Papers
Research papers from arXiv and related sources
Invariant-Stratified Propagation for Expressive Graph Neural Networks
Graph Neural Networks (GNNs) face fundamental limitations in expressivity and capturing structural heterogeneity. Standard message-passing architectures are constrained by the 1-dimensional Weisfei...
Asela Hevapathige, Ahad N. Zehmakan, Asiri Wijesinghe, Saman Halgamuge
UFO-4D: Unposed Feedforward 4D Reconstruction from Two Images
Dense 4D reconstruction from unposed images remains a critical challenge, with current methods relying on slow test-time optimization or fragmented, task-specific feedforward models. We introduce U...
Junhwa Hur, Charles Herrmann, Songyou Peng, Philipp Henzler, Zeyu Ma, Todd Zickler, Deqing Sun
DARE-bench: Evaluating Modeling and Instruction Fidelity of LLMs in Data Science
The fast-growing demands in using Large Language Models (LLMs) to tackle complex multi-step data science tasks create an emergent need for accurate benchmarking. There are two major gaps in existin...
Fan Shu, Yite Wang, Ruofan Wu, Boyi Liu, Zhewei Yao, Yuxiong He, Feng Yan
Do LLMs Benefit From Their Own Words?
Multi-turn interactions with large language models typically retain the assistant's own past responses in the conversation history. In this work, we revisit this design choice by asking whether lar...
Jenny Y. Huang, Leshem Choshen, Ramon Astudillo, Tamara Broderick, Jacob Andreas
CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation
GPU kernel optimization is fundamental to modern deep learning but remains a highly specialized task requiring deep hardware expertise. Despite strong performance in general programming, large lang...
Weinan Dai, Hanlin Wu, Qiying Yu, Huan-ang Gao, Jiahao Li, Chengquan Jiang, Weiqiang Lou, Yufan S...
Who Guards the Guardians? The Challenges of Evaluating Identifiability of Learned Representations
Identifiability in representation learning is commonly evaluated using standard metrics (e.g., MCC, DCI, R^2) on synthetic benchmarks with known ground-truth factors. These metrics are assumed to r...
Shruti Joshi, Théo Saulus, Wieland Brendel, Philippe Brouillard, Dhanya Sridhar, Patrik Reizinger
A Minimal Agent for Automated Theorem Proving
We propose a minimal agentic baseline that enables systematic comparison across different AI-based theorem prover architectures. This design implements the core features shared among state-of-the-a...
Borja Requena Pozo, Austin Letson, Krystian Nowakowski, Izan Beltran Ferreiro, Leopoldo Sarra
FaultXformer: A Transformer-Encoder Based Fault Classification and Location Identification model in PMU-Integrated Active Electrical Distribution System
Accurate fault detection and localization in electrical distribution systems is crucial, especially with the increasing integration of distributed energy resources (DERs), which inject greater vari...
Kriti Thakur, Alivelu Manga Parimi, Mayukha Pal
From Efficiency to Meaning: Adolescents' Envisioned Role of AI in Health Management
While prior research has focused on providers, caregivers, and adult patients, little is known about adolescents' perceptions of AI in health learning and management. Utilizing design fiction and c...
Jamie Lee, Kyuha Jung, Cecilia Lee, Lauren MacDonnell, Jessica Kim, Daniel Otterson, Erin Newman,...
Resolving the Metastable Si-XIII Structure through Convergent Theory and Experiment
Silicon is the undisputed cornerstone of modern technology, with applications ranging from micro- and opto-electronics to quantum technologies. Recently, the exploration of its allotropes has emerg...
Fabrizio Rovaris, Corrado Bongiorno, Anna Marzegalli, Mouad Bikerouin, Davide Spirito, Gerald J. ...
Data-Driven Linearization based Arc Fault Prediction in Medium Voltage Electrical Distribution System
High-impedance arc faults (HIAFs) in medium-voltage electrical distribution systems are difficult to detect due to their low fault current levels and nonlinear transient behavior. Traditional detec...
Mihir Sinha, Kriti Thakur, Prasanta K. Panigrahi, Alivelu Manga Parimi, Mayukha Pal
UXSim: Towards a Hybrid User Search Simulation
Simulating nuanced user experiences within complex interactive search systems poses distinct challenge for traditional methodologies, which often rely on static user proxies or, more recently, on s...
Saber Zerhoudi, Michael Granitzer
SafeGen-LLM: Enhancing Safety Generalization in Task Planning for Robotic Systems
Safety-critical task planning in robotic systems remains challenging: classical planners suffer from poor scalability, Reinforcement Learning (RL)-based methods generalize poorly, and base Large La...
Jialiang Fan, Weizhe Xu, Mengyu Liu, Oleg Sokolsky, Insup Lee, Fangxin Kong
Weighted Unequal Error Protection over a Rayleigh Fading Channel
We study a variant of unequal error protection in channel coding, where the message bit string is divided into a finite number of blocks and the maximization objective is a weighted sum of per-bloc...
Adeel Mahmood
Anansi: Scalable Characterization of Message-Based Job Scams
Job-based smishing scams, where victims are recruited under the guise of remote job opportunities, represent a rapidly growing and understudied threat within the broader landscape of online fraud. ...
Abisheka Pitumpe, Amir Rahmati
Comparing Classical and Quantum Variational Classifiers on the XOR Problem
Quantum machine learning applies principles such as superposition and entanglement to data processing and optimization. Variational quantum models operate on qubits in high-dimensional Hilbert spac...
Miras Seilkhan, Adilbek Taizhanov
Controllable Reasoning Models Are Private Thinkers
AI agents powered by reasoning models require access to sensitive user data. However, their reasoning traces are difficult to control, which can result in the unintended leakage of private informat...
Haritz Puerto, Haonan Li, Xudong Han, Timothy Baldwin, Iryna Gurevych
An Efficient Unsupervised Federated Learning Approach for Anomaly Detection in Heterogeneous IoT Networks
Federated learning (FL) is an effective paradigm for distributed environments such as the Internet of Things (IoT), where data from diverse devices with varying functionalities remains localized wh...
Mohsen Tajgardan, Atena Shiranzaei, Mahdi Rabbani, Reza Khoshkangini, Mahtab Jamali
Vacancy-induced local moments in quantum paramagnetic phases: An SU($N$) designer Hamiltonian study
We explore the effects of non-magnetic impurities (vacancy disorder) on the quantum paramagnetic phases stabilized by SU($N$) designer Hamiltonians on bipartite lattices. Using the results of our q...
Md Zahid Ansari, Souvik Kundu, Kedar Damle
Endpoint Estimates for Bergman Commutators and New Characterizations of the Bloch Space and $H^\infty$
We prove an $\LlogL $-type distributional inequality for the commutator of the Bergman projection with a conjugate Bloch symbol function on the unit ball. Such an inequality can be seen as a Bergma...
Adam B. Christopherson, Zhenghui Huo, Nathan A. Wagner, Yunus E. Zeytuncu