arXiv:2511.07657v1 Announce Type: new Abstract: Enterprise relational databases increasingly contain vast amounts of non-semantic data – IP addresses, product identifiers, encoded keys, and timestamps – that challenge traditional semantic analysis. This paper introduces a novel Character-Level Autoencoder (CAE) approach that…
arXiv:2511.08424v1 Announce Type: cross Abstract: Process-structure-property relationships are fundamental in materials science and engineering and are key to the development of new and improved materials. Symbolic regression serves as a powerful tool for uncovering mathematical models that describe these relationships.…
arXiv:2511.07658v1 Announce Type: new Abstract: Although recent advancements in learning-based analog circuit design automation have tackled tasks such as topology generation, device sizing, and layout synthesis, efficient performance evaluation remains a major bottleneck. Traditional SPICE simulations are time-consuming, while existing…
arXiv:2511.08577v1 Announce Type: cross Abstract: Improving reasoning capabilities of Large Language Models (LLMs), especially under parameter constraints, is crucial for real-world applications. Prior work proposes recurrent transformers, which allocate a fixed number of extra iterations per token to improve generation…
arXiv:2511.07694v1 Announce Type: new Abstract: Large Language Models (LLMs) exhibit strong performance across various natural language processing (NLP) tasks but remain vulnerable to hallucinations, generating factually incorrect or misleading outputs. Uncertainty estimation, often using predictive entropy estimation, is key to…
arXiv:2406.01539v3 Announce Type: replace Abstract: On the forefront of scientific computing, Deep Learning (DL), i.e., machine learning with Deep Neural Networks (DNNs), has emerged a powerful new tool for solving Partial Differential Equations (PDEs). It has been observed that DNNs…
arXiv:2511.07700v1 Announce Type: new Abstract: Artificial Intelligence (AI) models have demonstrated expert-level performance in melanoma detection, yet their clinical adoption is hindered by performance disparities across demographic subgroups such as gender, race, and age. Previous efforts to benchmark the performance…
arXiv:2501.06268v2 Announce Type: replace Abstract: We introduce a new method for clustering based on Cluster Catch Digraphs (CCDs). The new method addresses the limitations of RK-CCDs by employing a new variant of spatial randomness test that employs the nearest neighbor…
arXiv:2511.07701v1 Announce Type: new Abstract: Reinforcement learning (RL) systems, while achieving remarkable success across various domains, are vulnerable to adversarial attacks. This is especially a concern in vision-based environments where minor manipulations of high-dimensional image inputs can easily mislead the…
arXiv:2505.11770v2 Announce Type: replace Abstract: Interpretability research now offers a variety of techniques for identifying abstract internal mechanisms in neural networks. Can such techniques be used to predict how models will behave on out-of-distribution examples? In this work, we provide…
