Latest AI News and Developments

A curated selection of the most significant AI news and developments from trusted sources. Each article is directly linked to its original publication for full context and details.

Q-ARDNS-Multi: A Multi-Agent Quantum Reinforcement Learning Framework with Meta-Cognitive Adaptation for Complex 3D Environments

Published by arXiv AI on June 5, 2025

arXiv:2506.03205v1 Announce Type: new Abstract: This paper presents Q-ARDNS-Multi, an advanced multi-agent quantum reinforcement learning (QRL) framework that extends the ARDNS-FN-Quantum model, where Q-ARDNS-Multi stands for "Quantum Adaptive Reward-Driven Neural Simulator - Multi-Agent". It integrates quantum circuits with RY gates, meta-cognitive adaptation, and multi-agent coordination...

A Trustworthiness-based Metaphysics of Artificial Intelligence Systems

Published by arXiv AI on June 5, 2025

arXiv:2506.03233v1 Announce Type: new Abstract: Modern AI systems are man-made objects that leverage machine learning to support our lives across a myriad of contexts and applications. Despite extensive epistemological and ethical debates, their metaphysical foundations remain relatively under explored. The orthodox view simply suggests that AI systems, as artifacts, lack well-posed identity and...

Axiomatics of Restricted Choices by Linear Orders of Sets with Minimum as Fallback

Published by arXiv AI on June 5, 2025

arXiv:2506.03315v1 Announce Type: new Abstract: We study how linear orders can be employed to realise choice functions for which the set of potential choices is restricted, i.e., the possible choice is not possible among the full powerset of all alternatives. In such restricted settings, constructing a choice function via a relation on the alternatives is not always possible. However, we show...

Helpful Agent Meets Deceptive Judge: Understanding Vulnerabilities in Agentic Workflows

Published by arXiv AI on June 5, 2025

arXiv:2506.03332v1 Announce Type: new Abstract: Agentic workflows -- where multiple large language model (LLM) instances interact to solve tasks -- are increasingly built on feedback mechanisms, where one model evaluates and critiques another. Despite the promise of feedback-driven improvement, the stability of agentic workflows rests on the reliability of the judge. However, judges may...

Verification-Guided Falsification for Safe RL via Explainable Abstraction and Risk-Aware Exploration

Published by arXiv AI on June 5, 2025

arXiv:2506.03469v1 Announce Type: new Abstract: Ensuring the safety of reinforcement learning (RL) policies in high-stakes environments requires not only formal verification but also interpretability and targeted falsification. While model checking provides formal guarantees, its effectiveness is limited by abstraction quality and the completeness of the underlying trajectory dataset. We propose...

Sources

This curated overview brings together articles from the following trusted sources:

• arXiv AI

Note: All content is directly sourced from the original publications. Each article is properly cited with links to the original sources. For the most up-to-date information, please visit the original articles through the provided links.