Large language models (LLMs) have made significant advances in the field of natural language processing, but they still face challenges such as continuous decision-making, lack of long-term memory, and limited context windows in dynamic environments. To address these issues, this paper proposes an innovative framework Memory-Enhanced Agents with Reflective Self-improvement. The MARS framework comprises three agents: the User, the Assistant, and the Checker. By integrating iterative feedback, reflective mechanisms, and a memory optimization mechanism based on the Ebbinghaus forgetting curve, it significantly enhances the agents capabilities in handling multi-tasking and long-span information.
@article{arxiv.2503.19271,
title = {MARS: Memory-Enhanced Agents with Reflective Self-improvement},
author = {Xuechen Liang and Meiling Tao and Yinghui Xia and Jianhui Wang and Kun Li and Yijin Wang and Jingsong Yang and Tianyu Shi and Yuantao Wang and Miao Zhang and Xueqian Wang},
journal= {arXiv preprint arXiv:2503.19271},
year = {2025}
}
Comments
We are withdrawing this version because it duplicates our previous submission (arXiv:2409.00872)