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Carnegie Mellon researchers presented 156 papers at NeurIPS 2025 spanning diverse ML areas. Notable contributions include task-optimized neural networks for tactile processing that align with rodent brain activity, MeanFlow for one-step generative modeling achieving strong ImageNet results, and frameworks for computer-use agents and multi-agent collaboration. Research covers reinforcement learning scalability, LLM safety and alignment, speculative decoding optimization, vision-language models, and theoretical advances in game theory and causal inference. The work demonstrates CMU's breadth across foundational ML theory, practical systems, neuroscience-inspired models, and safety-critical applications.

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