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MARLA: A Conceptual Scaffold for Regulatory Learning under the EU AI Act

2026-09-04 · Alessio Buscemi, Tom Deckenbrunnen, Imane Hmiddou, Marco Billi, Livio Rubino, Silvia Rizzuto Ferruzza, Daniele Pagani, Antonino Rotolo arxiv

The EU AI Act positions regulation as part of the infrastructure for safe, trustworthy and market-ready innovation. Realising this ambition requires regulatory learning: the evidence generated during implementation must be translated into governance and legal knowledge that supports consistent interpretation, effective oversight, and adaptation as technologies evolve. Yet the actors who produce this evidence and those who rely on it operate in different professional worlds. This paper proposes MARLA (Map, Assess, Report, Learn, Adapt), a conceptual scaffold organising regulatory learning as a five-stage cycle centred on the implementation of legal requirements into socio-technical practices, situated at the Local, National and European levels of the AI Act's governance architecture. Deliberately non-prescriptive, MARLA gives technical and legal stakeholders a shared vocabulary in which each of the first three stages generates its own documentable form of regulatory learning. We illustrate the scaffold with two piloted case studies and a prospective National-to-European illustration.

📄 PDF Abstract BibTeX arXiv:2609.04877

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SMARLA: A Safety Monitoring Approach for Deep Reinforcement Learning Agents

2023-08-03 · Amirhossein Zolfagharian, Manel Abdellatif, Lionel C. Briand, Ramesh S

Deep Reinforcement Learning (DRL) has made significant advancements in various fields, such as autonomous driving, healthcare, and robotics, by enabling agents to learn optimal policies through interactions with their en…

Autonomous Driving Deep Reinforcement Learning reinforcement-learning Reinforcement Learning

MARLaaS: Multi-Tenant Asynchronous Reinforcement Learning as a Service

2026-05-08 · Timothy Tin Long Yu, Gursimran Singh, Ge Shi, Hanieh Sadri 외 arxiv

Reinforcement Learning from Verifiable Rewards (RLVR) has significantly improved the reasoning capabilities of large language models (LLMs), particularly in multi-turn agentic settings involving environment interaction l…

Reinforcement Learning

Deep Multi-Agent Reinforcement Learning for Decentralized Active Hypothesis Testing

2023-09-14 · Hadar Szostak, Kobi Cohen

We consider a decentralized formulation of the active hypothesis testing (AHT) problem, where multiple agents gather noisy observations from the environment with the purpose of identifying the correct hypothesis. At each…

Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning

Zero-to-One IDV: A Conceptual Model for AI-Powered Identity Verification

2025-03-11 · Aniket Vaidya, Anurag Awasthi

In today's increasingly digital interactions, robust Identity Verification (IDV) is crucial for security and trust. Artificial Intelligence (AI) is transforming IDV, enhancing accuracy and fraud detection. This paper int…

Fraud Detection

Hierarchical Scaffolding Enables Human-Like Cognitive Selectivity under Data Scarcity

2026-07-06 · Juhyoung Park, Jaehyuk Bae, Hyeonbo Yang, Se-Bum Paik arxiv

Modern machine learning systems demand extensive datasets for visual recognition. Conversely, humans learn with high efficiency despite severe data limitations, often by acquiring broad categorical structures before refi…

Representation Learning