EvoLib: Turning experience into evolving knowledge
EvoLib enables large language models to learn from their own experience during inference, without requiring ground-truth labels or external feedback. EvoLib transforms past attempts into reusable skills and reflective insights that can be applied to future tasks. Useful skills and insights are continually refined, consolidated, and reweighted, turning instance-specific observations into increasingly general knowledge over time.
- ▪EvoLib enables large language models to learn from their own experience during inference, without requiring ground-truth labels or external feedback.
- ▪EvoLib transforms past attempts into reusable skills and reflective insights that can be applied to future tasks.
- ▪Useful skills and insights are continually refined, consolidated, and reweighted, turning instance-specific observations into increasingly general knowledge over time.
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Record
| Original publisher | Microsoft Research |
| Canonical URL | https://www.microsoft.com/en-us/research/blog/evolib-turning-experience-into-evolving-knowledge/ |
| Publication time | Thu, 30 Jul 2026 16:00:00 +0000 |
| Retrieval time | 2026-07-30T18:57:29.406Z |
| Last seen | 2026-07-30T18:57:29.406Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | rn3nFcTA9VMR · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
EvoLib: Turning experience into evolving knowledge Published July 30, 2026 By Weijia Xu , Senior Researcher Alessandro Sordoni , Senior Principal Research Manager Zelalem Gero , Senior Researcher Michel Galley , Senior Principal Research Manager Eric Yuan , Principal Researcher Jianfeng Gao , Technical Fellow & Corporate Vice President Share this page Share on Facebook Share on X Share on LinkedIn Share on Reddit Subscribe to our RSS feed 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.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Microsoft Research.
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