arXiv:2509.01081v2 Announce Type: replace-cross Abstract: This paper evaluates the knowledge and reasoning capabilities of Large Language Models in Islamic inheritance law, known as ‘ilm al-mawarith. We assess the performance of seven LLMs using a benchmark of 1,000 multiple-choice questions covering…
arXiv:2509.14195v1 Announce Type: new Abstract: Mental representation, characterized by structured internal models mirroring external environments, is fundamental to advanced cognition but remains challenging to investigate empirically. Existing theory hypothesizes that second-order learning — learning mechanisms that adapt first-order learning (i.e.,…
arXiv:2509.11417v2 Announce Type: replace-cross Abstract: Vision-language-action (VLA) models finetuned from vision-language models (VLMs) hold the promise of leveraging rich pretrained representations to build generalist robots across diverse tasks and environments. However, direct fine-tuning on robot data often disrupts these representations…
arXiv:2010.01052v3 Announce Type: cross Abstract: The use of mechanistic models in clinical studies is limited by the lack of multi-modal patients data representing different anatomical and physiological processes. For example, neuroimaging datasets do not provide a sufficient representation of heart…
arXiv:2509.14001v1 Announce Type: cross Abstract: We introduce MOCHA (Multi-modal Objects-aware Cross-arcHitecture Alignment), a knowledge distillation approach that transfers region-level multimodal semantics from a large vision-language teacher (e.g., LLaVa) into a lightweight vision-only object detector student (e.g., YOLO). A translation module…
arXiv:2408.00208v1 Announce Type: cross Abstract: Purpose: Artificial intelligence (AI) techniques have been extensively utilized for diagnosing and prognosis of several diseases in recent years. This study identifies, appraises and synthesizes published studies on the use of AI for the prognosis…
arXiv:2406.12945v4 Announce Type: replace-cross Abstract: The ability to train generative models that produce realistic, safe and useful tabular data is essential for data privacy, imputation, oversampling, explainability or simulation. However, generating tabular data is not straightforward due to its heterogeneity,…
arXiv:2407.01613v2 Announce Type: replace-cross Abstract: Physics-informed deep learning has emerged as a promising alternative for solving partial differential equations. However, for complex problems, training these networks can still be challenging, often resulting in unsatisfactory accuracy and efficiency. In this work,…
arXiv:2509.13548v1 Announce Type: cross Abstract: We propose a novel mixture of experts framework for field-of-view enhancement in binaural signal matching. Our approach enables dynamic spatial audio rendering that adapts to continuous talker motion, allowing users to emphasize or suppress sounds…
arXiv:2509.05775v2 Announce Type: replace Abstract: Estimating heterogeneous treatment effects is critical in domains such as personalized medicine, resource allocation, and policy evaluation. A central challenge lies in identifying subpopulations that respond differently to interventions, thereby enabling more targeted and effective…
