, Shiqi Gao

The honesty of Large Language Models (LLMs) is increasingly important for safe deployment in high-stakes domains. However, this crucial trait is severely undermined by supervised fine-tuning (SFT), a common technique for model specialization. Existing recovery methods rely on data-intensive global parameter adjustments, implicitly assuming that SFT deeply co

Sixtus Dakurah

This work introduces a novel framework for testing topological variability in weighted networks by combining Hodge decomposition with Wasserstein variance minimization. Traditional approaches that analyze raw edge weights are susceptible to noise driven perturbations, limiting their ability to detect meaningful structural differences between network populati

Ayush Subedi, Anthony Torres, Jeff Ganley

ZBLAN glass is highly sensitive to thermal and mechanical stimuli, yet the associated surface chemical changes remain poorly understood. X-Ray Photoelectron Spectroscopy (XPS) measurements were performed on multiple ZBLAN samples representing distinct structural states: fully amorphous, incipiently crystalline, and highly crystalline, produced through therma

Quan-Hoang Vuong, Fatemeh Kianfar, Thi Mai Anh Tran, Ni Putu Wulan Purnama Sari

Human exceptionalism strongly shapes human-nature perceptions, thinking, values, and behaviors. Yet little is known about how virtual ecological environments influence this mindset. As digital worlds become increasingly immersive and ecologically sophisticated, they provide novel contexts for examining how human value systems are formed and transformed. This

Furui Xu, Shaobo Wang, Jiajun Zhang, Chenghao Sun

The growing scale of datasets in deep learning has introduced significant computational challenges. Dataset pruning addresses this challenge by constructing a compact but informative coreset from the full dataset with comparable performance. Previous approaches typically establish scoring metrics based on specific criteria to identify representative samples.

Daivik Patel, Shrenik Patel

Large reasoning models (LRMs) achieve strong accuracy through test-time scaling, generating longer chains of thought or sampling multiple solutions, but at steep costs in tokens and latency. We argue that memory is a core ingredient for efficient reasoning: when evidence already exists, models should think less by reusing structured memory instead of recompu

Abdelouahed Ben Mhamed, Assia Kamal-Idrissi, Amal El Fallah Seghrouchni

Branch-and-Bound (B\&B) is the dominant exact solution method for Mixed Integer Linear Programs (MILP), yet its exponential time complexity poses significant challenges for large-scale instances. The growing capabilities of machine learning have spurred efforts to improve B\&B by learning data-driven branching policies. However, most existing approaches rely

Minsoo Jo, Dongyoon Yang, Taesup Kim

Adversarial examples in neural networks have been extensively studied in Euclidean geometry, but recent advances in \textit{hyperbolic networks} call for a reevaluation of attack strategies in non-Euclidean geometries. Existing methods such as FGSM and PGD apply perturbations without regard to the underlying hyperbolic structure, potentially leading to ineff

Miryeong Park, Dongjin Cho, Sanghyun Kim, Younggun Cho

Planetary exploration robots must navigate uneven terrain while building reliable maps for space missions. However, most existing methods incorporate traversability constraints but may not handle high uncertainty in elevation estimates near complex features like craters, do not consider exploration strategies for uncertainty reduction, and typically fail to

Enze Hou, Yuzhi Liu, Linxuan Zhang, Difa Ye

The time-dependent Schr\"odinger equation (TDSE) in real space is fundamental to understanding the dynamics of many-electron quantum systems, with applications ranging from quantum chemistry to condensed matter physics and materials science. However, solving the TDSE for complex fermionic systems remains a significant challenge, particularly due to the need

Xuankun Rong, Wenke Huang, Tingfeng Wang, Daiguo Zhou

Multimodal large language models (MLLMs) have demonstrated impressive reasoning and instruction-following capabilities, yet their expanded modality space introduces new compositional safety risks that emerge from complex text-image interactions. Such cross-modal couplings can produce unsafe semantics even when individual inputs are benign, exposing the fragi

Palash Sarkar

The first contribution of the paper is to put forward an abstract definition of the Grain family of stream ciphers which formalises the different components that are required to specify a particular member of the family. Our second contribution is to provide new and strengthened definitions of the components. These include definining new classes of nonlinear

Hidetsugu Sakaguchi, Boris A. Malomed

It is known that the usual form of the Ehrenfest theorem (ET), which couples the motion of the center of mass (COM) of the one-dimensional (1D) wave function to the respective classical equation of motion, is not valid in the case of the potential box, confined by the zero boundary conditions. A modified form of the ET was proposed for this case, which inclu

Zhengchao Wang, Yitao Hu, Jianing Ye, Zhuxuan Chang

Retrieval-Augmented Generation (RAG) is a critical paradigm for building reliable, knowledge-intensive Large Language Model (LLM) applications. However, the multi-stage pipeline (retrieve, generate) and unique workload characteristics (e.g., knowledge dependency) of RAG systems pose significant challenges for serving performance optimization. Existing generi

Aishwarya Agarwal, Srikrishna Karanam, Vineet Gandhi

Contrastive vision-language models (VLMs) such as CLIP achieve strong zero-shot recognition yet remain vulnerable to spurious correlations, particularly background over-reliance. We introduce Cluster-based Concept Importance (CCI), a novel interpretability method that uses CLIP's own patch embeddings to group spatial patches into semantically coherent cluste

Yixuan Yang, Luyang Xie, Zhen Luo, Zixiang Zhao

Building interactive simulators and scalable robot-learning environments requires a large number of articulated assets. However, most existing 3D assets in simulation are rigid, and manually converting them into articulated objects is extremely labor- and cost-intensive. This raises a natural question: can we automatically identify articulable objects in a s

Yoonjae Seo, Ermal Elbasani, Jaehong Lee

Most neural network quantization methods apply uniform bit precision across spatial regions, disregarding the heterogeneous complexity inherent in visual data. This paper introduces MCAQ-YOLO, a practical framework for tile-wise spatial mixed-precision quantization in real-time object detectors. Morphological complexity--quantified through five complementary

Alexandru Hening, Nguyen T. Hieu, Dang H. Nguyen, Nhu Nguyen

In this paper we study the dynamics of stochastic microorganism flocculation models. Given the strong influence of environmental and seasonal fluctuations that are present in these models, we propose a stochastic model that includes multiple layers of stochasticity, from small Brownian fluctuations, to possibly large changes due to environmental `shifts'. We

Atsushi Nakayasu, Takayuki Yamada

This paper presents a mathematical analysis of an elliptic partial differential equation (PDE) designed to compute the geometric thickness of a given shape. The PDE-based formulation provides a direct and systematic approach to evaluate thickness through the elliptic equation, whose solution yields a vector field from which the thickness is extracted as the

Zhuo Chen, Gaoqiang Ji, Yiling He, Lei Wu

Decentralized finance (DeFi) is experiencing rapid expansion. However, prevalent code reuse and limited open-source contributions have introduced significant challenges to the blockchain ecosystem, including plagiarism and the propagation of vulnerable code. Consequently, an effective and accurate similarity detection method for EVM bytecode is urgently need

Xin Xia, Guan Tie Deng

This study investigates necessary and sufficient conditions for the boundedness of Forelli-Rudin type operators on weighted Lebesgue spaces associated with tubular domains over the forward light cone. We establish a complete characterization of the boundedness for two classes of multiparameter Forelli-Rudin type operators from the mixed-norm Lebesgue space $

Ziqiao Weng, Yaoyu Fang, Jiahe Qian, Xinkun Wang

Spatial transcriptomics (ST) bridges gene expression and tissue morphology but faces clinical adoption barriers due to technical complexity and prohibitive costs. While computational methods predict gene expression from H&E-stained whole-slide images (WSIs), existing approaches often fail to capture the intricate biological heterogeneity within spots and are

Ning Han, Zhenyu Ge, Feng Han, Yuhua Sun

Concept erasure aims to remove harmful, inappropriate, or copyrighted content from text-to-image diffusion models while preserving non-target semantics. However, existing methods either rely on costly fine-tuning or apply coarse semantic separation, often degrading unrelated concepts and lacking adaptability to evolving concept sets. In this paper, we propos

Xin Xia, GuanTie Deng

This study investigates conditions for the boundedness of Forelli-Rudin type operators on weighted Lebesgue spaces associated with tubular domains over the generalized light cone. We establish a complete characterization of the boundedness for two classes of Forelli-Rudin type operators from $L_{\boldsymbol{\alpha}}^{p}$ to $L_{\boldsymbol{\beta}}^{q}$, in t

Smitha Muthya Sudheendra, Zhongxing Zhang, Wenwen Cao, Jisu Huh

The scientific community increasingly relies on open data sharing, yet existing metrics inadequately capture the true impact of datasets as research outputs. Traditional measures, such as the h-index, focus on publications and citations but fail to account for dataset accessibility, reuse, and cross-disciplinary influence. We propose the X-index, a novel aut