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Learning physics into generative crystal models *after* training, rather than during inference, drastically improves structural validity and packing without sacrificing generation speed, and this benefit transfers across different model architectures.
VLSR's localize-then-reason approach boosts throughput by 9.6X, revolutionizing how we analyze molecular properties from images.
Mechanism-aware training boosts LLM performance in chemical reasoning, achieving an 8.3% exact match on complex reaction pathways—outperforming specialized models.
Achieving up to 4.05% bitrate savings in video quality metrics without the need for online fine-tuning could revolutionize how neural video codecs adapt to complex motion.
AI is poised to revolutionize protein dynamics research, but key challenges remain in ensuring scalability, thermodynamic consistency, and kinetic fidelity.
