Switchable Activation Networks
arXiv:2603.06601v1 Announce Type: new Abstract: Deep neural networks, and more recently large-scale generative models such as large language models (LLMs) and large vision-action models (LVAs), achieve remarkable performance across diverse domains, yet their prohibitive computational cost hinders deployment in resource-constrained...
Correlation Analysis of Generative Models
arXiv:2603.06614v1 Announce Type: new Abstract: Based on literature review about existing diffusion models and flow matching with a neural network to predict a predefined target from noisy data, a unified representation is first proposed for these models using two simple...
Distilling and Adapting: A Topology-Aware Framework for Zero-Shot Interaction Prediction in Multiplex Biological Networks
arXiv:2603.06618v1 Announce Type: new Abstract: Multiplex Biological Networks (MBNs), which represent multiple interaction types between entities, are crucial for understanding complex biological systems. Yet, existing methods often inadequately model multiplexity, struggle to integrate structural and sequence information, and face difficulties...
HEARTS: Benchmarking LLM Reasoning on Health Time Series
arXiv:2603.06638v1 Announce Type: new Abstract: The rise of large language models (LLMs) has shifted time series analysis from narrow analytics to general-purpose reasoning. Yet, existing benchmarks cover only a small set of health time series modalities and tasks, failing to...
Geodesic Gradient Descent: A Generic and Learning-rate-free Optimizer on Objective Function-induced Manifolds
arXiv:2603.06651v1 Announce Type: new Abstract: Euclidean gradient descent algorithms barely capture the geometry of objective function-induced hypersurfaces and risk driving update trajectories off the hypersurfaces. Riemannian gradient descent algorithms address these issues but fail to represent complex hypersurfaces via a...
Don't Freeze, Don't Crash: Extending the Safe Operating Range of Neural Navigation in Dense Crowds
arXiv:2603.06729v1 Announce Type: new Abstract: Navigating safely through dense crowds requires collision avoidance that generalizes beyond the densities seen during training. Learning-based crowd navigation can break under out-of-distribution crowd sizes due to density-sensitive observation normalization and social-cost scaling, while analytical...
Relational Semantic Reasoning on 3D Scene Graphs for Open World Interactive Object Search
arXiv:2603.05642v1 Announce Type: cross Abstract: Open-world interactive object search in household environments requires understanding semantic relationships between objects and their surrounding context to guide exploration efficiently. Prior methods either rely on vision-language embeddings similarity, which does not reliably capture task-relevant...
Boosting deep Reinforcement Learning using pretraining with Logical Options
arXiv:2603.06565v1 Announce Type: new Abstract: Deep reinforcement learning agents are often misaligned, as they over-exploit early reward signals. Recently, several symbolic approaches have addressed these challenges by encoding sparse objectives along with aligned plans. However, purely symbolic architectures are complex...
Offline Materials Optimization with CliqueFlowmer
arXiv:2603.06082v1 Announce Type: new Abstract: Recent advances in deep learning inspired neural network-based approaches to computational materials discovery (CMD). A plethora of problems in this field involve finding materials that optimize a target property. Nevertheless, the increasingly popular generative modeling...
JAWS: Enhancing Long-term Rollout of Neural Operators via Spatially-Adaptive Jacobian Regularization
arXiv:2603.05538v1 Announce Type: cross Abstract: Data-driven surrogate models improve the efficiency of simulating continuous dynamical systems, yet their autoregressive rollouts are often limited by instability and spectral blow-up. While global regularization techniques can enforce contractive dynamics, they uniformly damp high-frequency...
CBR-to-SQL: Rethinking Retrieval-based Text-to-SQL using Case-based Reasoning in the Healthcare Domain
arXiv:2603.05569v1 Announce Type: cross Abstract: Extracting insights from Electronic Health Record (EHR) databases often requires SQL expertise, creating a barrier for healthcare decision-making and research. While a promising approach is to use Large Language Models (LLMs) to translate natural language...
PRISM: Personalized Refinement of Imitation Skills for Manipulation via Human Instructions
arXiv:2603.05574v1 Announce Type: cross Abstract: This paper presents PRISM: an instruction-conditioned refinement method for imitation policies in robotic manipulation. This approach bridges Imitation Learning (IL) and Reinforcement Learning (RL) frameworks into a seamless pipeline, such that an imitation policy on...
IntSeqBERT: Learning Arithmetic Structure in OEIS via Modulo-Spectrum Embeddings
arXiv:2603.05556v1 Announce Type: new Abstract: Integer sequences in the OEIS span values from single-digit constants to astronomical factorials and exponentials, making prediction challenging for standard tokenised models that cannot handle out-of-vocabulary values or exploit periodic arithmetic structure. We present IntSeqBERT,...
A Novel Hybrid Heuristic-Reinforcement Learning Optimization Approach for a Class of Railcar Shunting Problems
arXiv:2603.05579v1 Announce Type: new Abstract: Railcar shunting is a core planning task in freight railyards, where yard planners need to disassemble and reassemble groups of railcars to form outbound trains. Classification tracks with access from one side only can be...
Bias In, Bias Out? Finding Unbiased Subnetworks in Vanilla Models
arXiv:2603.05582v1 Announce Type: new Abstract: The issue of algorithmic biases in deep learning has led to the development of various debiasing techniques, many of which perform complex training procedures or dataset manipulation. However, an intriguing question arises: is it possible...
Warm Starting State-Space Models with Automata Learning
arXiv:2603.05694v1 Announce Type: new Abstract: We prove that Moore machines can be exactly realized as state-space models (SSMs), establishing a formal correspondence between symbolic automata and these continuous machine learning architectures. These Moore-SSMs preserve both the complete symbolic structure and...
Predictive Coding Graphs are a Superset of Feedforward Neural Networks
arXiv:2603.06142v1 Announce Type: new Abstract: Predictive coding graphs (PCGs) are a recently introduced generalization to predictive coding networks, a neuroscience-inspired probabilistic latent variable model. Here, we prove how PCGs define a mathematical superset of feedforward artificial neural networks (multilayer perceptrons)....
Ensemble Graph Neural Networks for Probabilistic Sea Surface Temperature Forecasting via Input Perturbations
arXiv:2603.06153v1 Announce Type: new Abstract: Accurate regional ocean forecasting requires models that are both computationally efficient and capable of representing predictive uncertainty. This work investigates ensemble learning strategies for sea surface temperature (SST) forecasting using Graph Neural Networks (GNNs), with...
Possibilities of using artificial intelligence and natural language processing to analyse legal norms and interpret them
The study aaddressed the possibilities of using information technology and natural language in the study of legal norms. The study aimed to develop methods for using artificial intelligence and natural language processing to analyse jurisprudence. To achieve this goal, automatic...
In Defence of Principlism in AI Ethics and Governance
Demystifying the Draft EU Artificial Intelligence Act — Analysing the good, the bad, and the unclear elements of the proposed approach
AI standardization promises to support the implementation of EU legislation and promote the rapid transfer,transparency, and interoperability of this massively disruptive technology. However, apart from well-known practical difficulties stemming from the unique probabilistic nature and the rapid development of AI...
Protests During the Pandemic
As a general rule, the government is permitted to restrict activities, including protesting, during the COVID-19 pandemic. The government can regulate the time, place, and manner of speech in public forums with a content neutral restriction so long as the...
Shaping the future of AI in healthcare through ethics and governance
Abstract The purpose of this research is to identify and evaluate the technical, ethical and regulatory challenges related to the use of Artificial Intelligence (AI) in healthcare. The potential applications of AI in healthcare seem limitless and vary in their...
UW Theme 2.0 is now available!
Welcome the UW Theme! The kitchen sink page is your guide to all the visual page elements currently available through the theme. If you prefer to print out your options, please download the current component key (pdf). This WordPress theme...
Four Responsibility Gaps with Artificial Intelligence: Why they Matter and How to Address them
Abstract The notion of “responsibility gap” with artificial intelligence (AI) was originally introduced in the philosophical debate to indicate the concern that “learning automata” may make more difficult or impossible to attribute moral culpability to persons for untoward events. Building...
TDM copyright for AI in Europe: a view from Portugal
Abstract The development of artificial intelligence (AI) justified the introduction at the level of the European Union (EU) of a new copyright exception regarding text and data mining (TDM) for purposes of scientific research conducted by research organizations and entities...
Online Courts and the Future of Justice
In Online Courts and the Future of Justice, Richard Susskind, the world’s most cited author on the future of legal services, shows how litigation will be transformed by technology and proposes a solution to the global access-to-justice problem. In most...
Bias Preservation in Machine Learning: The Legality of Fairness Metrics Under EU Non-Discrimination Law
Artificial Intelligence Crime: An Interdisciplinary Analysis of Foreseeable Threats and Solutions
Artificial intelligence (AI) research and regulation seek to balance the benefits of innovation against any potential harms and disruption. However, one unintended consequence of the recent surge in AI research is the potential re-orientation of AI technologies to facilitate criminal...
The New Regulation of the European Union on Artificial Intelligence: Fuzzy Ethics Diffuse into Domestic Law and Sideline International Law