Self-evolving Agents with reflective and memory-augmented abilities
This work addresses the problem of improving agent performance in complex, continuous tasks for AI and NLP applications, representing an incremental advancement.
The paper tackles the challenge of continuous decision-making in large language models by proposing a framework that integrates iterative feedback, reflective mechanisms, and a memory optimization mechanism based on the Ebbinghaus forgetting curve, resulting in enhanced capabilities for handling multi-tasking and long-span information.
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. In this research, we propose a novel framework 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.