Hallucination in generative AI is often treated as a technical failure to produce factually correct output. Yet this framing underrepresents the broader significance of hallucinated content in language models, which may appear fluent, persuasive, and contextually appropriate while conveying distortions that escape conventional accuracy checks. This paper critically examines how regulatory and evaluation frameworks have inherited a narrow view of hallucination, one that prioritises surface verifiability over deeper questions of meaning, influence, and impact. We propose a layered approach to understanding hallucination risks, encompassing epistemic instability, user misdirection, and social-scale effects. Drawing on interdisciplinary sources and examining instruments such as the EU AI Act and the GDPR, we show that current governance models struggle to address hallucination when it manifests as ambiguity, bias reinforcement, or normative convergence. Rather than improving factual precision alone, we argue for regulatory responses that account for languages generative nature, the asymmetries between system and user, and the shifting boundaries between information, persuasion, and harm.
Interoperability is increasingly recognised as a foundational principle for fostering innovation, competition, and user autonomy in the evolving digital ecosystem. Existing research on interoperability predominantly focuses either on technological interoperability itself or on the legal regulations concerning interoperability, with insufficient exploration of their interdisciplinary intersection. This paper compares the technological interoperability in Web 3.0 with the theoretical framework of legal interoperability established by the EU Data Act, analysing the areas of convergence and mismatch. The goal is to align technical interoperability with legal concepts of interoperability, thereby enhancing the practical implementation of systematic interoperability in the next generation of the Web. This study finds that, firstly, Web 3.0's concept of interoperability spans data, systems, and applications, while the Data Act focuses solely on data. This narrow scope risks creating a fragmented ecosystem, where data exchange is possible, but full integration of systems and applications is hindered, leading to inefficiencies, and obstructing seamless data flow across platforms. Secondly, while Web 3.0 technically seeks to achieve interoperability through the integration of entire systems and decentralised applications, the compliance with Data Act might negatively limit such system and application interoperability through its data interoperability provisions. This paper suggests interdisciplinary recommendations to enhance the implementation and enforcement of interoperability. On one hand, the Data Act should broaden its concept of interoperability to encompass both the systems and applications layers. On the other hand, it is advisable to introduce provisions for standardised protocols through soft law mechanisms to address legal shortcomings and keep pace with technological advancements.
With data pipeline tools and the expressiveness of SQL, managing interdependent materialized views (MVs) are becoming increasingly easy. These MVs are updated repeatedly upon new data ingestion (e.g., daily), from which database admins can observe performance metrics (e.g., refresh time of each MV, size on disk) in a consistent way for different types of updates (full vs. incremental) and for different systems (single node, distributed, cloud-hosted). One missed opportunity is that existing data systems treat those MV updates as independent SQL statements without fully exploiting their dependency information and performance metrics. However, if we know that the result of a SQL statement will be consumed immediately after for subsequent operations, those subsequent operations do not have to wait until the early results are fully materialized on storage because the results are already readily available in memory. Of course, this may come at a cost because keeping results in memory (even temporarily) will reduce the amount of available memory; thus, our decision should be careful. In this paper, we introduce a new system, called S/C, which tackles this problem through efficient creation and update of a set of MVs with acyclic dependencies among them. S/C judiciously uses bounded memory to reduce end-to-end MV refresh time by short-circuiting expensive reads and writes; S/C's objective function accurately estimates time savings from keeping intermediate data in memory for particular periods. Our solution jointly optimizes an MV refresh order, what data to keep in memory, and when to release data from memory. At a high level, S/C still materializes all data exactly as defined in MV definitions; thus, it doesn't impact any service-level agreements. In our experiments with TPC-DS datasets (up to 1TB), we show S/C's optimization can speedup end-to-end runtime by 1.04x-5.08x with 1.6GB memory.
Abstract Quadrupedal animals show remarkable capabilities in traversing diverse terrains and display a range of behaviors and gait patterns. Achieving similar performance is a key goal for robotics researchers. We propose a bio-inspired approach to the design of quadrupeds that seeks to exploit the body and the passive properties of the robot. Using this novel approach we develop \textit{PAWS}, a Passive Automata With Synergies. By leveraging the principles of motor synergies, the design incorporates variable stiffness, biological anatomical insights, and self-organization to simplify control while maximizing its capabilities. The resulting synergy-based quadruped requires only four actuators and exhibits emergent, animal-like dynamical responses, including robustness to environmental perturbations and a wide range of behaviors. The finding contributes to the development of machine intelligence, and provides robots with more efficient and natural-looking robotic locomotion by combining synergistic actuation, compliant body properties, and embodied compensatory strategies.
