MARS: Memory-Enhanced Agents with Reflective Self-improvement

• URL: http://arxiv.org/abs/2503.19271v2
• Date: Wed, 09 Apr 2025 07:51:49 GMT
• Title: MARS: Memory-Enhanced Agents with Reflective Self-improvement
• Authors: Xuechen Liang, Meiling Tao, Yinghui Xia, Jianhui Wang, Kun Li, Yijin Wang, Jingsong Yang, Tianyu Shi, Yuantao Wang, Miao Zhang, Xueqian Wang,
• Abstract summary: This paper proposes an innovative framework Memory-Enhanced Agents with Reflective Self-improvement.<n>The framework comprises three agents: the User, the Assistant, and the Checker.
• Score: 18.974715067869877
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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