EvoLib: Turning experience into evolving knowledge

The company has launched EvoLib, a framework that enables large language models to learn from their own experiences during inference without needing ground-truth labels or external feedback. EvoLib transforms past attempts into reusable skills and reflective insights applicable to future tasks, enhancing learning effectiveness. This framework is designed to work with current AI models, as it does not require model updates, making it applicable to any language models deployed through APIs.