November 2025 arXiv papers — page 37

Kanako Narita, Bunyo Hatsukade, Seiji Fujimoto, Jorge González-López

We present results of a blind search for line-emitting galaxies using ALMA Lensing Cluster Survey data. We detected seven line emitters, one of which is [C\,{\sc ii}] at $z = 6.071$, four are CO at $z = 0.8$--1.1, and the remaining two are possibly CO or [C\,{\sc i}] within photometric redshift ranges. Three of the four CO emitters are multiple images of the

Takato Mori, Beni Yoshida

We revisit whether a semiclassical closed baby universe in AdS/CFT necessarily possess a trivial one-dimensional Hilbert space or may instead carry a large entropy. Recent results on Haar random encoding suggest a breakdown of complementary recovery, in which no logical operators can be reconstructed from individual bipartite subsystems. Motivated by this, w

M. J. Yantovski-Barth, Hengyue Zhang, Nolan Smyth, Connor Stone

We introduce a neural approach to dynamical modeling of galaxies that replaces traditional imaging-based deprojections with a differentiable mapping. Specifically, we train a neural network to translate Nuker profile parameters into analytically deprojectable Multi Gaussian Expansion components, enabling physically realistic stellar mass models without requi

Tooba Tehreem Sheikh, Jean Lahoud, Rao Muhammad Anwer, Fahad Shahbaz Khan

Traditional object detection models in medical imaging operate within a closed-set paradigm, limiting their ability to detect objects of novel labels. Open-vocabulary object detection (OVOD) addresses this limitation but remains underexplored in medical imaging due to dataset scarcity and weak text-image alignment. To bridge this gap, we introduce MedROV, th

Hidir Yesiltepe, Tuna Han Salih Meral, Adil Kaan Akan, Kaan Oktay

Current autoregressive video diffusion models are constrained by three core bottlenecks: (i) the finite temporal horizon imposed by the base model's 3D Rotary Positional Embedding (3D-RoPE), (ii) slow prompt responsiveness in maintaining fine-grained action control during long-form rollouts, and (iii) the inability to realize discontinuous cinematic transiti

Yunze Man, Shihao Wang, Guowen Zhang, Johan Bjorck

To act in the world, a model must name what it sees and know where it is in 3D. Today's vision-language models (VLMs) excel at open-ended 2D description and grounding, yet multi-object 3D detection remains largely missing from the VLM toolbox. We present LocateAnything3D, a VLM-native recipe that casts 3D detection as a next-token prediction problem. The key

Tahira Kazimi, Connor Dunlop, Pinar Yanardag

While recent text-to-video (T2V) diffusion models have achieved impressive quality and prompt alignment, they often produce low-diversity outputs when sampling multiple videos from a single text prompt. We tackle this challenge by formulating it as a set-level policy optimization problem, with the goal of training a policy that can cover the diverse range of

Xiaoye Wang, Chen Tang, Xiangyu Yue, Wei-Hong Li

This paper addresses the challenge of training a single network to jointly perform multiple dense prediction tasks, such as segmentation and depth estimation, i.e., multi-task learning (MTL). Current approaches mainly capture cross-task relations in the 2D image space, often leading to unstructured features lacking 3D-awareness. We argue that 3D-awareness is

Yongsheng Yu, Wei Xiong, Weili Nie, Yichen Sheng

Latent-space modeling has been the standard for Diffusion Transformers (DiTs). However, it relies on a two-stage pipeline where the pretrained autoencoder introduces lossy reconstruction, leading to error accumulation while hindering joint optimization. To address these issues, we propose PixelDiT, a single-stage, end-to-end model that eliminates the need fo

Adhiraj Ghosh, Vishaal Udandarao, Thao Nguyen, Matteo Farina

What data should a vision-language model be trained on? To answer this question, many data curation efforts center on the quality of a dataset. However, most of these existing methods are (i) offline, i.e. they produce a static dataset from a set of predetermined filtering criteria, and (ii) concept-agnostic, i.e. they use model-based filters which induce ad

Joseph W. Iverson, Kaysie Rose O

We make four contributions to the theory of optimal subspace packings and equi-isoclinic subspaces: (1) a new lower bound for block coherence, (2) an exact count of equi-isoclinic subspaces of even dimension $r$ in $\mathbb{R}^{2r+1}$ with parameter $\alpha \neq \tfrac{1}{2}$, (3) a new upper bound for the number of $r$-dimensional equi-isoclinic subspaces i

Wei Tang, Zuo-Zheng Wang, Kun Zhang, Tong Wei

Long-tailed multi-label visual recognition poses a significant challenge, as images typically contain multiple labels with highly imbalanced class distributions, leading to biased models that favor head classes while underperforming on tail classes. Recent efforts have leveraged pre-trained vision-language models, such as CLIP, alongside long-tailed learning

Ryan Burgert, Charles Herrmann, Forrester Cole, Michael S Ryoo

While generative video models have achieved remarkable fidelity and consistency, applying these capabilities to video editing remains a complex challenge. Recent research has explored motion controllability as a means to enhance text-to-video generation or image animation; however, we identify precise motion control as a promising yet under-explored paradigm

Ingrid Beltita, Daniel Beltita

We verify the conjecture on continuous-trace subquotients for $C^*$-algebras of nilpotent linear dynamical systems, where by linear dynamical system we mean a continuous action of the additive group of real numbers by linear maps on a finite-dimensional real vector space. In addition, we show that the dimension of the ambient vector space can be recovered fr

Frederik Zuiderveen Borgesius

The European Union Charter of Fundamental Rights only allows personal data processing if a data controller has a legal basis for the processing. This paper argues that in most circumstances the only available legal basis for the processing of personal data for behavioural targeting is the data subject's unambiguous consent. Furthermore, the paper argues that

Frederik Zuiderveen Borgesius

This chapter examines the policy implications of behavioural sciences insights for the regulation of privacy on the Internet, by focusing in particular on behavioural targeting. This marketing technique involves tracking people's online behaviour to use the collected information to show people individually targeted advertisements. Enforcing data protection l

Joris van Hoboken, Frederik Zuiderveen Borgesius

We use electronic communication networks for more than simply traditional telecommunications: we access the news, buy goods online, file our taxes, contribute to public debate, and more. As a result, a wider array of privacy interests is implicated for users of electronic communications networks and services. This development calls into question the scope of

Ziyue Wang, Yayati Jadhav, Peter Pak, Amir Barati Farimani

Mechanical design and manufacturing workflows conventionally begin with conceptual design, followed by the creation of a computer-aided design (CAD) model and fabrication through material-extrusion (MEX) printing. This process requires converting CAD geometry into machine-readable G-code through slicing and path planning. While each step is well established,

Monojit Bhattacharjee, Rajeev Gupta, Vidhya Venugopal

This work establishes a multivariable Wold-type decomposition for left-inverse commuting $n$-tuples of bounded operators, built on the hypothesis that each component admits a Wold-type decomposition. For pairs of operators, we obtain a complete analytic model: every left-inverse commuting analytic toral $2$-isometric pair is unitarily equivalent to the pair

Patrick Erik Bradley, Ángel Morán Ledezma

Using a previous novel way of defining kernel functions for Laplacian integral operators on a compact $p$-adic analytic manifold $X$, one such operator $\Delta_0^s$ with $s\in\mathds{R}$ is applied to hearing the Serre invariant $i(X)$ by showing that a wavelet eigenvalue is always congruent to $i(X)$ modulo $q-1$, where $q$ is the cardinality of the residue

Vaibhav Kumar, Kaiwalya Joshi, Bhavya Dixit, Gaurav S. Kasbekar

We propose a novel quantum-resistant mutual authentication scheme for radio-frequency identification (RFID) systems. Our scheme uses lattice-based cryptography and, in particular, achieves quantum-resistance by leveraging the hardness of the inhomogeneous short integer solution (ISIS) problem. In contrast to prior work, which assumes that the reader-server c

Chieh-Yun Chen, Zhonghao Wang, Qi Chen, Zhifan Ye

Reinforcement learning from human feedback (RLHF) with reward models has advanced alignment of generative models to human aesthetic and perceptual preferences. However, jointly optimizing multiple rewards often incurs an alignment tax, improving one dimension while degrading others. To address this, we introduce two complementary methods: MapReduce LoRA and

Maxwell Tang, Garrett Hinkley, Kenneth Goodenough, Stefan Krastanov

Optimal routing in quantum-repeater networks requires finding the best path that connects a pair of end nodes. Most previous work on routing in quantum networks assumes utility functions that are isotonic, meaning that the ordering of two paths does not change when extending both with the same edge. However, we show that utility functions that take into acco

Anastasia Mavridou, Divya Gopinath, Corina S. Păsăreanu

The integration of AI components, particularly Deep Neural Networks (DNNs), into safety-critical systems such as aerospace and autonomous vehicles presents fundamental challenges for assurance. The opacity of AI systems, combined with the semantic gap between high-level requirements and low-level network representations, creates barriers to traditional verif

Wei He, Kai Han, Hang Zhou, Hanting Chen

The optimization of large language models (LLMs) remains a critical challenge, particularly as model scaling exacerbates sensitivity to algorithmic imprecision and training instability. Recent advances in optimizers have improved convergence efficiency through momentum orthogonalization, but suffer from two key robustness limitations: dimensional fragility i

Duc V. Dinh, Jens Herfort, Andreas Fiedler, Oliver Brandt

The transport properties and electrical bandgap of nominally undoped ~75-nm-thick CrN layers simultaneously grown on AlN(0001) and AlN(11\bar{2}2) templates using plasma-assisted molecular beam epitaxy are investigated. The layers grown on AlN(0001) and AlN(11\bar{2}2) exhibit (111) and (113) surface orientations, respectively. All layers exhibit antiferroma

Matthew Frank

We show that every finite Boolean combination of polynomial equalities and inequalities in C^n admits two uniform normal forms: an $\exists\forall$ form and a $\forall\exists$ form, each using a single polynomial equation. Both forms use only one existentially quantified variable and one universally quantified variable. Optimality results demonstrate that no

Yangguang Li, Xianglong He, Zi-Xin Zou, Zexiang Liu

Inspired by generative paradigms in image and video, 3D shape generation has made notable progress, enabling the rapid synthesis of high-fidelity 3D assets from a single image. However, current methods still face challenges, including the lack of intricate details, overly smoothed surfaces, and fragmented thin-shell structures. These limitations leave the ge

David Szczecina, Senan Gaffori, Edmond Li

The widespread use of Large Language Models (LLMs) raises critical concerns regarding the unauthorized inclusion of copyrighted content in training data. Existing detection frameworks, such as DE-COP, are computationally intensive, and largely inaccessible to independent creators. As legal scrutiny increases, there is a pressing need for a scalable, transpar

Konstantin G. Chetyrkin

We extend in an extremely simple and straightforward way the well-known low-energy theorem for an effective Higgs-like scalar-gluon-gluon coupling [1] (as well as a similar one for for the effective coupling of the Higgs-like field to the light scalar quark currents) in QCD including arbitrary number of heavy quarks in addition to the light ones. The applica

Adam Karvonen, Daniel Reuter, Roy Rinberg, Luke Marks

As demand for LLM inference grows, it is becoming increasingly important that providers and their customers can verify that inference processes are performed correctly, without errors or tampering. However, re-running the same inference process twice often leads to different results due to benign numerical noise, making it difficult to distinguish legitimate

Xinhao Liu, Jiaqi Li, Youming Deng, Ruxin Chen

Reproducible closed-loop evaluation remains a major bottleneck in Embodied AI such as visual navigation. A promising path forward is high-fidelity simulation that combines photorealistic sensor rendering with geometrically grounded interaction in complex, open-world urban environments. Although recent video-3DGS methods ease open-world scene capturing, they

Wen-Tao Xu, Miguel Frías Pérez, Mingru Yang

Given a tensor network state, how can we determine conserved operators (including Hamiltonians) for which the state is an eigenstate? We answer this question by presenting a method to extract geometrically $k$-local conserved operators that have the given infinite projected entangled pair state (iPEPS) in 2D as an (approximate) eigenstate. The key ingredient

Noureldin Yosri, Dmytro Gavinsky, Dmitri Maslov

We present optimized quantum circuits for GF$(2^m)$ multiplication and division operations, which are essential computing primitives in various quantum algorithms. Our ancilla-free GF multiplication circuit has the gate count complexity of $O(m^{\log_2{3}})$, an improvement over the previous best bound of $O(m^2)$. This was achieved by developing an efficien

Saman Dehghan, Tianran Sun, Tianxiang Wu, Zihan Li

Existing C to Rust translation techniques fail to balance quality and scalability: transpilation-based approaches scale to large projects but produce code with poor safety, idiomaticity, and readability. In contrast, LLM-based techniques are prohibitively expensive due to their reliance on frontier models (without which they cannot reliably generate compilab

Yueming Shen, Christian Pean, David Dunson, Samuel Berchuck

Time-to-event models are commonly used to study associations between risk factors and disease outcomes in the setting of electronic health records (EHR). In recent years, focus has intensified on social determinants of health, highlighting the need for methods that account for patients' locations. We propose a Bayesian approach for introducing point-referenc

Ziheng Ouyang, Yiren Song, Yaoli Liu, Shihao Zhu

Previous works have explored various customized generation tasks given a reference image, but they still face limitations in generating consistent fine-grained details. In this paper, our aim is to solve the inconsistency problem of generated images by applying a reference-guided post-editing approach and present our ImageCritic. We first construct a dataset

Yujin Kim, Sarah Dean

Many consequential real-world systems, like wind fields and ocean currents, are dynamic and hard to model. Learning their governing dynamics remains a central challenge in scientific machine learning. Dynamic Mode Decomposition (DMD) provides a simple, data-driven approximation, but practical use is limited by sparse/noisy observations from continuous fields

Jing Wang, Tobias Schneider, Wei Hu, Stefan Schumacher

Zigzag chains allow for the formation of topological edge states. Several distinct chain architectures have been developed for this purpose. Here, we report a zigzag superlattice, containing two staggered sub-lattices, that supports multiple edge states, including higher-order modes. In such lattices, the intra- and intercell coupling is imbalanced by the tu

Gaspard Merten, Mahmoud Sakr, Gilles Dejaegere

Foundation models are transformative in artificial intelligence, but building them from scratch, especially for mobility trajectories, is not yet clear or documented. This tutorial bridges this gap by demonstrating the steps and code of a minimal implementation of a trajectory-focused foundation model starting from GPT-2. Through a concise, step-by-step, cod

Shurong Wang, Yuqi Pan, Zhuoyang Shen, Meng Zhang

Associative memory models are content-addressable memory systems fundamental to biological intelligence and are notable for their high interpretability. However, existing models evaluate the quality of retrieval based on proximity, which cannot guarantee that the retrieved pattern has the strongest association with the query, failing correctness. We reframe

Tomáš Bzdušek

Topological principles constitute at present an integral component of condensed matter physics, permeating the modern characterization of electronic states while also guiding materials design. In this brief Perspective, I highlight three research threads in single-particle topological band theory that have recently gained momentum: (i) the rise of the quantu

Nils Müller

We study the optimization of functions with $n>2$ arguments that have a representation as a sum of several functions that have only $2$ of the $n$ arguments each, termed sums of bivariates, on finite domains. The complexity of optimizing sums of bivariates is shown to be NP-equivalent and it is shown that there exists free lunch in the optimization of sums o

Yao Wu

Conventional models of matching markets assume that monetary transfers can clear markets by compensating for utility differentials. However, empirical patterns show that such transfers often fail to close structural preference gaps. This paper introduces a market microstructure framework that models matching decisions as a limit order book system with rigid

Xiwen Huang, Pierre Pinson

We introduce and analyse active learning markets as a way to purchase labels, in situations where analysts aim to acquire additional data to improve model fitting, or to better train models for predictive analytics applications. This comes in contrast to the many proposals that already exist to purchase features and examples. By originally formalising the ma

Yixin Liu, Pengfei Liu, Arman Cohan

Alignment with human preferences is an important evaluation aspect of LLMs, requiring them to be helpful, honest, safe, and to precisely follow human instructions. Evaluating large language models' (LLMs) alignment typically involves directly assessing their open-ended responses, requiring human annotators or strong LLM judges. Conversely, LLMs themselves ha

Winfrey Paul Sagayam Dennis

Urban Air mobility has gained momentum with recent advancements in the electric vertical take-off and landing (eVTOL) vehicles, offering faster point-to-point air taxi services that could help relieve traffic congestion in chronically overburdened cities. The research assesses the feasibility and systems-of-systems level adoption potential of UAM operations

Nitin Jha, Abhishek Parakh, Mahadevan Subramaniam

Quantum Key Distribution (QKD) is revolutionizing cryptography by promising information-theoretic security through the immutable laws of quantum mechanics. Yet, the challenge of transforming these idealized security models into practical, resilient systems remains a pressing issue, especially as quantum computing evolves. In this review, we critically dissec

Heman Shakeri

Deep sequence models for blood glucose forecasting consistently fail to leverage clinically informative drivers--insulin, meals, and activity--despite well-understood physiological mechanisms. We term this Driver-Blindness and formalize it via $\Delta_{\text{drivers}}$, the performance gain of multivariate models over matched univariate baselines. Across the

En-Hung Chao

In the past decades, significant improvements have been made on standard-model predictions on kaon decays using lattice quantum chromodynamics. In these proceedings, I review selected works on long-distance contributions to kaon decays and developments on QED corrections to those.

S. Ramachandran, S. Jensen, Y. Alhassid

The two-species cold atomic Fermi gas with attractive short-range interactions in two spatial dimensions undergoes a Bardeen-Cooper-Schrieffer (BCS) to a Bose-Einstein Condensate (BEC) crossover as a function of $\ln (k_F a)$, where $a$ is the scattering length. However, the nature of this crossover in the strong coupling regime $\ln(k_F a) \sim 1$ remains p

Mahtab A. Khan, Michael N. Leuenberger

We develop a microscopic theory of superfluidity for hard-core dark excitons on the triangular lattice by mapping the large-$U$ Bose--Hubbard model to an effective XXZ spin-$\frac{1}{2}$ Hamiltonian including virtual hopping processes. Within this framework, we identify the superfluid phase that emerges between the two Mott-insulating endpoints at fillings 0

Kaiyuan Zhang, Mark Tenenholtz, Kyle Polley, Jerry Ma

The integration of artificial intelligence (AI) agents into web browsers introduces security challenges that go beyond traditional web application threat models. Prior work has identified prompt injection as a new attack vector for web agents, yet the resulting impact within real-world environments remains insufficiently understood. In this work, we examine

Alex Laguë, Mathew S. Madhavacheril, Josh Borrow, Kendrick M. Smith

The complex processes of baryonic feedback associated with galaxy evolution are still poorly understood, and their impact on the clustering of matter on small scales remains difficult to quantify. While many fitting functions and emulators exist to model the matter power spectrum, their input parameters are not directly observable. However, recent studies us

Shitao Fan, Ilsang Ohn, David Dunson, Lizhen Lin

Variational Bayes methods are popular due to their computational efficiency and adaptability to diverse applications. In specifying the variational family, mean-field classes are commonly used, which enables efficient algorithms such as coordinate ascent variational inference (CAVI) but fails to capture parameter dependence and typically underestimates uncer

Allen Emmanuel Binny, Mahathi Anand, Hugo T. M. Kussaba, Lingyun Chen

Learning safe and stable robot motions from demonstrations remains a challenge, especially in complex, nonlinear tasks involving dynamic, obstacle-rich environments. In this paper, we propose Safe and Stable Neural Network Dynamical Systems S$^2$-NNDS, a learning-from-demonstration framework that simultaneously learns expressive neural dynamical systems alon

Mingxing Rao, Bowen Qu, Daniel Moyer

The recovery of training data from generative models ("model inversion") has been extensively studied for diffusion models in the data domain as a memorization/overfitting phenomenon. Latent diffusion models (LDMs), which operate on the latent codes from encoder/decoder pairs, have been robust to prior inversion methods. In this work we describe two key find

Charlotte Beylier, Hannah Selder, Arthur Fleig, Simon M. Hofmann

While deep reinforcement learning agents demonstrate high performance across domains, their internal decision processes remain difficult to interpret when evaluated only through performance metrics. In particular, it is poorly understood which input features agents rely on, how these dependencies evolve during training, and how they relate to behavior. We in

Jakub Muszyński, Ignacy Walużenicz, Patryk Zan, Zofia Wrona

Microgrids are deployed to reduce purchased grid energy, limit exposure to volatile tariffs, and ensure service continuity during disturbances. This requires coordinating heterogeneous distributed energy resources across multiple time scales and under variable conditions. Among existing tools, typically, power-system simulators capture physical behaviour but

J. A. J. Alford, J. D. Gelfand, M. Abdelmaguid, P. Slane

We investigate the origin of unidentified, extended TeV source 1LHAASO J0500$+$4454, considering three possible origins: cosmic rays interacting with a molecular cloud (MC), particles accelerated in a currently undetected supernova remnant (SNR), and an energetic outflow powered by a pulsar. Upper limits on the CO and X-ray emission from the $\gamma$-ray emi

Mario Gauvrit, Paul Laurain, Tristan Rivière

We establish the lower semi continuity of the Morse index and the upper continuity of the Morse Index plus nullity of sequences of critical points of the Sacks-Uhlenbeck type relaxation of the Yang-Mills Energy in 4 dimension. The result is known not to be true in general for the ``cousin problem'' of hamonic maps from surfaces into arbitrary manifolds. This

Karim Kadry, Abdallah Abdelwahed, Shoaib Goraya, Ajay Manicka

We present Anatomica: an inference-time framework for generating multi-class anatomical voxel maps with localized geo-topological control. During generation, we use cuboidal control domains of varying dimensionality, location, and shape to slice out relevant substructures. These local substructures are used to compute differentiable penalty functions that st

Carolina Bolognani, Ulrich Nierste, Stefan Schacht, K. Keri Vos

We derive Standard Model predictions for the CP asymmetries of singly-Cabibbo suppressed $D\rightarrow Pη'$ decays, where $P=K,π,η$. Our predictions are based on the approximate SU(3)$_F$ symmetry of QCD and include first-order symmetry-breaking effects in a systematic way. The underlying symmetry leads to correlations between different decay modes. To t

Shuo Xie, Tianhao Wang, Beining Wu, Zhiyuan Li

Adaptive optimizers can reduce to normalized steepest descent (NSD) when only adapting to the current gradient, suggesting a close connection between the two algorithmic families. A key distinction between their analyses, however, lies in the geometries, e.g., smoothness notions, they rely on. In the convex setting, adaptive optimizers are governed by a stro

A. D. Biggs

We have measured a time delay of 25.3 +/- 2.0 d (1-sigma confidence) in the Einstein ring gravitational lens system PKS 1830-211 from an analysis of archival VLA and ATCA monitoring data observed between 1997 and 2004. A small portion of the ATCA data was previously used to determine a time delay and our result is consistent with the previous value, but with

Paolo Garbarino, Massimiliano Grazzini, Stefan Kallweit, Chiara Savoini

Triboson production processes play a crucial role in probing the electroweak sector of the Standard Model, as they involve quartic gauge-boson couplings already at the tree level. With these measurements entering the precision era at the Large Hadron Collider (LHC), accurate theoretical predictions become indispensable. We present the computation of the next

Ethan Stace, Aaron Smith, Kevin Lorinc, Olof Nebrin

Resonance lines encode rich information about astrophysical sources and their environments, yet fully analytic treatments of multi-line radiative transfer remain almost entirely unexplored. We present exact, closed-form solutions for steady-state resonant-line radiative transfer in "V-shaped" atomic networks, where a single ground state couples to multiple t

Peter A. Perry

This paper corrects several errors in the author's previous papers (Journal of Spectral Theory 2016, Analysis and PDE 2014) on the Davey-Stewartson II (DS II) and modified Novikov-Veselov (mNV) equations. In each of these papers a proof was given that the solution by inverse scattering yields a classical solution to the PDE. The mNV equation lies in the inte

Ziang Cui, Shanyong Wang, Yining Zhao, Yiran Wang

Haptic feedback is essential for human-machine interaction, as it bridges physical and digital experiences and enables immersive engagement with virtual environments. However, current haptic devices are frequently tethered, lack portability and flexibility. They also have limited ability to deliver fine-grained, multi-dimensional feedback. To address these c

Yingjia Lin, Abhinav Anand, Kenneth R. Brown

Quantum error correction typically requires repeated syndrome extraction due to measurement noise, which results in substantial time overhead in fault-tolerant computation. Single-shot error correction aims to suppress errors using only one round of syndrome extraction. However, for most codes, it requires high-weight checks, which significantly degrade, and

Nick Polson, Vadim Sokolov

In this paper, we design $MC^2$ algorithms for Mixed Integer and Linear Programming. By expressing a constrained optimisation as one of simulation from a Boltzmann distribution, we reformulate integer and linear programming as Monte Carlo optimisation problems. The key insight is that solving these optimisation problems requires the ability to simulate from

Chenhui Gou, Zilong Chen, Zeyu Wang, Feng Li

This paper studies Visual Question-Visual Answering (VQ-VA): generating an image, rather than text, in response to a visual question -- an ability that has recently emerged in proprietary systems such as NanoBanana and GPT-Image. To also bring this capability to open-source models, we introduce VQ-VA World, a data-centric framework built around an agentic pi

Mohamadreza Delbari, George C. Alexandropoulos, Robert Schober, H. Vincent Poor

Near-field (NF) communications is receiving renewed interest in the context of multiple-input multiple-output (MIMO) systems involving large physical apertures with respect to the signal wavelength. While line-of-sight (LOS) links are typically expected to dominate in NF scenarios, the impact of non-LOS (NLOS) components at both in centimeter- and millimeter

Jae Kwan Im, Hyeonjun An, Seob-Gu Kim, Jae-Hong Lim

A sessile water droplet on a cold substrate freezes into a shape with a sharp apex because of water's expansion upon freezing, yielding a universal tip angle across various conditions. Using \textit{in situ} X-ray imaging, we report that this angle changes with substrate temperature, and the deviation originates from bubble formation during freezing. Three-d

Tasha Kim, Oiwi Parker Jones

Safety-critical assistive systems that directly decode user intent from neural signals require rigorous guarantees of reliability and trust. We present GUARDIAN (Gated Uncertainty-Aware Runtime Dual Invariants), a framework for real-time neuro-symbolic verification for neural signal-controlled robotics. GUARDIAN enforces both logical safety and physiological

William E. East

We study the threshold of gravitational collapse in spherically symmetric spacetimes governed by the Einstein-Maxwell-Vlasov equations. We numerically construct solutions describing a collapsing distribution of charged matter that either forms a charged black hole or eventually disperses. We first consider a region of parameter space where the solutions at t

Yanjun Guo, Chao Liu, ZhiCun Liu, Chunyan Li

Runaway stars depart their birthplaces with high peculiar velocities. Two mechanisms are commonly invoked to explain their origin, the binary supernova scenario (BSS) and the dynamical ejection scenario (DES). Investigating the kinematic properties of runaway stars is key to understanding their origins.We intend to investigate the origins of 39 B-type runawa

Mingkai Jia, Mingxiao Li, Zhijian Shu, Anlin Zheng

Recent advances in visual generation have emphasized the importance of Latent Generative Models (LGMs), which critically depend on effective visual tokenizers to bridge pixels and semantic representations. However, tokenizers constructed on pre-trained vision foundation models (VFMs) often struggle to balance semantic richness and reconstruction fidelity in

Rui Xue, Shichao Zhu, Liang Qin, Tianfu Wu

Graph Neural Networks (GNNs) have emerged as powerful tools for modeling graph-structured data and have been widely used in recommender systems, such as for capturing complex user-item and item-item relations. However, most industrial deployments adopt a two-stage pipeline: GNNs are first pre-trained offline to generate node embeddings, which are then used a

Shengqiong Wu, Weicai Ye, Yuanxing Zhang, Jiahao Wang

Diffusion Transformers have significantly improved video fidelity and temporal coherence, however, practical controllability remains limited. Concise, ambiguous, and compositionally complex user inputs contrast with the detailed prompts used in training, yielding an intent-output mismatch. We propose ReaDe, a universal, model-agnostic interpreter that conver

Yuwei Niu, Weiyang Jin, Jiaqi Liao, Chaoran Feng

Recent years have witnessed significant progress in Unified Multimodal Models, yet a fundamental question remains: Does understanding truly inform generation? To investigate this, we introduce UniSandbox, a decoupled evaluation framework paired with controlled, synthetic datasets to avoid data leakage and enable detailed analysis. Our findings reveal a signi

Aditya R. Sengupta, Jordan Diaz, Matthew DeMartino, Rebecca Jensen-Clem

Ground-based direct imaging of exoplanets at high contrast requires precise correction of atmospheric turbulence using adaptive optics (AO). The planet-to-star contrast ratio at small angular separations from the host star is often limited by non-common-path aberrations (NCPAs) seen only in the science plane. The photonic lantern (PL) can be used to sense ab

Martín Blufstein, Katherine Goldman, Koichi Oyakawa

We prove the dichotomy that every Coxeter group either has a strongly solid group von Neumann algebra or contains the product of an infinite cyclic group and a free group of rank 2. This generalizes the same dichotomy for right-angled Coxeter groups by Borst-Caspers. However, our proof is conceptually different, which leads to a significantly streamlined arg

Xintong Li, Haoran Zhang, Xiao Zhou

The abundance of fine-grained spatio-temporal data, such as traffic sensor networks, offers vast opportunities for scientific discovery. However, inferring causal relationships from such observational data remains challenging, particularly due to unobserved confounders that are specific to units (e.g., geographical locations) yet influence outcomes over time

N. Bostan, R. H. Dejrah, C. Dioguardi, A. Racioppi

$F(R)$ Palatini gravity provides a robust framework for constructing viable inflationary potentials. In this study, we examine natural inflation and show that its consistency with observational data can be restored when the model is embedded within $F(R)$ Palatini gravity, specifically for $F(R) = R + \alpha R^n$ with $7/4 \lesssim n \leq 2$. For completenes

Avi Mayorcas, Łukasz Mądry

We show existence and uniqueness of invariant measures for SDE of the form \[ dX_t = g(X_t)dt + u(X_t)dt + dW^H_t \] where $W^H$ is a fractional Brownian motion (fBm) with Hurst parameter $H\in (0,\frac{1}{2})$, $u$ is a linearly dispersive term and $g$ is any $B^\alpha_{\infty,\infty}(\mathbb{R}^d)$ distribution in the class treated by Catellier--Gubinelli

Momoko Shiraishi, Yinzhi Cao, Takahiro Shinagawa

Command-line interface (CLI) fuzzing tests programs by mutating both command-line options and input file contents, thus enabling discovery of vulnerabilities that only manifest under specific option-input combinations. Prior works of CLI fuzzing face the challenges of generating semantics-rich option strings and input files, which cannot reach deeply embedde

Aditya Shah, Tyler Menezes

Many studies have aimed to broaden participation in computing (BPC) through extracurricular educational initiatives. When these initiatives are structured as open-enrollment extracurricular programs, their success often depends on their marketing approach. However, there is little in the computing education research literature about how to conduct effective

Zheqi Hu, Xuanjing Chen, Jinlin Hu

With the rapid growth of AI-generated content (AIGC) across domains such as music, video, and literature, the demand for emotionally aware recommendation systems has become increasingly important. Traditional recommender systems primarily rely on user behavioral data such as clicks, views, or ratings, while neglecting users' real-time emotional and intention

Michał Kowalczyk, Yvan Martel

For a class of nonlinear Klein-Gordon equations, we prove that in the small energy limit, any sequence of breathers decomposes into a finite sum of decoupled, periodically modulated canonical solitons. Each of these solitons is asymptotically equal to an explicit sine-Gordon breather and the distance between them grows to infinity as the energy decreases to

Haoyu Wang, Andrea Alfonsi, Roberto Ponciroli, Richard Vilim

The behavior of a dynamical system under a given set of inputs can be captured by tracking the response of an optimal subset of process variables (\textit{state variables}). For many engineering systems, however, first-principles, model-based identification is impractical, motivating data-driven approaches for Digital Twins used in control and diagnostics. I

Tatiana Gelvez-Barrera, Barbara Nicolas, Denis Kouamé, Bruno Gilles

Passive acoustic mapping enables the spatial mapping and temporal monitoring of cavitation activity, playing a crucial role in therapeutic ultrasound applications. Most conventional beamforming methods, whether implemented in the time or frequency domains, suffer from limited axial resolution due to the absence of a reference emission onset time. While frequ

Dustin Bryant, Jonathan Julian Huerta y Munive, Simon Foster

Modern machine learning pipelines are built on numerical algorithms. Reliable numerical methods are thus a prerequisite for trustworthy machine learning and cyber-physical systems. Therefore, we contribute a framework for verified numerical methods in Isabelle/HOL based on ITrees. Our user-friendly specification language enables the direct declaration of num

Pouyan Nasiri, Leonard S. Fifield, Hadis Nouri, Roozbeh Dargazany

We present a physics-informed neural network framework for predicting the mechanical performance of elastomers exposed to concurrent thermal and gamma-radiation exposure, such as elastomers in nuclear cables or space electronics. Our demonstrated approach integrates the dual-network hypothesis with the microsphere concept to represent soft and brittle sub-ne

Guanjie Chen, Shirui Huang, Kai Liu, Jianchen Zhu

Diffusion Models have emerged as a leading class of generative models, yet their iterative sampling process remains computationally expensive. Timestep distillation is a promising technique to accelerate generation, but it often requires extensive training and leads to image quality degradation. Furthermore, fine-tuning these distilled models for specific ob

M. Sumetsky

Photonic circuits modulated in time can convert the input light frequency $\omega_0$ shifting it by multiples of the modulation frequency $\omega_p$ and, in certain cases, amplify the total input light power. Of special interest are photonic circuits employing microwave capacitors, which instantaneously modulate photonic waveguides with frequency $\omega_p \

Farjana Sultana Mim, Shuchin Aeron, Eric Miller, Kristen Wendell

Identifying discourse features in student conversations is quite important for educational researchers to recognize the curricular and pedagogical variables that cause students to engage in constructing knowledge rather than merely completing tasks. The manual analysis of student conversations to identify these discourse features is time-consuming and labor-

Aatman Vaidya, Harsh Bhagat, Seema Nagar, Amit A. Nanavati

Hate speech on online platforms has been credibly linked to multiple instances of real world violence. This calls for an urgent need to understand how toxic content spreads and how it might be mitigated on online social networks, and expectedly has been the topic of extensive research in recent times. Prior work has largely modelled hate through epidemic or

Tushya Kalpada, Aayush Vijayvargia, Ezra Day-Roberts, Onur Erten

Quantum circuits provide an emerging controllable platform to realize novel dynamical non-equilibrium phases including topologically ordered states. The Kitaev model has become a cornerstone of quantum magnetism due to its quantum spin liquid ground state and rich phase diagram. The Kitaev model has also been treated in the monitored circuit setting, giving

Alhasan Abdellatif, Hannah P. Menke, Florian Doster, Kamaljit Singh

The UNet-enhanced Fourier Neural Operator (UFNO) extends the Fourier Neural Operator (FNO) by incorporating a parallel UNet pathway, enabling the retention of both high- and low-frequency components. While UFNO improves predictive accuracy over FNO, it inefficiently treats scalar inputs (e.g., temperature, injection rate) as spatially distributed fields by d