这篇论文给了一套测试方法，来验证 LLM 智能体到底会不会把经验变成可复用的技能，而不是光靠记住上下文。
新基准 ContinualSkillBench 用于评估智能体的持续技能学习，覆盖五个领域、100 个按难度递增且可复用技能的互联子任务。实验表明序列执行总体能提升性能，但收益因模型和领域而异。上下文学习与显式技能维护平均表现相当，显式技能只在需要可复用流程或精确输出的任务上更有优势。较弱模型反而会积累更大、更碎片化的任务技能集合。
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 representative domains, each containing 100 interconnected subtasks ordered by increasing difficulty and opportunities for cross-task skill reuse. Our experiments show that sequential execution generally improves performance, but the gains vary substantially across models and domains. Moreover, in-context learning performs comparably to explicit skill maintenance on average, suggesting that much of the improvement arises from adaptation to prior context and feedback rather than reusable skill abstraction alone. Explicit skills nevertheless provide selective benefits for tasks requiring reusable procedures or precise outputs. We further find that less capable models tend to accumulate larger, more fragmented collections of task-specific skills. These findings show that current in-context skill evolution mechanisms can support continual adaptation, but still struggle to consistently consolidate experience into robust and transferable skills.