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20242026
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cs.AI2026

From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review

Mohamed Amine Ferrag, Norbert Tihanyi, Merouane Debbah

Large language models and autonomous AI agents have evolved rapidly, resulting in a diverse array of evaluation benchmarks, frameworks, and collaboration protocols. Driven by the g…

cs.AI2026

AgentDrive: An Open Benchmark Dataset for Agentic AI Reasoning with LLM-Generated Scenarios in Autonomous Systems

Mohamed Amine Ferrag, Abderrahmane Lakas, Merouane Debbah

The rapid advancement of large language models (LLMs) has sparked growing interest in their integration into autonomous systems for reasoning-driven perception, planning, and decis…

cs.AI2025

UAVBench: An Open Benchmark Dataset for Autonomous and Agentic AI UAV Systems via LLM-Generated Flight Scenarios

Mohamed Amine Ferrag, Abderrahmane Lakas, Merouane Debbah

Autonomous aerial systems increasingly rely on large language models (LLMs) for mission planning, perception, and decision-making, yet the lack of standardized and physically groun…

cs.AI2025

Reasoning Language Models for Root Cause Analysis in 5G Wireless Networks

Mohamed Sana, Nicola Piovesan, Antonio De Domenico +4

Root Cause Analysis (RCA) in mobile networks remains a challenging task due to the need for interpretability, domain expertise, and causal reasoning. In this work, we propose a lig…

cs.AI2025

TeleMath: A Benchmark for Large Language Models in Telecom Mathematical Problem Solving

Vincenzo Colle, Mohamed Sana, Nicola Piovesan +3

The increasing adoption of artificial intelligence in telecommunications has raised interest in the capability of Large Language Models (LLMs) to address domain-specific, mathemati…

cs.AI2024

Dynamic Intelligence Assessment: Benchmarking LLMs on the Road to AGI with a Focus on Model Confidence

Norbert Tihanyi, Tamas Bisztray, Richard A. Dubniczky +11

As machine intelligence evolves, the need to test and compare the problem-solving abilities of different AI models grows. However, current benchmarks are often simplistic, allowing…