From Retrieval to Reasoning: Advancing AI Agents for Knowledge Discovery and Collaboration

The web is the world's largest knowledge repository, yet as AI systems become increasingly integrated into our digital infrastructure, the ability to retrieve, reason, and collaborate effectively has become paramount. Large Language Models (LLMs) are evolving from passive responders to active knowledge agents that can retrieve complex information, validate hypotheses, and optimize interactions over multiple turns. In this talk, I will explore the frontiers of AI-driven knowledge retrieval and reasoning, drawing from recent research on knowledge graphs, semi-structured retrieval, adaptive tool use, and multi-turn AI collaboration. I will also discuss how agentic frameworks enable rigorous, automated hypothesis validation through sequential falsifications. Together, these advancements push beyond traditional search and QA systems, unlocking new capabilities for knowledge discovery, scientific research, and human-AI collaboration. Finally, I will highlight key challenges and opportunities in building AI systems that are not just accurate, but also interactive, explainable, and aligned with human needs.