arXiv:2509.13642v1 Announce Type: new Abstract: We propose LLM-Interleaved (LLM-I), a flexible and dynamic framework that reframes interleaved image-text generation as a tool-use problem. LLM-I is designed to overcome the “one-tool” bottleneck of current unified models, which are limited to synthetic…
arXiv:2509.05037v3 Announce Type: replace Abstract: Accurate prediction of time-to-event outcomes is a central challenge in oncology, with significant implications for treatment planning and patient management. In this work, we present ModaliSurv, a multimodal deep survival model utilising DeepHit with a…
arXiv:2509.13636v1 Announce Type: new Abstract: This study introduces a novel method that transforms multimodal physiological signalsphotoplethysmography (PPG), galvanic skin response (GSR), and acceleration (ACC) into 2D image matrices to enhance stress detection using convolutional neural networks (CNNs). Unlike traditional approaches…
arXiv:2502.12484v2 Announce Type: replace Abstract: Neural solvers have shown significant potential in solving the Traveling Salesman Problem (TSP), yet current approaches face significant challenges. Supervised learning (SL)-based solvers require large amounts of high-quality labeled data, while reinforcement learning (RL)-based solvers,…
arXiv:2509.13634v1 Announce Type: new Abstract: Federated learning (FL) has gained popularity as a privacy-preserving method of training machine learning models on decentralized networks. However to ensure reliable operation of UAV-assisted FL systems, issues like as excessive energy consumption, communication inefficiencies,…
arXiv:2407.06544v2 Announce Type: replace Abstract: We explore multiple instance verification, a problem setting in which a query instance is verified against a bag of target instances with heterogeneous, unknown relevancy. We show that naive adaptations of attention-based multiple instance learning…
arXiv:2509.13520v1 Announce Type: new Abstract: Neural surrogates and operator networks for solving partial differential equation (PDE) problems have attracted significant research interest in recent years. However, most existing approaches are limited in their ability to generalize solutions across varying non-parametric…
arXiv:2509.13523v1 Announce Type: new Abstract: Generative machine learning offers new opportunities to better understand complex Earth system dynamics. Recent diffusion-based methods address spectral biases and improve ensemble calibration in weather forecasting compared to deterministic methods, yet have so far proven…
arXiv:2509.13527v1 Announce Type: new Abstract: Chemists in search of structure-property relationships face great challenges due to limited high quality, concordant datasets. Machine learning (ML) has significantly advanced predictive capabilities in chemical sciences, but these modern data-driven approaches have increased the…
arXiv:2509.13621v1 Announce Type: new Abstract: This paper introduces an automated fault analysis framework for the Advanced Light Source (ALS) that processes real-time event logs from its EPICS control system. By treating log entries as natural language, we transform them into…
