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@omarsar0

A Multi-Agent Framework for Synthetic Data Generation Presents MAG-V, a multi-agent framework that first generates a dataset of questions that mimic customer queries. It then reverse engineer alternate questions from responses to verify agent trajectories. Reports that the generated synthetic data can improve agent performance on actual customer queries. Finds that for trajectory verification "simple ML baselines with feature engineering can match the performance of more expensive and capable models."

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