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

概要

arXiv:2510.19897v3 Announce Type: replace-cross Abstract: 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…