Guan Wu

CLEAR: Context Augmentation from Contrastive Learning of Experience via Agentic Reflection

CLEAR first employs a reflection agent to perform contrastive analysis over past execution trajectories and summarize useful context for each observed task to train a context augmentation model (CAM), which further optimize CAM using reinforcement learning, where the reward signal is obtained by running the task execut...