AI Multi-agents

Leveraging Large Language Models for Complex Problem Solving 

Investigate how large language models (LLMs) can collaborate as multi-agent systems to tackle intricate tasks, such as autonomously coding and logically reasoning through multifaceted processes. Explore the coordination of agents, role assignment, and dynamic task distribution to maximize efficiency. Evaluate the scalability and adaptability of these systems in increasingly complex scenarios. Students will aim to design and test innovative frameworks for agent collaboration, ensuring a seamless and robust performance that pushes the boundaries of current AI capabilities.

Developing and Evaluating Cutting-Edge Testing Scenarios

Design rigorous and forward-looking evaluation frameworks to assess the capabilities of LLM-based agents in realistic use cases. Focus on creating testing scenarios that challenge agents in areas such as adaptive reasoning, ethical decision-making, and real-time problem solving. Ensure scenarios are grounded in practical utility while exploring their implications for future AI applications. Students will also explore metrics for success, addressing both technical and user-centric dimensions of evaluation.

Requirements

For students eager to shape the next wave of AI innovations, this project requires a deep foundation in at least one of the following areas: i) machine learning and AI, ii) programming, or iii) statistics. Participants will combine technical expertise with creativity to develop novel AI methods, positioning themselves as contributors to the future of transformative AI technologies.