arXiv:2510.03534v4 Announce Type: replace-cross Abstract: We study the problem of long-term (multiple days) mapping of a river plume using multiple autonomous underwater vehicles (AUVs), focusing on the Douro river representative use-case. We propose an energy – and communication – efficient multi-agent reinforcement learning approach in which a central coordinator intermittently communicates with the AUVs, collecting measurements and issuing commands. Our approach integrates spatiotemporal Gaussian process regression (GPR) with a multi-head Q-network controller that regulates direction and speed for […]

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arXiv:2602.00881v1 Announce Type: new Abstract: Legal Statute Identification (LSI) for a given situation is one of the most fundamental tasks in Legal NLP. This task has traditionally been modeled using facts from court judgments as input queries, due to their abundance. However, in practical settings, the input queries are likely to be informal and asked by laypersons, or non-professionals. While a few laypeople LSI datasets exist, there has been little research to explore the differences between court and […]

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arXiv:2602.02533v1 Announce Type: new Abstract: Vision Language Action (VLA) models have recently shown great potential in bridging multimodal perception with robotic control. However, existing methods often rely on direct fine-tuning of pre-trained Vision-Language Models (VLMs), feeding semantic and visual features directly into a policy network without fully addressing the unique semantic alignment challenges in the VLA domain. In this paper, we propose HMVLA, a novel VLA framework that exploits the inherent hierarchical structures in vision and language for […]

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Key Highlights: There has been an active debate on the internet about whether tech giants should push AI features to users or let them choose. Microsoft and Google have been two of the notable tech companies that have been increasingly experimenting with AI features. While Google’s implementation is often praised, there’s a section that has been heavily criticizing Microsoft for pushing AI nonsensically all over Windows. Users have complained that rather than fixing Windows 11 at its core, […]

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Class imbalance induces systematic bias in deep neural networks by imposing a skewed effective class prior. This work introduces the Neural Prior Estimator (NPE), a framework that learns feature-conditioned log-prior estimates from latent representations. NPE employs one or more Prior Estimation Modules trained jointly with the backbone via a one-way logistic loss. Under the Neural Collapse regime, NPE is analytically shown to recover the class log-prior up to an additive constant, providing a theoretically grounded adaptive signal without […]

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arXiv:2602.16758v1 Announce Type: new Abstract: This paper presents a smooth trajectory generation method for a four-degree-of-freedom parallel kinematic milling robot. The proposed approach integrates B-spline and Quaternion interpolation techniques to manage decoupled position and orientation data points. The synchronization of orientation and arc-length-parameterized position data is achieved through the fitting of smooth piece-wise Bezier curves, which describe the non-linear relationship between path length and tool orientation, solved via sequential quadratic programming. By leveraging the convex hull properties of […]

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Before training a model, you have to preprocess data. This is necessary to transform raw data into clean data suitable for analysis. In this guide, we will cover essential steps to preprocess data using Python. These include splitting the dataset into training and validation sets, handling missing values, managing categorical features, and normalizing the dataset. Why do you need to preprocess data? Data preprocessing is important for several reasons: Improves data quality. Data preprocessing techniques such as handling […]

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arXiv:2603.07371v1 Announce Type: cross Abstract: The success of deep generative models in scientific discovery requires not only the ability to generate novel candidates but also reliable guarantees that these candidates indeed satisfy desired properties. Recent conformal-prediction methods offer a path to such guarantees, but its application to generative modeling in drug discovery is limited by budget constraints, lack of oracle access, and distribution shift. To this end, we introduce ConfHit, a distribution-free framework that provides validity guarantees under […]

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