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StudentSim outperforms existing models, achieving a remarkable behavioral fidelity of 0.51 and guidance responsiveness of 0.91 in chess, setting a new standard for AI tutors.
Current video generation models struggle with visual reasoning, with the best achieving only 51% accuracy on a new benchmark designed to probe their capabilities.
Ditching the always-on LLM for proactive agents can boost speed by up to 83x and improve F1 scores by 16.7, simply by processing structured event data directly with a temporal graph learning model.
Forget human-readable models: Agentic-imodels evolves ML models that are optimized for LLM interpretability, boosting agentic data science performance by up to 73%.
