CASCADE: Case-Based Continual Adaptation for Large Language Models During Deployment

Short summary

CASCADE formalizes deployment-time learning as a third LLM lifecycle stage, using episodic memory and contextual bandits to enable agents to improve from experience without parameter updates. Achieves 20.9% improvement over zero-shot prompting across medical, legal, code, search, and embodied tasks. Framework establishes foundation for continually improving AI systems post-deployment.

• •Introduces CASCADE framework for deployment-time learning in LLMs
• •Achieves 20.9% improvement over zero-shot across 16 diverse tasks
• •Enables LLM agents to adapt post-deployment through episodic memory

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