Artificial Intelligence · 30.07.2026, 16:04 UTC
EvoLib: Turning experience into evolving knowledge
| Schweregrad | info |
|---|---|
| Kategorie | Artificial Intelligence |
| Quelle | Microsoft Research ↗ |
| Veröffentlicht | 30.07.2026 UTC |
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At a glance
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. A collection of past conversations, reasoning traces, or action histories can quickly grow into a vast archive of experiences, making it difficult to identify the most relevant knowledge for a new task—let alone refine and evolve this knowledge to improve performance over time.
Humans learn differently. We do not remember every detail of our past experiences. Instead, we remember what matters: strategies that work, mistakes to avoid, and skills that transfer across situations. Over time, these lessons are refined into …