让智能体像人一样反思并优化记忆，处理复杂任务更持久高效。
Self-evolving Agents with reflective and memory-augmented abilities
- 引入反思机制与埃宾浩斯遗忘曲线优化记忆
- 在多任务和长序列任务中表现显著提升
- 适合需要持续决策的复杂智能体系统
大型语言模型（LLMs）在自然语言处理领域取得显著进展，但仍面临持续决策等挑战。本研究提出一种新框架，整合迭代反馈、反思机制与基于埃宾浩斯遗忘曲线的记忆优化机制，显著提升了智能体在多任务处理和长跨度信息理解方面的能力。实验表明，该方法在包含复杂推理与长期依赖的任务中表现出更强的稳定性与准确性，尤其在长时间运行场景下优于基准模型。
原文摘要 · Abstract (English)
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.
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