THIRDWAVE

Artificial Intelligence (AI) is transforming research, industry, and society, with Large Language Models (LLMs) playing a central role. While LLMs excel in natural language understanding, reasoning, and content generation, they also exhibit hallucinations, security vulnerabilities, ethical concerns, and regulatory issues. These challenges are particularly critical in healthcare and education, where accuracy, reliability, and fairness are essential. Addressing these shortcomings requires AI paradigms that enhance interpretability, robustness, and compliance. THIRDWAVE aims to establish an international, interdisciplinary network to advance LLM-driven neuro-symbolic AI, integrating symbolic AI with LLMs to create interpretable, reliable, and domain-aware systems. This approach enables AI to leverage structured knowledge, improve decision-making, and comply with domain-specific constraints, making it more applicable to real-world challenges. The project is structured around four key objectives: O1) Understanding LLMs: Analyzing capabilities and limitations to improve performance, usability, and trustworthiness. O2) Enhancing LLMs: Improving fairness, factual accuracy, and robustness through external knowledge sources and human collaboration. O3) Advancing LLM-driven Neuro-Symbolic AI: Developing hybrid systems that combine LLMs with symbolic reasoning for structured knowledge representation and better decision support. O4) Use Cases & Evaluation: Applying LLM-driven neuro-symbolic AI in healthcare, education, geodata, and food information engineering, validating scalability and societal impact. By fostering collaboration among AI researchers, domain experts, and industry partners, THIRDWAVE will bridge the gap between data-driven and knowledge-driven AI, ensuring LLMs become interpretable, ethically aligned, and domain-aware. The project’s findings will inform AI regulation, advance research, and drive innovation, contributing to responsible AI development.