Reflections on NeurIPS 2025: Advancing evaluation and continual learning in AI

NeurIPS 2025 highlighted key themes in AI research, emphasizing the critical role of evaluation and benchmarking in advancing AI systems, amidst challenges such as data contamination and pattern matching. The conference underscored the importance of creating high-quality datasets and benchmarks to assess AI capabilities more reliably, with a focus on tasks that reflect real-world use cases and expose failure modes. Reinforcement learning was highlighted as a framework for building interactive, continually learning AI systems, though practical implementations remain limited. A shift towards more realistic and open-ended benchmarks was noted, along with a growing acceptance of prosaic alignment, suggesting that Artificial General Intelligence might be achieved with existing machine learning techniques. Labelbox supports these advancements by developing high-quality, expert-curated datasets and abstract evaluation methodologies to rigorously test AI models, ensuring that research drives better data and better data drives research, ultimately accelerating the AI ecosystem.

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