October 2025 arXiv papers — page 122

Weikang Yu, Vincent Nwazelibe, Xianping Ma, Xiaokang Zhang

Mining activities are essential for industrial and economic development, but remain a leading source of environmental degradation, contributing to deforestation, soil erosion, and water contamination. Sustainable resource management and environmental governance require consistent, long-term monitoring of mining-induced land surface changes, yet existing data

Linyue Ma, Yilong Xu, Xiang Long, Zhi Zheng

Search augmentation empowers Large Language Models with retrieval capabilities to overcome the limitations imposed by static parameters. Recently, Reinforcement Learning leverages tailored reward signals as a viable technique to enhance LLMs performing tasks involving search. However, existing reward modeling for search-augmented LLMs faces several limitatio

Yi-Shan Chu, Yueh-Cheng Kuo

We revisit the Universal Approximation Theorem(UAT) through the lens of the tropical geometry of neural networks and introduce a constructive, geometry-aware initialization for sigmoidal multi-layer perceptrons (MLPs). Tropical geometry shows that Rectified Linear Unit (ReLU) networks admit decision functions with a combinatorial structure often described as

Amarjit Budhiraja, Francesco Coghi

We consider a pure jump process $\{X_t\}_{t\ge 0}$ with values in a finite state space $S= \{1, \ldots, d\}$ for which the jump rates at time instant $t$ depend on the occupation measure $L_t \doteq t^{-1} \int_0^t \delta_{X_s}\,ds$. Such self-interacting chains arise in many contexts within statistical physics and applied probability. Under appropriate cond

Nathaniel Vaduthala

A partial field is an algebraic object that allows one to simultaneously abstract several different representability properties of matroids. In this paper we study partial fields as algebraic objects in their own right. We characterize the weak and strong characteristic sets of partial fields and show that the class of partial fields is not well-quasi ordere

Kieran Carrigg, Rob van Gastel, Melda Yeghaian, Sander Dalm

Masked Autoencoder (MAE) pre-training of vision transformers (ViTs) yields strong performance in low-label data regimes but comes with substantial computational costs, making it impractical in time- and resource-constrained industrial settings. We address this by integrating Decorrelated Backpropagation (DBP) into MAE pre-training, an optimization method tha

Zhuo Cao, Lena Krieger, Hanno Scharr, Ira Assent

Galaxy morphologies play an essential role in the study of the evolution of galaxies. The determination of morphologies is laborious for a large amount of data giving rise to machine learning-based approaches. Unfortunately, most of these approaches offer no insight into how the model works and make the results difficult to understand and explain. We here pr

Mathias Klahn, Gaute Linga, Tanguy Le Borgne, Joachim Mathiesen

A key challenge in multiphase flow through porous media is to understand and predict the conditions under which trapped fluid clusters become mobilized. Here, we investigate the stability of such clusters in two-phase flow and present a simple, quasistatic model that accurately determines the critical Bond number (that is, the critical ratio between the aver

Sven Tarlowski, Lutz Eckstein

This paper addresses the challenge of ensuring realistic traffic conditions by proposing a methodology that systematically identifies traffic simulation requirements. Using a structured approach based on sub-goals in each study phase, specific technical needs are derived for microscopic levels, agent models, and visual representation. The methodology aims to

Xiaomin Chen, Chuan Li, Zigong Xu, Georgios Nicolaou

Local particle acceleration in the shock sheath region formed during the interaction between multiple coronal mass ejections (CMEs) is a complicated process that is still under investigation. On March 23, 2024, the successive eruption of two magnetic flux ropes (MFRs) from the solar active region 3614 produced twin CMEs, as identified in coronagraph images.

Carl Tipler

We study rank 2 torus-equivariant torsion-free sheaves on the complex projective space. For reflexive sheaves we derive a simple formula for the Chern polynomial, and in the general torsion-free case we introduce an iterative construction method based on elementary injections, allowing us to prescribe Chern classes. This yields infinite families of explicit

Hongbin Cui

We present two generalizations for the celebrated works of Ferus-Karcher-M\"unzner \cite{FKM81} and Wang \cite{W94}. We first show that an isoparametric foliation on $\mathbb{S}^{2n+1}$ constructed by Ferus-Karcher-M\"unzner naturally yields an isoparametric foliation on its submanifold $\mathbb{S}^n \times \mathbb{S}^n$ with one same focal variety. The seco

Gyoseung Lee, In-soo Kim, Yonina C. Eldar, A. Lee Swindlehurst

In this paper, we investigate channel estimation for reconfigurable intelligent surface (RIS) empowered millimeter-wave (mmWave) multi-user single-input multiple-output communication systems using low-resolution quantization. Due to the high cost and power consumption of analog-to-digital converters (ADCs) in large antenna arrays and for wide signal bandwidt

Xinyao Liao, Xianfang Zeng, Ziye Song, Zhoujie Fu

Despite the rapid progress of instruction-based image editing, its extension to video remains underexplored, primarily due to the prohibitive cost and complexity of constructing large-scale paired video editing datasets. To address this challenge, we introduce a low-cost pretraining strategy for instruction-based video editing that leverages in-context learn

Jialei Huang, Yang Ye, Yuanqing Gong, Xuezhou Zhu

Dexterous manipulation requires precise geometric reasoning, yet existing visuo-tactile learning methods struggle with sub-millimeter precision tasks that are routine for traditional model-based approaches. We identify a key limitation: while tactile sensors provide rich contact information, current learning frameworks fail to effectively leverage both the p

Laura Antonelli, Valentina De Simone, Marco Viola

Magnetic Resonance Imaging (MRI) is essential for noninvasive generation of high-quality images of human tissues. Accurate segmentation of MRI data is critical for medical applications like brain anatomy analysis and disease detection. However, challenges such as intensity inhomogeneity, noise, and artifacts complicate this process. To address these issues,

Vignesh V Menon, Adam Wieckowski, Yiquin Liu, Benjamin Bross

The demand for efficient multi-rate encoding techniques has surged with the increasing prevalence of ultra-high-definition (UHD) video content, particularly in adaptive streaming scenarios where a single video must be encoded at multiple bitrates to accommodate diverse network conditions. While Versatile Video Coding (VVC) significantly improves compression

Sandra Albrechtsen, Marc Distel, Agelos Georgakopoulos

We prove that for every $t \in \mathbb{N}$, the graph $K_{2,t}$ satisfies the fat minor conjecture of Georgakopoulos and Papasoglu: for every $K\in \mathbb{N}$ there exist $M,A\in \mathbb{N}$ such that every graph with no $K$-fat $K_{2,t}$ minor is $(M,A)$-quasi-isometric to a graph with no $K_{2,t}$ minor. We use this to obtain an efficient algorithm for ap

Lara Brudermüller, Brandon Hung, Xinghao Zhu, Jiuguang Wang

We present a generative predictive control (GPC) framework that amortizes sampling-based Model Predictive Control (SPC) by bootstrapping it with conditional flow-matching models trained on SPC control sequences collected in simulation. Unlike prior work relying on iterative refinement or gradient-based solvers, we show that meaningful proposal distributions

Andrei Seoev, Leonid Gremyachikh, Anastasiia Smirnova, Yash Madhwal

In blockchain networks, the strategic ordering of transactions within blocks has emerged as a significant source of profit extraction, known as Maximal Extractable Value (MEV). The transition from spam-based Priority Gas Auctions (PGA) to structured auction mechanisms like Polygon Atlas has transformed MEV extraction from public bidding wars into sealed-bid

Zhibo Wu, Yunfan Wu, Lin Jiang, Ping Yang

Cross-domain recommendation forms a crucial component in recommendation systems. It leverages auxiliary information through source domain tasks or features to enhance target domain recommendations. However, incorporating inconsistent source domain tasks may result in insufficient cross-domain modeling or negative transfer. While incorporating source domain f

I-Fan Lin, Faegheh Hasibi, Suzan Verberne

In this paper, we propose an intuitive, training-free and label-free method for intent clustering in conversational search. Current approaches to short text clustering use LLM-generated pseudo-labels to enrich text representations or to identify similar text pairs for pooling. The limitations are: (1) each text is assigned only a single label, and refining r

Hendrik De Bie, Antonino De Martino, Kamal Diki

We introduce a polyanalytic extension of the Gaussian radial basis function (RBF) kernel by computing the action of the convolution operator on normalized Hermite functions. In particular, using the Zaremba-Bergman formula we derive an explicit closed form for this new reproducing kernel function. We then establish an isomorphism relating the reproducing ker

Silvia Lucia Sanna, Leonardo Regano, Davide Maiorca, Giorgio Giacinto

According to a recent EUROPOL report, cybercrime is still recurrent in Europe, and different activities and countermeasures must be taken to limit, prevent, detect, analyze, and fight it. Cybercrime must be prevented with specific measures, tools, and techniques, for example through automated network and malware analysis. Countermeasures against cybercrime c

David L. Carl, Simone A. Padoan, Stefano Rizzelli

Accurately quantifying tail risks-rare but high-impact events such as financial crashes or extreme weather-is a central challenge in risk management, with serially dependent data. We develop a Bayesian framework based on the Generalized Pareto (GP) distribution for modeling threshold exceedances, providing posterior distributions for the GP parameters and ta

Shefali for the IceCube Collaboration

The prototype station of the Surface Array Enhancement at the IceCube Neutrino Observatory has been taking data in its final design since 2023. This station is part of the planned extension within the footprint of the existing surface array, IceTop. One station consists of 8 scintillator detectors, 3 radio antennas, and a central DAQ. The final upgrade of th

Qingyao Li, Xinyi Dai, Weiwen Liu, Xiangyang Li

Large Language Models (LLMs) excel at code generation, yet their outputs often contain subtle bugs, for which effective test cases are a critical bottleneck. Existing test generation methods, whether based on prompting or supervised fine-tuning, rely on static datasets. This imposes a ``fixed-difficulty ceiling'', fundamentally limiting their ability to unco

Jihyun Yu, Yoojin Oh, Wonho Bae, Mingyu Kim

Test-time adaptation (TTA) aims to correct performance degradation of deep models under distribution shifts by updating models or inputs using unlabeled test data. Input-only diffusion-based TTA methods improve robustness for classification to corruptions but rely on gradient guidance, limiting exploration and generalization across distortion types. We propo

Sinjini Chandra, Rupa Chatterjee, Zubayer Ahammed

Prompt photon measurements in relativistic nuclear collisions serve as an essential comparative basis for heavy ion studies enabling the separation of medium induced effects. However, the identification of prompt photons is experimentally challenging due to substantial backgrounds from photons produced in hadron decays and jet fragmentation. Appropriate isol

Cristóbal Loyola

In this article we prove global propagation of analyticity in finite time for solutions of semilinear Schr\"odinger equations with analytic nonlinearity from a region $\omega$ where the Geometric Control Condition holds. Our approach refines a recent technique introduced by Laurent and the author, which combines control theory techniques and Galerkin approxi

Uélison Jean Lopes dos Santos, Alessandro Ferri, Szilard Nistor, Riccardo Tommasini

In this paper, we present a vision for a new generation of multimodal streaming systems that embed MLLMs as first-class operators, enabling real-time query processing across multiple modalities. Achieving this is non-trivial: while recent work has integrated MLLMs into databases for multimodal queries, streaming systems require fundamentally different approa

Ming Gui, Johannes Schusterbauer, Timy Phan, Felix Krause

We introduce Representation Tokenizer (RepTok), a generative modeling framework that represents an image using a single continuous latent token obtained from self-supervised vision transformers. Building on a pre-trained SSL encoder, we fine-tune only the semantic token embedding and pair it with a generative decoder trained jointly using a standard flow mat

Jiani Huang, Xingchen Zou, Lianghao Xia, Qing Li

The application of Large Language Models (LLMs) in recommender systems faces key challenges in delivering deep personalization and intelligent reasoning, especially for interactive scenarios. Current methods are often constrained by limited context windows and single-turn reasoning, hindering their ability to capture dynamic user preferences and proactively

Qing Yang, Zhenghao Liu, Yangfan Du, Pengcheng Huang

Recent advances in Text-To-Speech (TTS) synthesis have achieved near-human speech quality in neutral speaking styles. However, most existing approaches either depend on costly emotion annotations or optimize surrogate objectives that fail to adequately capture perceptual emotional quality. As a result, the generated speech, while semantically accurate, often

Yao Zhong, Hanzhi Chen, Simon Schaefer, Anran Zhang

Robots are expected to serve as intelligent assistants, helping humans with everyday household organization. A central challenge in this setting is the task of object placement, which requires reasoning about both semantic preferences (e.g., common-sense object relations) and geometric feasibility (e.g., collision avoidance). We present GOPLA, a hierarchical

Zhibo Wu, Yunfan Wu, Quan Liu, Lin Jiang

Multi-interest recommendation has gained attention, especially in industrial retrieval stage. Unlike classical dual-tower methods, it generates multiple user representations instead of a single one to model comprehensive user interests. However, prior studies have identified two underlying limitations: the first is interest collapse, where multiple represent

Mehrdad Saadatmand, Abbas Khan, Beatriz Marin, Ana C. R Paiva

The evolving landscape of software development demands that software testers continuously adapt to new tools, practices, and acquire new skills. This study investigates software testing competency needs in industry, identifies knowledge gaps in current testing education, and highlights competencies and gaps not addressed in academic literature. This is done

Natan Bagrov, Eugene Khvedchenia, Borys Tymchenko, Shay Aharon

Vision-language models (VLMs) have recently expanded from static image understanding to video reasoning, but their scalability is fundamentally limited by the quadratic cost of processing dense frame sequences. Long videos often exceed the token budget of modern language models, leading to severe context limitations and latency issues. We introduce Efficient

Zhuo Cao, Xuan Zhao, Lena Krieger, Hanno Scharr

The growing integration of machine learning (ML) and artificial intelligence (AI) models into high-stakes domains such as healthcare and scientific research calls for models that are not only accurate but also interpretable. Among the existing explainable methods, counterfactual explanations offer interpretability by identifying minimal changes to inputs tha

Miryeong Kwon, Donghyun Gouk, Hyein Woo, Junhee Kim

MPI implementations commonly rely on explicit memory-copy operations, incurring overhead from redundant data movement and buffer management. This overhead notably impacts HPC workloads involving intensive inter-processor communication. In response, we introduce MPI-over-CXL, a novel MPI communication paradigm leveraging CXL, which provides cache-coherent sha

Yuanyi Song, Heyuan Huang, Qiqiang Lin, Yin Zhao

The rapid advancement of multimodal large language models has enabled agents to operate mobile devices by directly interacting with graphical user interfaces, opening new possibilities for mobile automation. However, real-world mobile tasks are often complex and allow for multiple valid solutions. This contradicts current mobile agent evaluation standards: o

Kedi Chen, Zhikai Lei, Xu Guo, Xuecheng Wu

Large language models (LLMs) make remarkable progress in reasoning tasks. Among different reasoning modes, inductive reasoning, due to its better alignment with human learning, attracts increasing interest. However, research on inductive reasoning faces certain challenges. First, existing inductive data mostly focuses on superficial regularities while lackin

Sara Ayhan

A bilateralist take on proof-theoretic semantics can be understood as demanding of a proof system to display not only rules giving the connectives' provability conditions but also their refutability conditions. On such a view, then, a system with two derivability relations is obtained, which can be quite naturally expressed in a proof system of natural deduc

Ming Li, Xiang Chen, Wen-Qi Duan, Yuan-Hao Yang

The integrated devices that generate structured optical fields with non-trivial orbital angular momentum (OAM) hold great potential for advanced optical applications, but are restricted to complex nanostructures and static functionalities. Here, we demonstrate a reconfigurable OAM beam generator from a simple microring resonator without requiring grating-lik

Ning Ding, Keisuke Fujii, Toru Tamaki

Tactical understanding in badminton involves interpreting not only individual actions but also how tactics are dynamically executed over time. In this paper, we propose \textbf{Shot2Tactic-Caption}, a novel framework for semantic and temporal multi-scale video captioning in badminton, capable of generating shot-level captions that describe individual actions

Shuangshuang Ying, Yunwen Li, Xingwei Qu, Xin Li

Current preference learning methods achieve high accuracy on standard benchmarks but exhibit significant performance degradation when objective quality signals are removed. We introduce WritingPreferenceBench, a dataset of 1,800 human-annotated preference pairs (1,200 English, 600 Chinese) across 8 creative writing genres, where responses are matched for obj

Edward Sandra, Lander Vanroye, Dries Dirckx, Ruben Cartuyvels

Classical methods in robot motion planning, such as sampling-based and optimization-based methods, often struggle with scalability towards higher-dimensional state spaces and complex environments. Diffusion models, known for their capability to learn complex, high-dimensional and multi-modal data distributions, provide a promising alternative when applied to

Gyudong Kim, Hyukju Na, Jin Hyeon Kim, Hyunsung Jang

As training billion-scale transformers becomes increasingly common, employing multiple distributed GPUs along with parallel training methods has become a standard practice. However, existing transformer designs suffer from significant communication overhead, especially in Tensor Parallelism (TP), where each block's MHA-MLP connection requires an all-reduce c

Hippolyte Quelquejay Leclere, Kunyang Li, Marta Volonteri, Stanislav Babak

We use the Horizon-AGN cosmological simulation to study the properties of supermassive black hole binaries (MBHBs) contributing most to the gravitational wave background (GWB) signal expected in the pulsar timing array (PTA) band. We develop a pipeline to generate realistic populations of MBHBs, allowing us to estimate both the characteristic strain and GWB

Gabriel Fischer Abati, João Carlos Virgolino Soares, Giulio Turrisi, Victor Barasuol

This paper presents a novel approach for representing proprioceptive time-series data from quadruped robots as structured two-dimensional images, enabling the use of convolutional neural networks for learning locomotion-related tasks. The proposed method encodes temporal dynamics from multiple proprioceptive signals, such as joint positions, IMU readings, an

Markus Klar, Sebastian Stein, Fraser Paterson, John H. Williamson

We explore the use of Active Inference (AIF) as a computational user model for spatial pointing, a key problem in Human-Computer Interaction (HCI). We present an AIF agent with continuous state, action, and observation spaces, performing one-dimensional mouse pointing and clicking. We use a simple underlying dynamic system to model the mouse cursor dynamics

Naoki Kato

Dekimpe and Ongenae constructed infinitely many pairwise non-isomorphic complete left-symmetric structures on $\mathbb{R}^n$ for $n\geq 6$. In this paper, we construct a family of complete left-symmetric structures on the cotangent Lie algebra $T^*\mathfrak{g}$ of a certain $n$-dimensional almost abelian nilpotent Lie algebra $\mathfrak{g}$ and give a condit

Satyaki Manna, Anandamay Das Bhowmik

The notion of antidistinguishability captures the possibility of ruling out certain alternatives in a quantum experiment without identifying the actual outcome. Although extensively studied for quantum states, the antidistinguishability of quantum channels remains largely unexplored. In this work, we investigate the single-shot antidistinguishability of unit

Lina Deschamps, Levin Maier, Tom Stalljohann

In this paper, we prove that for any given closed contact manifold, there exists an infinite-dimensional space of Riemannian metrics which can be identified with the space of bundle metrics on the induced contact distribution. For each such metric, and for all energy levels, the number of embedded periodic orbits of the corresponding magnetic geodesic flow g

Francesco Vona, Evelyn Romanjuk, Sina Hinzmann, Julia Schorlemmer

The risk of isolation in virtual reality (VR) stems from the immersive nature of the technology. VR can transport users to entirely virtual environments, often disconnecting them from the physical world and real-life interactions. Asymmetric multiplayer options have been explored to address this issue and encourage social interaction by requiring players to

Rinto Thomas, Praveen Ranganath Prabhakar, Douglas J. Tobias, Michael von Domaros

Human skin oils are a major sink for ozone in densely occupied indoor environments. Understanding how the resulting volatile and semivolatile organic oxidation products influence indoor air chemistry requires accurate representations not only of their emission into indoor air but also of their transport across the outermost skin barrier, the stratum corneum.

Yuyang Hong, Jiaqi Gu, Qi Yang, Lubin Fan

Knowledge-based visual question answering (KB-VQA) requires visual language models (VLMs) to integrate visual understanding with external knowledge retrieval. Although retrieval-augmented generation (RAG) achieves significant advances in this task by combining knowledge-base querying, it still struggles with the quality of multimodal queries and the relevanc

Thomas Feuillen, Amirafshar Moshtaghpour

The International Symposium on Computational Sensing (ISCS) brings together researchers from optical microscopy, electron microscopy, RADAR, astronomical imaging, biomedical imaging, remote sensing, and signal processing. With a particular focus on applications and demonstrators, the purpose of this symposium is to be a forum where researchers in computation

Francesco Vona, Michael Stern, Navid Ashrafi, Julia Schorlemmer

This study investigates the potential of virtual reality (VR) for enhancing sales skills training using a Cave Automatic Virtual Environment (CAVE). VR technology enables users to practice interpersonal and negotiation skills in controlled, immersive environments that mimic real-world scenarios. In this study, participants engaged in sales simulations set in

Jakub Koncki, Richárd Rimányi

A map between manifolds induces stratifications of both the source and the target according to the occurring multisingularities. In this paper, we study universal expressions-called higher Thom polynomials-that describe the Segre-Schwartz-MacPherson class of such multisingularity loci. We prove a Structure Theorem reducing these Thom polynomials to the data

Fan Cao, Yuqi Yuan, Xiaohui Yan, Bohan Zhang

In the literature of cognitive neuroscience, researchers tend to assume a linear relationship between brain activation level and task performance; however, controversial findings have been reported in participants at different ages and different proficiency levels. Therefore, there may be a non-linear relationship between task performance and brain activatio

Ben Schweizer, David Wiedemann

We study the time-harmonic Maxwell equations on bounded Lipschitz domains with an impedance boundary condition. The impedance coefficient can be matrix valued such that, in particular, a polarization dependent impedance is modeled. We derive a Fredholm alternative for this system. As a consequence, we obtain the existence of weak solutions for arbitrary sour

Michal Konopa, Jan Fesl, Ladislav Ber ánek

The increasing complexity and temporal variability of workloads on MIG-enabled GPUs challenge the scalability of traditional centralized scheduling. Building upon the SJA concept, this paper introduces JASDA-a novel paradigm that extends SJA from a largely centralized scheduling model toward a fully decentralized negotiation process. In JASDA, jobs actively

Francesco Vona, Giulia Valcamonica, Franca Garzotto

Tangible Augmented Reality (TAR) is an interaction paradigm that integrates physical and digital worlds to create immersive, interactive experiences. This paper explores two TAR applications, Holomarket and Along the Oceanic Flow (ATOF), and presents insights from two exploratory studies evaluating their usability and likeability among individuals with neuro

T. Thallapalli, A. Alpana, A. M. Iyer, S. Sharma

Studies at the $Z$-pole have played an important role in developing our understanding of the Standard Model (SM). Continuing the explorations in this regime, we consider the possibility of the production of two $b$-quarks and a photon in proton-proton collisions at the HL-LHC. While such a final state is possible in the SM by means of the process $Z\rightarr

Hugo Markoff, Jevgenijs Galaktionovs

Camera traps generate millions of wildlife images, yet many datasets contain species that are absent from existing classifiers. This work evaluates zero-shot approaches for organizing unlabeled wildlife imagery using self-supervised vision transformers, developed and tested within the Animal Detect platform for camera trap analysis. We compare unsupervised c

Jawaher Kaldari, Shehbaz Tariq, Saif Al-Kuwari, Samuel Yen-Chi Chen

As quantum machine learning continues to evolve, reinforcement learning stands out as a particularly promising yet underexplored frontier. In this survey, we investigate the recent advances in QRL to assess its potential in various applications. While QRL has generally received less attention than other quantum machine learning approaches, recent research re

Hugo Markoff, Jevgenijs Galaktionovs

State-of-the-art animal classification models like SpeciesNet provide predictions across thousands of species but use conservative rollup strategies, resulting in many animals labeled at high taxonomic levels rather than species. We present a hierarchical re-classification system for the Animal Detect platform that combines SpeciesNet EfficientNetV2-M predic

Yuman He, Wentao Jiang, Siqi Wu, Xuzhe Ying

Recent experiment on Fe-doped CoSn has uncovered a series of correlated phases upon hole doping of the kagome flat bands. Among the phases observed, a nematic phase with a six- to two-fold rotation symmetry breaking is found to prevail over a wide doping and temperature range. Motivated by these observations, we investigate the interaction-driven phases real

Rashmi R, Vidyadhar Upadhya

Current Retrieval-Augmented Generation (RAG) systems primarily operate on unimodal textual data, limiting their effectiveness on unstructured multimodal documents. Such documents often combine text, images, tables, equations, and graphs, each contributing unique information. In this work, we present a Modality-Aware Hybrid retrieval Architecture (MAHA), desi

Michelle S. Lam, Omar Shaikh, Hallie Xu, Alice Guo

Large language models promise a broad set of functions, but when not given a specific objective, they default to generic results. We demonstrate that inferring the user's in-the-moment objective, then rapidly optimizing for that singular objective, enables LLMs to produce specialized tools, interfaces, and responses. Our work introduces just-in-time objectiv

F. Herklotz, E. V. Lavrov, A. Herklotz, V. V. Melnikov

The configurational behavior of sulfur in antimony triselenide (Sb$_2$Se$_3$) is investigated by combining infrared absorption spectroscopy with density functional theory. Four sulfur-related local vibrational modes are identified at 249, 273, 283, and 312~cm$^{-1}$ in melt-grown single crystals prepared from Sb$_2$Se$_3$ granulate. Their assignment to sulfu

Vaishnavi Sundararajan, Rithwik

Tracking devices, while designed to help users find their belongings in case of loss/theft, bring in new questions about privacy and surveillance of not just their own users, but in the case of crowd-sourced location tracking, even that of others even orthogonally associated with these platforms. Apple's Find My is perhaps the most ubiquitous such system whi

Zhifei Chen, Tianshuo Xu, Leyi Wu, Luozhou Wang

Video generation has recently made striking visual progress, but maintaining coherent object motion and interactions remains difficult. We trace two practical bottlenecks: (i) human-provided motion hints (e.g., small 2D maps) often collapse to too few effective tokens after encoding, weakening guidance; and (ii) optimizing for appearance and motion in a sing

Tomasz Serafin, Weronika Nitka

Calibration sample selection and forecast combination are two simple yet powerful tools used in forecasting. They can be combined with a variety of models to significantly improve prediction accuracy, at the same time offering easy implementation and low computational complexity. While their effectiveness has been repeatedly confirmed in prior scientific lit

Kazuya Nakayama, Wataru Buz Iwakiri, Teruaki Enoto, Shun Inoue

Algol is a well-known eclipsing binary hosting an active and variable star that exhibits frequent stellar flares. Here, we report our pre-planned and coordinated rapid X-ray follow-up observations of an eclipsing flare on Algol. The Monitor of All-sky X-ray Image (MAXI) detected a flare on Algol at 05:52 UT on 2018 July 4. Subsequently, we carried out a prom

Daria Frolova, Talgat Daulbaev, Egor Sevriugov, Sergei A. Nikolenko

Accurate prediction of protein-ligand binding poses is crucial for structure-based drug design, yet existing methods struggle to balance speed, accuracy, and physical plausibility. We introduce Matcha, a novel molecular docking pipeline that combines multi-stage flow matching with physically-aware post-processing. Our approach consists of three sequential st

Matan Rusanovsky, Shimon Malnick, Shai Avidan

Vision-language models have achieved remarkable success in cross-modal understanding. Yet, these models remain limited to object-level or region-level grounding, lacking the capability for pixel-precise keypoint comprehension through natural language. We introduce a novel framework for pixel level grounding. The framework consists of two complementary compon

Mátyás Schubert, Tom Claassen, Sara Magliacane

Causal discovery methods can identify valid adjustment sets for causal effect estimation for a pair of target variables, even when the underlying causal graph is unknown. Global causal discovery methods focus on learning the whole causal graph and therefore enable the recovery of optimal adjustment sets, i.e., sets with the lowest asymptotic variance, but th

Huipeng Huang, Wenbo Liao, Huajun Xi, Hao Zeng

Obtaining high-quality labels for large datasets is expensive, requiring massive annotations from human experts. While AI models offer a cost-effective alternative by predicting labels, their label quality is compromised by the unavoidable labeling errors. Existing methods mitigate this issue through selective labeling, where AI labels a subset and human lab

Hyein Woo, Miryeong Kwon, Jiseon Kim, Eunjee Na

This paper proposes ScalePool, a novel cluster architecture designed to interconnect numerous accelerators using unified hardware interconnects rather than traditional long-distance networking. ScalePool integrates Accelerator-Centric Links (XLink) and Compute Express Link (CXL) into a unified XLink-CXL hybrid fabric. Specifically, ScalePool employs XLink fo

Meseret Asrat

We consider a one parameter family of holographic solutions in classical string theory in three spacetime dimensions. In Euclidean space, the solutions interpolate smoothly without developing a conical singularity between the cigar black hole times a (non contractible) spatial circle and a thermal solution which has a (non contractible) temporal circle. We s

Dongwook Lee, Sol Han, Jinwhan Kim

This paper presents CALM-Net, a curvature-aware LiDAR point cloud-based multi-branch neural network for vehicle re-identification. The proposed model addresses the challenge of learning discriminative and complementary features from three-dimensional point clouds to distinguish between vehicles. CALM-Net employs a multi-branch architecture that integrates ed

David Martínez-Gómez

Magneto-acoustic waves in partially ionized plasmas are damped due to elastic collisions between charged and neutral particles. Here, we use a linearized two-fluid model to describe the influence of this collisional interaction on the properties of small-amplitude waves propagating in a uniform and static background. Mainly focusing on the case of waves gene

Jie Feng, Zhenbing Liu, Junjie Dai, Hongbin Chen

Conventional beamforming with fixed-orientation antenna (FOA) arrays may struggle to effectively enhance signal and/or suppress interference due to significant variations in antenna directive gains over different steering angles. To break this limitation, we investigate in this paper the rotatable antenna (RA)-enhanced single/multi-beam forming by exploiting

Felix Koch, Marcel Wever, Fabian Raisch, Benjamin Tischler

Recent advancements in foundation models for tabular data, such as TabPFN, demonstrated that pretrained Transformer architectures can approximate Bayesian inference with high predictive performance. However, Transformers suffer from quadratic complexity with respect to sequence length, motivating the exploration of more efficient sequence models. In this wor

S. V. Goloskokov, Ya-Ping Xie

Study of exclusive photoproduction of $J/\Psi$ mesons was carried out in a factorization approach. Generalized parton distributions (GPDs) for gluons, which play an important role here, are constructed using the double distribution representation. The obtained cross sections of $J/\Psi$ production in a wide energy range are in good agreement with the experim

Hui Wang, Jinghua Zhao, Junyang Cheng, Cheng Liu

Text-to-audio (TTA) generation is advancing rapidly, but evaluation remains challenging because human listening studies are expensive and existing automatic metrics capture only limited aspects of perceptual quality. We introduce AudioEval, a large-scale TTA evaluation dataset with 4,200 generated audio samples (11.7 hours) from 24 systems and 126,000 rating

Alexandre Borovik, Şükrü Yalçınkaya

We briefly explain how to implement the morphisms in our paper ``Natural representations of black box groups encrypting $SL_2(\mathbb{F})$" and provide some examples.

Niccolo' Castronuovo, Alberto Dennunzio, Luciano Margara

Group cellular automata are continuous, shift-commuting endomorphisms of $G^\mathbb{Z}$, where $G$ is a finite group. We provide an easy-to-check characterization of expansivity for group cellular automata on abelian groups and we prove that expansivity is a decidable property for general (non-abelian) groups. Moreover, we show that the class of expansive gr

Zhe Hou, Hailong Li, Qing Yan, Yu-Hang Li

In disordered lattices, itinerant electrons typically undergo Anderson localization due to random phase interference, which suppresses their motion. By contrast, in flat-band systems where electrons are intrinsically localized owing to their vanishing group velocity, the role of disorder remains elusive. Twisted bilayer graphene (TBG) at the magic angle $\si

Gerard Asbert, Pau Torras, Lei Kang, Alicia Fornés

The field of Optical Music Recognition (OMR) is currently hindered by the scarcity of real annotated data, particularly when dealing with handwritten historical musical scores. In similar fields, such as Handwritten Text Recognition, it was proven that synthetic examples produced with image generation techniques could help to train better-performing recognit

Kyubyung Chae, Gihoon Kim, Gyuseong Lee, Taesup Kim

Recent trends in LLMs development clearly show growing interest in the use and application of sovereign LLMs. The global debate over sovereign LLMs highlights the need for governments to develop their LLMs, tailored to their unique socio-cultural and historical contexts. However, there remains a shortage of frameworks and datasets to verify two critical ques

Junyi Wu, Jiaming Xu, Jinhao Li, Yongkang Zhou

3D Gaussian Splatting (3DGS) has emerged as a promising 3D reconstruction technique. The traditional 3DGS training pipeline follows three sequential steps: Gaussian densification, Gaussian projection, and color splatting. Despite its promising reconstruction quality, this conventional approach suffers from three critical inefficiencies: (1) Skewed density al

N. V. Krasnikov

We prove the internal inconsistency of the supersymmetric Wess-Zumino model. Our proof is based on three assumptions. The first assumption is that in the full theory the structure of counter temcs coincides with the structure of the counter terms in the perturbation theory. The second assumption is the positivity of norm states - no ghosts in the spectrum of

Yue Hou, He Zhu, Ruomei Liu, Yingke Su

Distributional discrepancy between training and test data can lead models to make inaccurate predictions when encountering out-of-distribution (OOD) samples in real-world applications. Although existing graph OOD detection methods leverage data-centric techniques to extract effective representations, their performance remains compromised by structural redund

Keiko I. Nagao, Yuga Sakano, Takashi Shinohara, Yuji Matsuda

In this study, we conducted an experiment to estimate $\pi$ using body-to-body and body-to-wall collisions. By geometrically analyzing the system's motion, we first review how the collision count corresponds to the digits of $\pi$. This method utilizes the property that the number of collisions corresponds to $\pi$ to the $n$-th decimal place by setting the

Yulin Zhang, Cheng Shi, Yang Wang, Sibei Yang

Envision an AI capable of functioning in human-like settings, moving beyond mere observation to actively understand, anticipate, and proactively respond to unfolding events. Towards this vision, we focus on the innovative task where, given ego-streaming video input, an assistant proactively answers diverse, evolving questions at the opportune moment, while m

En-Yu Lai, Jih-Chang Yu, Yen-Tsung Huang

Understanding causal mechanisms in complex systems requires evaluating path-specific effects (PSEs) in multi-mediator models. Identification of PSEs traditionally relies on the demanding cross-world independence assumption. To relax this, VanderWeele et al. (2014) proposed an interventional approach that redefines PSEs, while Stensrud et al. (2021) introduce

Amir Mohammad Fadaei Ayyam, Michael Sammler

Recent years have witnessed the rise of compositional semantics as a foundation for formal verification of complex systems. In particular, interaction trees have emerged as a popular denotational semantics. Interaction trees achieve compositionality by providing a reusable library of effects. However, their notion of effects does not support higher-order eff