Romania

Challenges and Opportunities for Safe and Reliable Autonomous Agents in the Era of Large Language Models

Date: June 23, 2025 | Location: Cluj-Napoca, Romania

Introduction

As autonomous agents continue to evolve, the integration of Large Language Models (LLMs) is reshaping their capabilities and impact. However, this new frontier presents unique challenges and opportunities in ensuring the safety, reliability, and ethical deployment of these systems. This session explores the complexities of designing and managing autonomous agents in the age of LLMs, focusing on issues such as robustness, explainability, bias, and accountability. Experts will discuss the latest advancements, potential risks, and strategies for fostering trust in these powerful technologies, while identifying key opportunities for innovation and responsible development.

Agenda (Tentative)

08:45
Welcome & Opening Remarks
Dr. Zheng Hu
09:00
Keynote: Explainable Attribution Technology and its Application in Autonomous Agent Reliability
Prof. Xiaochun Cao
Sun Yat-sen University
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10:00
Invited Talk: Probabilistic Verification of AI Agents
Dr. Xingyu Zhao
University of Warwick
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11:00
Invited Talk: Bridging the Gap between Interpretable AI Research and Real World AI Agents
Dr. Ziquan Liu
Queen Mary University of London
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12:00
Lunch
13:30
Invited Talk: The Importance of Starting Small with Baby Robots: Developmental Robotics for Language Grounding
Prof. Angelo Cangelosi
University of Manchester
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14:20
Invited Talk: Reliable Visual-Language Grounding for Embodied AI Agents
Prof. Lorenzo Baraldi
University of Modena and Reggio Emilia
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15:20
Coffee Break
15:30
Invited Talk: Multi-robot Control Using Bayesian Machine Learning.
Prof. Wei Pan
University of Manchester
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16:30
Panel Discussion: Challenges and Opportunities in the Era of Large Language Models
Moderator: Dr. Qunli Zhang
Panelists: All Invited Speakers
17:00
Closing Remarks
Dr. Xiao Chen

Organizers

Zifan Zeng

Huawei&Technical University of Munich

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Dr. Qunli Zhang

Huawei

RAMS Lab

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Dr. Ziquan Liu

Queen Mary University of London

Computer Vision Group

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Prof. Shaogang Gong

Queen Mary University of London

Computer Vision Group

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