MARS: Memory-Enhanced Agents with Reflective Self-improvement
This addresses memory and context limitations in LLMs for dynamic environments, but appears incremental as it builds on existing agent-based approaches.
The paper tackles the challenges of continuous decision-making, lack of long-term memory, and limited context windows in LLMs by proposing the MARS framework, which integrates iterative feedback, reflective mechanisms, and memory optimization based on the Ebbinghaus forgetting curve to enhance agent capabilities in multi-tasking and long-span information handling.
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.