December 2025 arXiv papers — page 5

Hongbo Jin, Kuanwei Lin, Wenhao Zhang, Yichen Jin

Reinforcement Learning (RL) is crucial for empowering VideoLLMs with complex spatiotemporal reasoning. However, current RL paradigms predominantly rely on random data shuffling or naive curriculum strategies based on scalar difficulty metrics. We argue that scalar metrics fail to disentangle two orthogonal challenges in video understanding: Visual Temporal P

Elohan Veillon, Astrid Klipfel, Adlane Sayede, Zied Bouraoui

Generative models hold great promise for accelerating materials discovery, but their evaluation often overlooks the chemical validity and stability requirements crucial to real-world applications. Density Functional Theory (DFT) simulations are the gold standard for evaluating such properties but are computationally intensive and inaccessible to non-experts.

Anush Tserunyan, Robin Tucker-Drob

We study locally countable acyclic measure-class-preserving (mcp) Borel graphs by analyzing their "topography" -- the interaction between the geometry and the associated Radon--Nikodym cocycle. We identify three notions of topographic significance for ends in such graphs and show that the number of nonvanishing ends governs both amenability and smoothness. M

Yichen Liu, Kesava Viswanadha, Zhongyu Li, Nelson Lojo

An important function of autonomous microrobots is the ability to perform robust movement over terrain. This paper explores an edge ML approach to microrobot locomotion, allowing for on-device, lower latency control under compute, memory, and power constraints. This paper explores the locomotion of a sub-centimeter quadrupedal microrobot via reinforcement le

Yuan-Kuei Wu, Yang Liu, Yiteng Huang, Zhaojun Yang

Spoken Language Models (SLMs) are increasingly central to modern speech-driven applications, but performance degrades under acoustic shift - real-world noise, reverberation, and microphone variation. Prior solutions rely on offline domain adaptation, which is post-hoc, data-intensive, and slow. We introduce the first test-time adaptation (TTA) framework for

Michael Hilke

Quantum topology categorizes physical systems in integer invariants, which are robust to some deformations and certain types of disorder. A prime example is the Su-Schrieffer-Heeger (SSH) model, which has two distinct topological phases, the trivial phase with no edge states and the non-trivial phase with zero-energy edge states. The energy dispersion of the

C. Harshitha, C. G. Venketasubramanian

Let $F$ be a non-archimedean local field or a finite field. Let $\pi$ be a principal series representation of $GL_{2n}(F)$ induced from any of its maximal standard parabolic subgroups. Let $N$ be the unipotent radical of the maximal parabolic subgroup $P$ of $GL_{2n}(F)$ corresponding to the partition $(n,n).$ In this article, we describe the structure of th

Mandar Parab

Recent work on language model self-improvement shows that models can refine their own reasoning through reflection, verification, debate, or self-generated rewards. However, most existing approaches rely on external critics, learned reward models, or ensemble sampling, which increases complexity and training instability. We propose Counterfactual Self-Questi

Zhaolin Hu

Risk management often plays an important role in decision making under uncertainty. In quantitative risk management, assessing and optimizing risk metrics requires efficient computing techniques and reliable theoretical guarantees. In this paper, we introduce several topics on quantitative risk management and review some of the recent studies and advancement

Yulong Gao, Wan Jiang, Mingzhe Cao, Xuepu Wang

In the realm of online advertising, automated bidding has become a pivotal tool, enabling advertisers to efficiently capture impression opportunities in real-time. Recently, generative auto-bidding has shown significant promise, offering innovative solutions for effective ad optimization. However, existing offline-trained generative policies lack the near-te

Liping Li, Zhangjie Wang

We establish an invariance principle connecting boundary random walks on $\mathbb N$ with Feller's Brownian motions on $[0,\infty)$. A Feller's Brownian motion is a Feller process on $[0,\infty)$ whose excursions away from the boundary $0$ coincide with those of a killed Brownian motion, while its behavior at the boundary is characterized by a quadruple $(p_

Sibo Wei, Peng Chen, Lifeng Dong, Yin Luo

Multi-omics studies often rely on pathway enrichment to interpret heterogeneous molecular changes, but pathway enrichment (PE)-based workflows inherit structural limitations of pathway resources, including curation lag, functional redundancy, and limited sensitivity to molecular states and interventions. Although recent work has explored using large language

Li Guo, Hongyu Xiang, Bin Zhang

The classical quasi-shuffle algebra for multiple zeta values have a well-known Hopf algebra structure. Recently, the shuffle algebra for multiple zeta values are also equipped with a Hopf algebra structure. This paper shows that these two Hopf algebras are isomorphic utilizing quasi-symmetric functions. This Hopf algebra isomorphism is compared with with the

Chenghong Ji, Chaoying Zhao

The research on open quantum systems is important for both quantum computing and quantum sensing. So far, we can only use the main equation to make an approximate description. The dynamics of a single Fluxonium qubit under Markovian environment satisfied Lindblad Master Equation. In experiments, pulse sequence dynamic decoupling (DD) can enhance the coherenc

Kota Okajima, Koji Hukushima

Brownian circuits perform computations using stochastic transitions driven by thermal fluctuations. While the energetic costs of such fluctuation-driven computation have been extensively studied within stochastic thermodynamics, much less is known about its computational complexity, in particular, how computation time scales with circuit size. In this work,

Jaehong Jo, Jihun Park, Yo-Seb Jeon, H. Vincent Poor

In this paper, we propose a novel hierarchical sensing framework for wideband integrated sensing and communications with uniform planar arrays (UPAs). Leveraging the beam-squint effect inherent in wideband orthogonal frequency-division multiplexing (OFDM) systems, the proposed framework enables efficient two-dimensional angle estimation through a structured

Dmitri Bykov, Savva Kutsubin, Andrew Kuzovchikov

We study the situation when the T-dual of a toric K\"ahler geometry is a generalized K\"ahler geometry involving semi-chiral fields. We explain that this situation is generic for polycylinders, tori and related geometries. Gauging multiple isometries in this case requires the introduction of semi-chiral gauge fields on top of the standard ones. We then apply

Lili Wang, Tao Wang

In this paper, we introduce Steklov and Neumann isocapacitary constants for the $p$-Laplacian on compact manifolds. These constants yield two-sided bounds for the $(p,\alpha)$-Sobolev constants, which degenerate to upper and lower bounds for the first nontrivial Steklov and Neumann eigenvalues of the $p$-Laplacian when $\alpha= 1$.

Jibin Song, Mingi Kwon, Jaeseok Jeong, Youngjung Uh

In this work, we show that the impact of model capacity varies across timesteps: it is crucial for the early and late stages but largely negligible during the intermediate stage. Accordingly, we propose FlowBlending, a stage-aware multi-model sampling strategy that employs a large model and a small model at capacity-sensitive stages and intermediate stages,

Swathi S Krishna, S. K. Mishra, S. Jaiswal

The nonlinear waves excited by the movement of a charged body in the dusty plasma medium are studied. A charged body moving through a dusty plasma medium can generate diverse nonlinear waves, such as precursors and pinned solitons. These wave excitations under weakly nonlinear and dispersive limits are described theoretically by the forced Korteweg-de Vries

Bohan Liang, Zijian Chen, Qi Jia, Kaiwei Zhang

Stock prediction, a subject closely related to people's investment activities in fully dynamic and live environments, has been widely studied. Current large language models (LLMs) have shown remarkable potential in various domains, exhibiting expert-level performance through advanced reasoning and contextual understanding. In this paper, we introduce PriceSe

Beren Millidge

The hippocampus appears to implement two core but highly distinct functions in the brain: long term memory retrieval and planning and spatial navigation. Naively, these functions appear very different algorithmically. In this short note, we demonstrate that two powerful algorithms that have each independently been proposed to underlie the hippocampal operati

Xiu-Lei Ren

We investigate the $s$-wave $KN$ scattering up to next-to-leading order within a renormalizable framework of covariant chiral effective field theory. Using time-ordered perturbation theory, the scattering amplitude is obtained by treating the leading-order interaction non-perturbatively and including the higher-order corrections perturbatively via the subtra

Ch. Li, A. Yu. Orlov

We consider products of $n$ random Hermitian matrices which generalize the one-matrix model and show its relation to Hurwitz numbers which count ramified coverings of certain type. Namely, these Hurwitz numbers count $2k$-fold ramified coverings of the Riemann sphere with arbitrary ramification type over $0$ and $\infty$ and ramifications related to the part

Jia Wang, Ming Zeng, Dazhang Li, Wentao Wang

Laser wakefield acceleration, characterized by the extremely high electric field gradient exceeding 100GV/m, is regarded as a compact and cost affordable technology for the next generation of particle colliders and light sources. However, it has always been a major challenge to effectively increase the energy transfer efficiency from the laser to the acceler

Hai Zhong, Qianqian Hu, Zhiyue Zuo, Zhipeng Wang

Continuous-variable quantum key distribution (CVQKD) features a high key rate and compatibility with classical optical communication. Developing expandable and efficient CVQKD networks will promote the deployment of large-scale quantum communication networks in the future. This paper proposes a CVQKD network based on the entangled states of an optical freque

Nguyen Van Tuyen, Minh N. Dao, Tran Van Nghi

In this paper, we consider a class of constrained multiobjective optimization problems, where each objective function can be expressed by adding a possibly nonsmooth nonconvex function and a differentiable function with Lipschitz continuous gradient, then subtracting a weakly convex function. This encompasses multiobjective optimization problems involving di

Chin-Hao Tseng, Atsushi Uchida, Sheng-Kwang Hwang

The exponential growth of data transmission and processing speeds in modern digital infrastructure requires entropy sources capable of producing large volumes of true randomness for information security. Chaotic emissions from semiconductor lasers are attractive in this context because of their fast dynamics and nonrepetitive behavior. Their spectral bandwid

Kang Fu, Honglei Zhang, Xuechao Zou, Yidong Li

Federated recommendations (FRs) provide personalized services while preserving user privacy by keeping user data on local clients, which has attracted significant attention in recent years. However, due to the strict privacy constraints inherent in FRs, access to user-item interaction data and user profiles across clients is highly restricted, making it diff

Xiang Gao, Cody Hyndman

We first review the convolution fast-Fourier-transform (CFFT) approach for the numerical solution of backward stochastic differential equations (BSDEs) introduced in (Hyndman and Oyono Ngou, 2017). We then propose a method for improving the boundary errors obtained when valuing options using this approach. We modify the damping and shifting schemes used in t

Fen-Yu Hsieh, Yun-Chang Teng, Ding-Yong Hong, Jan-Jan Wu

Large language models (LLMs) have demonstrated remarkable performance across a wide range of language processing tasks. However, this success comes at the cost of substantial computation and memory requirements, which significantly impedes their deployment in resource-constrained environments. To address this challenge, this work introduces an automation fra

Qian Cheng, Weitao Zhou, Cheng Jing, Nanshan Deng

Real-world autonomous driving must adhere to complex human social rules that extend beyond legally codified traffic regulations. Many of these semantic constraints, such as yielding to emergency vehicles, complying with traffic officers' gestures, or stopping for school buses, are intuitive for humans yet difficult to encode explicitly. Although large vision

Kangyang Luo, Shuzheng Si, Yuzhuo Bai, Cheng Gao

In the era of large language models (LLMs), supervised neural methods remain the state-of-the-art (SOTA) for Coreference Resolution. Yet, their full potential is underexplored, particularly in incremental clustering, which faces the critical challenge of balancing efficiency with performance for long texts. To address the limitation, we propose \textbf{MEIC-

Zhangjian Hu, Ermin Wang

In this paper, for $p> 1 $ and $r \ge 1$ we provide a complete characterization of the positive Borel measures $\mu$ on the unit ball $\B_n$ of $\mathbb {C}^n$ for which the induced Toeplitz operator $T_\mu$ is $r$-summing on the Bergman space $A^{p}$. We prove that the $r$-summing norm of $T_\mu: A^p\to A^p$ is equivalent to $\|\widetilde{\mu}\|_{L^{\kappa}

Hisham Sati, Urs Schreiber

We apply methods of equivariant homotopy theory, which may not previously have found due attention in condensed matter physics, to classify first the fragile/unstable topological phases of 2D crystalline Chern insulator materials, and second the possible topological order of their fractional cousins. We highlight that the phases are given by the equivariant

András Millinghoffer, András Formanek, András Antos, Péter Antal

The challenge of effectively transferring knowledge across multiple tasks is of critical importance and is also present in downstream tasks with foundation models. However, the nature of transfer, its transitive-intransitive nature, is still an open problem, and negative transfer remains a significant obstacle. Selection of beneficial auxiliary task sets in

Monika Schleier-Smith

These lecture notes discuss applications of atom-light interactions in cavities to quantum metrology, simulation, and computation. A focus is on nonlocally interacting spin systems realized by coupling many atoms to a delocalized mode of light. We will build up from the fundamentals: understanding how a cavity enables light to coherently imprint information

Zhongyang Shen

We propose a new outlier detection method for multi-dimensional data. The method detects outliers based on vector cosine similarity, using a new dataset constructed by adding a dimension with zero values to the original data. When a point in the new dataset is selected as the measured point, an observation point is created as the origin, differing only in th

Buqing Cao, Qian Peng, Xiang Xie, Liang Chen

Real-world graphs or networks are usually heterogeneous, involving multiple types of nodes and relationships. Heterogeneous graph neural networks (HGNNs) can effectively handle these diverse nodes and edges, capturing heterogeneous information within the graph, thus exhibiting outstanding performance. However, most methods of HGNNs usually involve complex st

Hongjie Dong, Junhee Ryu

We consider nonlocal operators of the form \begin{equation*} L_t u(x) = \int_{\mathbb{R}^d} \left( u(x+y)-u(x)-\nabla u(x)\cdot y^{(\sigma)} \right) \nu_t(dy), \end{equation*} where $\nu_t$ is a general L\'evy measure of order $\sigma \in(0,2)$. We allow this class of L\'evy measures to be very singular and impose no regularity assumptions in the time variab

Duanyuan Gao, Hao-Jui Kuan, Yurui Zhou, Zhiqiang Miao

We calculate the $f$-mode frequency and tidal overlap of quark stars using the full general relativity method. We verify the universal relations obtained from conventional neutron stars in the case of quark stars and explore the cases with different values of parameters of the quark star equation of state. Since quark stars have significantly smaller radii c

Kai Ye, Xiaotong You, Jianghang Lin, Jiayi Ji

Reasoning Segmentation requires models to interpret complex, context-dependent linguistic queries to achieve pixel-level localization. Current dominant approaches rely heavily on Supervised Fine-Tuning (SFT) or Reinforcement Learning (RL). However, SFT suffers from catastrophic forgetting and domain dependency, while RL is often hindered by training instabil

Chenyu Ge

Neural Radiance Fields (NeRF) have achieved photorealistic novel view synthesis but suffer from computational inefficiency due to dense ray sampling during volume rendering. We propose SAC-NeRF, a reinforcement learning framework that learns adaptive sampling policies using Soft Actor-Critic (SAC). Our method formulates sampling as a Markov Decision Process

Evagoras Makridis, Gabriele Oliva, Apostolos I. Rikos, Themistoklis Charalambous

This paper proposes a deterministic distributed algorithm, referred to as PP-ACDC, that achieves exact average consensus over possibly unbalanced directed graphs using only a fixed and a priori specified number of quantization bits. The method integrates Push-Pull (surplus) consensus dynamics with a dynamic quantization framing scheme combining zooming and m

Matteo Ruggiero

We show that any holomorphic germ $f \colon (X,x_0) \to (Y,y_0)$ of topological degree $1$ between normal surface singularities can be written as $f=\pi \circ \sigma$, where $\pi \colon Y' \to (Y,y_0)$ is a modification and $\sigma \colon (X,x_0) \to (Y',y_1)$ is a local isomorphism sending $x_0$ to a point $y_1 \in \pi^{-1}(y_0)$. A result by Fantini, Favre

Nathan Bagshaw, Giuseppe Dibitetto

We consider 4D maximal $\mathrm{ISO}(7)$ gauged supergravity, which is known to arise from a consistent truncation of massive IIA supergravity on a six-sphere. Within its $\mathrm{SU}(3)$ invariant sector, the theory is known to possess eight AdS vacua, preserving various amounts of residual supersymmetry and bosonic symmetry. By making use of fake supergrav

Amir Asiaee, Samhita Pal, James O'quinn, James P. Long

We study causal discovery from observational data in linear Gaussian systems affected by \emph{mixed latent confounding}, where some unobserved factors act broadly across many variables while others influence only small subsets. This setting is common in practice and poses a challenge for existing methods: differentiable and score-based DAG learners can misi

Ali Behrouz, Meisam Razaviyayn, Peilin Zhong, Vahab Mirrokni

Despite the recent progresses, particularly in developing Language Models, there are fundamental challenges and unanswered questions about how such models can continually learn/memorize, self-improve, and find effective solutions. In this paper, we present a new learning paradigm, called Nested Learning (NL), that coherently represents a machine learning mod

Reza Jahani, Md Farhamdur Reza, Richeng Jin, Huaiyu Dai

Decentralized Federated Learning (DFL) has emerged as a privacy-preserving machine learning paradigm that enables collaborative training among users without relying on a central server. However, its performance often degrades significantly due to limited connectivity and data heterogeneity. As we move toward the next generation of wireless networks, mobility

Wenzhe Li, Shujian Zhang, Wenxuan Zhou, John Lambert

Evaluating the quality of multi-turn conversations is crucial for developing capable Large Language Models (LLMs), yet remains a significant challenge, often requiring costly human evaluation. Multi-turn reward models (RMs) offer a scalable alternative and can provide valuable signals for guiding LLM training. While recent work has advanced multi-turn \texti

Evgeny Ivanov, Stepan Sidorov

We define two new indecomposable (not fully reducible) ${\cal N}=8$, $d=1$ off-shell multiplets and consider the corresponding models of ${\cal N}=8$ supersymmetric mechanics with spin variables. Each multiplet is described off shell by a scalar superfield which is a nonlinear deformation of the standard scalar superfield $X$ carrying the $d=1$ multiplet ${\

Narasimha Reddy Gosala, Arundhati Dasgupta

Quark-Gluon Plasma (QGP), a strongly interacting state of the early universe, exhibits remarkably fluid-like behavior despite its underlying non-Abelian dynamics. Motivated by these features, we explore time-dependent SU(2) Yang-Mills condensates as non-linear classical background fields to model QGP. We first study quarks in gluon backgrounds and show that

Gautami Bhowmik, Masao Tsuzuki

We count the maximal lattices over $p$-adic fields and the rational number field. For this, we use the theory of Hecke series for a reductive group over nonarchimedean local fields, which was developed by Andrianov and Hina-Sugano. By treating the Euler factors of the counting Dirichlet series for lattices, we obtain zeta functions of classical groups, which

Bence Csajbók, Máté Róbert Kepes, Eszter Robin, Bence Sógor

A $t$-fold blocking set of the finite Desarguesian plane $\mathrm{PG}(2,p^n)$, $p$ prime, is a set of points meeting each line of the plane in at least $t$ points. The minimum size of such sets is of interest for numerous reasons; however, even the minimum size of nontrivial blocking sets (i.e. $1$-fold blocking sets not containing a line) in \(\mathrm{PG}(2

Zhehan Li, Zheng Wang, Jiadong Lu, Qi Liu

Relative localization is critical for cooperation in autonomous multi-robot systems. Existing approaches either rely on shared environmental features or inertial assumptions or suffer from non-line-of-sight degradation and outliers in complex environments. Robust and efficient fusion of inter-robot measurements such as bearings, distances, and inertials for

Wenbo Qiao, Peng Zhang, Qinghua Hu

Visual word sense disambiguation focuses on polysemous words, where candidate images can be easily confused. Traditional methods use classical probability to calculate the likelihood of an image matching each gloss of the target word, summing these to form a posterior probability. However, due to the challenge of semantic uncertainty, glosses from different

Songqi Zhou, Ruixue Liu, Boman Su, Jiazhou Wang

Fault diagnosis of lithium-ion batteries is critical for system safety. While existing deep learning methods exhibit superior detection accuracy, their "black-box" nature hinders interpretability. Furthermore, restricted by binary classification paradigms, they struggle to provide root cause analysis and maintenance recommendations. To address these limitati

Xuyang Huang, Han-Ze Li, Ching Hua Lee, Jian-Xin Zhong

Quantum advantage is widely understood to rely on key quantum resources beyond entanglement, among which nonstabilizerness (quantum ``magic'') plays a central role in enabling universal quantum computation. However, the exact evaluation of the second-order stabilizer R\'enyi entropy for generic many-body quantum states remains computationally challenging, wi

Maoyuan Li, Zhongsheng Wang, Haoyuan Li, Jiamou Liu

We present R-Debater, an agentic framework for generating multi-turn debates built on argumentative memory. Grounded in rhetoric and memory studies, the system views debate as a process of recalling and adapting prior arguments to maintain stance consistency, respond to opponents, and support claims with evidence. Specifically, R-Debater integrates a debate

Ke Xie, Xingyi Zhao, Min-Yue Chen, Yu-An Chen

The complexity, interdependence, and rapid evolution of 3GPP specifications present fundamental challenges for ensuring the security of modern cellular networks. Manual reviews and existing automated approaches, which often depend on rule-based parsing or small sets of manually crafted security requirements, fail to capture deep semantic dependencies, cross-

Mingxiu Wang, Jiawei Wang, Xiao Cheng

Loop invariants are fundamental for reasoning about the correctness of iterative algorithms. However, deriving suitable invariants remains a challenging and often manual task, particularly for complex programs. In this paper, we introduce BALI, a branch-aware framework that integrates large language models (LLMs) to enhance the inference and verification of

Kangjie Zhou, Zhaoyang Li, Han Gao, Yao Su

Target search and tracking (SAT) is a fundamental problem for various robotic applications such as search and rescue and environmental exploration. This paper proposes an informative trajectory planning approach, namely ReSPIRe, for SAT in unknown cluttered environments under considerably inaccurate prior target information and limited sensing field of view.

Hsien-Hsin Chou

Panchromatic absorbing materials are widely regarded as a key strategy for enhancing solar energy utilization and photocurrent generation. However, in artificial molecular systems, broadening the absorption spectrum is often accompanied by fundamental challenges, including bandgap narrowing, poor energy-level alignment, and limited charge-transfer kinetics,

Evgenii O. Vasiliev

Destruction of the interstellar dust proceeds primary behind supernova shocks. The previous estimates of the mass of the interstellar dust destroyed in the SN remnant do not take into account the physical properties of the ambient medium. Here we consider how some parameters, i.e. gas density and metallicity, can influence the destruction of the interstellar

Kuangyu Ding, Marie Maros, Gesualdo Scutari

We study finite-sum nonlinear programs with localized variable coupling encoded by a (hyper)graph. We introduce a graph-compliant decomposition framework that brings message passing into continuous optimization in a rigorous, implementable, and provable way. The (hyper)graph is partitioned into tree clusters (hypertree factor graphs). At each iteration, agen

Kallal Pal, Sumit Chandok

Motivated by the work of Baronti et al. [J. Math. Anal. Appl. 252(2000) 124-146], where they defined the supremum of an arithmetic mean of the side lengths of a triangle, summing antipodal points on the unit sphere, we introduce a new geometric constant for Banach spaces, utilizing the Heinz means that interpolate between the geometric and arithmetic means a

Ruiyang Zhao, Fan Lam

We present a new approach for representing and reconstructing multidimensional magnetic resonance imaging (MRI) data. Our method builds on a novel, learned feature-based image representation that disentangles different types of features, such as geometry and contrast, into distinct low-dimensional latent spaces, enabling better exploitation of feature correl

Yongsheng Zhao, Lei Zhao, Baoping Cheng, Gongxin Yao

Vision-Language-Action (VLA) models have achieved remarkable breakthroughs in robotics, with the action chunk playing a dominant role in these advances. Given the real-time and continuous nature of robotic motion control, the strategies for fusing a queue of successive action chunks have a profound impact on the overall performance of VLA models. Existing me

Tomohiro Fujita, Tomo Takahashi, Sora Yamashita

We develop an effective theory for biased tracers formulated at the level of the Boltzmann equation, providing a unified description of density and velocity bias. We introduce a general effective collision term in the tracer Boltzmann equation to encode tracer dynamics that are intrinsically different from those of dark matter. This collision operator leads

Mary Kreidel, Julian Ingham, Xuanjing Chu, Jesse Balgley

Identifying the pairing symmetry of unconventional superconductors plays an essential role in the ongoing quest to understand correlated electronic matter. A long-standing approach is to study the temperature dependence of the London penetration depth $\lambda$ for evidence of nodal points where the superconducting gap vanishes. However, experimental reports

Evgenii O. Vasiliev, Biman B. Nath

Dust lifetime derived from an isolated supernova (SN) evolution in the interstellar medium is known to be an order of magnitude shorter than the time needed to replenish dust mass by its production in various Galactic sources. We show, using 3-D numerical hydrodynamical simulations, that destruction of dust in the case of multiple SNe in a star cluster is ma

Wanteng Ma, T. Tony Cai

Contextual bandits are a central framework for sequential decision-making, with applications ranging from recommendation systems to clinical trials. While nonparametric methods can flexibly model complex reward structures, they suffer from the curse of dimensionality. We address this challenge using a single-index model, which projects high-dimensional covar

J. W. Park, H. Kim, H. W. Yeom

Magnetic impurities in strongly correlated electronic systems serve as sensitive probes to a wide range of many-body quantum phenomena. Broken symmetries in such a system can lead to inequivalent lattice sites, and magnetic impurities may interact selectively with particular orbitals or sublattices. However, the microscopic mechanisms behind such site-specif

Mingyi Li, Xiao Zhang, Ruisheng Zheng, Hongjian Shi

With the development of large-scale models, traditional distributed bilevel optimization algorithms cannot be applied directly in low-resource clients. The key reason lies in the excessive computation involved in optimizing both the lower- and upper-level functions. Thus, we present the first resource-adaptive distributed bilevel optimization framework with

Dong Hao Ou Yang

In this paper, we study the momentum distribution of an electron gas in a $3$-dimensional torus. The goal is to compute the occupation number of Fourier modes for some trial state obtained through random phase approximation. We obtain the mean-field analogue of momentum distribution formulas for electron gas in [Daniel and Voskov, Phys. Rev. \textbf{120}, (1

Honglin Gao, Lan Zhao, Junhao Ren, Xiang Li

Heterogeneous graph neural networks (HGNNs) have achieved strong performance in many real-world applications, yet targeted backdoor poisoning on heterogeneous graphs remains less studied. We consider backdoor attacks for heterogeneous node classification, where an adversary injects a small set of trigger nodes and connections during training to force specifi

Weixiang Ye

Halls exact variance decomposition [Phys. Rev. A 64, 052103 (2001)] splits the quantum variance of an observable into the ensemble variance of an optimal position based estimate and a residual nonclassical inaccuracy. We evaluate this decomposition in Bohmian mechanics. For momentum, the optimal estimate coincides with the Bohmian guidance field, and the ina

Maolin Wang, Bowen Yu, Sheng Zhang, Linjie Mi

Tensor network structure search (TN-SS) aims to automatically discover optimal network topologies and rank configurations for efficient tensor decomposition in high-dimensional data representation. Despite recent advances, existing TN-SS methods face significant limitations in computational tractability, structure adaptivity, and optimization robustness acro

Yongsoo Jho, Jeonghwan Park, Min Gi Park, Seong Chan Park

We examine Sommerfeld enhancement in dark matter annihilation as a potential origin of the halo-like gamma-ray excess near $E_\gamma \simeq 20$ GeV reported by Totani. A minimal model with a light CP-even scalar mediator naturally produces a velocity-dependent annihilation cross section consistent with thermal freeze-out, the Milky Way excess, and limits fro

Casey O. Barkan, Sid Black, Oliver Sourbut

We investigate whether large language models (LLMs) can predict whether they will succeed on a given task and whether their predictions improve as they progress through multi-step tasks. We also investigate whether LLMs can learn from in-context experiences to make better decisions about whether to pursue a task in scenarios where failure is costly. All LLMs

Takashi Hirotsu

In this article, we generalize the following problem, which is called the rational angle bisection problem, to the $n$-dimensional space $k^n$ over a subfield $k$ of $\mathbb R$: in the coordinate plane, for which rational numbers $a$ and $b$ are the slopes of the angle bisectors between the two lines with slopes $a$ and $b$ rational? First, we provide sever

Donghyeon Song, Yeongjun Jang, Joowon Lee, Junsoo Kim

This paper aims to provide an efficient implementation of encrypted linear dynamic controllers that perform recursive multiplications on a Ring-Learning With Errors (Ring-LWE) based cryptosystem. By adopting a system-theoretical approach, we significantly reduce both time and space complexities, particularly the number of homomorphic operations required for

Sungjae Min, Hyungjoo Kim, David Hyunchul Shim

Humanoid robots toward human-level dexterity require robotic hands capable of simultaneously providing high grasping force, rapid actuation speeds, multiple degrees of freedom, and lightweight structures within human-like size constraints. Meeting these conflicting requirements remains challenging, as satisfying this combination typically necessitates heavie

Mir Mohammad Yousuf, Shabir Ahmad Sofi

Quantum Software Engineering (QSE) is essential for ensuring the reliability and maintainability of hybrid quantum-classical systems, yet empirical evidence on how bugs emerge and affect quality in real-world quantum projects remains limited. This study presents the first ecosystem-scale longitudinal analysis of software bugs across 123 open source quantum r

Neeraj Pandey, Abhineet Agarwal, Raju Roychowdhury, Karmeshu

Analysis of the urban population fraction data for sixteen populous countries over the last fifty years reveals a universal increase in urbanization, exhibiting four qualitatively distinct temporal patterns: (i) continuously accelerating growth, (ii) continuously decelerating growth, (iii) two-phase growth transitioning from acceleration to deceleration, and

Tatsuaki Tsuruyama

We develop an axiomatic reconstruction of thermodynamics based entirely on two primitive components: a description of what aspects of a system are observed and a reference measure that encodes the underlying descriptive convention. These ingredients define an "information volume" for each observational cell. By incorporating the logarithm of this volume as a

Ye Ma, Shixin Lin, Shengxing Fu, Yuwei Liu

Running is a fundamental form of human locomotion and a key task for evaluating neuromuscular control and lower-limb coordination. In recent years, muscle synergy analysis based on surface electromyography (sEMG) has become an important approach in this area. This review focuses on muscle synergies during running, outlining core neural control theories and b

Chengkai Hou, Kun Wu, Jiaming Liu, Zhengping Che

While data-driven imitation learning has revolutionized robotic manipulation, current approaches remain constrained by the scarcity of large-scale, diverse real-world demonstrations. Consequently, the ability of existing models to generalize across long-horizon bimanual tasks and mobile manipulation in unstructured environments remains limited. To bridge thi

Le Yang, Weijing You, Huiyang He, Kailiang Ji

Multi-Party Private Set Intersection (MP-PSI) with threshold enhances the flexibility of MP-PSI by disclosing elements present in at least $t$ participants' sets, rather than requiring elements to appear in all $n$ sets. In scenarios where each participant is responsible for its dataset, e.g., digital forensics, MP-PSI with threshold should disclose both int

Yury Kolomeytsev, Dmitry Golembiovsky

Autonomous mobile robots operating in complex, dynamic environments face the dual challenge of navigating large-scale, structurally diverse spaces with static obstacles while safely interacting with various moving agents. Traditional graph-based planners excel at long-range pathfinding but lack reactivity, while Deep Reinforcement Learning (DRL) methods demo

James H. Adler, Xiaozhe Hu, Seulip Lee

We propose a unified four-dimensional (4D) spatiotemporal formulation for time-dependent convection-diffusion problems that preserves underlying physical structures. By treating time as an additional space-like coordinate, the evolution problem is reformulated as a stationary convection-diffusion equation on a 4D space-time domain. Using exterior calculus, w

Fan Wen

We establish periodic quasiconformal extension theorems for periodic orientation-preserving quasisymmetric self homeomorphisms of quasicircles or quasi-round carpets. As applications, we prove that, if $f$ is a periodic orientation-preserving quasisymmetric self homeomorphism of a quasi-round carpet $S$ of measure zero in $\mathbb{C}$, which has a fixed poin

Hollis Williams

Acoustic levitation is widely used for contactless droplet manipulation, yet the standard Gor'kov description obtained by time averaging the acoustic field lacks a quantitative validity criterion. In this work, we derive Gor'kov theory as the leading-order slow time limit of the instantaneous radiation force, compute the first finite-inertia correction, and

Hemant Mishra, Shubham Singh, Rajeev Singh, Amit Raj Singh

We investigate the application of quantum computing algorithms to enhance the efficiency of enumerating self-avoiding walks (SAWs), utilizing quantum properties such as superposition and interference. A Quantum Amplitude Estimation (QAE)-based algorithm is developed to enumerate SAWs on both 2D and 3D lattices. In case of 2D square lattice, SAWs up to N=71 s

Qiling Gu, Wenlong Zhang, Zhidong Zhang

This paper develops a discrete data-driven approach for solving the inverse source problem of the wave equation with final time measurements. Focusing on the $L^2$-Tikhonov regularization method, we analyze its convergence under two different noise models, using noisy discrete spatial observations. By exploiting the spectral decomposition of the forward oper

Gaoqing Cao, Xiaofeng Luo, Weijie Fu

In this work, we propose dilepton emission rate (DER) as a sensitive probe of QCD critical point based on the extended Polyakov-quark-meson model. The model could successfully capture two main mechanisms for dilepton production, $\pi^\pm$ and quark-antiquark annihilations on one hand, and self-consistently account for chiral transition and (de-)confinement o

Cheng Zhu, Jing Han, Qianshuai Xue, Kehan Wang

Currently, artificial intelligence is profoundly transforming the audio domain; however, numerous advanced algorithms and tools remain fragmented, lacking a unified and efficient framework to unlock their full potential. Existing audio agent frameworks often suffer from complex environment configurations and inefficient tool collaboration. To address these l

Daniel Hedman

Single-walled carbon nanotubes (SWCNTs) are promising for nanoscale electronics and photonics, but practical deployment requires chirality control. Most catalytic chemical vapor deposition (CCVD) growth of SWCNTs proceeds on liquid metal nanoparticles via a vapor-liquid-solid (VLS) mechanism and yields broad chirality distributions, whereas improved selectiv

Malikussaid, Septian Caesar Floresko, Ade Romadhony, Isman Kurniawan

Lipophilicity (logP) prediction remains central to drug discovery, yet linear regression models for this task frequently violate statistical assumptions in ways that invalidate their reported performance metrics. We analyzed 426,850 bioactive molecules from a rigorously curated intersection of PubChem, ChEMBL, and eMolecules databases, revealing severe heter

Kwangok Seo, Johan Lim, Seokho Lee, Jong Hee Park

Ideal point estimation methods face a significant challenge when legislators engage in protest voting -- strategically voting against their party to express dissatisfaction. Such votes introduce attenuation bias, making ideologically extreme legislators appear artificially moderate. We propose a novel statistical framework that extends the fast EM-based esti