arXiv (All)
open access
SkillAdam: Stable and Efficient Skill Evolution for Agents
Agent skills provide a lightweight way to equip frozen language-model agents with domain knowledge and procedural guidance, yet obtaining high-quality skills remains costly and difficult to scale. Expert-written skills require substantial human effort. Recent skill self-evolution methods automate an
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Retrieval-Augmented Skill Optimization via Cross-Harness Adaptation#1142325
- Retrieval-Augmented Skill Optimization via Cross-Harness Adaptation#1122330
- Skill Retrieval Augmentation for Agentic AI#138972
- Progressive Agent Skill Generation via Reinforcement Learning#998318
- Set-shifting Behavioral Test for Harnessed Agents#684535
Record · ID 986563
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive