From bigger models to better intelligence: what NeurIPS 2025 tells us about progress

NeurIPS 2025 highlights a shift in the AI community from prioritizing the sheer scale of models to focusing on efficiency and capability-driven approaches. Rather than merely building larger models, the emphasis is on optimizing model architecture and system performance, as demonstrated by innovations such as sparse attention and diffusion models. The conference also underscored the importance of dynamic benchmarks that evaluate AI on long-horizon and abstract tasks rather than static, easily overfit assessments, pointing to a need for diverse and pluralistic evaluation metrics. The role of world models and agents in AI development is increasingly recognized, with an emphasis on continual learning and multimodal alignment to facilitate interaction with the real world. The discussions suggest a broader understanding that superintelligence may emerge not from individual breakthroughs but from systems that integrate efficient core models, interactive world models, and continual adaptation. As the field progresses, NeurIPS remains a platform where future trends and realities converge, heralding a transition from the era of scaling to an era of nuanced research.