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LEAP reveals that separating evidence evaluation can drastically enhance the accuracy and interpretability of probabilistic forecasts in LLM applications.
LLMs can significantly boost forecasting accuracy, but their effectiveness is hampered by measurement limitations and sensitivity to input changes.
PertMind reveals that leveraging cellular perturbation data can significantly enhance LLMs' biological reasoning capabilities without extensive task-specific retraining.
Capturing structured relationships in images can boost open-vocabulary object detection performance by over 10% on novel categories.
Today's AI agent security frameworks are failing to keep pace with the rising tide of threats arising from autonomous decision-making and environmental interaction.
