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Amazon researchers have released Mitra, a novel foundation model pretrained on synthetic data that advances tabular machine learning through in-context learning and delivers state-of-the-art results on major benchmarks.
Discover key lessons from the Manus project on context engineering that improve AI agents' performance by optimizing how they process and manage decision-making information.
New research demonstrates that inference-time prompting can effectively approximate fine-tuned transformer models, offering a resource-efficient approach to NLP tasks without retraining.
New research reveals how integrating in-context learning insights into fine-tuning datasets significantly improves language model generalization on complex reasoning tasks.