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Carnegie Mellon University researchers are presenting 156 papers at NeurIPS 2025, covering advances across machine learning domains. Key contributions include task-optimized neural networks for tactile processing that align with rodent brain activity, MeanFlow for one-step generative modeling achieving strong image generation results, and frameworks for computer-use agents and multi-agent collaboration. Research spans reinforcement learning with horizon reduction for scalability, speculative decoding optimizations for LLM inference, safety benchmarks for AI agents, and methods for improving language model alignment through checklist-based feedback. Additional work addresses 3D scene understanding, speech quality evaluation, molecular property prediction, and data attribution in large language models.

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