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

arXiv:2604.07487v3 Announce Type: replace Abstract: Large language model agents rely on effective model context to obtain task-relevant information for decision-making. Many existing context engineering approaches primarily rely on the context generated from t