Papers
Research papers from arXiv and related sources
On the Pólya Frequency Order of the de Bruijn Newman Kernel. Certified Failure at Order Five and the Toeplitz Threshold Phenomenon
We prove that the classical de Bruijn--Newman kernel $K(u) = Φ(|u|)$, arising in the study of the Riemann zeta function via the de Bruijn--Newman constant, is not a Pólya frequency function of orde...
Wojciech Michałowski
In-context Pre-trained Time-Series Foundation Models adapt to Unseen Tasks
Time-series foundation models (TSFMs) have demonstrated strong generalization capabilities across diverse datasets and tasks. However, existing foundation models are typically pre-trained to enhanc...
Shangqing Xu, Harshavardhan Kamarthi, Haoxin Liu, B. Aditya Prakash
Quantifying the Expectation-Realisation Gap for Agentic AI Systems
Agentic AI systems are deployed with expectations of substantial productivity gains, yet rigorous empirical evidence reveals systematic discrepancies between pre-deployment expectations and post-de...
Sebastian Lobentanzer
PhantomRun: Auto Repair of Compilation Errors in Embedded Open Source Software
Continuous Integration (CI) pipelines for embedded software sometimes fail during compilation, consuming significant developer time for debugging. We study four major open-source embedded system pr...
Han Fu, Andreas Ermedahl, Sigrid Eldh, Kristian Wiklund, Philipp Haller, Cyrille Artho
Neural Bayesian updates to populations with growing gravitational-wave catalogs
As gravitational-wave catalogs grow, they will become increasingly computationally expensive to analyze in their entirety, especially when inferring astrophysical source populations with high-dimen...
Noah E. Wolfe, Matthew Mould, John Veitch, Salvatore Vitale
The Truthfulness Spectrum Hypothesis
Large language models (LLMs) have been reported to linearly encode truthfulness, yet recent work questions this finding's generality. We reconcile these views with the truthfulness spectrum hypothe...
Zhuofan Josh Ying, Shauli Ravfogel, Nikolaus Kriegeskorte, Peter Hase
Pseudo-Newtonian potential for accretion disks in a modified gravity spacetime around the black hole and underlying properties
We construct a pseudo-Newtonian potential (PNP) corresponding to a rotating black hole solution in a modified gravity (MGR) framework using a metric-based prescription. The motivation is to enable ...
Sriraj Chandra, Banibrata Mukhopadhyay
Tests of general relativity in pseudo-Newtonian approach
We investigate the extent to which pseudo-Newtonian gravitational potentials can reproduce classic tests of general relativity without resorting to full general relativistic formalisms. This is use...
Naman Goyal, Banibrata Mukhopadhyay, Ashish Kumar Meena
The origin of isolated millisecond pulsars in globular clusters
A significant fraction of millisecond pulsars (MSPs) in globular clusters (GCs) are observed as isolated objects, despite the widely accepted scenario in which MSPs are formed through recycling in ...
Raniere de Menezes
Galactic Center Gamma-Ray Excess from a Generic Triaxial Halo
Recent studies of Galactic surveys, such as Gaia, have revealed that the Milky Way's gravitational potential comes from a matter distribution that is triaxial and rotated with respect to the Galact...
Leo Qiyuan Hu, Ilias Cholis, Yi-Ming Zhong
The ALPINE-CRISTAL-JWST Survey: Chemical Abundance Comparison Between the ISM and CGM of Main-Sequence Galaxies at z=4-6
Gaseous halos around galaxies play an important role in galaxy evolution. The exchange of metals from the interstellar medium (ISM) to the circumgalactic medium (CGM) are caused by the formation, f...
Wuji Wang, Andreas L. Faisst, Kyle Finner, Livia Vallini, Andrea Pallottini, Enrico Veraldi, Bahr...
Mobile-O: Unified Multimodal Understanding and Generation on Mobile Device
Unified multimodal models can both understand and generate visual content within a single architecture. Existing models, however, remain data-hungry and too heavy for deployment on edge devices. We...
Abdelrahman Shaker, Ahmed Heakl, Jaseel Muhammad, Ritesh Thawkar, Omkar Thawakar, Senmao Li, Hish...
tttLRM: Test-Time Training for Long Context and Autoregressive 3D Reconstruction
We propose tttLRM, a novel large 3D reconstruction model that leverages a Test-Time Training (TTT) layer to enable long-context, autoregressive 3D reconstruction with linear computational complexit...
Chen Wang, Hao Tan, Wang Yifan, Zhiqin Chen, Yuheng Liu, Kalyan Sunkavalli, Sai Bi, Lingjie Liu, ...
Skill-Inject: Measuring Agent Vulnerability to Skill File Attacks
LLM agents are evolving rapidly, powered by code execution, tools, and the recently introduced agent skills feature. Skills allow users to extend LLM applications with specialized third-party code,...
David Schmotz, Luca Beurer-Kellner, Sahar Abdelnabi, Maksym Andriushchenko
Agentic AI for Scalable and Robust Optical Systems Control
We present AgentOptics, an agentic AI framework for high-fidelity, autonomous optical system control built on the Model Context Protocol (MCP). AgentOptics interprets natural language tasks and exe...
Zehao Wang, Mingzhe Han, Wei Cheng, Yue-Kai Huang, Philip Ji, Denton Wu, Mahdi Safari, Flemming H...
PackFlow: Generative Molecular Crystal Structure Prediction via Reinforcement Learning Alignment
Organic molecular crystals underpin technologies ranging from pharmaceuticals to organic electronics, yet predicting solid-state packing of molecules remains challenging because candidate generatio...
Akshay Subramanian, Elton Pan, Juno Nam, Maurice Weiler, Shuhui Qu, Cheol Woo Park, Tommi S. Jaak...
Development of a Cherenkov-Based Time-of-Flight Detector Using Silicon Photomultipliers
The aim of this work is to develop high precision Time-of-Flight (TOF) devices based on high refractive index solid Cherenkov radiators read out by silicon photomultipliers (SiPMs). Cherenkov light...
Liliana Congedo, Giuseppe De Robertis, Antonio Di Mauro, Mario Giliberti, Francesco Licciulli, An...
Do Large Language Models Understand Data Visualization Rules?
Data visualization rules-derived from decades of research in design and perception-ensure trustworthy chart communication. While prior work has shown that large language models (LLMs) can generate ...
Martin Sinnona, Valentin Bonas, Emmanuel Iarussi, Viviana Siless
KNIGHT: Knowledge Graph-Driven Multiple-Choice Question Generation with Adaptive Hardness Calibration
With the rise of large language models (LLMs), they have become instrumental in applications such as Retrieval-Augmented Generation (RAG). Yet evaluating these systems remains bottlenecked by the t...
Mohammad Amanlou, Erfan Shafiee Moghaddam, Yasaman Amou Jafari, Mahdi Noori, Farhan Farsi, Behnam...
AdaEvolve: Adaptive LLM Driven Zeroth-Order Optimization
The paradigm of automated program generation is shifting from one-shot generation to inference-time search, where Large Language Models (LLMs) function as semantic mutation operators within evoluti...
Mert Cemri, Shubham Agrawal, Akshat Gupta, Shu Liu, Audrey Cheng, Qiuyang Mang, Ashwin Naren, Lut...