Learning from Supervision with Semantic and Episodic Memory: A Reflective Approach to Agent Adaptation

We investigate how agents built on pretrained large language models (LLMs) can learn target classification functions from labeled examples without parameter updates. While conventional approaches like fine-tuning are often costly, inflexible, and opaque, we propose a memory-augmented framework that leverages LLM-generated critiques grounded in labeled data. Our framework uses episodic memory to store instance-level critiques—capturing specific past experiences—and semantic memory to distill these into reusable, task-level guidance.