arXiv AI

Transparency as Answerability: Role-Dependent Information Flow Modeling

arXiv AI
Jun 9

"So There's a Catch-22 Here": How Early Adopters Who Build Multi-Agent LLM Systems Conceptualize Transparency

arXiv:2606. 08323v1 Announce Type: cross Abstract: Multi-agent large language model (LLM) systems are rapidly emerging, yet transparency, a cornerstone of responsible AI, remains under-defined in these distributed architectures, which have complexities of inter-agent coordination and orchestration.

By Suchismita Naik, Samir Passi, Mihaela Vorvoreanu, Scott Saponas, Amanda Hall
arXiv AI
Oct 2

The AI Assessment Sandbox Configurator: A Framework to Support Technical Assessment in AI Regulatory Sandboxes

The paper introduces the AI Assessment Sandbox Configurator, an open‑source framework designed to support technical assessment in AI Regulatory Sandboxes (AIRS) mandated by the EU Artificial Intelligence Act. It outlines 11 architectural and governance requirements for infrastructure that enables large‑scale, structured technical testing, and presents a catalogue of tests, a shared data model, dashboards, and reporting tools that harmonise heterogeneous outputs. An early‑stage pilot demonstrated the framework’s harmonisation and reporting capabilities within a live AIRS engagement, contributing to an official Exit Report.

By Alessio Buscemi, German Castignani, Daniele Pagani, Maxime Cordy, Jordi Cabot
arXiv AI
Sep 1

Operationalising AI Regulatory Sandboxes: Activities, Requirements, and Technical Assessment under the EU AI Act

The paper "Operationalising AI Regulatory Sandboxes: Activities, Requirements, and Technical Assessment under the EU AI Act" outlines a detailed framework for implementing AI Regulatory Sandboxes (AIRS) under the EU AI Act. It maps the sandbox lifecycle into 29 activities, distinguishes between a Core AIRS and an Extended AIRS that includes an AI Technical Sandbox (AITS), and derives 15 infrastructural and governance requirements linked to these activities and provider obligations. The authors also introduce the Sandbox Configurator, an open‑source tool to instantiate AITS environments, aiming to provide structured workflows for regulators, robust evaluation methods for experts, and a transparent compliance pathway for AI providers.

By Alessio Buscemi, Thibault Simonetto, Daniele Pagani, German Castignani, Maxime Cordy, Jordi Cabot
arXiv AI
Jun 17

Large Language Models for Agentic NetOps and AIOps: Architectures, Evaluation, and Safety

arXiv:2605. 12729v2 Announce Type: replace-cross Abstract: Large language models are increasingly being used to support network operations (NetOps) and artificial intelligence for IT operations (AIOps), including incident investigation, root-cause analysis, configuration synthesis, and limited self-healing.

By Muhammad Bilal, Jon Crowcroft, Ruizhi Wang, Xiaolong Xu, Schahram Dustdar
arXiv AI
Jun 30

Towards Harnessing the Collaborative Power of Large and Small Models for Domain Tasks

arXiv:2504. 17421v2 Announce Type: replace-cross Abstract: Large language models (LMs) offer broad generalization capabilities but require vast amounts of data and computational resources for domain-specific tasks; small models (SMs), in contrast, are more efficient and tailored to specific domains yet lack general-purpose coverage.

By Yang Liu, Kejia Zhang, Bingjie Yan, Tianyuan Zou, Jianqing Zhang, Zixuan Gu, Xiangsen Chen, Jianbing Ding, Xidong Wang, Jingyi Li, Xiaozhou Ye, Ye Ouyang, Qiang Yang, Ya-Qin Zhang
arXiv AI
Aug 18

ETHOS: Towards a Modular Ethics Framework for Clinical Multi-Agent Systems

arXiv:2608. 15424v1 Announce Type: cross Abstract: The rapid adoption of large language models has enabled the development of clinical multi-agent systems (MAS) capable of integrating multimodal patient data and supporting increasingly complex clinical decision-making.

By Rakesh Sharma, Sydney Pugh, Cameron Beeche, Pankhuri Singhal, Rachel Wu, Margaret Eby, Jeffrey Duda, James Gee, Kyra O'Brien, Hersh Sagreiya, Marina Serper, Victoria Gershuni, Angela Bradbury, Anurag Verma, Eric Eaton, Kevin B. Johnson, Walter Witschey