Contrastive Learning of Experience via Agentic Reflection

CLEAR framework enhances LLM agents with contrastive learning for context augmentation

Researchers have introduced CLEAR, a novel framework designed to enhance the context augmentation capabilities of large language model agents. This method utilizes contrastive learning and agentic reflection to generate…