ContinualSkillBench: Can LLM Agents Truly Evolve Their Capabilities?
Modern agent frameworks equip large language models with external skill libraries to solve complex tasks. However, it remains unclear whether these systems can effectively evolve their skills and whether the resulting skills improve task-solving capabilities. To bridge this gap, we introduce ContinualSkillBench, a dynamic evaluation framework for in-context continual skill learning. It covers five domains, each with 100 tasks ordered by difficulty and opportunities for skill transfer. Our experiments show that sequential execution generally improves performance, but the gains vary substantially across models and domains. Moreover, across two models and three domains, retaining context and...
| Authors | Tianyi Guan, Yiding Wang, Haotong Yang et al. |
|---|---|
| Primary category | cs.AI |
| Published | 4 August 2026 |
| Last updated at source | 15 October 2026 |
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