16 citations · 18 across the 15 of their papers we have counts for
6 papers · 1 filter
When Numbers Start Talking: Numerical Signalling and Strategic Behaviour Among LLMs
Alessio Buscemi, Daniele Proverbio, Alessandro Di Stefano +3
Large language model (LLM)-based agents increasingly operate in multi-agent systems (MAS) characterised by strategic interaction. However, little is known about whether, and to wha…
Designing a Boundary Negotiating Artifact for Collaborative Socio-Technical Sense-Making in AI Regulatory Sandboxes
Idoia Landa-Oregi, Tom Deckenbrunnen, Alessio Buscemi +2
The rapid, unpredictable advancements in AI system capabilities has seen regulators take adaptive and experimental approaches to policymaking. Established in other domains as instr…
MARLA: A Conceptual Scaffold for Regulatory Learning under the EU AI Act
Alessio Buscemi, Tom Deckenbrunnen, Imane Hmiddou +5
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 eviden…
Payoff scaling shapes cooperation in LLM agents across languages
Trung-Kiet Huynh, Dao-Sy Duy-Minh, Thanh-Bang Cao +13
Large language models (LLMs) are increasingly deployed as autonomous agents that negotiate, coordinate, and act on behalf of users. Whether they cooperate in such settings is no lo…
Bathtubs, Boundaries, and Sandboxes: AI Regulatory Learning under Legal Uncertainty
Tom Deckenbrunnen, Alessio Buscemi, Marco Almada +2
Effective regulation of AI is a defining policy challenge, driven by their integration into all aspects of society. To remain responsive to their rapid development and emergent pro…
When Numbers Start Talking: Implicit Numerical Coordination Among LLM-Based Agents
Alessio Buscemi, Daniele Proverbio, Alessandro Di Stefano +3
LLMs-based agents increasingly operate in multi-agent environments where strategic interaction and coordination are required. While existing work has largely focused on individual…