Microsoft Research· Research / Phi

EvoLib: Turning experience into evolving knowledge

Self-supervised. EvoLib enables large language models to learn from their own experience during inference, without requiring ground-truth labels or external feedback. From experience to knowledge. EvoLib transforms past attempts into reusable skills and reflective insights that can be applied to future tasks. Knowledge that evolves. Useful skills and insights are continually refined, consolidated, and reweighted, turning instance-specific observations into increasingly general knowledge over time. Learning that transfers across tasks. By turning experience into reusable knowledge, EvoLib helps AI models learn from past successes and failures and evolve the knowledge that has the highest potential on improving future performance. Built for today’s AI models. As EvoLib does not require model updates, it can be applied to any black-box language models and AI systems deployed through APIs. Memory has become an important AI agent capability: the ability to store and retrieve past experiences. But memory alone is not learning. …

Announced

30 Jul 2026 16:00 UTC