MUSE-Autoskill: Self-Evolving Agents via Skill Creation, Memory, Management, and Evaluation
The MUSE-Autoskill framework introduces a new approach for self-evolving agents that enhances their ability to create and manage skills. This framework allows agents to continuously improve their task-solving capabilities through a unified lifecycle of skill management. Initial experiments suggest that this method can significantly enhance task success and efficiency.
- ▪MUSE-Autoskill is a skill-centric agent framework designed for continuous improvement in task-solving capabilities.
- ▪The framework enables agents to create, reuse, and refine skills while managing them through a unified lifecycle.
- ▪Experiments indicate that lifecycle-managed skills can improve task success, efficiency, and cross-agent transfer.
arXiv cs.AI files mainly under ai research. We currently carry 1,128 of its stories.
Story provenance
Source · retrieval · rights · ranking — open for full record
inspect →
Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.27366 |
| Publication time | Wed, 27 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-27T04:07:56.398Z |
| Last seen | 2026-05-27T04:07:56.398Z |
| 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 | qAJg90n5bnY7 |
| 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
Computer Science > Artificial Intelligence arXiv:2605.27366 (cs) [Submitted on 26 May 2026] Title:MUSE-Autoskill: Self-Evolving Agents via Skill Creation, Memory, Management, and Evaluation Authors:Huawei Lin, Peng Li, Jie Song, Fuxin Jiang, Tieying Zhang View a PDF of the paper titled MUSE-Autoskill: Self-Evolving Agents via Skill Creation, Memory, Management, and Evaluation, by Huawei Lin and 4 other authors View PDF HTML (experimental) Abstract:Large language model (LLM) agents rely on reusable skills to solve complex tasks. However, existing skill creation approaches treat skills as isolated and static artifacts, limiting their reusability, reliability, and long-term improvement.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
Discussion
0 commentsMore from arXiv cs.AI
