  We introduce an innovative RAG-based framework with an ever-improving memory.
Inspired by humans'pedagogical process, RAM utilizes recursively
reasoning-based retrieval and experience reflections to continually update the
memory and learn from users' communicative feedback, namely communicative
learning. Extensive experiments with both simulated and real users demonstrate
significant improvements over traditional RAG and self-knowledge methods,
particularly excelling in handling false premise and multi-hop questions.
Furthermore, RAM exhibits promising adaptability to various feedback and
retrieval methods, showcasing its potential for advancing AI capabilities in
dynamic knowledge acquisition and lifelong learning.
