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Self-evolving Agents with reflective and memory-augmented abilities
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Self-evolving Agents with reflective and memory-augmented abilities
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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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Cited by 10 Pith papers
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