4 papers
Mitigating Anchoring Bias in LLM-Based Agents for Energy-Efficient 6G Autonomous Networks
Hatim Chergui, Claudia Carballo González, Farhad Rezazadeh +1
This paper presents an autonomous agentic resource negotiation framework designed to enable zero-touch network slicing in 6G architectures using Large Language Model (LLM) agents.…
A Tutorial on Cognitive Biases in Agentic AI-Driven 6G Autonomous Networks
Hatim Chergui, Farhad Rezazadeh, Merouane Debbah +1
The path to higher network autonomy in 6G lies beyond the mere optimization of key performance indicators (KPIs), requiring systems that perceive and reason over the network enviro…
LLM-Based Agentic Negotiation for 6G: Addressing Uncertainty Neglect and Tail-Event Risk
Hatim Chergui, Farhad Rezazadeh, Mehdi Bennis +2
A critical barrier to the trustworthiness of sixth-generation (6G) agentic autonomous networks is the uncertainty neglect bias; a cognitive tendency for large language model (LLM)-…
Towards Cloud-Native Agentic Protocol Learning for Conflict-Free 6G: A Case Study on Inter-Slice Resource Allocation
Juan Sebastián Camargo, Farhad Rezazadeh, Hatim Chergui +2
In this paper, we propose a novel cloud-native architecture for collaborative agentic network slicing. Our approach addresses the challenge of managing shared infrastructure, parti…