CASCADE: Cumulative Agentic Skill Creation through Autonomous Development and Evolution

• URL: http://arxiv.org/abs/2512.23880v1
• Date: Mon, 29 Dec 2025 21:50:23 GMT
• Title: CASCADE: Cumulative Agentic Skill Creation through Autonomous Development and Evolution
• Authors: Xu Huang, Junwu Chen, Yuxing Fei, Zhuohan Li, Philippe Schwaller, Gerbrand Ceder,
• Abstract summary: Large language model (LLM) agents currently depend on predefined tools or brittle tool generation.<n>We introduce CASCADE, a self-evolving agentic framework representing an early instantiation of the transition from "LLM + tool use" to "LLM + skill acquisition"
• Score: 7.266404572341558
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language model (LLM) agents currently depend on predefined tools or brittle tool generation, constraining their capability and adaptability to complex scientific tasks. We introduce CASCADE, a self-evolving agentic framework representing an early instantiation of the transition from "LLM + tool use" to "LLM + skill acquisition". CASCADE enables agents to master complex external tools and codify knowledge through two meta-skills: continuous learning via web search and code extraction, and self-reflection via introspection and knowledge graph exploration, among others. We evaluate CASCADE on SciSkillBench, a benchmark of 116 materials science and chemistry research tasks. CASCADE achieves a 93.3% success rate using GPT-5, compared to 35.4% without evolution mechanisms. We further demonstrate real-world applications in computational analysis, autonomous laboratory experiments, and selective reproduction of published papers. Along with human-agent collaboration and memory consolidation, CASCADE accumulates executable skills that can be shared across agents and scientists, moving toward scalable AI-assisted scientific research.

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