Self-Evolving Large Language Model Agent with Adaptive Long-Term Memory and Autonomous Knowledge Updating

Abstract

Large Language Model (LLM) agents are increasingly being used for multi-step reasoning, tool use, information retrieval, and autonomous task execution. However, conventional LLM agents are largely session-oriented: they have limited persistent memory, may repeatedly relearn previously observed information, and can generate responses from stale knowledge. This paper proposes a self-evolving LLM agent architecture that combines adaptive long-term memory, autonomous knowledge updating, confidence-aware retrieval, and controlled self-reflection. The proposed framework separates short-term conversational context from long-term episodic, semantic, and procedural memory. A memory controller determines which experiences should be stored, strengthened, summarized, replaced, or forgotten. An autonomous knowledge updater periodically discovers candidate information from trusted external sources, evaluates evidence, detects conflicts with stored knowledge, and updates the knowledge base only when predefined confidence and consistency conditions are satisfied. The framework also introduces temporal validity, provenance tracking, contradiction detection, and rollback mechanisms to reduce knowledge drift and memory corruption. A comprehensive evaluation methodology is proposed using long-horizon task completion, memory precision and recall, knowledge freshness, update accuracy, contradiction rate, context efficiency, and recovery from incorrect updates. The research aims to provide a practical architecture for LLM agents that can continuously improve while maintaining traceability and reliability