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Sun Yat-sen University
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Log-based anomaly detection models are missing 90% of the picture, but AnomalyGen uses LLMs and static analysis to hallucinate realistic training data and close the gap.
LLMs can now explain chemical reactions from 4D molecular trajectories, thanks to a new benchmark and model designed to bridge the gap between dynamic molecular simulations and natural language understanding.
