CASCADE: Cumulative Agentic Skill Creation through Autonomous Development and Evolution

Understand

Large language model (LLM) agents currently depend on predefined tools or early-stage tool generation, limiting their adaptability and scalability 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, code extraction, and memory utilization; self-reflection via introspection, knowledge graph exploration, and others.
• We evaluate CASCADE on SciSkillBench, a benchmark of 116 materials science and chemistry research tasks.