Papers
Research papers from arXiv and related sources
Radio selection of heavily obscured AGN in the J1030 field: unraveling a missing Compton-thick population
We tested the effectiveness of radio selection to discover heavily obscured AGNs, particularly at high-z, and we measured their abundance for the first time from a radio perspective. We consider th...
Giovanni Mazzolari, Roberto Gilli, Marco Mignoli, Marcella Brusa, Isabella Prandoni, Fabio Vito, ...
Bias in Universal Machine-Learned Interatomic Potentials and its Effects on Fine-Tuning
Universal machine learned interatomic potentials (uMLIPs) embody a growing area of interest due to their transferability across the periodic table, displaying an error of about 0.6 kcal/mol against...
Nicolas Wong, Julia H. Yang
Towards macroeconomic analysis without microfoundations: measuring the entropy of simulated exchange economies
The theory of thermal macroeconomics (TM) analyses economic phenomena within the mathematical framework of classical thermodynamics, using a set of axioms that apply to the purely macroscopic aspec...
Yihang Luo, Robert S. MacKay, Nick Chater
Shrinkage Regularization for (Non)Linear Serial Dependence Test
This paper introduces a regularized test of the null hypothesis of the absence of linear and nonlinear serial dependence for high-dimensional non-Gaussian time series. Our approach extends the port...
Francesco Giancaterini, Alain Hecq, Joann Jasiak, Aryan Manafi Neyazi
A neural operator for predicting vibration frequency response curves from limited data
In the design of engineered components, rigorous vibration testing is essential for performance validation and identification of resonant frequencies and amplitudes encountered during operation. Pe...
D. Bluedorn, A. Badawy, B. E. Saunders, D. Roettgen, A. Abdelkefi
Reason and Verify: A Framework for Faithful Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) significantly improves the factuality of Large Language Models (LLMs), yet standard pipelines often lack mechanisms to verify inter- mediate reasoning, leaving ...
Eeham Khan, Luis Rodriguez, Marc Queudot
Uncertainty-Aware Deep Hedging
Deep hedging trains neural networks to manage derivative risk under market frictions, but produces hedge ratios with no measure of model confidence -- a significant barrier to deployment. We introd...
Manan Poddar
Adiabatic evolution of asymmetric binaries on generic orbits with new fundamental fields I: characterization of gravitational wave fluxes
We investigate the dynamics of asymmetric binaries in extensions of General Relativity featuring a massless scalar field non-minimally coupled to gravity, focusing on the interplay between eccentri...
Sara Gliorio, Matteo Della Rocca, Susanna Barsanti, Leonardo Gualtieri, Andrea Maselli, Thomas P....
The Cosmological Simulation Code OpenGadget3 -- Implementation of Self-Interacting Dark Matter
Dark matter (DM) could be subject to non-gravitational self-interactions which is relevant to resolve potential problems of cold DM on small scales. Their impact on astrophysical objects such as ga...
Moritz S. Fischer, Marc Wiertel, Cenanda Arido, Yashraj Patil, Antonio Ragagnin, Klaus Dolag, Mar...
CREATE: Testing LLMs for Associative Creativity
A key component of creativity is associative reasoning: the ability to draw novel yet meaningful connections between concepts. We introduce CREATE, a benchmark designed to evaluate models' capacity...
Manya Wadhwa, Tiasa Singha Roy, Harvey Lederman, Junyi Jessy Li, Greg Durrett
Understanding the Use of a Large Language Model-Powered Guide to Make Virtual Reality Accessible for Blind and Low Vision People
As social virtual reality (VR) grows more popular, addressing accessibility for blind and low vision (BLV) users is increasingly critical. Researchers have proposed an AI "sighted guide" to help us...
Jazmin Collins, Sharon Y Lin, Tianqi Liu, Andrea Stevenson Won, Shiri Azenkot
Think Before You Lie: How Reasoning Improves Honesty
While existing evaluations of large language models (LLMs) measure deception rates, the underlying conditions that give rise to deceptive behavior are poorly understood. We investigate this questio...
Ann Yuan, Asma Ghandeharioun, Carter Blum, Alicia Machado, Jessica Hoffmann, Daphne Ippolito, Mar...
Towards a Neural Debugger for Python
Training large language models (LLMs) on Python execution traces grounds them in code execution and enables the line-by-line execution prediction of whole Python programs, effectively turning them ...
Maximilian Beck, Jonas Gehring, Jannik Kossen, Gabriel Synnaeve
Towards Flexible Spectrum Access: Data-Driven Insights into Spectrum Demand
In the diverse landscape of 6G networks, where wireless connectivity demands surge and spectrum resources remain limited, flexible spectrum access becomes paramount. The success of crafting such sc...
Mohamad Alkadamani, Amir Ghasemi, Halim Yanikomeroglu
Model Merging in the Era of Large Language Models: Methods, Applications, and Future Directions
Model merging has emerged as a transformative paradigm for combining the capabilities of multiple neural networks into a single unified model without additional training. With the rapid proliferati...
Mingyang Song, Mao Zheng
Adaptive Clinical-Aware Latent Diffusion for Multimodal Brain Image Generation and Missing Modality Imputation
Multimodal neuroimaging provides complementary insights for Alzheimer's disease diagnosis, yet clinical datasets frequently suffer from missing modalities. We propose ACADiff, a framework that synt...
Rong Zhou, Houliang Zhou, Yao Su, Brian Y. Chen, Yu Zhang, Lifang He, Alzheimer's Disease Neuroim...
Optical and orbital characterization of spherically symmetric static black holes of self-gravitating new nonlinear electrodynamics model
Horizon scale imaging and precision lensing have turned black holes into quantitative laboratories for strong gravity and for non standard electromagnetic physics. We study the optical appearance a...
İlim İrfan Çimdiker, Ali Övgün, Yosef Verbin
AI-Enabled Data-driven Intelligence for Spectrum Demand Estimation
Accurately forecasting spectrum demand is a key component for efficient spectrum resource allocation and management. With the rapid growth in demand for wireless services, mobile network operators ...
Colin Brown, Mohamad Alkadamani, Halim Yanikomeroglu
NanoBench: A Multi-Task Benchmark Dataset for Nano-Quadrotor System Identification, Control, and State Estimation
Existing aerial-robotics benchmarks target vehicles from hundreds of grams to several kilograms and typically expose only high-level state data. They omit the actuator-level signals required to stu...
Syed Izzat Ullah, Jose Baca
Thinking to Recall: How Reasoning Unlocks Parametric Knowledge in LLMs
While reasoning in LLMs plays a natural role in math, code generation, and multi-hop factual questions, its effect on simple, single-hop factual questions remains unclear. Such questions do not req...
Zorik Gekhman, Roee Aharoni, Eran Ofek, Mor Geva, Roi Reichart, Jonathan Herzig