There’s still a lot of active research in traditional ML areas that LLMs haven’t solved. Causal inference, robustness to distribution shifts, and adversarial resilience remain open challenges. Continual and online learning, where models adapt without forgetting, are crucial for real-world deployment. Multi-modal learning beyond text, especially fusing vision, time series, and structured data, is another tough frontier. Interpretability, especially in high-stakes domains, still requires far more than attention maps. LLMs are impressive, but they haven’t made most of classical ML research obsolete.