arXiv:2509.19315v2 Announce Type: replace-cross
Abstract: Arrhythmias are a major cause of sudden cardiac death in children, making automated rhythm classification from electrocardiograms (ECGs) clinically important. However, pediatric arrhythmia anal…
arXiv:2603.26996v1 Announce Type: cross
Abstract: We present FormalProofBench, a private benchmark designed to evaluate whether AI models can produce formally verified mathematical proofs at the graduate level. Each task pairs a natural-language probl…
arXiv:2508.01277v2 Announce Type: replace-cross
Abstract: Automated bioacoustic analysis is essential for biodiversity monitoring and conservation, requiring advanced deep learning models that can adapt to diverse bioacoustic tasks. This article prese…
arXiv:2603.26993v1 Announce Type: cross
Abstract: This technical note studies the reliability limits of LLM-based multi-agent planning as a delegated decision problem. We model the LLM-based multi-agent architecture as a finite acyclic decision networ…
arXiv:2506.04450v5 Announce Type: replace-cross
Abstract: Large Language Models (LLMs) are increasingly adopted across domains such as education, healthcare, and finance. In healthcare, LLMs support tasks including disease diagnosis, abnormality class…
arXiv:2603.26983v1 Announce Type: cross
Abstract: Art. 50 II of the EU Artificial Intelligence Act mandates dual transparency for AI-generated content: outputs must be labeled in both human-understandable and machine-readable form for automated verifi…
arXiv:2505.17288v2 Announce Type: replace-cross
Abstract: Using the bit string generation problem as a case study, we theoretically compare two standard methods for adapting large language models to new tasks. The first, referred to as supervised fine…
arXiv:2603.26982v1 Announce Type: cross
Abstract: Reinforcement learning algorithms have been widely used for decision-making tasks in various domains. However, the performance of these algorithms can be impacted by high variance and instability, part…
arXiv:2505.12412v3 Announce Type: replace-cross
Abstract: We introduce a novel particle-based algorithm for end-to-end training of latent diffusion models. We reformulate the training task as minimizing a free energy functional and obtain a gradient f…
arXiv:2603.26971v1 Announce Type: cross
Abstract: Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by atypical brain connectivity. One of the crucial steps in addressing ASD is its early detection. This study introduces a…