arXiv:2512.13907v2 Announce Type: replace-cross
Abstract: The implementation of the AI Act requires practical mechanisms to verify compliance with legal obligations, yet concrete and operational mappings from high-level requirements to verifiable assessment activities remain limited, contributing to uneven readiness across Member States. This paper presents a structured mapping that translates high-level AI Act requirements into concrete, implementable verification activities applicable across the AI lifecycle. The mapping is derived through a systematic process in which legal requirements are decomposed into operational sub-requirements and grounded in authoritative standards and recognised practices. From this basis, verification activities are identified and characterised along two dimensions: the type of verification performed and the lifecycle target to which it applies. By making explicit the link between regulatory intent and technical and organisational assurance practices, the proposed mapping reduces interpretive uncertainty and provides a reusable reference for consistent, technology-agnostic compliance verification under the AI Act.
Learning Evolving Latent Strategies for Multi-Agent Language Systems without Model Fine-Tuning
arXiv:2512.20629v1 Announce Type: cross Abstract: This study proposes a multi-agent language framework that enables continual strategy evolution without fine-tuning the language model’s parameters. The core