Self-evolving Agents with reflective and memory-augmented abilities
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—Self-evolving Agents with Reflective and Memory-augmented Abilities (SAGE). The SAGE 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. The agents, through self-evolution, can adaptively adjust strategies, optimize information storage and transmission, and effectively reduce cognitive load. We evaluate the performance of the SAGE framework on AgentBench and long text tasks. Experimental results show that SAGE significantly improves model performance, achieving a 2.26X improvement on closed-source models and an improvement ranging from 57.7\% to 100\% on open-source models, with particularly notable effects on smaller models.
Paper Type: Long
Research Area: Efficient/Low-Resource Methods for NLP
Research Area Keywords: Human-Centered NLP,Efficient/Low-Resource Methods for NLP
Contribution Types: NLP engineering experiment
Languages Studied: English
Submission Number: 1095
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