Beneath every capable AI agent lies a stack of model architectures and training strategies that determine how it reasons, plans, and adapts. This panel will dive into the core “thinking layers” of agents—from large language models and world models to planning modules, memory systems, and tool-use capabilities. Panelists will discuss the latest approaches to pre-training and fine-tuning, the role of reinforcement learning and simulation environments, and emerging architectures for multi-step reasoning and long-horizon planning. The conversation will highlight both the technical trade-offs and the open research questions: how do we balance generality vs. specialization, how do we evaluate reasoning quality, and what’s needed to make agents robust outside of demos? Attendees will gain a deeper understanding of the model and training foundations that underpin effective agentic systems—and where research is headed next.
