Papers
Research papers from arXiv and related sources
Self-testing with untrusted random number generators
Self-testing--the attractive possibility to infer the underlying physics of a quantum device in a black-box scenario--has gained increased traction in recent years, with applications to device-inde...
Moisés Bermejo Morán, Ravishankar Ramanathan
Planning for isolation? The role of urban form and function in shaping mobility in Brasília
Brasília offers a rare test of how urban form shapes experienced segregation. Built almost at once around modernist neighbourhood units, then expanded through planned satellites and informal periph...
Andrew Renninger
ESG Reporting Lifecycle Management with Large Language Models and AI Agents
Environmental, Social, and Governance (ESG) standards have been increasingly adopted by organizations to demonstrate accountability towards ethical, social, and sustainability goals. However, gener...
Thong Hoang, Mykhailo Klymenko, Xiwei Xu, Shidong Pan, Yi Ding, Xushuo Tang, Zhengyi Yang, Jieke ...
Making Bielik LLM Reason (Better): A Field Report
This paper presents a research program dedicated to evaluating and advancing the reasoning capabilities of Bielik, a Polish large language model. The study describes a number of stages of work: ini...
Adam Trybus, Bartosz Bartnicki, Remigiusz Kinas
Splat2Real: Novel-view Scaling for Physical AI with 3D Gaussian Splatting
Physical AI faces viewpoint shift between training and deployment, and novel-view robustness is essential for monocular RGB-to-3D perception. We cast Real2Render2Real monocular depth pretraining as...
Hansol Lim, Jongseong Brad Choi
Flexible Multi-Target Angular Emulation for Over-the-Air Testing of Large-Scale ISAC Base Stations: Principle and Experimental Verification
Over-the-air (OTA) emulation of diverse sensing target characteristics in a controlled laboratory environment is pivotal for advancing integrated sensing and communication (ISAC) technology, as it ...
Chunhui Li, Hao Sun, Wei Fan
Reinforcement Learning with Conditional Expectation Reward
Reinforcement Learning with Verifiable Rewards (RLVR) has proven effective in enhancing the reasoning capabilities of large language models, particularly in domains such as mathematics where reliab...
Changyi Xiao, Caijun Xu, Yixin Cao
QuantumX: an experience for the consolidation of Quantum Computing and Quantum Software Engineering as an emerging discipline
The first edition of the QuantumX track, held within the XXIX Jornadas de Ingeniería del Software y Bases de Datos (JISBD 2025), brought together leading Spanish research groups working at the inte...
Juan M. Murillo, Ignacio García Rodríguez de Guzmán, Enrique Moguel, Javier Romero-Álvarez, Jaime...
Disentangling Similarity and Relatedness in Topic Models
The recent advancement of large language models has spurred a growing trend of integrating pre-trained language model (PLM) embeddings into topic models, fundamentally reshaping how topics capture ...
Hanlin Xiao, Mauricio A. Álvarez, Rainer Breitling
Formulation of intrinsic nonlinear thermal conductivity for bosonic systems using quantum kinetic equation
Nonlinear responses in transport phenomena have attracted significant attention because they can arise even when linear responses are forbidden by symmetry, with the quantum geometry of Bloch wave ...
Aoi Kuwabara, Joji Nasu
Trajectory-Informed Memory Generation for Self-Improving Agent Systems
LLM-powered agents face a persistent challenge: learning from their execution experiences to improve future performance. While agents can successfully complete many tasks, they often repeat ineffic...
Gaodan Fang, Vatche Isahagian, K. R. Jayaram, Ritesh Kumar, Vinod Muthusamy, Punleuk Oum, Gegi Th...
Layer Consistency Matters: Elegant Latent Transition Discrepancy for Generalizable Synthetic Image Detection
Recent rapid advancement of generative models has significantly improved the fidelity and accessibility of AI-generated synthetic images. While enabling various innovative applications, the unprece...
Yawen Yang, Feng Li, Shuqi Kong, Yunfeng Diao, Xinjian Gao, Zenglin Shi, Meng Wang
Strong Gaussian approximation for U-statistics in high dimensions and beyond
We establish a strong Gaussian approximation for high-dimensional non-degenerate U-statistics with diverging dimension. Under mild assumptions, we construct, on a sufficiently rich probability spac...
Weijia Li, Leheng Cai, Qirui Hu
The Asteroid Framing Cameras on ESA's Hera mission
As the first asteroid deflection test, NASA's successfully hit asteroid Dimorphos (secondary of the binary asteroid 65803 Didymos) with the DART kinetic impactor on September 26, 2022. To fully cha...
Jean-Baptiste Vincent, Gábor Kovács, Balázs V. Nagy, Frank Preusker, Naomi Murdoch, Maurizio Pajo...
Exact Interpolation under Noise: A Reproducible Comparison of Clough-Tocher and Multiquadric RBF Surfaces
This paper presents a reproducible comparison of cubic and radial basis function (RBF) interpolants for multivariate surface analysis. To eliminate evaluation bias, both methods are assessed under ...
Mirkan Emir Sancak
Does LLM Alignment Really Need Diversity? An Empirical Study of Adapting RLVR Methods for Moral Reasoning
Reinforcement learning with verifiable rewards (RLVR) has achieved remarkable success in logical reasoning tasks, yet whether large language model (LLM) alignment requires fundamentally different a...
Zhaowei Zhang, Xiaohan Liu, Xuekai Zhu, Junchao Huang, Ceyao Zhang, Zhiyuan Feng, Yaodong Yang, X...
Distilling LLM Semantic Priors into Encoder-Only Multi-Talker ASR with Talker-Count Routing
Large language models (LLMs) provide strong semantic priors that can improve multi-talker automatic speech recognition (MT-ASR), but using an LLM as an autoregressive decoder is computationally exp...
Hao Shi, Yusuke Fujita, Roman Koshkin, Mengjie Zhao, Yuan Gao, Lianbo Liu, Yui Sudo
Need for Speed: Zero-Shot Depth Completion with Single-Step Diffusion
We introduce Marigold-SSD, a single-step, late-fusion depth completion framework that leverages strong diffusion priors while eliminating the costly test-time optimization typically associated with...
Jakub Gregorek, Paraskevas Pegios, Nando Metzger, Konrad Schindler, Theodora Kontogianni, Lazaros...
Attribution as Retrieval: Model-Agnostic AI-Generated Image Attribution
With the rapid advancement of AIGC technologies, image forensics will encounter unprecedented challenges. Traditional methods are incapable of dealing with increasingly realistic images generated b...
Hongsong Wang, Renxi Cheng, Chaolei Han, Jie Gui
Implicit Statistical Inference in Transformers: Approximating Likelihood-Ratio Tests In-Context
In-context learning (ICL) allows Transformers to adapt to novel tasks without weight updates, yet the underlying algorithms remain poorly understood. We adopt a statistical decision-theoretic persp...
Faris Chaudhry, Siddhant Gadkari